FIELD OF THE INVENTION

The present invention generally relates to a system and method for reducing peak network loads in wireless sensor networks. The invention further relates to a sensor apparatus for use in the system and a computer program product for carrying out the method.

BACKGROUND

Wireless sensor networks in general consist of a collection of small, low- power nodes that collect environmental information and transmit this information wirelessly to a central device. This central device can use the sensed environmental information to control apparatuses or can collect the sensed environmental information for data analysis.

Single environmental events may trigger several sensors at the same time, resulting in a traffic burst in which several sensors attempt to notify the controller at the same time. Typically, all these sensors are within each other's radio range. Therefore, traffic bursts lead to peak network loads in wireless sensor networks. These peak network loads can result in packet collisions that decrease performance of the wireless sensor networks. Moreover, as sensor apparatuses are generally configured to transmit periodic messages when no environmental changes are detected, more peak network loads follow periodically on an initial traffic burst.

In wireless lighting systems, sensors are coupled to controllers of luminaires to enable the adaption of the emitted light to environmental inputs. The luminaires, for example, may be dimmed when there is an abundance of daylight. During sudden changes of lighting conditions, a peak in the number of messages transmitted may be observed. This can adversely lead to instantaneous impact on the network and latency in dimming. Also, after the instantaneous impact a periodic impact can be observed as well.

Traffic bursts in wireless sensor networks are an acknowledged problem. Multiple methods of solving this problem have been proposed. An example of such a method is proposed in the patent application WO 2014/097036 Al.

US 7,436,789 discloses a wireless network comprising one or more sensor nodes and/or one or more control nodes. The sensor node transmits in response to a sensed event and/or a request from a control node. Transmission/routing of data between a sensor node and/or a control node may be subject to a policy constraint and a resource constraint.

SUMMARY OF THE INVENTION

It is an object of the invention to provide a system and a method for reducing peak network loads in wireless sensor networks while maintaining similar performance of the wireless sensor network.

According to a first aspect of the invention the object is achieved by a system for reducing peak network loads in a wireless sensor network, comprising a central device and a plurality of sensor apparatuses,

wherein the sensor apparatuses are arranged to sense environmental events and to notify the central device when an environmental event is sensed using incidental messages;

wherein the sensor apparatuses are arranged to notify the central device periodically in the absence of environmental events using periodic messages;

and wherein the sensor apparatuses are configured to:

- choose a transmission time T between t _{i A} and t _{i B} , for incidental messages, and between t _{p A} and t _{p S} , for periodic messages, where T, t _{i A} , t _{i B} , t _{p A} and t _{p B} are measures of time, where t _{i A} = At _t - τ _ίΛ , t _{i B} = At _t + τ _{ί Β} , t _{p A} = At _p - τ _{ρ Α} and t _{p B} = At _p + τ _{ρ Β} , wherein At _t is given by max(0, T _min — T _event + T _iast ), wherein T _min is a minimum waiting time between the time at which the last message is sent, T _iast , and the time at which a message is transmitted, T, wherein T _event is the time at which the environmental event is sensed, wherein At _p is a default interval for the periodic messages and wherein for τ _{ί Α} holds 0 < T < Ate

- and to transmit the messages wirelessly to the central device at transmission time T, whereby at least a plurality of sensor apparatus have transmission times which differ from each other.

The system is advantageous as it selects the transmission time from an interval for both incidental and periodic messages. By selecting the transmission time from an interval for incidental messages, a first peak, that occurs when an environmental event triggers multiple sensors, is spread out. By also selecting the transmission time from an interval for the periodic messages, the peak network load observed periodically will be spread out. Note that by spreading initial peaks, later peaks will be spread out even more. Note that, as the transmission times for the periodic messages are chosen from an interval the messages are not periodic anymore in the strictest interpretation of the word periodic.

However, due to their periodic component At _p we will refer to them as periodic messages in the course of the text. Note that it would also be possible to refer to them as quasi-periodic or updating messages. However, in order to compare the present invention with the prior art we prefer to refer to them as periodic messages.

It is advantageous if different sensors in the wireless sensors networks have different intervals from which they choose their transmission times as this also spreads the peaks. An environmental event can be defined by the crossing of a threshold sensing value. When the light turns on or off in a room, the threshold sensing value for the light intensity will be crossed such that an environmental event is sensed. Environmental events are not limited to the crossing of threshold values. Environmental events can also occur from more complex phenomena. It could be that information should be combined before the sensor registers an environmental event. For example, a sensor could be configured to only register an environmental event when during a certain time interval multiple values are sensed.

In an embodiment of the system, the sensor apparatuses are configured to choose the transmission times according to a probability distribution.

This is advantageous as the probability distribution can be chosen to optimally spread the peaks.

In an embodiment of the system, the central device is a control device, wherein the control device is coupled to the sensor apparatuses for setting t _{i A} , t _{i B} , t _{p A} and t _{p B} and for determining the probability distribution.

The above embodiment is beneficial as it allows the control over the time interval in which the transmission times are chosen and allows control over the probability distribution. This enables optimization of the intervals and probability distributions for the different sensors to optimally decrease the peak network loads.

In an embodiment of the system, wherein the control device is configured to retrieve location information, indicative of locations of the sensor apparatuses coupled to the control device, and or retrieve cross-correlation information, indicative of cross-correlations between transmission times of sensor apparatuses, the control device is configured to set t _{i A} , t _{i B} , t _{p A} and t _{p B} and to determine the probability distribution based on the location information and or the cross-correlation information.

It is advantageous to set the time intervals and probability distribution based on the location information as sensors that are placed in close proximity to each other are likely to transmit at the same time as they likely sense the same environmental changes. Therefore, it is useful to designate different time intervals and or probability distributions to sensors that are placed in close proximity. Additionally, it is favorable to set the time intervals and probability distribution based on cross-correlation information as the cross- correlation information indicates which sensors transmit messages at the same time. It is useful to designate different time intervals and or probability distributions to sensors that transmit messages at the same time. Setting the time intervals and or probability distributions based on the location information or cross-correlation information is beneficial by itself. Therefore, combining both the location and cross-correlation information to set the time intervals and probability distributions of the sensors is propitious as well. Note that when a combination of the location information and the cross-correlation information is used, it could occur that the control device receives contradictory instructions. For example, two sensors can be placed close to each other but never transmit messages at the same time. In this case the location information and cross-correlation information will be contradictory. The central device will be configured to handle such contradictory information types. In general, the information originating from the cross-correlation information will prevail over the location information as the cross-correlation is a direct indication of which sensors generate peak network loads.

In an embodiment of the system, wherein at least two of the sensor apparatuses are light sensors and wherein the control device is configured to retrieve illuminance information indicative of cross-correlations between illuminance levels of neighboring sensors, the control device is configured to set t _{i A} , t _{i B} , t _{p A} and t _{p B} and to determine the probability distribution based on the illuminance information.

Light sensors that sense the same illuminance levels are likely to cause peak network loads in case of a sudden change in the lighting conditions. Therefore, it is beneficial to set the time intervals and or probability distributions based on the illuminance information.

In an embodiment of the system, wherein at least two of the sensor apparatuses are light sensors, the control device is configured to retrieve daylight proximity information indicative of which light sensors sense incoming daylight and the control device is configured to set t _{i A} , t _{i B} , t _{p A} and t _{p B} and to specify the probability distribution based on said information.

It is advantageous to set the time intervals and probability distribution based on the daylight proximity information as light sensors that sense incoming daylight are likely to cause peak network loads due to sudden changes in the daylight conditions. Therefore, it is beneficial to set the time intervals and or probability distributions based on the illuminance information. It could for example be useful to designate different time intervals and or probability information to different sensors that sense incoming daylight.

In an embodiment of the system, the system further comprises at least one controllable apparatus controlled by the control device, wherein the control device is configured to control the apparatus based on the messages transmitted by the sensor apparatuses.

It is advantageous to use the information originating from the sensors to control devices. It is advantageous for example to control sprinklers to irrigate a field when sensors sense that the sun is shining heavily.

In an embodiment of the system, wherein there are multiple controllable apparatuses, wherein the apparatuses are luminaires and wherein the control device is configured to control dimming and other properties of the luminaire.

It is advantageous to, for example, dim luminaires in an area when much daylight is sensed in that area.

According to a second aspect of the present invention the object is achieved by a sensor apparatus arranged to sense environmental events and to notify a central device when an environmental event is sensed using incidental messages;

wherein the sensor apparatuses are arranged to notify the central device periodically in the absence of environmental events using periodic messages;

and wherein the sensor apparatuses are configured to:

- choose a transmission time T between t _{i A} and t _{i B} , for incidental messages, and between t _{p A} and t _{p S} , for periodic messages, where T, t _{i A} , t _{i B} , t _{p A} and t _{p B} are measures of time, where t _{i A} = At _t - τ _ίΛ , t _{i B} = At _t + τ _{ί Β} , t _{p A} = At _p - τ _{ρ Α} and t _{p B} = At _p + τ _{ρ Β} , wherein At _t is given by max(0, T _min — T _event + T _iast ), wherein T _min is a minimum waiting time between the time at which the last message is sent, T _iast , and the time at which a message is transmitted, T, wherein T _event is the time at which the environmental event is sensed, wherein At _p is a default interval for the periodic messages and wherein for τ _{ί Α} holds 0 < T < Ate

- and to transmit the message wirelessly to the central device at transmission time T, whereby at least a plurality of sensor apparatus have transmission times which differ from each other.

In an embodiment, the sensor apparatus comprises:

a sensor for sensing environmental inputs to generate sensor data; a transmitting module coupled to the central device for transmitting messages to a central device;

a receiving module configured to receive messages from the central device; and

- a processing unit coupled to the sensor for receiving the sensor data, coupled to the receiving module to process the received messages and coupled to the transmitting module to control the transmitting module, wherein the processing unit is configured to process the sensor data to determine if an environmental event is sensed; control the transmitting module to send an incidental or periodic message, and choose the transmission time T for the incidental or periodic message.

This is advantageous as a transmitting module, receiving module and processing unit enable a sensor apparatus to communicate with a variety of devices.

Additionally, the processing unit allows the sensors apparatus to process incoming or outgoing information. Moreover, this is advantageous as the processing unit enables the sensors apparatus to process the raw sensor data.

In an embodiment, the processing unit is configured to choose the transmission times according to a probability distribution.

In an embodiment, the processing unit is configured to process the received messages and to set t _{i A} , t _{i B} , t _{p A} and t _{p B} and to specify the probability distribution based on the received messages.

According to a third aspect of the present invention the object is achieved by a method for reducing peak network loads in a wireless sensor network which comprises a central device and a plurality of sensor apparatuses, the method comprising:

sensing environmental events;

notifying the central device when an environmental event is sensed using incidental messages;

notifying the central device in the absence of environmental events using periodic messages;

choosing a transmission time T between t _{i A} and t _{i B} for incidental messages and between t _{p A} and t _{p B} for periodic messages, where T, t _{i A} , t _{i B} , t _{p A} and t _{p B} are measures of time, where t _{i A} = M _t - τ _ίΛ , t _{i B} = M _t + τ _{ί Β} , t _{p A} = At _p - τ _{ρ Α} and t _{p B} = At _p + τ _{ρ Β} , wherein At^ is given by max(0, T _min — T _event + T _iast ), wherein T _min is a minimum waiting time between the time at which the last message is sent, T _iast , and the time at which a message is transmitted, T, wherein T _event is the time at which the environmental event is sensed, wherein At _p is a default interval for the periodic messages and wherein for τ _{ί Α} holds 0 < T < Ate

and transmitting the message wirelessly to the device at transmission time T, whereby at least a plurality of sensor apparatus have transmission times which differ from each other.

In an embodiment, the method further comprises choosing the transmission times according to a probability distribution.

According to a fourth aspect of the present invention the object is achieved by a computer program product downloadable from a communication network and/or stored on a computer readable medium, the computer program product comprising machine readable instructions that, when executed on a computing system, causes a processor to perform the steps of said method.

It is advantageous to have computer program product that enables a wireless sensor network to carry out the steps of the method.

BRIEF DESCRIPTION OF THE DRAWINGS

The above, as well as additional objects, features and advantages of the disclosed system, sensor apparatus, method and computer program product, will be better understood through the following illustrative and non-limiting detailed description of embodiments of devices and methods, with reference to the appended drawings Fig. 1 to 8, wherein

Fig. 1 schematically shows a wireless sensor network,

Fig. 2 illustrates peak network loads following on an environmental event in a prior art network,

Fig. 3 illustrates decreased peak network loads following on an environmental event as the effect of the present invention,

Fig. 4 illustrates different probability distributions,

Fig. 5 illustrates the effect of the present invention using a uniform probability distribution,

Fig. 6 schematically shows a sensor apparatus, the environment and a central device,

Fig. 7 schematically shows a control device in contact with controlled devices and wirelessly connected to sensors, and Fig. 8 schematically shows the method of decreasing the peak network loads, All the figures are schematic, not necessarily to scale, and generally only show parts which are necessary in order to elucidate the invention, wherein other parts may be omitted or merely suggested.

DESCRIPTION

The present invention generally relates to a system and method for reducing peak network loads in wireless sensor networks. The invention further relates to a sensor apparatus for use in the system and a computer program product for carrying out the method.

Fig. 1. schematically illustrates a wireless sensor network comprising a central device 100 and plurality of sensor apparatuses, depicted as circles 102. The sensor apparatuses 102, can receive messages from the central device 100. This is schematically illustrated by dotted arrows 106. Additionally, the sensor apparatuses can send messages to the central device 100, schematically illustrated as solid arrows 104.

Many wireless sensor networks comprising a central device and a plurality of sensor apparatuses exist. Examples are sensor networks in offices buildings or homes where sensors detect presence of people, (day)light and temperature; conveyor belt systems where sensors monitor the conveyor belt; and lab facilities where sensors monitor the air quality and the presence of dangerous chemicals.

Various wireless communication technologies that are known in the art may be used to couple the sensors to the central device. Examples are Bluetooth, Wi-Fi and ZigBee.

Reoccurring peak network loads in wireless sensor networks are an

acknowledged problem in the prior art as illustrated in Fig. 2. The figure schematically shows the transmission of events for two sensors indicated by the axis 1 and 2. Additionally, it schematically shows the total network load for a plurality of sensors. In general these sensors are configured to send incidental messages (200) when an environmental event (202) is sensed occurs and periodic messages (204) when no environmental changes occurs. The time at which the environmental event is sensed is denoted as T _event throughout the text and figures. Generally, in order to prevent a cascade of transmissions when events keep triggering a sensor, sensors are configured to wait a minimum time, T _min , after the last message that is transmitted before transmitting a new message. When multiple sensor apparatuses are affected by the same environmental change (202), they will all try to send an incidental message (200) at the same time resulting in a peak in the network load (206) as shown in Fig. 2. These peak network loads can result in packet collisions that decrease performance of the wireless sensor networks. Moreover, as sensors are generally configured to transmit periodic messages, after a time At _p , when no environmental changes are detected, more peak network loads (208) follow as the sensor apparatuses are synchronized by the first environmental event.

A solution for these reoccurring peak network loads is given by the present invention. The peak networks loads are addressed by spreading the messages that are being sent over a transmission interval. A difference with prior art is that in the present invention the spreading is done for both the incidental and the periodic message hereby addressing both the peak network loads due to incidental messages as well as the peak network loads due to periodic messages.

Fig. 3 shows the effect of the present invention. When an environmental event (300) triggers an incidental message 306, the sensor apparatus is configured to choose a transmission time T within a transmission interval between t _{i A} and t _{i B} , where t _{i A} = At _t — T , t _{i B} = Ati + τ _{ί Β} . Ati is given by max(0, + Tiast) , wherein T _last is the time at which the last message is transmitted. t _{i A} , Ati, τ _{ί Α} , t _{i B} , and τ _{ί Β} are measures of time. The mathematical expression for AT _t means that, when there is a sufficient time difference, a time difference larger than T _min , between the last message that is transmitted and the sensing of an environmental event, the incidental message is transmitted immediately. When the difference is smaller than T _min , AT _t induces a waiting time to prevent a cascade of transmissions. Note that 0 < τ _{ί Α} < At _t such that a sensor cannot be configured to transmit a message in the past. Note that τ _{ί Α} and τ _{ί Β} can be different and that the transmission intervals can be different for different sensor apparatuses. In general, different sensor apparatuses will chose different transmission times within a transmission interval such that the peak network load 302 is decreased with respect to the peak network load 206 when the incidental events are sent all at the same time. In Fig. 3. it is shown that sensor 1 has to wait At _t before transmitting its incidental message and that At _t + 5 _έ is the transmission time T that is chosen in the interval between t _{i A} and t _{i B} . 5 _έ is thus smaller than τ _{ί Β} .

As the transmission times are spread in the initial peak network load 302, the transmission times in the subsequent peak network loads 304 will automatically be spread equally. However, then, still peak network loads remain. Therefore, when a periodic message 308 is sent, the sensor apparatus is configured to choose a transmission time T in the transmission interval given by t _{p A} .and t _{p S} , where t _{p A} = At _p — τ _{ρ Α} and t _{p B} = At _p + τ _{ρ Β} . At _p represents the default interval for the periodic messages and is in general larger than At _t as the goal of the periodic messages is just to update the central device that the sensor is operating correctly. Note that as the transmission times for the periodic messages are chosen from an interval the messages are not periodic anymore in the strictest interpretation of the word periodic. However, due to their periodic component At _p we will refer to them as periodic messages in the course of the text. Note that it would also be possible to refer to them as quasi-periodic messages or updating messages. However, in order to compare the present invention with the prior art we prefer to refer to them as periodic messages.

Again, τ _{ρ Α} and τ _{ρ Β} can be different and the time interval can be different for different sensor apparatuses. Since different sensor apparatuses in general will choose different transmission times, the periodic peak network 304 loads will be spread further and further as shown in Fig. 3. In Fig. 3 it is shown that sensor 1 transmits a message after At _p + δ _{ρ 1} which lies between t _{p A} and t _{p B} and for which δ _{ρ 1} < τ _{ρ Β} and subsequently transmits a message after At _p — δ _{ρ 2} which will also lie between t _{p A} and t _{p B} and for which δ _{ρ 2} ≤ τ _{ρ Α} .

The sensor apparatuses can be configured to choose the transmission times within the time interval according to a specific probability distribution with a variance and a mean, shown in Fig. 4. By making the sensor apparatuses choose the transmission time randomly within the transmission interval, a uniform distribution 400 can be achieved. Note that the probability distribution is the probability distribution of a single sensor apparatus. By choosing these probability distributions correctly a probability distribution for all messages sent by the plurality of sensor apparatuses can be achieved. For example, if all sensor apparatuses are configure to transmit according to a uniform distribution, the total distribution will be uniform as well.

The sensor apparatuses can also be configured to choose the transmission times according to a different probability distribution such as, but not limited to, a binomial 402 or Gaussian 404 distribution. What distribution to choose may depend on the sensor network, the bandwidth over which the data needs to be transmitted and the configurability of the sensor apparatuses.

Fig. 5 shows a schematic overview of the effect of choosing a uniform probability distribution for the transmission times. With respect to Fig. 3 it is clear that the peak network loads are decreased even further. Note that the figure is not to scale regarding the surface area below the peak network load plot. It should be clear that, as there is less peak network traffic, there will be, in general, less collisions and thus less retransmissions. When the total traffic would be measured, the total traffic, given by the surface area below the network load plot, would be lower for an implementation of the present invention as shown in Fig. 5 than for an implementation as shown in Fig. 3.

A sensor apparatus 600, as shown in Fig. 6, may comprise a sensor 602 for sensing environmental inputs 604. By sensing environmental inputs, the sensor will generate sensor data that will be send to a processing unit 606 that will process the sensor data. The processing unit can for example be a microcontroller or microprocessor.

The sensor apparatus may also comprise a transmitting module 608 and a receiving module 610. The transmitting module and receiving module can be combined as a transceiver such as, for example, a two-way radio. The modules can also be installed separately as a wireless radio and a radio receiver for example.

The transmitting module can be controlled to transmit messages to the central device by the processing unit. The processing unit can control the time at which the transmitting module sends messages as well as the content of the messages. The content, in general, will be based on the sensor data.

The receiving module 610 can receive messages sent by the central device and will send these message to the processing unit. The processing unit can process these messages and can base its operation on these messages. In this way, the central device can control the sensor apparatus to, for example, transmit its messages according to a probability distribution as before mentioned.

The time intervals and probability distribution for the transmission times can be configured when the sensor apparatuses are made, when the wireless sensor network is commissioned but also during operation by the central device. This enables the central device to tune the operation of the wireless sensor network. Additional information types can also be taken into account by the central device in order to configure the time intervals and probability distribution for the transmission times. The central device in general will be a smart device such as, but not limited to, a computer, a server, a smartphone and a tablet. The central device will in general thus have processing power, a memory and means for connecting the central device to a network such as, but not limited to, the World Wide Web. Various wireless communication technologies that are known in the art may be used to couple the central device to the network. Examples are Bluetooth, Wi-Fi, 802.15.4, Thread, and ZigBee. Any information that is available in the network can be retrieved by the central device. The central device, also referred to as control device when it controls sensors or apparatuses, could retrieve location information, indicative of locations of the sensor apparatuses coupled to the central device and base t _{i A} , t _{i B} , t _{p A} and t _{p B} , the probability distribution, the variance and the mean on the location information. It is likely that sensor apparatuses in close proximity to each other transmit messages at the same time. Therefore, the central device could chose minimally overlapping transmission intervals for two sensors in close proximity to each other. To ensure that the transmission times of the two sensors do not overlap, the central device could adapt the probability distribution, i.e., two non- overlapping binomial distributions would decrease the probability of the same transmission time.

The control device could also retrieve cross-correlation information, indicative of cross-correlations between transmission times of sensor apparatuses and base t _{i A} , t _{i B} , t _{p A} and t _{p s} , the probability distribution, the variance and the mean on the cross-correlation information. If there is a cross-correlation between the transmission time of sensor apparatuses they often transmit message at the same time. Therefore, the central device can configure, as described above, them such that it is less likely that they send messages at overlapping transmission times.

An example of an application of the present invention is a lighting network. More and more sensors are added to lighting networks to provide the luminaires with information on the occupancy of rooms and the presence of natural light. Therefore, when many people enter a room and or the conditions of the natural light change, many sensors can be affected leading to peak network load.

In this case the central device could retrieve illuminance information indicative of cross-correlations between illuminance levels of neighboring sensors. If there is a cross-correlation between the illuminance level of neighboring sensors it is likely that they will send a message when the natural light in a room changes. Therefore, the central device can base t _{i A} , t _{i B} , t _{p A} and t _{p B} , the probability distribution, the variance and the mean on the illuminance information to prevent peak network loads. Same measures as mentioned above can be implemented.

In the case of a lighting network, the central device could retrieve daylight proximity information indicative of which light sensors sense incoming daylight. It is likely that the light sensors that sense incoming daylight transmit messages at the same time.

Therefore, the central device can base t _{i A} , t _{i B} , t _{p A} and t _{p S} , the probability distribution, the variance and the mean on the daylight proximity information to prevent peak network loads. Same measures as mentioned above can be implemented.

The central device could use a combination of the location information and or the cross-correlation information and or the illuminance information and or the daylight proximity information to determine the settings of t _{i A} , t _{i B} , t _{p A} and t _{p s} , the probability distribution, the variance and the mean more accurately. Note that when a combination of information types is used, it could occur that two information types give contradictory instructions. For example, two sensors can be placed close to each other but never transmit messages at the same time. In this case the location information and cross-correlation information will be contradictory. The central device will be configured to handle such contradictory information types. In general, the information originating from the cross- correlation information will prevail over the other information types as this is a direct indication of which sensors generate peak network loads.

The central device could retrieve the location information from a network, a memory or from the sensors (if the sensors know their positions). Moreover, the central device could retrieve the cross-correlation, the illuminance information and the daylight proximity information from a network or memory. The central device could also use its processing powers, and its data on the messages the central device receives to determine the cross-correlation, illuminance or daylight proximity information itself.

As shown in Fig. 7, a central device 700 can be a control device that communicates with sensors, depicted as circles 702 to the controlled devices, depicted as rounded squares 708. The control device can be connected to the devices by wire or wirelessly.

In one embodiment, the sensors are presence, light and temperature sensors and the controlled devices are luminaires and HVAC systems. Vision sensors could be added to this embodiment.

In another embodiment, the sensors are light sensors, air quality sensors and presence sensors and the controlled devices are outdoor luminaires. Many environmental sensors could be added to this embodiment.

In yet another embodiment, the sensors are pollution sensors, air quality sensors, presence sensors and fire sensors and the controlled devices are for example warning systems able to warn about fire, dangerous chemicals in the air. Additionally the controlled devices could be sprinkler systems. Many more wireless sensor network exist. Applications can for example be process management, area monitoring, health care monitoring, environmental sensing, air pollution monitoring, forest fire detection, landslide detection, water quality monitoring, chemical agent detection, machine health monitoring, data logging and waste monitoring.

In Fig. 8 a method for reducing peak network loads in a wireless sensor network which comprises a central device and a plurality of sensor apparatuses is schematically depicted. The steps done by the sensor apparatus to achieve the object of the present invention are shown. The sensor apparatus is arranged to sense 800 environmental events. The sensor apparatus will notify a central device when an environmental event is sensed using incidental messages and the sensor apparatuses will notify the central device periodically in the absence of environmental events using periodic messages.

The sensor apparatuses are configured to: choose 802 a transmission time T for the message it transmits. For incidental messages these transmission times will lie between t _{i A} and t _{i B} and for periodic messages these transmission times will lie between t _{p A} and t _{p B} . The sensor apparatuses are configured to transmit (804) the message wirelessly to the central device at transmission time T, whereby at least a plurality of sensor apparatus have transmission times which differ from each other.

In an embodiment, the method, wherein the central device is a control device, wherein the control device is coupled to the sensors, further comprises setting t _{i A} , t _{i B} , t _{p A} and t _{p B} by the control device, and specifying the probability distribution by the control device.

In an embodiment, the method further comprises controlling at least one controllable apparatus by the control device.

In various implementations, a processor may be associated with one or more storage media (generically referred to herein as "memory," e.g., volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, USB sticks, SD cards and Solid State Drives etc.). In some implementations, the storage media may be encoded with one or more programs that, when executed on one or more processors, perform at least some of the functions discussed herein. Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller so as to implement various aspects of the present invention discussed herein. The terms "program" or "computer program" are used herein in a generic sense to refer to any type of computer code (e.g., software or microcode) that can be employed to program one or more processors or controllers.

The term "network" as used herein refers to any interconnection of two or more devices (including processors) that facilitates the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. As should be readily appreciated, various implementations of networks suitable for interconnecting multiple devices may include any of a variety of network topologies and employ any of a variety of communication protocols.

Additionally, in various networks according to the present disclosure, any one connection between two devices may represent a dedicated connection between the two systems, or alternatively a non-dedicated connection. In addition to carrying information intended for the two devices, such a non-dedicated connection may carry information not necessarily intended for either of the two devices (e.g., an open network connection).

Furthermore, it should be readily appreciated that various networks of devices as discussed herein may employ one or more wireless, wire/cable, and/or fiber optic links to facilitate information transport throughout the network.

It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein should be accorded a meaning most consistent with the particular concepts disclosed herein.