IN A DEFINED SURVEYED AREA REFERENCE TO CO-PENDING APPLICATIONS

Priority is claimed from US provisional application No. 62021723 and from US provisional application No. 62048330, both entitled "A Method and system for detection of changes in a defined surveyed area" and filed respectively on 8 July 2014 and 10 September 2014.

FIELD OF THIS DISCLOSURE

The present invention relates to the field of monitoring systems. More particularly, the invention relates to a method and system of identifying deviation from steady-state conditions using wireless signals.

BACKGROUND FOR THIS DISCLOSURE

Conventional technology constituting background to certain embodiments of the present invention is described as follows and in the following publications inter alia:

Motion detection, occupancy detection and similar schemes are used in a variety of daily applications such as intrusion detection (alarm systems), smart home (activation of subsystems or appliances), counting systems and more. Typical implementations of such schemes involve installation of dedicated hardware and software elements, based on technologies such as but not limited to:

Infrared (passive and active sensors)

Optics (video and camera systems)

Radio Frequency Energy (radar, microwave and tomographic motion detection) Sound (microphones and acoustic sensors)

Vibration (triboelectric, seismic, and inertia- switch sensors)

Magnetism (magnetic sensors and magnetometers)

Beamforming, aka spatial filtering, includes signal processing techniques used in sensor arrays e.g. high-end WiFi routers which controls directionality of transmission and/or reception of signals such as a radio or Wifi signal. Absent beamforming, when a router sends out a wireless signal, the signal gets wider as it travels, losing strength in exchange for coverage which may not be necessary. Beamforming may send router signals directly to devices in a straight line, minimizing surrounding signal interference and/or increasing strength of signal per device. Beamforming has various names e.g. Asus uses the term "AiRadar".

Conventional beamforming, e.g. by phase shifting, is described inter alia at the following http location: chimera.labs.oreilly.com/books/1234000001739/ch04.html.

Directional signal transmission or reception may be achieved by combining elements in a phased array such that signals at some angles experience constructive interference, whereas others experience destructive interference. Beamforming may be used at both transmitting and receiving ends to achieve spatial selectivity.

In Explicit Beamforming the transmitter e.g. router and receiver (device on network) exchange information. Implicit Beamforming does not require support at both ends of the wireless network.

A beamforming matrix may be used to describe the phase shifts required by each antenna element. For example, the array may apply a phase shift to each phase array element so that (say) an element on the right transmits first and an element on the left transmits last, thereby to cause the transmissions from the array to converge along a new path shifted left relative to the previous path.

A downloadable motion detection solution which turns a tablet into a smart home security solution is described at the following http location:

/unbouncepages.com/gethiwifi/.

Patent document US 20050055568 to Agrawala et al describes "a security system which exploits wireless fidelity (Wi-Fi) infrastructure in an area of interest in order to detect the presence of objects in the area of interest" as well as "the ability to track the presence and movement of objects in the area of interest in real time and to report the movement of the detected objects". Monitoring points, interconnected with transmitters (or an access point network of the Wi-Fi infrastructure), measure RSSI values of signals transmitted by the transmitters and supply the RSSI values to a security system server which determines whether these RSSI values deviate from RSSI values predetermined when no unwanted object was present. A thermostat which learns the preferences of occupants of a house is described at the following http www location: forbes.com/sites/aarontilley/2014/01/13/google- acquires-nest-for-3-2-billion/.

A white paper at the following http location: cdn2.hubspot.net/hub/13940/file- 257413033- pdf/images/miscellaneous_to_sort_out_later/docs/smart_buildi ngs_white_paper_aug_201 3.pdf describes use of IR sensors "to make buildings more intelligent".

The disclosures of all publications and patent documents mentioned in the specification, and of the publications and patent documents cited therein directly or indirectly, are hereby incorporated by reference. Materiality of such publications and patent documents to patentability is not conceded.

SUMMARY OF CERTAIN EMBODIMENTS

Certain embodiments of the present invention seek to provide a system which is capable of identifying the presence, or changes in presence, of objects (in particular human beings) in a surveyed area.

Certain embodiments of the present invention seek to provide a system and method for motion detection, occupancy detection or detection of other changes in a defined area using simple sensors that monitor changes in parameters of wireless signals.

Certain embodiments of the present invention seek to provide a method for detecting whether or not there is a new presence in a monitored area.

Certain embodiments of the present invention seek to provide use of beamforming (typically explicit beamforming e.g. 802.11.ac) data to identify change in object presence/absence, implying object movement, rather than using that beamforming data simply to form optimal beams. A passive sniffer such as the SAVVIUS device described in the following http www link:

wildpackets.com/use_cases/wireless_analysis/80211ac_analy sis may be used to monitor the exchange, e.g. from beamformee to beamformer, of beamforming parameters e.g. a V-Matrix. Any change in these parameters indicates a change in the monitored area and a suitable notification message may be sent to any suitable control functionality, e.g. a Smart House system, for further action.

Alternatively, rather than employing a passive sniffer to listen to a legacy network operational at a site e.g. a home or organization or outdoor site, and to report to a cooperating control element e.g. Smart Home or any other control system using object presence as an input to its logic, the "sniffer" functionality may be embedded in a dedicated beamformer (and/or beamformee). Dedicated beamformers and/or beamformee devices may be deployed at the site and their explicit beamforming V matrices (say) may be monitored. These devices need not even have router functionality nor even interface to the Internet. Any changes e.g. in beamforming-related matrices detected by the specially deployed devices may be reported directly to a cooperating control element. The dedicated beamformer and/or beamformee may for example implement some or all of the beamforming functionality provided in the 802.1 lac protocol.

Certain embodiments of the present invention seek to generate object movement/presence data without any need for deploying new hardware, except for a passive "sniffer" element; e.g. by utilizing legacy deployments of devices with beamforming functionality (e.g. IEEE 802.11.ac Access points (or routers) and receivers.

A legacy deployment may for example comprise legacy IOT (Internet of Things) devices using a suitable protocol such as but not limited to 802.1 lac which may have explicit beamforming functionality, or other legacy IOT (Internet of Things) devices using protocols which do not support beamforming but their transmissions may be used to measure their RSSI at sensor locations (e.g. 802.11η, Bluetooth, Bluetooth low energy). For example, a household appliance or processor-controlled device deployed in an organization, or any other beamformee, may tell an associated router that beam forming parameters between them should be changed, from which, according to certain embodiments of the invention, the system shown and described herein may infer that there is a change e.g. new human presence in the vicinity of that appliance. This may be done by deploying a suitable sniffer at a location enabling the sniffer to monitor data flowing between the appliance and the router.

Embodiments of the present invention include but are not limited to:

Embodiment bl: A method for detection of changes in a defined surveyed area, comprising:

a. intercepting, by at least one sensor, parameters of wireless signals and accordingly generating parameter values at steady-state conditions; Such parameters may for example be beamforming feedback packets when the wireless signal used complies with a standard that supports explicit beamforming such as 802.1 lac, or intercepted power level (RSSI) for wireless signals such as other 802.11 verities and Bluetooth.

b. continuously comparing the intercepted parameters versus the steady-state conditions, to detect threshold-surpassing changes which may indicate a change in the surveyed area; and

c. once a qualified situation is determined due to detection of threshold-surpassing changes, generating an alert response.

Embodiment b2: A method according to embodiment bl, wherein the steady-state conditions include evaluation of interception parameters values of wireless signals at steady-state (uninterrupted, idle state) conditions, based on accumulation of parameters at initial start-up interval (following "arming" of the system), wherein such values may be subsequently updated if changes occur which are not determined to be alarm situations. Embodiment b3: A method according to embodiment bl, further comprising applying a training session by deliberately introducing changes, resulting in deviations in the interception parameters that will be useful in identifying a specific alert situation, such that the characteristics measured in the training session may become part of the steady-state conditions, enabling more specific alert information and dynamic steady- state conditions.

Embodiment b4: A method according to embodiment b3, wherein steady-state conditions may be dynamically updated by the system based on changes in intercepted parameters that are determined to be legitimate changes in the monitored area that do not constitute an alarm.

Embodiment b5: A method according to embodiment bl, further comprising: a. intercepting beamforming feedback packets sent from the at least one station (node, Beamformee) to the at least one source (Beamformer); and

b. processing the intercepted packets in order to identify instances when significant changes occur in the vicinity of the Beamformer and Beamformee, which affect the transmission media and thus necessitate a change in the pattern of the beam formed by the at least one source.

Embodiment b6: A method according to embodiment b5, wherein the packets are intercepted by a computer software or hardware adapted to intercept and log traffic passing over a digital network.

Embodiment b7: A method according to embodiment b5, further comprising analyzing the changes in the transmission medium e.g. air between the beamformer and the beamformee, to identify the nature, direction or location of an object that causes the changes.

Embodiment b8: A method according to embodiment b5, wherein the source is a wireless router as the beamformer, and the sensor is an appliance passively connected to the wireless network, intercepting and analyzing communications between the wireless router and one or more station (node) as a beamformee.

Embodiment b9: A method according to embodiment 8, further comprising tracking changes in the location of a moving beamformee (a mobile sensor) in the surveyed area, by observing changes in the direction of the beam formed from the beamformer to the beamformee, thereby estimating the direction in which a user who carries the beamformee moves. Embodiment blO: A method according to embodiment b8, further comprising determining the location of a mobile sensor by combining information from stationary and mobile sensors.

Embodiment bl l: A method according to embodiment b5, wherein the station (node, beamformee) is a wearable Wi-Fi equipped device.

Embodiment bl2: A method according to embodiment bll, further comprising providing gesture recognition capabilities by processing the intercepted packets in order to detect the geometry between the beamformer and the wearable station (node).

Embodiment bl3: A method according to embodiment b8, wherein the source is an electronic device (e.g., a specially developed gadget) having basic router functionality such as beamforming to stations (nodes) on the network, with or without connection to the Internet, with capability to generate reports (such as alarm reports or updates to smart home system) when changes in beam parameters are detected.

The following terms may be construed either in accordance with any definition thereof appearing in the prior art literature or in accordance with the specification, or as follows: access point (AP): device that allows wireless devices to connect to a wired network using a wireless technology such as but not limited to Wi-Fi. The access point may connect to a router (e.g. via a wired network) or may be an integral

component of the router itself. Examples of commercially available access points which support 802.1 lac include: Aruba 270 series, Cisco Aironet 3600, HP 560, Ruckus T301 or any other suitable typically 802.1 lac wireless access point devices e.g. as described at the following http www link:

tomsitpro.com/articles/802. llac-access-points, 2-722-2.html.

For example, an access point (AP) may be operative, e.g. using an 802.11 standard, for sending data such as IP packets to a recipient device e.g. laptop.

For embodiments where RSSI is measured, a variety of wireless transmission standards may be used - intercepted and analyzed - such as but not limited to 802.11 and Bluetooth.

Access points supporting 802.1 lac may use any suitable phased antenna array e.g. on-chip or on-antenna.

Access points supporting 802.1 lac may listen to a client and use analysis to determine a best-suited beam along which to boost power. "Beamforming data" as used herein is intended to include any feedback data generated for sending back to a beamformer for use in forming optimal (as defined in a specific use-case) beams, e.g. data including channel measurements characterizing a channel between a beamformer and a client device. This data may for example be employed to determine how to use the available transmit power to best reach a client device e.g. by focusing energy toward a client or recipient.

Beamforming may be either explicit or implicit. For example, beamforming may follow the 802.11η standard or any specific one of the multiple beamforming methods described therein.

Beamformer: Any device that shapes its transmitted frames. A beamformer may generate a steering matrix (or any other suitable representation of the frequency response from each transmission chain in the array over each transmission stream) for determining how an antenna array should use each of its array elements to select a spatial path for the transmission.

Beamformee: any recipient or client device operative for receiving beamformed signals e.g. shaped frames from a beamformer.

Note: an AP (access point) which supports beamforming may initiate a frame exchange to a recipient device, typically beginning by exchanging frames to measure the AP-recipient radio channel. Channel calibration may also be termed "channel sounding" e.g. in the 802.1 lac standard. After this measurement has been completed, the AP may initially serve as a beamformer including sending spatially focused frames to the recipient. However, at the conclusion of the data transmission, the laptop must, if certain protocols e.g. 802.11 are employed, positively acknowledge receipt of the data. This acknowledgement may also be beamformed, in which case the recipient device may serve as a beamformer for the transmission of such acknowledgements. More generally, in any exchange between devices, either or both devices may calibrate the radio channel between them for beamforming purposes.

The beamformee may generate and transmit a feedback matrix which tells the beamformer how to steer the frame to the beamformee. A compressed beamforming report frame may include a feedback matrix and field/s enabling the beamformer to interpret the feedback matrix e.g. if (as in 802.1 lac) phase shift information is transmitted as a long string of bits; fields may be provided which indicate where to split the bits into individual matrix elements. Channel sounding: Beamforming may employ channel calibration or "channel sounding" e.g. as per the 802.1 lac or any other suitable standard, to determine how to radiate energy in a suitable direction. This term is intended to include any method having some or all of the following steps, suitably ordered e.g. as follows: (1) beamformer transmits a Null Data Packet (NDP) Announcement frame, to gain control of the channel and identify beamformees. Beamformees respond to the NDP Announcement, whereas other stations may defer channel access until sounding has been completed.

(2) beamformer transmits at least one null data packet which receiver may use to analyze OFDM training fields thereby to compute a channel response hence steering matrix. For multi-user transmissions, multiple NDPs may be transmitted. (3) beamformee analyzes training fields in received NDP and computes a feedback matrix; and (4) beamformer receives feedback matrix e.g. from beamformee/s and computes steering matrix to govern direction of subsequent transmissions toward the beamformee. Using the steering matrix, the beamformer may then transmit frames biased in a suitable direction.

"Explicit beamforming" as used herein is intended to include any beamforming technology in which separate frames are provided for or dedicated to channel measurements. For example: 802.1 lac-standard beamforming. "Explicit beamforming" as used herein is intended to include any beamforming technology in which the downstream channel is measured at a receiver (beamformee), and relayed back to a transmitter (beamformer) which uses the measured channel information to derive transmit beamforming parameters.

"Implicit beamforming". As used herein "Implicit beamforming" is intended to include any beamforming technology in which an upstream wireless channel is measured by the beamformer, and the resulting measurement is used to derive at least one parameter for at least one subsequent downstream beamformed transmission. In "Implicit beamforming" the beamformee need not measure and send channel state information to the beamformer.

802.1 lac standard: as used herein, is intended to include (a) any standard in which beamforming operates on pairwise relationships between a beamformer and a beamformee; and (b) any standard which uses null data packet (NDP) sounding technology.

Sniffer: Any device operative for capturing traffic flowing e.g. on a wirelesss network, such as the device distributed by SAVVIUS and described in the following http www link: wildpackets.com/use_cases/wireless_analysis/80211ac_analysis . A sniffer includes any device operative to analyze data and/or control ackets sent over a network e.g. in accordance with the 802.1 lac protocol.

Passive sensor/sniffer: intended to include any electronic device which monitors data sent between beamformee and beamformer.

In the embodiments herein, sensors may be replaced by sniffers as appropriate.

Station: may be both a beamformer and a beamformee.

V matrix: any feedback data computed by beamformee and transmitted to the beamformer to facilitate beamforming e.g. the V matrix of the 802.1 lac specification.

The term "mobile", "phone", "communication device", smart phone and similar as used herein is intended to include but not be limited to any of the following: mobile telephone, smart phone, playstation, iPad, TV, remote desktop computer, game console, tablet, mobile e.g. laptop or other computer terminal, embedded remote unit.

There is thus also provided, in accordance with at least one embodiment of the present invention, at least the following embodiments:

Embodiment 1. A method for detection of at least one of presence and motion of at least one object within a defined surveyed area characterized by presence of a transmission medium, the method comprising:

monitoring, using at least one processor, at least one parameter of at least one wireless signal passing through the transmission medium in the surveyed area over time, for at least one change pattern deemed suggestive of at least one of presence and motion within the surveyed area; and

responsive to at least the change pattern, generating an output signal alerting that a change in a motion pattern of at least one object may have occurred within the defined surveyed area,

wherein the wireless signal comprises beamforming feedback data sent from at least one Beamformee node ("station" ) back to at least one Beamformer node (source).

It is appreciated that identification of change "surpassing" a threshold is intended to include inter alia a measured change which is greater than (>), or greater than or equal to ( >=), a specific numerical threshold, or which is less than (<) a threshold e.g. the change may "surpass" the threshold, in certain embodiments, when the change drops below the threshold in which case changes below the threshold may be considered to have surpassed the threshold and would trigger action e.g. generation of an output signal; whereas changes above the threshold may be considered not to have "surpassed" the threshold and may not trigger action e.g. may not result in generation of the output signal.

Change patterns deemed suggestive of at least one of presence and motion within the surveyed area include but are not limited to, occurrence of certain predetermined values, or predetermined changes or rates of changes, in values e.g. including any of the possible implementations described herein.

Embodiment 2. A method according to any of the preceding embodiments wherein the change in the motion pattern may comprise a lack of motion as opposed to motion detected in the surveyed area in the past.

Embodiment 3. A method according to any of the preceding embodiments wherein the change in the motion pattern may comprise presence of motion as opposed to lesser or no motion detected in the surveyed area in the past.

Embodiment 4. A method according to any of the preceding embodiments wherein a sensor/sniffer, separate from the beamformee node, is operative for intercepting at least one parameter of the beamforming feedback data sent from the

Beamformee node back to at least one Beamformer node, for use in the monitoring.

Embodiment 5. A method according to any of the preceding embodiments and also comprising a Smart House system; and wherein the output signal is provided to the Smart House system which responsively performs at least one action suitable for being performed differentially depending at least on whether or not motion of at least one object has occurred within the defined surveyed area, thereby to provide an activity- triggered smart home.

Embodiment 6. A method according to any of the preceding embodiments wherein the monitoring comprises quantifying the change by comparing at least one parameter as intercepted to steady-state conditions previously measured at the surveyed area.

Embodiment 7. A method according to any of the preceding embodiments and also comprising a set-up stage during which conditions at the surveyed area are measured, thereby to establish the steady-state conditions, previous to the monitoring.

Embodiment 8. A method according to any of the preceding embodiments wherein the wireless signal comprises at least one explicit beamforming protocol packet sent from the at least one beamformee node to the at least one beamformer node. Embodiment 9. A method according to any of the preceding embodiments wherein the wireless signal comprises at least one 802.11.ac-protocol packet sent from the at least one beamformee node to the at least one source. Embodiment 10. A method according to any of the preceding embodiments wherein at least one parameter of at least one wireless signal for the monitoring is received from the at least one node.

Embodiment 11. A method according to any of the preceding embodiments wherein the 802.11.ac-protocol packet comprises at least one V-matrix including an array of V-matrix elements and wherein the change comprises change in at least one of the V-matrix elements.

Embodiment 12. A method according to any of the preceding embodiments wherein the Beamformee node is a legacy device which is already deployed.

Embodiment 13. A method according to any of the preceding embodiments wherein the Beamformer node is a legacy device which is already deployed.

Embodiment 14. A method according to any of the preceding embodiments and also comprising use of a training and Calibration scheme to process beamforming data accumulated using at least one sniffer distributed in a setting and operative for gathering data on N beams between beamformer(s) and beamformees, the scheme comprising:

during a training mode/stage, monitoring an object moving between a predetermined sequence of M locations including storing beamforming data on all N beams in server memory as the object reaches each of the M locations, thereby to define M calibration vectors each with N components, and

subsequently, in operational mode/stage, storing an N-component vector x representing beamforming data on N beams from the sniffers; and

computing a calibration vector closest to vector x, by finding the minimal distance from among the distances between vector x and each of the M calibration vectors,

wherein, if the minimal distance is smaller than a predefined threshold, the output signal comprises a notification that an object whose presence has been detected is in a location from among the M locations which corresponds to the calibration vector closest to vector x. Embodiment 15. A method according to any of the preceding embodiments and also comprising use of a route Calibration scheme to process beamforming data accumulated using at least one sniffer distributed in a setting, the scheme comprising: accumulating beamforming data as an object moves along a route of interest within the setting, during a training session in which no other moving objects are present, including recording beamforming data from all of the sniffers at P points in time thereby to define P calibration vectors each with N components; and

subsequently, in operational mode/stage, identifying a best fit between current beamforming data and the P calibration vectors.

Embodiment 16. A method according to any of the preceding embodiments wherein the Smart House system responsively performs the at least one action dependent also on pre-stored data representing at least one habit of at least one Smart House inhabitant.

Embodiment 17. A method according to any of the preceding embodiments wherein the pre-stored data is pre-learned using machine learning.

Embodiment 18. A method according to any of the preceding embodiments wherein the monitoring includes identifying change surpassing at least one threshold in the at least one parameter.

Embodiment 19. A method according to any of the preceding embodiments wherein each component comprises at least one V-matrix.

Embodiment 20. A method according to any of the preceding embodiments wherein each component comprises a plurality of V-matrices, one for each of a plurality of subcarriers.

Embodiment 21. A method for detection of at least one of presence and motion of at least one object within a defined surveyed area characterized by presence of a transmission medium, the method comprising:

monitoring, for at least one change pattern deemed suggestive of at least one of presence and motion within the surveyed area, at least one parameter of at least one wireless signal passing through the transmission medium in the surveyed area over time; and

responsive to at least the change pattern, generating an output signal alerting that a change in a motion pattern of at least one object may have occurred within the defined surveyed area, the method comprising use of a training and calibration scheme to process data accumulated using at least one sensor distributed in a setting, the scheme comprising: during a training mode/stage, monitoring an object moving between a

predetermined sequence of M locations including storing the measured parameter data in server memory as the object reaches each of the M locations, thereby to define M calibration vectors each with N components, and

subsequently, in operational mode/stage, storing an N-component vector x representing data from the sensors; and

computing a calibration vector closest to vector x, by finding the minimal distance from among the distances between vector x and each of the M calibration vectors,

wherein, if the minimal distance is smaller than a predefined threshold, the output signal comprises a notification that an object whose presence has been detected is in a location from among the M locations which corresponds to the calibration vector closest to vector x.

Embodiment 22. A method for detection of motion of at least one object within a defined surveyed area characterized by presence of a transmission medium, the method comprising:

monitoring, for at least one change pattern deemed suggestive of at least one of presence and motion within the surveyed area, at least one parameter of at least one wireless signal passing through the transmission medium in the surveyed area over time; and

responsive to at least the change pattern, generating an output signal alerting that a change in a motion pattern of at least one object may have occurred within the defined surveyed area,

the method also comprising use of a route calibration scheme to process data accumulated using at least one sniffer distributed in a setting, the scheme comprising: accumulating data as an object moves along a route of interest within the setting, during a training session in which no other moving objects are present, including recording data from all of the sensors at P points in time thereby to define P calibration vectors each with N components,; and

subsequently, in operational mode/stage, identifying a best fit between current data and the P calibration vectors.

Embodiment 23. A method according to any of the preceding embodiments wherein each component comprises a scalar. Embodiment 24. A method according to any of the preceding embodiments wherein each component comprises at least one matrix.

Embodiment 25. A system for detection of at least one of presence and motion of at least one object within a defined surveyed area characterized by presence of a transmission medium, the system comprising:

At least one processor operative to monitor at least one parameter of at least one wireless signal passing through the transmission medium in the surveyed area over time, for at least one change pattern deemed suggestive of at least one of presence and motion within the surveyed area; and

An alert generator operative responsive to at least the change pattern, for generating an output signal alerting that a change in a motion pattern of at least one object may have occurred within the defined surveyed area,

wherein the wireless signal comprises beamforming feedback data sent from at least one Beamformee node ("station" ) back to at least one Beamformer node (source).

Embodiment 26. A system for detection of at least one of presence and motion of at least one object within a defined surveyed area characterized by presence of a transmission medium, the system comprising:

Apparatus, including a processor, for monitoring at least one parameter of at least one wireless signal passing through the transmission medium in the surveyed area over time for at least one change pattern deemed suggestive of at least one of presence and motion within the surveyed area ;

An alert generator operative responsive to at least the change pattern, for generating an output signal alerting that a change in a motion pattern of at least one object may have occurred within the defined surveyed area; and

Server memory,

wherein at least one of the apparatus for monitoring and alert generator employs a training and calibration scheme to process data accumulated using at least one sensor distributed in a setting, the scheme comprising:

monitoring an object moving between a predetermined sequence of M locations during a training mode/stage, including storing the measured parameter data in the server memory as the object reaches each of the M locations, thereby to define M calibration vectors each with N components, and

in operational mode/stage, storing an N-component vector x representing data from the sensors; and computing a calibration vector closest to vector x, by finding the minimal distance from among the distances between vector x and each of the M calibration vectors,

wherein, if the minimal distance is smaller than a predefined threshold, the output signal comprises a notification that an object whose presence has been detected is in a location from among the M locations which corresponds to the calibration vector closest to vector x.

Embodiment 27. A system for detection of motion of at least one object within a defined surveyed area characterized by presence of a transmission medium, the system comprising:

Apparatus, including a processor, for monitoring at least one parameter of at least one wireless signal passing through the transmission medium in the surveyed area over time , for at least one change pattern deemed suggestive of at least one of presence and motion within the surveyed area; and

An alert generator, operative responsive to at least the change pattern, for generating an output signal alerting that a change in a motion pattern of at least one object may have occurred within the defined surveyed area,

wherein at least one of the apparatus for monitoring and alert generator employs a route calibration scheme to process data accumulated using at least one sniffer distributed in a setting, the scheme comprising accumulating data as an object moves along a route of interest within the setting, during a training session in which no other moving objects are present, including recording data from all of the sensors at P points in time thereby to define P calibration vectors each with N components; and in operational mode/stage, identifying a best fit between current data and the P calibration vectors.

Embodiment 28. A computer program product, comprising a non-transitory tangible computer readable medium having computer readable program code embodied therein, the computer readable program code adapted to be executed to implement any method shown and described herein.

Also provided, excluding signals, is a computer program comprising computer program code means for performing any of the methods shown and described herein when the program is run on at least one computer; and a computer program product, comprising a typically non-transitory computer-usable or -readable medium e.g. non-transitory computer -usable or -readable storage medium, typically tangible, having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement any or all of the methods shown and described herein. The operations in accordance with the teachings herein may be performed by at least one computer specially constructed for the desired purposes or general purpose computer specially configured for the desired purpose by at least one computer program stored in a typically non-transitory computer readable storage medium. The term "non-transitory" is used herein to exclude transitory, propagating signals or waves, but to otherwise include any volatile or non-volatile computer memory technology suitable to the application.

Any suitable processor/s, display and input means may be used to process, display e.g. on a computer screen or other computer output device, store, and accept information such as information used by or generated by any of the methods and apparatus shown and described herein; the above processor/s, display and input means including computer programs, in accordance with some or all of the embodiments of the present invention. Any or all functionalities of the invention shown and described herein, such as but not limited to operations within flowcharts, may be performed by any one or more of: at least one conventional personal computer processor, workstation or other programmable device or computer or electronic computing device or processor, either general-purpose or specifically constructed, used for processing; a computer display screen and/or printer and/or speaker for displaying; machine -readable memory such as optical disks, CDROMs, DVDs, BluRays, magnetic-optical discs or other discs; RAMs, ROMs, EPROMs, EEPROMs, magnetic or optical or other cards, for storing, and keyboard or mouse for accepting. Modules shown and described herein may include any one or combination or plurality of: a server, a data processor, a memory/computer storage, a communication interface, a computer program stored in memory/computer storage.

The term "process" as used above is intended to include any type of computation or manipulation or transformation of data represented as physical, e.g. electronic, phenomena which may occur or reside e.g. within registers and /or memories of at least one computer or processor. The term processor includes a single processing unit or a plurality of distributed or remote such units.

The above devices may communicate via any conventional wired or wireless digital communication means, e.g. via a wired or cellular telephone network or a computer network such as the Internet.

The apparatus of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instructions which, when executed by the machine, implements some or all of the apparatus, methods, features and functionalities of the invention shown and described herein. Alternatively or in addition, the apparatus of the present invention may include, according to certain embodiments of the invention, a program as above which may be written in any conventional programming language, and optionally a machine for executing the program such as but not limited to a general purpose computer which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may, wherever suitable, operate on signals representative of physical objects or substances.

The embodiments referred to above, and other embodiments, are described in detail in the next section.

Any trademark occurring in the text or drawings is the property of its owner and occurs herein merely to explain or illustrate one example of how an embodiment of the invention may be implemented.

Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions, utilizing terms such as, "processing", "computing", "estimating", "selecting", "ranking", "grading", "calculating", "determining", "generating", "reassessing", "classifying", "generating", "producing", "stereo-matching", "registering", "detecting", "associating", "superimposing", "obtaining" or the like, refer to the action and/or processes of at least one computer/s or computing system/s, or processor/s or similar electronic computing device/s, that manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories, into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices. The term "computer" should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, computing system, communication devices, processors (e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.

The present invention may be described, merely for clarity, in terms of terminology specific to particular programming languages, operating systems, browsers, system versions, individual products, and the like. It will be appreciated that this terminology is intended to convey general principles of operation clearly and briefly, by way of example, and is not intended to limit the scope of the invention to any particular programming language, operating system, browser, system version, or individual product. Elements separately listed herein need not be distinct components and alternatively may be the same structure. A statement that an element or feature may exist is intended to include (a) embodiments in which the element or feature exists; (b) embodiments in which the element or feature does not exist; and (c) embodiments in which the element or feature exist selectably e.g. a user may configure or select whether the element or feature does or does not exist.

Any suitable input device, such as but not limited to a sensor, may be used to generate or otherwise provide information received by the apparatus and methods shown and described herein. Any suitable output device or display may be used to display or output information generated by the apparatus and methods shown and described herein. Any suitable processor/s may be employed to compute or generate information as described herein e.g. by providing one or more modules in the processor/s to perform functionalities described herein. Any suitable computerized data storage e.g. computer memory may be used to store information received by or generated by the systems shown and described herein. Functionalities shown and described herein may be divided between a server computer and a plurality of client computers. These or any other computerized components shown and described herein may communicate between themselves via a suitable computer network.

BRIEF DESCRIPTION OF THE DRAWINGS Certain embodiments of the present invention are illustrated in the following drawings:

Fig. 1 schematically illustrates an exemplary embodiment of the invention;

Fig. 2 schematically illustrates a second exemplary embodiment of the invention;

Fig. 3 is a simplified flowchart illustration of a method provided in accordance with certain embodiments and including some or all of the illustrated operations, suitably ordered e.g. as shown, e.g. for combination with any of the systems of Figs. 1 - 2, 5 - 6.

Fig. 4 is an example table which may be stored in memory in accordance with certain embodiments. Any tables herein may include some or all of the illustrated fields and/or records.

Figs. 5, 6 schematically illustrate embodiments of the invention which may be combined with other embodiments, including calibration methods shown and described herein, in any suitable manner.

Methods and systems included in the scope of the present invention may include some (e.g. any suitable subset) or all of the functional blocks shown in the specifically illustrated implementations by way of example, in any suitable order e.g. as shown.

Computational components described and illustrated herein can be implemented in various forms, for example, as hardware circuits such as but not limited to custom VLSI circuits or gate arrays or programmable hardware devices such as but not limited to FPGAs, or as software program code stored on at least one tangible or intangible computer readable medium and executable by at least one processor, or any suitable combination thereof. A specific functional component may be formed by one particular sequence of software code, or by a plurality of such, which collectively act or behave or act as described herein with reference to the functional component in question. For example, the component may be distributed over several code sequences such as but not limited to objects, procedures, functions, routines and programs and may originate from several computer files which typically operate synergistically.

Any method described herein is intended to include within the scope of the embodiments of the present invention also any software or computer program performing some or all of the method's operations, including a mobile application, platform or operating system e.g. as stored in a medium, as well as combining the computer program with a hardware device to perform some or all of the operations of the method.

Data can be stored on one or more tangible or intangible computer readable media stored at one or more different locations, different network nodes or different storage devices at a single node or location.

It is appreciated that any computer data storage technology, including any type of storage or memory and any type of computer components and recording media that retain digital data used for computing for an interval of time, and any type of information retention technology, may be used to store the various data provided and employed herein. Suitable computer data storage or information retention apparatus may include apparatus which is primary, secondary, tertiary or off-line; which is of any type or level or amount or category of volatility, differentiation, mutability, accessibility, addressability, capacity, performance and energy use; and which is based on any suitable technologies such as semiconductor, magnetic, optical, paper and others.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

Methods and systems are described herein which are operative to monitor for and detect change in a surveyed area such as threshold-surpassing e.g. abrupt changes resulting from presence of new objects in the monitored area - in parameters such as but not limited to Beamforming parameters (e.g. V-Matrix in Wifi 802.1 lac) or Intercepted power parameters (e.g. RSSI - received signal strength indicator— in all types of communications). A processor is used to identify changes deemed suggestive of at least one of presence and motion e.g. predefined change/s of monitored value/s or in rate/s of changes of monitored value/s, or occurrence of predetermined values in the monitored parameters, e.g. including any of the possible implementations described herein.

Certain embodiments seek to observe changes in parameters of wireless signals caused by changes in presence of objects (in particular human beings) in a surveyed area, or changes in the location of a sensing device, etc.

Certain embodiments seek to provide a system and method for motion detection, occupancy detection or detection of other changes in a defined area using simple sensors that monitor changes in parameters of wireless signals. Additionally, embodiments of the present invention also provide a system and method for detecting changes in the location of stations e.g. of a wireless network. Throughout this description the term "sensor" is used to include any element which - when connected to a wireless network - may intercept and evaluate parameters and/or contents of data transmission, analyze them to generate various types of alert information when appropriate, and optionally communicate with other elements. Typical sensors may comprise computers with dedicated software application, or dedicated gadgets or devices having the required functionality (e.g., a dedicated device embedded with local area wireless technology such as Wi-Fi). In some instances, the sensor may be embedded in an appliance - for example special software in a router or Wi-Fi circuitry and a processing element in a smart TV. Unless otherwise indicated, the functions described herein may be performed by executable code and instructions stored in a computer readable medium and running on one or more processor-based systems. However, state machines, and/or hardwired electronic circuits may also be utilized. Further, with respect to the example processes described herein, not all the process states need to be reached, nor do the states have to be performed in the illustrated order. Further, certain process states that are illustrated as being serially performed may be performed in parallel.

While embodiments of the invention will be described in the general context of program modules that execute in conjunction with an application program that runs on an operating system, those skilled in the art will recognize that the embodiments may also be implemented in combination with other program modules and/or other devices.

In this context, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types adapted to intercept and evaluate parameters and contents of relevant types of wireless communications.

According to an embodiment of the present invention, applicable for wireless networks which make use of beamforming mechanisms (such as the one defined for 802.11.ac protocol based systems, for example), a "sniffer" element monitors packets transmitted from/to a router and a "station" on a Wi-Fi network, and in particular tracks data associated with beamforming information. An threshold-surpassing change in a beam formed between the router (beamformer) and the "station" (beamformee) is interpreted as indicating a change in the area in which one or both are installed.

In the prior-art, beamforming is used to overcome intercepted signal degradation caused by phenomena such as multipath. To overcome such problems, beamforming uses multiple transmitting antennas, where the same signal is applied to multiple antennas - however with pre-determined shifts in phase and/or gain that are introduced into the transmission circuitry. The shifts are intended to cause the various signals, transmitted from the multiple antennas (e.g., from the transmitter), to reach the intercepting antenna(s) (e.g., the receiver) in a manner resulting in a summation that yields a signal that will provide better system performance.

The IEEE 802.11.ac wireless networking standard specifies the mechanism of explicit feedback beamforming to optimize this beamforming process. This process is started by the Beamformer (typically an Access Point, AP) sending a signal called VHT Null Data Packet (VHT NDP) to the intended recipients (e.g., the receiving units - the Beamformees). Each of those Beamformees computes and generates - based on the parameters of the intercepted signal - a matrix that characterizes the channel between the AP and the receiver. This matrix, usually referred to as the V matrix, is sent back to the AP (as feedback data) where it is used to generate the parameters that will result in the best beam to be used to exchange data between the AP and the specific Beamformee.

According to an embodiment of the present invention, the system intercepts, recognizes and decodes the explicit beamforming feedback data sent from a beamformee to the AP. This is achieved by a computer software or hardware that connects to the wireless network and passively "sniffs" the packets. By intercepting the packets and processing them, the system is able to identify instances in which threshold-surpassing changes in the beam parameters are taking place, presumably due to changes which occur in the vicinity of the Beamformer and Beamformee. Such changes affect the transmission media and thus necessitate a change in the pattern of the beam formed by the AP.

The mode of operation described above uses stationary receivers (beamformees), and thus observed changes will be attributed mainly to changes in the surveyed area, namely by changes in the presence and location of objects (in particular human beings) in the vicinity of the beamformer and the beamformee. Identification of such changes may be used to trigger an alarm in the case of an intrusion detection system, or as an input to another system (such as a Smart Home system).

Examples of elements that will take part in such an environment are a wireless router as the beamformer, and any appliance equipped with wireless connection to that router as a beamformee. Special gadgets may be developed for the system and used as either beamformers or beamformees, using a dedicated wireless network or an existing one. According to some embodiments of the present invention, special algorithms are used to analyze the changes in the channel between the beamformer and the beamformee, as reflected in the V Matrix values, to identify the nature, direction or location of the object causing the changes in the channel.

A different mode of operation is to track changes in the location of a mobile receiver (beamformee) - for example a cellular phone carried by a person who moves in the surveyed area. By observing changes in the direction of the beam formed from the beamformer to the beamformee, the system may estimate the direction in which the person moves (important information for Smart Home systems, for example).

By combining information from stationary and mobile receivers, improved performance may be achieved in determining the exact location (as opposed to direction from the beamformer) of a mobile sensor. Furthermore, by switching the connection of a mobile receiver between two or more APs and obtaining the directions of the respective beams formed, triangulation schemes may be used to derive the receiver's location.

According to an embodiment of the present invention, the process of motion detection, occupancy detection or detection of other changes in a defined area involve some or all of the following steps, suitably ordered e.g. as shown:

a. the beam forming parameters, as sampled by the sensor at a "quiet time", are used to generate a Baseline state - e.g., values that define steady-state (uninterrupted) conditions.

b. Subsequently, newly acquired parameters are continuously compared versus the Baseline state, to detect threshold-surpassing changes which may indicate a change in the surveyed area or a change in the location of the receiver (station) with respect to the surveyed area (e.g., due to the exiting of the receiver (station) from the surveyed area). c. Once a qualified situation (e.g., threshold-surpassing change in parameters) is determined, an alert response is generated - Local audible and/or visual alarm, SMS, mobile messaging app such as "WhatsApp" or other messaging over Internet, phone call, logging of alarm state in local or remote database. Optionally, activation of local camera to take a snapshot or video, to (optionally) be transmitted along with alert message.

In the case of integration with a smart home system, a message to that system is issued, d. If minor changes in monitored parameters are detected (or changes that are not determined by the system to justify an alarm), the new parameters may be used by the system to update/modify the originally set baseline state. Example Architectures may include the following:

a. Passive, existing network, single sensor: In this architecture a passive "sniffer-like" unit is connected to an existing Wi-Fi network. Monitoring the data transferred between the AP and existing nodes (which may be computers or any Wi-Fi-equipped appliances - including refrigerator, coffee machine or any other adequately equipped appliance) the unit will:

a. Estimate parameters at baseline state;

b. Perform evaluation of parameters in monitor mode;

c. Determine deviation from baseline state (significant changes in connection parameters - particularly the beam to be formed);

d. Generate alarm response, issue message to smart home system, or update baseline when appropriate.

b. Passive, existing network, multiple sensors: In this architecture, multiple sensors (each as per the example above) are connected - each sensor to a different network, and/or several sensors sharing the load on a single network. With all sensors reporting their results to a central control center (local or remote), the control center may apply special algorithms to enhance and fine tune the overall picture obtained regarding changes in the surveyed area (typically a larger area than one that may be covered with a single sensor).

c. Active, stand-alone network: In this architecture a dedicated "private" network is formed which includes Beamformers and Beamformees. Typically such a network will comprise special gadgets developed for the system. A beamformer gadget may also include software that will implement the "sniffing" functionality described earlier.

Typical Operation modes may include all or any subset of the following:

1. Baseline setting - evaluation of parameters values of wireless signals at steady-state (uninterrupted, idle state) conditions, based on accumulation of parameters at initial start-up interval (following "arming" of the system). Baseline state values may be subsequently updated by the system if changes occur which are not determined to be alarm situations.

2. Training session - rather than setting baseline values by evaluating parameters at an idle state, it might be advisable in some cases to deliberately introduce changes, resulting in deviations in the parameters that will be useful in identifying specific alarm situation. As an example, a person moving in a certain area during the training session will presumably cause changes in the parameters that will be similar to those caused by an intruder during monitoring mode. The characteristics measured in a training session may become part of the baseline settings or other data structure, enabling more specific alarm information.

3. Monitoring mode - ongoing evaluation of parameters, using a variety of algorithms which may include basic ones such as threshold detection, or sophisticated machine- learning schemes, to determine gross deviations from baseline settings, possibly indicating an alarm situation. Some changes identified during monitoring mode may be used to update the baseline settings.

4. Alert condition - gross deviations from the baseline settings may be a cause to generate an alert which may comprise of local audible and/or visual alarm, SMS,

Whatsapp or other messaging over Internet, phone call, logging of alarm state in local or remote database, activation of local camera to take a snapshot or video, to (optionally) be transmitted along with alarm message.

5. Change occurrence - in smart home applications, rather than setting (or reporting) an alarm situation, gross deviations from the baseline settings may cause sending a message to another system. That system may in turn perform an activity such as turning lights off in a vacated room, turning air-conditioning on upon entrance of a person to a room, etc. Typical types of messages sent may include a person entering a room, a person leaving a room, movement in a certain direction, room left empty, etc.

All the above will be better understood through the following illustrative and non- limitative implementation examples.

Fig. 1 shows a "sniffer" device 14 that may be used in conjunction with embodiments of the invention. The device illustrated in this figure is particularly convenient because it may be passively connected to the network 13 and used as a sensor without the need to carry out alterations in the structure. In this embodiment, a dedicated application that resides in the device 14 is adapted to monitor data exchanged in the network between an access point as indicated by numerals 10 and stations as indicated by numerals 11 and 12, and in particular to detect threshold-surpassing changes in the parameters of the beam formed between those elements (for example by interpreting the V matrix data as defined in the 802.1 l.ac protocol) which may indicate a change in the surveyed area. When a qualified situation is determined device 14 may generate an alert or report via Internet connection 15 to a server 16 which in turn will take further action. Where exemplary embodiments herein refers to a "sniffer" element, or to a smartphone, a wide variety of computer or electronic systems/devices equipped with a wireless network interface controller may be used, such as, without limitation, specific software in the system's router, a tablet, a smart television, a network-enabled personal digital assistant (PDA), a network game console, a networked entertainment device, a digital camera, a home appliance and so on.

According to some embodiments of the invention, a wireless network source may be a dedicated wireless network transmitter (WNT) gadget configured to generate wireless signals using any wireless distribution method, such as Wi-Fi , which supports beamforming as per standard 802.1 l.ac (for example), or Bluetooth. As the sensor may only need to intercept beamforming information, the wireless network transmitter does not necessarily need to have any Internet connection ability (although it is possible to manufacture a wireless network transmitter with such functionally, if required). Therefore, in its compact form, the wireless network transmitter may include only the component/circuitry required for a sensor, and/or for generating only the wireless network signals for communicating with stations on the network using beamforming to optimize the performance of the communications channel. For example, the wireless network transmitter may be used in areas where the existing wireless network (which usually has a fixed location) lacks proper coverage for a desired area, or for areas where no wireless network exists.

According to some embodiments of the invention, the "station" (or sensor) may be a dedicated gadget having a processing unit configured to intercept wireless signals and to operate according to the method and embodiments described in a relevant wireless communications protocol (802.1 l.ac, for example), in particular with respect to data exchange to enable forming a beam that will optimize the performance of the communications channel.

Fig. 2 schematically illustrates a system for detection of changes in a defined surveyed area, according to an embodiment of the invention. The system comprises a wireless network transmitter (WNT) 20, and "stations" as indicated by numerals 21, 22 and 23 connected through network 24.

WNT (wireless network transmitter ) 20 is an example of a dedicated gadget that includes circuitry and software that performs the explicit beamforming functionality usually found in a router vis-a-vis stations, as well a processing that analyzes changes in the beamforming feedback information (the V matrix in case of 802.1 l.ac), which, in case of threshold-su assing ones, may be determined (by the WNT itself or by a server 26 to which the sensor sends the data via internet connection 25) as an indication of a change in the surveyed area.

It is appreciated that devices that support beamforming focus their signals toward each of one or several Wi-Fi clients in a room, concentrating the data transmission so that more data reaches each targeted device instead of broadcasting a signal to a wide area (e.g. approximately equally in all or many directions ) thereby radiating out unnecessarily into the atmosphere. If a Wi-Fi client also supports beamforming, router and client may exchange information about their respective locations to determine an optimal signal path between them.

According to certain embodiments, a system is provided, such as but not limited to any of those described and/or illustrated in the drawings, which is operative to capture (e.g. in a 802.1 lac or other Wifi scenario, particularly if explicit beamforming is supported) data defining how a beam should be formed by at least one beamformer, and to use logic to deduce therefrom, that change may have occurred in a physical space between the beamformer and at least one beamformee, under the assumption that change in a beam formation recommendation generated e.g. by a beamformee, implies a change in the physical space between the beamformer and beamformee.

A suitable method which may be employed to identify changes in a channel between a beamformer and beamformee, by analyzing beamforming data e.g. V Matrix values, is illustrated in Fig. 3.

The method of Fig. 3 may include some or all of the following steps, suitably ordered e.g. as shown:

a. following startup of the system, "no activity" is ensured for an initial set-up time- period, e.g. by selecting a suitable time, known to be quiet, to set up a site, and/or by instructing human users of the site to refrain from use thereof during this time period. During this time period, the system may be said to be in "baseline setting" operation mode.

b. an "inactive" sample of beamforming parameters (configurable number) is collected. c. check "no activity" (e.g. check standard deviation of the sample collected in (b) is lower than a (configurable) threshold, if not, return to (a). If "no activity", proceed, d. build an "inactive" baseline: compute mean (or other central tendency) and standard deviation of sample collected in (b), typically assuming normal (Gaussian) distribution of the sampled parameter values. e. continue to collect beamforming parameters. When a sample of same deviates from the baseline using a (configurable) criterion e.g. a (configurable) difference, in %, between current parameter or mean of parameters (over a "window" of configurable or predetermined size) and baseline mean, proceed to step (f) to start building a

characterization of an "active" group of parameters.

f. characterize "active" parameters: compute mean (or other central tendency) and standard deviation of sample found in (e) to deviate from inactive baseline, typically assuming normal (Gaussian) distribution of the sampled parameter values.

g. continue to collect beamforming parameters, monitor to find out whether or not there is a deviation from the mean computed in (f) using a (configurable) criterion for deviation e.g. a (configurable) difference, in %, between current parameter or mean of parameters (over a "window" of configurable or predetermined size) and "active" mean computed in step (f).

h. for as long as no deviation is found by monitoring of step g, continue to update "active" mean computed in step (f).

i. If monitoring of step g identifies a sample that does not, e.g. using the (configurable) criteria of steps e.g. respectively, seem to belong to either of the "inactive" and "active" groups, an activity probability value is assigned to the sample based on the sample's distance, using any suitable distance/similarity metric , from the inactive and active groups of parameters. Typically, if the new sample is more similar to the inactive group, the level of the activity probability value may be low, whereas if the new sample is more similar to the active group, the level of the activity probability value may be high.

For example consider a situation where the mean "Inactive" level is 4 and the mean "Active" level is 40, and the standard deviation of each is known. Assuming for simplicity and by way of example that both standard deviations are the same, if the new sample yields a Value of 8, the new sample may be considered "Inactive" whereas if a new sample yields a value of 32 the new sample may be considered "Active". A new sample having a value of 22 might be "Active" with 50% probability; standard deviation values may also be used to estimate the probability. Subsequent final decision may be based on the probability inter alia. For example, one input says 50% whereas another says 2%; the final decision combining both these inputs would probably be "Inactive", j. periodically or upon occasion, e.g. after processing a (configurable) number of samples similar to the "inactive" group of parameters , re-build the "inactive" baseline e.g. by using a FIFO (first-in first-out) mechanism to recompute mean and standard deviation based at least in part on newer inactive sampled parameters not used previously, thereby to identify drift in the characteristics of the inactive state.

Beamforming parameters may for example include some or all values in the V Matrix being transmitted between beamformee and beamformer. Parameter processing steps in Fig. 3 may therefore be performed separately for several or all of the values in the V matrix. The data generated by the parameter processing steps of Fig. 3 for each of several V matrix values may be combined logically or computationally in any suitable manner to determine that a deviation indicative of a channel change, has occurred. For example, even one single element or a small number of elements that has (or each have) changed very significantly may be deemed indicative of a channel change, and/or a smaller degree of change in each of a larger number of V matrix elements may be deemed indicative of a channel change. Furthermore, a single transmission typically includes several subcarriers in which case there may be a V Matrix for each subcarrier; the values for each may be suitably combined over positions in the matrix and/or over subcarriers, in any suitable manner, to yield one or more suitable criteria of channel change. In an application-specific set-up stage, one or more suitable combination formulae and cut-off points for the output of the formulae may be selected, from among various tested possibilities, to determine an optimal combination formula and cut-off point whose performance in identifying artificially introduced change (e.g. change deliberately induced in the set-up stage in a controlled manner) is found to be best.

An example of deliberate change introduction used in training session mode is as follows; the example is described in terms of beamforming measurements rather than, say, RSSI measurements however this is not intended to be limiting:

A household appliance or processor-controlled device e.g. a TV is operating as a beamformee. It is desired to identify situations in which a person enters the room to interact with the appliance e.g. to watch TV. Rather than merely notifying that the matrix governing beamforming to the TV set has changed, the system may (a) observe in a "training" (set-up e.g.) session, changes that occur when a human is asked to simulate, once or multiple times, a typical interaction with the appliance e.g. to walk into the room and watch TV. (b) store those changes in a "calibration table", and (c) when in real operation, look for patterns of change resembling those in the "calibration table", using suitable logic which may for example be based on identifying commonalities between change patterns detected each of the multiple times in which the human walks into the room and watches TV, in the set-up/training stage. It is appreciated that this embodiment may be implemented inter alia for beamforming V matrix evaluation; by replacing or augmenting analytic analysis of the changes in the V-matrix parameters, with a calibration table built based on set-up "simulations" and storing indications of specific types of interferences or events which cause specific change patterns in the matrix. Then, in run time, if these change patterns in the matrix are identified, it may be deemed/deduced that the corresponding events may have occurred.

It is appreciated that any suitable training and calibration schemes may be employed. An example training and calibration scheme is now described with reference to the table of Fig. 4.

According to certain embodiments, data such as RSSI or beamforming e.g. V- matrix data may be accumulated by N sniffers/sensors distributed in any desired manner in a home setting, rather than by a single sniffer.

For example, N sniffers may be provided in various rooms of a home, providing data on 8 beamformers/beamformee pairs (beams) e.g.:

· Living room -3 beams: SI 1,S12, SB

• Family room -2 beams: Sfl,Sf2

• Corridor -1 beam: Scl

• Bedroom - 2 beams: Sbl,Sb2

The specific V-matrix (say) data employed and stored may be installation- dependent. For example, all entries of the V-matrix or any subset thereof may be employed; for all of multiple subcarriers (one matrix for each subcarrier, e.g.) or for only one of some subcarriers, and so on.

During training, a person is monitored moving between a predetermined sequence of M locations (e.g. locations 1 - 10 in the apartment or home which correspond respectively to the M=10 rows in the table of Fig. 4). Whenever s/he reaches each of the 10 points, beamforming data from all N sensors monitoring the L = 8 (in this example) beams may be stored in server memory (optionally after averaging several measurements) e.g. as shown in the table of Fig. 4 in which each row may be regarded as a vector with 8 components (or more generally, L components where L is the number of beams on which data is collected).

Later, when the system is in operational mode rather than training mode, the vector of measurements from all sensors is found to be Sll(x) S12(x) S13(x) Sfl(x) Sf2(x) Scl(x) Sbl(x) Sb2(x). The method of the present invention may according to certain embodiments find a calibration point closest to point x corresponding to the above vector, by finding the minimal distance (root mean square) between the measured vector x and each of the M calibration vectors, where the M calibration vectors are those stored in the training stage e.g. as represented by the M = 10 respective rows of Fig. 4, in the illustrated example. For example, the distance from vector 1 may be computed as follows (formula 1):

Sqrt((Sll(x)-Sll(l))**2 + (S12(x)-S12(l))**2+(S13(x)-S13(l))**2+

+ (Sfl(x)-Sfl(l))**2 + (Sf2(x)-Sf2(l))**2+(Scl(x)-Scl(l))**2+

+ (Sbl(x)-Sbl(l))**2+(Sb2(x)-Sb2(l))**2)

The above formula assumes each element is a scalar. However, more generally each element may comprise a matrix (e.g. the V-matrix), or a plurality of matrices (e.g. V- matrices corresponding to each of a corresponding plurality of sub -carriers), or any other suitable combination of measured values.

If the minimum distance obtained (between measured vector and each calibration vector) is smaller than a predefined threshold, the method may notify that the object whose presence is detected is in the vicinity of the relevant calibration point. For example, if the row corresponding to the corridor sensor is the one which yields the minimum distance, the method may notify that the object whose presence is detected is in the corridor.

A confidence level may be provided, and used as a factor in any suitable action decision logic, e.g. by determining whether the minimum distance is much smaller than all other distances between the measured vector and calibration vectors (high confidence) or whether the minimum distance is just barely smaller than various other measured vector minus calibration vector distances (low level of confidence).

According to certain embodiments, a plane (say) may be fit between the minimum point and other points in its vicinity. For example, 3 (say) points e.g. the 3 points in the living room in the illustrated embodiment, may be weighted by their respective distance values computed above, to obtain a point typically in between the 3 points, that does not necessarily coincide with a calibration point. The method may then report that the detected presence appears to have been located at this intermediate point.

According to certain embodiments, local minima may be employed. For example, a person in the living room will impact the sensors in the living room, but probably not impact the sensors in the bedroom. Thus the calibration table may be used to identify existence of more than one person/object. For example, a good fit may be found, between the measured vector's components Sll, S12 and S13 and the corresponding 3 components in line 5 of the calibration table of Fig. 4 (assume Fig. 4's line 5 corresponds to a person standing in the living room during calibration). Also, good fit may be found between the measured vector's components Sbl, Sb2 and the corresponding pair of components in line 8 of the calibration table of Fig. 4 (assume line 8 corresponds to a person standing in the bedroom during calibration).

The above situation may be interpreted as being indicative of two instances of new presence: one new person/object in the living room and another in the bedroom.

According to certain embodiments, a route (living room to kitchen, for example) may be calibrated using a variation of the above method. For example, beamforming (or RSSI or other wireless signal behavior) data may be accumulated as a person walks from one place to another during a training session in which no other moving persons or objects are present. Values from all sensors are typically recorded at P points in time, typically separated by uniform time intervals, as the person traverses the route being calibrated. Thus for each route pre-calibrated, data similar to that shown in the table of Fig. 4 may be stored but the P rows of the table now respectively correspond to the P points in time intervals rather than to locations of multiple "interferers" (e.g. persons, during calibration as described above).

In operation, the method may then attempt to identify a best fit between gathered data (e.g. values obtained from all sensors over time) to calibrated routes.

Example: Upon entering her or his apartment via the front door, a person called James typically takes one of three routes:

a. front door to kitchen to living room;

b. front door to bathroom to living room;

c. front door to bathroom to bedroom.

Each of those routes may be for example take approximately 20 seconds.

In calibration (training) James walks each route, and the output of each sensor is sampled periodically e.g. once per second. A table similar to the table of Fig. 4 is generated for each route. Each (consecutive) row corresponds, therefore, to a set of measurements taken in a particular (consecutive) second. In the tables corresponding to the first and second routes, the first row represents presence of a person near the door, and the last row represents presence of a person in the living room; but the intermediate rows of the two respective tables are different. In the table corresponding to the third route, the table's first row represents presence of a person near the door, and the last row represents presence of a person in the bedroom.

In normal operation, when is has been determined that someone has entered the apartment, the sensors may be sampled periodically, typically using the same period or intervals as were employed during calibration/training e.g. once per second for 20 seconds. A table is generated representing the sensor readings over time, as was done during calibration. Then, an attempt may be made to find a good match (e.g. using least squares over all cells in all tables) between the table generated during normal operation, and one of the tables gathered during calibration.

As a person might be walking faster or slower along the route than during training/calibration, several matching attempts may be made, corresponding, respectively, to different walking speeds. For example, a first matching attempt may attempt to match the 20 seconds as sensed to the calibration data, line by line. Then, another matching attempt corresponding to a possible faster, 10 second walk, may be made by comparing the gathered data with every second line in the calibration data. If it is desired to check a match assuming a non-integer ratio between calibration walking speed and normal walking speed, gathered data may be compared to values interpolated between appropriate cells in the calibration table, or vice versa, interpolated gathered data may be compared to appropriate cells in the calibration table.

According to one embodiment, the output signal may be provided to an automated system and may be regarded as indicative of human presence, or absence, in a surveyed area, and, responsively, suitable action may be taken by the automated system. For example, if the output signal indicates less motion suggesting human absence from the area, energy-consuming devices serving humans in the surveyed area, such as heating, air- conditioning and illumination, may be turned off. If the output signal indicates more motion suggesting that humans are now present in the area, energy-consuming devices serving humans in the surveyed area, such as heating, air-conditioning and illumination, may be turned on. The criterion for turning off (or on) such devices may logically combine the output signal value with temporal data e.g. the duration for which human absence is detected and/or the time of day at which human absence is detected. For example, heaters may be turned off only if human absence persists for at least 0.5 hours and may be left on otherwise. Channel 2 may be turned on if the time is 6 pm and stored results of machine learning suggest that human/s who return to the surveyed area after 5 pm usually turn on channel 2. Channel 1 may be turned on if stored results of machine learning suggest that human/s who return to the surveyed area before 5 pm usually turn on channel 1 and the time is now 3 pm, and so forth.

More generally, a Smart House system may perform at least one action responsive to any suitable combination of:

logic derived from behavior of wireless signals collected from the area of interest e.g. beamforming data or RSSI measurements (e.g. as described above and below); pre-stored data entered by a human operator and representing at least one habit of at least one smart house inhabitant; and

pre-stored data representing at least one habit of at least one smart house inhabitant which is pre-learned using machine learning.

According to certain embodiments, when a multiple sensor/sniffer architecture is employed e.g. as described hereinabove, the processing load may be shared between the sniffers e.g. if many beamformees are involved as may occur e.g. in a IOT scenario in which many household appliances or processor-controlled devices are deployed at a single premises, and/or if it is desired to "sniff" a plurality of networks. According to certain embodiments, a trajectory of a moving object is deduced, using suitable logic, if changes are found to occur in relatively rapid succession, and the timing of the changes suggests that a human carrying, bearing or wearing a cellphone or Wifi equipped device (say) has moved from one appliance's vicinity to another. For example, a change is detected between a router and a TV, then between a refrigerator and the router, then between a bathroom appliance and the same router. If the time differences between these events are compatible with normal human velocity, detection of this sequence may trigger an indication or suggestion of an object's (e.g. human's) trajectory from the TV to the refrigerator to the bathroom.

A particular advantage of certain embodiments is that no cooperation from the manufacturer of a (legacy or off-the-shelf) system that is generating beamforming data is required, because the protocol governing the exchange of such data is normally known. Therefore, beamforming data e.g. packets of V matrix data, typically resulting from an interrogation process between a router e.g. beamformer and beamformee which may respond via a conventional local network, may be sniffed e.g. passively read and used to identify any change in the Matrix values.

According to certain embodiments, the processing components shown and described herein e.g. the logic of Fig. 3, may interface to a central control system connected to "Internet of things" devices. The system may be designed as a cost effective DIY (do it yourself) system with a simple application download, thereby rendering itself suitable for end users which require low cost technology.

It is appreciated that the embodiments shown and described herein, including but not limited to embodiments employing V-matrix measurements and embodiments employing RSSI measurements, have a wide variety of uses and possible interfaces, including but not limited to the following:

a. security systems which provide remote access and control e.g. via an end-user's smart phone or tablet, to alert the end-user of intrusions e.g. by recognizing a new "presence" in an end-user's premises. Premises may be a dwelling, commercial or security premises, or organizational premises such as a museum in which it is desired to provide a system to support museum security staff, e.g. by sending alerts of possible intruder presence to staff's mobile communication devices.

b. Smart Home and Smart buildings -as opposed to devices which merely support remote end-user control of household appliances typically via the end-user's smartphone, the system shown and described herein may be used to support a (human/pet/object) activity- triggered based smart home which may even include a learning system that learns the habits of the people in the house then is operative for controlling smart systems on the premises e.g. for energy efficiency and money saving, including any or all of turning on/off lights, air conditioning and heating, TV, radio, and other entertainment control, window, home/garage door and shutter control, home security system modes.

In Smart buildings such as but not limited to office buildings, large residential buildings, schools, libraries, museums, sport facilities, stores/markets/malls e.g. in fitting rooms, public toilets, and workplaces including manufacturing facilities, use of occupancy detection as an input to systems which control lights, air-conditioning/heating, power- on/off of computers and other electrical devices and more, can achieve significant advantages in terms of, say, energy efficiency.

Similarly, smart signs and TV screens for commercial use in public spaces may turn on/off music or any other content triggered, at least in part, on occupancy detection.

Reference is now made to Figs. 5, 6 which illustrate other embodiments of the present invention which may be combined with the above teachings, e.g. the above calibration methods, in any suitable manner. For example, embodiments described herein in a beam-forming context, e.g. using V matrices, may alternatively be implemented using measurements other than V matrices such as but not limited to RSSI. These other embodiments of the present invention seek to provide a system which is capable of detecting behavior associated with identifying changes in surveyed area and/or the exiting of a sensing device from a specific region covered by one or more wireless networks.

These embodiments include:

Embodiment al : A method for detection of changes in a defined surveyed area, comprising some or all of the following steps suitably ordered e.g. as follows: a) intercepting parameters of wireless signals from at least one source that generates wireless signals and accordingly generating interception parameter values at steady-state conditions;

b) continuously comparing the intercepted parameters versus the steady-state conditions, to detect threshold-surpassing changes which may indicate a change in the surveyed area; and

c) once a qualified situation is determined, generating an alert response.

Embodiment a2: A method according to embodiment al, wherein the steady-state conditions includes evaluation of interception parameters' values of wireless signals at steady-state (uninterrupted, idle state) conditions, based on accumulation of parameters during an initial start-up interval (following "arming" of the system), wherein such values may be subsequently updated if changes occur which are deemed not to be alarm situations.

Embodiment a3: A method according to embodiment al, further comprising applying a training session by deliberately introducing changes, resulting in deviations in the interception parameters that will be useful in identifying specific alert situations, such that the characteristics measured in the training session may become part of the steady-state conditions, enabling more specific alert information and dynamic steady-state conditions.

Embodiment a4: A method according to embodiment a3, wherein steady-state conditions may be dynamically updated by the system based on changes in intercepted parameters that are determined to be legitimate changes in the monitored area that do not constitute an alarm.

According to an embodiment of the present invention, the interception parameters processed by each sensor may comprise technical parameters such as the ID of the wireless network source, Amplitude (e.g. RSSI), Phase, time of arrival, and frequency (Doppler shift) of one or more intercepted Wi-Fi, Bluetooth or other wireless communications networks, at one or more sensors.

A mobile device 110 may be provided which may be used as a sensor without needing to carry out alterations in the structure. The mobile device may be equipped with a wireless network interface controller such as an iPhone by Apple Inc. or other type of smartphone. In this embodiment, a dedicated application which resides in the mobile device is adapted to monitor changes in intercepted parameters of wireless signals as received from one or more wireless network sources such as common wireless routers or access points 111, 112, to detect threshold-surpassing changes which may indicate a change in the surveyed area, and to generate an alert once a qualified situation is determined.

According to an embodiment of the present invention, the process of motion detection, occupancy detection or detection of other changes in a defined area involves some or all of the following steps suitably ordered e.g. as shown:

a. Initially, the intercepted parameters, as sampled by the sensor at a specific location in a surveyed area, are used to generate a Baseline state e.g. interception parameters values of wireless signals that define steady-state (uninterrupted) conditions.

b. Subsequently, the intercepted parameters are continuously compared versus the Baseline state, to detect threshold-surpassing changes which may indicate a change in the surveyed area or a change in the location of the sensor with respect to the surveyed area (e.g., due to the exiting of the sensor from the surveyed area).

c. Once a qualified situation (e.g. threshold-surpassing change in intercepted parameters) is determined, an alert response is generated such as but not limited to a local audible and/or visual alarm, SMS, mobile messaging app such as "WhatsApp" or other messaging over Internet, phone call, logging of alarm state in local or remote database.

Optionally, activation of local camera to take a snapshot or video, to (optionally) be transmitted along with alert message.

If minor changes in monitored parameters are detected (detected changes that the system deems insufficient to justify an alarm), the new parameters may be used by the system to update/modify the originally set baseline state.

Typical Architectures:

1. Standalone - all processing performed by single sensor, which is operative for some or all of:

a. Estimates interception parameters at baseline state; b. Performs evaluation of interception parameters in monitor mode;

c. Determines deviation from baseline state;

d. Generates alarm response or updates baseline when appropriate.

2. Local networked system - multiple sensors, each reporting intercepted parameters to a central control station, which is operative for some or all of:

a. Estimates interception parameters at baseline state based on multiple sensors; b. Performs evaluation of interception parameters in monitor mode based on multiple sensors;

c. Determines deviation from baseline state based on multiple sensors;

d. Generates alarm response or updates baseline when appropriate.

3. Remotely controlled networked system - multiple sensors, each reporting intercepted parameters to remote control station (either directly or through a local control station) which are operative for some or all of:

a. Estimates interception parameters at baseline state based on multiple sensors; b. Performs evaluation of interception parameters in monitor mode e.g. based on multiple sensors;

c. Determines deviation state based on multiple sensors;

d. Generates alarm response or updates baseline when appropriate.

Implementation examples include but are not limited to:

a. Stand-alone sensor, minimalistic implementation, security oriented application - sensor set to monitor intercepted wireless networks, e.g. IDs of those networks. Change in the IDs of intercepted networks indicates the sensor was moved. Example: sensor operating in a parked vehicle and intercepts wireless networks IDs from one or more nearby businesses or residences. If the vehicle is moved from its initial location, a whole new set of wireless networks (with different IDs) are intercepted, or the original wireless network IDs are no longer available, indicating the sensor (and the car, in this example) were moved. b. Stand-alone sensor, advanced implementation, security oriented application - system is initiated remotely or locally. If initiation is local - a suitable "idle" time interval e.g. a few seconds may be allowed to enable the user to distance himself from the sensor. The sensor maps the intercepted networks, selects the most viable ones and generates baseline data e.g. by accumulating and processing relevant intercepted parameters over a suitable time window e.g. a few minutes. If determined that there is no sufficient data to support coefficient operation, a warning indication may be issued. Once baseline parameters are set, the sensor switches to Monitoring mode, and if threshold-surpassing changes are detected, an alert is generated.

c. Stand-alone sensor, smart home oriented application - use a sensor (which may be, for example, built-in Wi-Fi circuitry of a smart TV) to determine that a viewer left the area, and automatically turned the TV off.

d. Multiple sensors network, security oriented application - upon initiation of the system, the sensors start sending intercepted parameters to the central processing element (co- located or remote, such as cloud-based), which maps the intercepted networks, selects the most viable ones and generates baseline data e.g. by accumulating and processing relevant intercepted parameters over a suitable time window e.g. a few minutes. If determined that there is no sufficient data to support efficient operation, in all or part of the physical regions covered by the deployed system, a warning indication may be issued. Once baseline parameters are set, the sensor switches to monitoring mode, and if threshold- surpassing changes are detected, the alarm is generated.

e. Multiple sensors network, smart home oriented application - use information derived from several sensors to detect movement of people from one room to another, and to activate subsystems (lights, air conditioning, etc.) accordingly.

A system 120 (Fig. 6 e.g.) may be provided for detection of changes in a defined surveyed area, according to an embodiment of the invention. The system typically comprises a wireless transmitter (WNT) 121 and one or more sensors 122, 123, ... also termed herein S2, S3.... e.g. as shown in Fig. 6 herein.

S2 (sensor 122) is an example of a dedicated gadget that includes a processing unit that intercepts wireless signals and has Internet connection to send data to a remote server. S3 (sensor 123) is an example of a dedicated gadget that includes a processing unit that intercepts wireless signals with no Internet connection which can identify changes and trigger alarm at specific situations (e.g., activates an alarm element 125).

Remote server 124 can be part of a cloud system which receives data from different sensors (e.g., such as the processing unit S2) and recognizes alarm situations (due to threshold-surpassing change in intercepted parameters of the wireless signals).

When alarm needs to be triggered, this might be initiated directly by the remote server, or by indicating the processing unit S2 that accordingly can activate the alarm element (e.g., a local audible element). According to some embodiments of the present invention, the sensor is provided with the ability to communicate with the transmitter (e.g. standard wireless equipment such as a router or access point, or dedicated WNT gadget) in order to change and control some parameters of the transmission. For example, the sensor can send requests to change parameters such as transmission power, data transmission rate, control beamforming ( e.g. concentrate the wireless signals and aim them directly at the sensor or other desired target), etc. In the case of standard wireless equipment, affected parameters will typically be technical parameters, the control of which is facilitated by the applicable standard communications protocol. In the case of a WNT gadget, any parameters may be controlled and manipulated, provided appropriate processing capability is supported by the WNT gadget and the sensor.

It is appreciated that terminology such as "mandatory", "required", "need" and "must" refer to implementation choices made within the context of a particular implementation or application described herewithin for clarity and are not intended to be limiting since, in an alternative implementation, the same elements might be defined as not mandatory and not required or might even be eliminated altogether.

It is appreciated that software components of the present invention including programs and data may, if desired, be implemented in ROM (read only memory) form including CD-ROMs, EPROMs and EEPROMs, or may be stored in any other suitable typically non-transitory computer-readable medium such as but not limited to disks of various kinds, cards of various kinds and RAMs. Components described herein as software may, alternatively, be implemented wholly or partly in hardware and/or firmware, if desired, using conventional techniques, and vice-versa. Each module or component may be centralized in a single location or distributed over several locations.

Included in the scope of the present disclosure, inter alia, are electromagnetic signals in accordance with the description herein. These may carry computer-readable instructions for performing any or all of the operations of any of the methods shown and described herein, in any suitable order including simultaneous performance of suitable groups of operations as appropriate; machine-readable instructions for performing any or all of the operations of any of the methods shown and described herein, in any suitable order; program storage devices readable by machine, tangibly embodying a program of instructions executable by the machine to perform any or all of the operations of any of the methods shown and described herein, in any suitable order; a computer program product comprising a computer useable medium having computer readable program code, such as executable code, having embodied therein, and/or including computer readable program code for performing, any or all of the operations of any of the methods shown and described herein, in any suitable order; any technical effects brought about by any or all of the operations of any of the methods shown and described herein, when performed in any suitable order; any suitable apparatus or device or combination of such, programmed to perform, alone or in combination, any or all of the operations of any of the methods shown and described herein, in any suitable order; electronic devices each including at least one processor and/or cooperating input device and/or output device and operative to perform e.g. in software any operations shown and described herein; information storage devices or physical records, such as disks or hard drives, causing at least one computer or other device to be configured so as to carry out any or all of the operations of any of the methods shown and described herein, in any suitable order; at least one program pre-stored e.g. in memory or on an information network such as the Internet, before or after being downloaded, which embodies any or all of the operations of any of the methods shown and described herein, in any suitable order, and the method of uploading or downloading such, and a system including server/s and/or client/s for using such; at least one processor configured to perform any combination of the described operations or to execute any combination of the described modules; and hardware which performs any or all of the operations of any of the methods shown and described herein, in any suitable order, either alone or in conjunction with software. Any computer- readable or machine-readable media described herein is intended to include non-transitory computer- or machine-readable media.

Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any operation or functionality described herein may be wholly or partially computer-implemented e.g. by one or more processors. The invention shown and described herein may include (a) using a computerized method to identify a solution to any of the problems or for any of the objectives described herein, the solution optionally include at least one of a decision, an action, a product, a service or any other information described herein that impacts, in a positive manner, a problem or objectives described herein; and (b) outputting the solution.

The system may if desired be implemented as a web-based system employing software, computers, routers and telecommunications equipment as appropriate.

Any suitable deployment may be employed to provide functionalities e.g. software functionalities shown and described herein. For example, a server may store certain applications, for download to clients, which are executed at the client side, the server side serving only as a storehouse. Some or all functionalities e.g. software functionalities shown and described herein may be deployed in a cloud environment. Clients e.g. mobile communication devices such as smartphones may be operatively associated with, but external to, the cloud.

The scope of the present invention is not limited to structures and functions specifically described herein and is also intended to include devices which have the capacity to yield a structure, or perform a function, described herein, such that even though users of the device may not use the capacity, they are, if they so desire, able to modify the device to obtain the structure or function.

Features of the present invention, including operations, which are described in the context of separate embodiments, may also be provided in combination in a single embodiment. For example, a system embodiment is intended to include a corresponding process embodiment and vice versa. Also, each system embodiment is intended to include a server-centered "view" or client centered "view", or "view" from any other node of the system, of the entire functionality of the system, computer-readable medium, apparatus, including only those functionalities performed at that server or client or node. Features may also be combined with features known in the art and particularly although not limited to those described in the Background section or in publications mentioned therein.

Conversely, features of the invention, including operations, which are described for brevity in the context of a single embodiment or in a certain order may be provided separately or in any suitable subcombination, including with features known in the art (particularly although not limited to those described in the Background section or in publications mentioned therein) or in a different order, "e.g." is used herein in the sense of a specific example which is not intended to be limiting. Each method may comprise some or all of the operations illustrated or described, suitably ordered e.g. as illustrated or described herein.

Embodiments such as but not limited to training and calibration methods shown and described herein, which are said to utilize beamforming data may alternatively be implemented to utilize data, other than beam-forming data, which represents behavior of wireless signals passing through a surveyed area. For example, certain aspects e.g.

monitoring, baseline setting, calibration may be described herein in terms of an 802.1 lac embodiment monitoring V-Matrix values; these may alternatively be provided in conjunction with an embodiment handling wireless transmission such as but not limited to any version of Bluetooth or of Wifi, which may monitor values rather than v- matrix values such as RSSI values. The converse also holds (aspects described in the context of Bluetooth/Wifi may be provided instead in a beamforming context).

Certain aspects may be described in conjunction with parameter Measurement using Passive sniffing of data packets e.g. as in 802.1 l.ac. Alternatively however these aspects may be provided in conjunction with an embodiment which is operative for reading RSSI levels from standard communications interfaces; the converse also holds.

Certain aspects may be described in conjunction with training/calibration schemes, in which the values calibrated per calibrated point may be V-Matrices of multiple beams. Alternatively however these aspects may be provided in conjunction with an embodiment which employs training/calibration schemes, in which the values calibrated per calibrated point are RSSI levels obtained at multiple points of interest; the converse also holds.

Certain aspects may be described in conjunction with an alarm system operative to generate an alarm intelligible by a human user. Alternatively however these aspects may be provided in conjunction with a system which provides a control input to a cooperating computerized system such as but not limited to a Smart home system which may or may not be employed at the surveyed area.

Devices, apparatus or systems shown coupled in any of the drawings may in fact be integrated into a single platform in certain embodiments or may be coupled via any appropriate wired or wireless coupling such as but not limited to optical fiber, Ethernet, Wireless LAN, HomePNA, power line communication, cell phone, PDA, Blackberry GPRS, Satellite including GPS, or other mobile delivery. It is appreciated that in the description and drawings shown and described herein, functionalities described or illustrated as systems and sub-units thereof can also be provided as methods and operations therewithin, and functionalities described or illustrated as methods and operations therewithin can also be provided as systems and sub-units thereof. The scale used to illustrate various elements in the drawings is merely exemplary and/or appropriate for clarity of presentation and is not intended to be limiting.