Patent Description:
Work of the presently named inventors, to the extent it is described in the background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present disclosure.

Electronic sensors which are able to measure an environment parameter such as temperature, humidity, pressure and the like and output data indicative of the measured parameter have been known for some time. With improvements in device interconnectivity and processing power and reductions in sensor cost, however, it is now envisaged that data output by a large number of such sensors can be analysed in real time in order to monitor particular environments more effectively. There are many particular types of environment this type of data analysis could be applied to, such as road networks, pipelines, agricultural land and pedestrianized areas, to name a few examples.

In order to obtain useful environmental data from such sensors, it is necessary to distribute a relatively large number of sensors appropriately throughout the environment which is to be monitored. This is costly, both in terms of the cost of the sensors themselves and in securing each sensor at an appropriate position within the environment so as to allow it to make accurate measurements and to reduce the risk of it becoming dislodged. For example, if sensors are placed along a road in order to measure the temperature at the road surface, it may be necessary to bury, embed or encapsulate in for example a drainage feature each individual sensor in the road close to the road surface. This ensures that the road surface temperature can be accurately measured and reduces the risk of vehicles, pedestrians, animals or the like from dislodging the sensors. Once a set of sensors have been appropriately distributed over a particular environment, low maintenance of the sensors is therefore desirable. In particular, it is envisaged that each sensor may comprise a small battery in order to power it in making measurements and to power a transmitter provided with the sensor to wirelessly output data indicative of those measurements to a receiver. It is therefore desirable that this battery does not need to be changed very often (thereby limiting how often it is necessary to undertake the costly operation of finding each sensor, removing it from its position, replacing the battery and replace the sensor, for example).

Cited references include <CIT>, <CIT>, <CIT> and <CIT>.

The scope of the present invention is defined by the claims.

<FIG> shows a system <NUM> according to an embodiment. The system comprises a plurality of sensors <NUM> and a data processing apparatus <NUM>. Each of the sensors periodically measures the value of one or more environmental parameters and wirelessly transmits sensor data indicative of the measured parameter value(s) to the data processing device <NUM> for processing. The data processing device <NUM>, in this example, is in data communication with a display device <NUM> (e.g. a smartphone or tablet computer with an electronic display) to which display data generated by the processing of the sensor data is sent for display on the display device. The data processing device <NUM> may take any suitable form, for example, an application server or central server. The data processing device <NUM> is not necessarily connected to a display device in the way as described in the embodiment of <FIG>. The display device <NUM> is only an example and, more generally, the data processing device <NUM> is configured to output data to any suitable audio and/or visual device which, in turn, is configured to output information to a user based on that received data. The output information takes the form of measured or estimated parameter values at each of the sensor locations (as will be described) or information such as alerts determined based on such measured or estimated parameter values.

In this example, the sensors are distributed along a road <NUM> (e.g. partially or fully buried in the asphalt) and measure parameters of interest to an organisation (e.g. local authority) responsible for maintaining the road. The sensor data may be indicative of one or more of the temperature (e.g. to warn of the potential of ice (in particular, black ice) on the road when the temperature is within a predetermined threshold of <NUM>), humidity, an amount of a predetermined air pollutant (e.g. NOx or particulate pollutants), a vehicle count (e.g. determined according to a detectable change in noise, vibration and/or pressure as a vehicle passes a particular sensor, e.g. through a change of inductance in a coil), a presence and/or depth of standing water, snow or other settled precipitation (e.g. determined based on detecting the presence of moisture and/or an increase in pressure at the sensor location) and a presence of oil-based substance (e.g. in the case of an oil or diesel spillage), for example. Regular measurement of parameters such as these along the road is useful in helping the local authority manage traffic, plan road repairs, advertise travel conditions and the like. It will be appreciated that the system <NUM> could be implemented in a different environment (e.g. with sensors distributed along a pipeline, across agricultural land or across pedestrianized areas, for example) and that the sensor data will then indicate measurements of parameters relevant to that different environment. However, for the sake of demonstration of the present technique, a road environment is described here. The disclosure is not limited to arranging the sensors in a <NUM> dimensional array.

<FIG> respectively show the components of a sensor <NUM> and the data processing device <NUM>, according to an embodiment. Each sensor in <FIG> has the components shown in <FIG>.

The sensor <NUM> comprises a communication interface <NUM> for wirelessly transmitting signals to (and, optionally, wirelessly receiving signals from) the data processing device <NUM> via antenna <NUM>, sensor hardware <NUM> for measuring one or more parameters of the environment, a controller <NUM> and a battery <NUM> for powering each of the other components. The controller <NUM> controls the operation of each of the communication interface <NUM>, sensor hardware <NUM> and battery <NUM>. Each of the components of the sensor is implemented, where necessary, using appropriate circuitry, for example.

The data processing device <NUM> comprises a communication interface <NUM> for wirelessly receiving signals from (and, optionally, wirelessly transmitting signals to) each of the sensors <NUM> via antenna <NUM>, a processor <NUM> for processing electronic instructions, memory <NUM> for storing the electronic instructions to be processed and input and output data associated with the electronic instructions, a storage medium <NUM> (e.g. in the form of a hard disk drive, solid state drive, tape drive or the like) for long term storage of electronic information and a display interface <NUM> for outputting the display data to the display device <NUM> (or other audio and/or visual device). The processor <NUM> controls the operation of each of the communication interface <NUM>, memory <NUM>, storage medium <NUM> and display output <NUM>. Each of the components of the data processing device is implemented, where necessary, using appropriate circuitry, for example. Signals received from the sensors comprise the sensor data.

In an embodiment, the signals transmitted between the sensor <NUM> and the data processing device <NUM> are radio signals. More particularly, they may be radio signals transmitted over a low-power wide-area network (LPWAN) such as ELTRES ® by Sony ®, or LoRa ®, or Sigfox ®, that are designed to operate in frequency bands in a duty-cycle constrained manner as mandated by regional regulators such as ETSI (European Telecommunications Standards Institute) in Europe. In an embodiment, each sensor has a periodic active time period (in which the sensor is able to send and/or receive data) and a periodic inactive time period (in which the sensor is not able to send and/or receive data). The periodic switching between the active state and the inactive state of the sensor is controlled by the controller <NUM>. Sensor data is periodically transmitted to the device <NUM> by each sensor <NUM> during a subset of the periodic active time periods of that sensor. In the case that the sensor data is transmitted over an LPWAN, the length of the periodic active time period during which the sensor data can be transmitted is constrained according to the LPWAN duty-cycle constraint. In embodiments, the LPWAN may be implemented as part of a licenced or unlicensed / license-exempt system.

Power is required to be supplied from the battery <NUM> each time a sensor <NUM> makes a measurement and transmits corresponding sensor data to the data processing device <NUM>. Thus, the more often each sensor is required to make a measurement and transmit sensor data, the faster the battery will be depleted and the more often the battery will need to be changed or charged. Regularly changing the battery of each of a relatively large number of sensors distributed over an environment is a costly process. Even if the battery is rechargeable (e.g. using an energy harvesting technique such as capturing solar energy using a solar panel (not shown) comprised within the sensor), there is still a desire to reduce the use of battery power to alleviate the risk of the battery becoming depleted between charges (e.g. during winter when there is little sunlight to reach a sensor's solar panel).

In order to reduce the battery depletion rate of each sensor <NUM>, in an embodiment, the sensors are divided into groups, each group comprising a plurality of sensors and being associated with a respective one of the sensors (this is referred to as the group sensor). For each group, each sensor in the group then takes a measurement and transmits corresponding sensor data to the data processing device <NUM> at a respective one of a plurality of successive times. Once each sensor in the group has taken a measurement and transmitted its sensor data, the process is then repeated. Based on the value of the parameter measured by each sensor of the group at its respective time, the data processing apparatus <NUM> then determines an estimated value of the parameter at the location of the group sensor at each of the successive times. This allows a value of the parameter (as actually measured or as estimated) to be determined at the location of the group sensor at each of the successive times, even though the group sensor itself only actually makes a measurement and transmits sensor data at one of those times. Each sensor is the group sensor of one group and may also be a sensor in one or more other groups associated with respective other sensors (the other sensors being the group sensors of these respective other groups). This means that a value of the parameter is determined for each sensor each time that a measurement is taken, even though that sensor only takes a measurement at a portion of those times.

This arrangement allows the parameter to be monitored over the entire environment over which the sensors are distributed whilst reducing the number of times each individual sensor must make a measurement and transmit sensor data, thereby reducing the battery depletion rate of each individual sensor and reducing how often sensor batteries must be replaced. The reduction in sensor data transmitted (since only one sensor in each group need transmit sensor data at each successive time rather than all sensors) also increases network capacity and reduces the relative cost of network ownership.

This is demonstrated in <FIG>, which show the road <NUM> and sensors of <FIG> at a plurality of successive times t<NUM> to t<NUM>. The sensors are individually labelled Sn-<NUM>, Sn-<NUM>, Sn, Sn+<NUM> and Sn+<NUM> and form a group of sensors in which Sn is the group sensor. At time t<NUM> (<FIG>), a first sensor in the group Sn (which, in this case, is the group sensor) takes a measurement of a parameter and transmits sensor data indicative of the measured parameter value to the data processing device <NUM>. Let us assume that the parameter in this example is temperature. It will be appreciated that another parameter could be measured, however. At time t<NUM> (<FIG>), a second sensor in the group Sn+<NUM> takes a temperature measurement and transmits sensor data indicative of the measured temperature value to the data processing device <NUM>. At time t<NUM> (<FIG>), a third sensor in the group Sn+<NUM> takes a temperature measurement and transmits sensor data indicative of the measured temperature value to the data processing device <NUM>. At time t<NUM> (<FIG>), a fourth sensor in the group Sn-<NUM> takes a temperature measurement and transmits sensor data indicative of the measured temperature value to the data processing device <NUM>. Finally, at time t<NUM> (<FIG>), a fifth sensor in the group Sn-<NUM> takes a temperature measurement and transmits sensor data indicative of the measured temperature value to the data processing device <NUM>. All sensors in the group have therefore provided a temperature measurement to the data processing device.

The process of each sensor in the group successively making one measurement and transmitting corresponding sensor data until all sensors in the group have done so may be referred to as a measurement cycle. In this example, the measurement of the temperature by the sensor Sn is the first measurement of the measurement cycle and the measurement of the temperature by the sensor Sn-<NUM> is the final measurement of the measurement cycle. The measurement cycle is continuously repeated so that, after measurement by Sn-<NUM> at t<NUM> in one measurement cycle, the next measurement is taken by Sn at t<NUM> in the next measurement cycle. Each measurement cycle thus has a respective t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> value. For example, if measurements are taken every <NUM> minutes with the first measurement cycle beginning at time <NUM>:<NUM>, then, for the first measurement cycle, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM> and t<NUM> = <NUM>:<NUM>. For the second measurement cycle, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM> and t<NUM> = <NUM>:<NUM>. For the third measurement cycle, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM>, t<NUM> = <NUM>:<NUM> and t<NUM> = <NUM>:<NUM>, and so on.

The processor <NUM> of the data processing device <NUM> then determines, for each successive time at which a measurement is made during each measurement cycle, a temperature value for the group sensor. In an embodiment, this is done by determining a weighted sum of the latest measured temperature of each sensor in the group. The weighting associated with each sensor is determined according to any suitable factor(s), such as the amount of time elapsed since that sensor last took a measurement and the location of that sensor (e.g. the spatial separation of that sensor from the group sensor). The weighted sum providing the calculated temperature TCn of the group sensor Sn is: <MAT>.

Here, Wi is the weighting associated with sensor Si and Ti is the latest measured temperature of the sensor Si. The measured temperatures used to calculated TCn after each measurement taken during the measurement cycle and the extent to which they influence the value of TCn, are reflected in the weightings Wn, Wn+<NUM>, Wn+<NUM>, Wn-<NUM> and Wn-<NUM>. The weightings are appropriately adjusted each time a new measurement is taken during the measurement cycle. An example set of weightings is shown in Table <NUM>:.

According to Table <NUM>, for each measurement cycle, when Sn makes the first measurement Tn at t<NUM>, the measured temperature used to calculate TCn is Tn only (since this is the actual temperature measured by Sn). Thus, Wn = <NUM> and Wn+<NUM>, Wn+<NUM>, Wn-<NUM> and Wn-<NUM> = <NUM>. When Sn+<NUM> makes the next measurement Tn+<NUM> at t<NUM>, the measured temperatures used to calculate TCn are Tn (as previously measured at t<NUM>) and Tn+<NUM> with weightings Wn = <NUM> and Wn+<NUM> = <NUM>, respectively. Here, Wn+<NUM>, Wn-<NUM> and Wn-<NUM> = <NUM>. When Sn+<NUM> makes the next measurement Tn+<NUM> at t<NUM>, the measured temperatures used to calculate TCn are Tn (as previously measured at t<NUM>), Tn+<NUM> (as previously measured at t<NUM>) and Tn+<NUM> with weightings Wn =<NUM>, Wn+<NUM> = <NUM> and Wn+<NUM> = <NUM>, respectively. Here, Wn-<NUM> and Wn-<NUM> = <NUM>. When Sn-<NUM> makes the next measurement Tn-<NUM> at t<NUM>, the measured temperatures used to calculate TCn are Tn (as previously measured at t<NUM>), Tn+<NUM> (as previously measured at t<NUM>), Tn+<NUM> (as previously measured at t<NUM>) and Tn-<NUM> with weightings Wn = <NUM>, Wn+<NUM> = <NUM>, Wn+<NUM> = <NUM> and Wn-<NUM> = <NUM>, respectively. Here, Wn-<NUM> = <NUM>. Finally, when Sn-<NUM> makes the next measurement Tn-<NUM> at t<NUM>, the measured temperatures used to calculate TCn are Tn (as previously measured at t<NUM>), Tn+<NUM> (as previously measured at t<NUM>), Tn+<NUM> (as previously measured at t<NUM>), Tn-<NUM> (as previously measured at t<NUM>) and Tn-<NUM> with weightings Wn = <NUM>, Wn+<NUM> = <NUM>, Wn+<NUM> = <NUM> and Wn-<NUM> = <NUM> and Wn-<NUM> = <NUM>.

It is noted that the weightings in Table <NUM> at each of the times t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> also take into account the location of each sensor so that, for example, a sensor closer to the group sensor Sn will have a higher weighting than a sensor further from the group sensor Sn. For example, at time t<NUM> in Table <NUM>, Sn+<NUM> is the sensor which takes the temperature measurement (the temperature measurement, Tn+<NUM>, being the most recent temperature measurement in the measurement cycle and thus, temporally, the most relevant) and is allocated a weighting of Wn+<NUM> = <NUM>. On the other hand, at time t<NUM> in Table <NUM>, although Sn+<NUM> is the sensor which takes the temperature measurement (the temperature measurement, Tn+<NUM>, being the most recent temperature measurement in the measurement cycle and thus, temporally, the most relevant) it is allocated a weighting of only Wn+<NUM> = <NUM>. This is because Sn+<NUM> is located further away from the group sensor Sn than Sn+<NUM> (and therefore the temperature at Sn+<NUM> is considered to have less relevance to the temperature at Sn than the temperature at Sn+<NUM>). It will thus be appreciated that, in an embodiment, the weightings of each sensor at each measurement time of the measurement cycle take into account both the time elapsed since that sensor last made a measurement and the location of that sensor. It will be appreciated that the weightings in Table <NUM> (and Tables <NUM> to <NUM>, described later) are only examples and that, in practice, the weightings will be determined according to the specific characteristics of the environment in which the measurements are made and the parameter(s) which are measured.

Each sensor will have its own respective group of sensors with a corresponding set of measurement times (at each of which at least one sensor in the group will take a measurement) and weightings (defined for each sensor in the group for each of the measurement times). In an embodiment, each group comprises the group sensor plus one or more sensors located in the vicinity of the group sensor.

For example, for sensors arranged along a pathway such as a road, each group may comprise the group sensor plus a predetermined number of sensors either side of the group sensor. In the example of <FIG>, each group comprises two sensors on each side of the group sensor. It will be appreciated that, in reality, the road <NUM> of <FIG> extends further than shown and comprises a larger number of sensors. Thus, the sensor Sn-<NUM> will have a group comprising the sensors Sn-<NUM> (not shown), Sn-<NUM>, Sn-<NUM>, Sn and Sn+<NUM>, sensor Sn+<NUM> will have a group comprising the sensors Sn-<NUM>, Sn, Sn+<NUM>, Sn+<NUM> and Sn+<NUM> (not shown), and so on (it will thus be seen that each sensor is both a group sensor and is a sensor within the respective groups of one or more other sensors). Each measurement taken by each sensor is used for each of the groups to which that sensor belongs (with an appropriate weighting for each measurement time for each group). For example, assume that the weightings of the groups of sensors for Sn-<NUM>, Sn and Sn+<NUM> are given by Tables <NUM>, <NUM> and <NUM>, respectively.

Note that the times t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> of each group are the measurement times of the measurement cycle of that group and are defined individually for each group (that is, tj of group Sn is not necessarily the same as time tj of group Sn-<NUM> or Sn+<NUM>). Considering the sensors Sn-<NUM>, Sn-<NUM>, Sn, Sn+<NUM> and Sn+<NUM>, in this example, the time at which sensor Sn makes a single measurement is defined as t<NUM> in group Sn-<NUM> (with weighting Wn = <NUM>), t<NUM> in group Sn (with weighting Wn = <NUM>) and t<NUM> in group Sn+<NUM> (with weighting Wn = <NUM>). The time at which sensor Sn+<NUM> makes a single measurement is defined as t<NUM> in group Sn-<NUM> (with weighting Wn+<NUM> = <NUM>), t<NUM> in group Sn (with weighting Wn+<NUM> = <NUM>) and t<NUM> in group Sn+<NUM> (with weighting Wn+<NUM> = <NUM>). The time at which sensor Sn+<NUM> makes a single measurement is not defined in group Sn-<NUM> (since sensor Sn+<NUM> is not part of the group of sensor Sn-<NUM>), but is defined as t<NUM> in group Sn (with weighting Wn+<NUM> = <NUM>) and t<NUM> in group Sn+<NUM> (with weighting Wn+<NUM> = <NUM>). In this example, when sensor Sn+<NUM> makes a measurement, sensor Sn-<NUM> also makes a measurement (and does so at t<NUM> in group Sn-<NUM> with weighting Wn-<NUM> = <NUM>). The time at which sensor Sn-<NUM> makes a single measurement is defined as t<NUM> in group Sn-<NUM> (with weighting Wn-<NUM> = <NUM>) and t<NUM> in group Sn (with a weighting Wn-<NUM> = <NUM>), but is not defined for group Sn+<NUM> (since sensor Sn-<NUM> is not part of the group of sensor Sn+<NUM>). In this example, when sensor Sn-<NUM> makes a measurement, sensor Sn+<NUM> also makes a measurement (and does so at t<NUM> in group Sn+<NUM> with weighting Wn+<NUM> = <NUM>). The time at which sensor Sn-<NUM> makes a single measurement is defined as t<NUM> in group Sn-<NUM> (with weighting Wn-<NUM> = <NUM>), t<NUM> in group Sn (with weighting Wn-<NUM> = <NUM>) and t<NUM> in group Sn+<NUM> (with weighting Wn-<NUM> = <NUM>). It is thus demonstrated how a single measurement taken by each sensor is simultaneously used by each different group to which that sensor belongs, thus facilitating efficient use of sensor data.

The groups of group sensors located in the vicinity of the limits of the sensor distribution (e.g. at the ends of the road <NUM> past which there are no more sensors) may have a reduced number of sensors. For example, if sensor Sn-<NUM> in <FIG> is at the end of the road <NUM>, then there are no other sensors towards its left hand side. The group of sensor Sn-<NUM> may therefore comprise only sensors Sn-<NUM>, Sn-<NUM> and Sn. In this case, it will be appreciated that only temperatures Tn-<NUM>, Tn-<NUM> and Tn will be measured during the measurement cycle for the Sn-<NUM> group and that there will be two missing measurements compared to groups of sensors (e.g. the group of Sn) located away from the end of the road. In this case, weightings are still defined for the same number of measurement times (i.e. for t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> of the Sn-<NUM> group). However, the weightings will only be defined for Tn-<NUM>, Tn-<NUM> and Tn. Alternatively, the number of group sensors may be constant, and the groups of group sensors located in the vicinity of the limits of the sensor distribution may comprise additional sensors in order to make up the required total number. For example, if sensor Sn-<NUM> in <FIG> is at the end of the road <NUM>, then the group of sensor Sn-<NUM> may comprise sensors Sn+<NUM> and Sn+<NUM> as well as Sn-<NUM>, Sn-<NUM> and Sn in order to ensure a total of five sensors in the group. Alternatively, one or more of the sensors in groups of sensors located in the vicinity of the limits of the sensor distribution may be configured to make measurements more often in order to provide improved accuracy. For example, if the group of sensor Sn-<NUM> at the end of the road comprises only sensors Sn-<NUM>, Sn-<NUM> and Sn, then one or more of these sensors may be configured to take one or more additional measurements in order to ensure that one measurement is taken per time t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> of the group. This improves the accuracy of the calculated temperature at the location of Sn-<NUM> whilst only requiring a small proportion of the sensors (i.e. a portion of those located in the group of Sn-<NUM> rather than all sensors) to make more frequent measurements. Thus, even if these sensors require a battery replacement more frequently, this is not the case for most of the other sensors.

It will be appreciated that the sensors in each group may be determined according to one or more other parameters. For example, each group may comprise all sensors within a predetermined distance of the group sensor. The weightings and any additional measurements of sensors in the group are then adjusted depending on the number of sensors in each group.

Sensor variables such as the number of sensors in each group, the relative spatial separation of the sensors in each group, the interval between the successive times at which each sensor in the group takes a measurement and transmits sensor data and the weightings allocated to each sensor at each of those successive times are determined in advance depending on the nature of the one or more parameters which are measured by each sensor. For example, if a measured parameter is known to have greater spatial variation, then the relative spatial separation of the sensors in each group is less and/or the rate at which the sensor weighting is reduced with distance from the group sensor is greater. Conversely, if the measured parameter is known to have less spatial variation, then the relative spatial separation of the sensors in each group is greater and/or the rate at which the sensor weighting is reduced with distance from the group sensor is less. In another example, if a measured parameter is known to have greater temporal variation, then the number of sensors in each group is less, the interval between the successive times at which each sensor in the group takes a measurement and transmits sensor data is less and/or the rate at which the weighting of each sensor is reduced with time since the last measurement of that sensor was taken is greater. Conversely, if the measured parameter is known to have less temporal variation, then the number of sensors in each group is greater, the interval between the successive times at which each sensor in the group takes a measurement and transmits sensor data is greater and/or the rate at which the weighting of each sensor is reduced with time since the last measurement of that sensor was taken is less. This allows an appropriate balance between the accuracy of estimated parameter values and the reduction in sensor battery depletion rate. It will be appreciated that, depending on the one or more parameters measured, each of the sensor variables may be chosen accordingly based on experimental or computer modelled data, for example.

In an embodiment, one or more of the sensor variables may be adjusted according to different times at which the amount of spatial and/or temporal variation of one or more of the measured parameters is different. For example, if the measured parameter is temperature, then it may be the case that, during the night (e.g. between the hours of 10pm and 4am when there is no sunlight), the temporal and/or spatial variation of temperature is lower than during the day (e.g. between the hours of 4am and 10pm when there are varying amounts of sunlight cast from different angles). One or more of the sensor variables may therefore be adjusted to reflect this.

In a first example, the number of sensors in each group is reduced during the night and increased during the day. This correspondingly increases and decreases the relative spatial separation of the sensors in each group during the night and day, respectively. This allows the number of measurements made by a portion of the sensors in the group to be reduced, thereby further reducing the power consumption of these sensors. For example, during the day, the group of sensor Sn in <FIG> may comprises the five sensors Sn-<NUM>, Sn-<NUM>, Sn, Sn+<NUM> and Sn+<NUM> whereas, during the night, the group of sensor Sn may comprise only three sensors Sn-<NUM>, Sn and Sn+<NUM> (i.e. sensors Sn-<NUM> and Sn+<NUM> do not perform any measurements during the night). This means that measurements are made at times t<NUM>, t<NUM> and t<NUM> only. The weightings of the sensors Sn-<NUM>, Sn and Sn+<NUM> at each of the times t<NUM>, t<NUM> and t<NUM> are adjusted accordingly (so as to ensure that the sum of the weightings is <NUM>). In an embodiment, weightings may continue to be calculated for times t<NUM> and t<NUM> so as to determine values of TCn at these times (even though no new measurements are taken at these times), thereby allowing an updated value of TCn to be provided at each time of the measurement cycle (t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM>).

In an embodiment, the sensors included in the reduced number of sensors during the night is varied so as to achieve more uniform battery depletion. For example, for each night, a different three of the five sensors in each sensor group may be selected (and allocated appropriate weightings) so that, over time, the battery depletion rate of the sensors is approximately uniform. On average, each sensor will only be operational during <NUM> out of <NUM> nights, thereby reducing the sensor power consumption of all sensors. In an example, each sensor in each group is configured with a predefined schedule to determine whether or not it is to take measurements on a given night. In this case, the controller <NUM> of each sensor comprises a storage medium to store information indicative of the predefined schedule and timing circuitry (e.g. implementing a clock and calendar) to determine (according to the schedule) whether the sensor is to transmit or not at any given time. During the night, the device <NUM> then determines which of the sensors are taking measurements (and which should thus be included in the sensor group for the night) based on the sensor data it receives (it will receive sensor data only from sensors which are taking measurements). In another example (e.g. in which the sensors can receive data from as well as transmit data to the device <NUM>), the device <NUM> may transmit a scheduling signal to the sensors in each group each night indicating which sensors of the group are to take measurements that night. The scheduling signal may include, for example, information indicative of a list of the unique sensor identifiers of the sensors which are to perform measurements that night. Each sensor then performs measurements and transmits sensor data during the night only if its unique sensor identifier is included in the list. In an embodiment, information indicative of sets of weightings for each possible subset of sensors of each group is stored in the storage medium <NUM>. One of the sets of weightings is then selected based on the subset of sensors selected to take measurements during a particular night.

In a second example (which may be combined with the first example), the times at which measurements are made are less frequent during the night and more frequent during the day. For example during the day, each consecutive measurement time t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> may be separated by a time interval of <NUM> minutes whereas, during the night, the separation interval may increase to <NUM> minutes. This halves the number of measurements which must be made during the night, thereby reducing sensor power consumption. This may be accompanied by adjustments to the sensor weightings for each measurement time, if appropriate. In this example, the device <NUM> may transmit a scheduling signal indicating a new time interval between which consecutive measurements should be made. Each sensor then adjusts how often it takes a measurement and transmits sensor data to the device <NUM>. For example, if the time interval is doubled (e.g. from <NUM> minutes to <NUM> minutes), then each sensor should make a measurement and transmit sensor data half as often, if the time interval is tripled (e.g. from <NUM> minutes to <NUM> minutes), then each sensor should make and transmit sensor data a third as often, and so on. Alternatively (e.g. in the case that each sensor can only receive but not transmit signals), each sensor may be preconfigured to change how often it takes a measurement and transmits sensor data based on whether it is the day or night. In this case, the controller <NUM> of each sensor comprises a storage medium to store information indicative of how often to undertake measurement and transmission at a given time (e.g. at a certain periodicity during the day and at half the periodicity at night) and timing circuitry (e.g. implementing a clock) to determine whether it is day or night.

In a third example (which may be combined with the first and/or second examples), the weightings may be adjusted according to whether it is day or whether it is night. During the day (when the spatial and/or temporal variation in temperature is greater), the rate at which the sensor weighting is reduced with distance from the group sensor and/or with time since the last measurement was taken is greater. During the night (when the spatial and/or temporal variation in temperature is less), the rate at which the sensor weighting is reduced with distance from the group sensor and/or with time since the last measurement was taken is less.

In an embodiment, each of the sensors <NUM> is associated with a unique identifier which is included with the sensor data each time sensor data is transmitted to the data processing device <NUM>. This allows the device <NUM> to associate each instance of received sensor data with a particular sensor. Information indicative of which sensor (identified by its unique identifier) is in each group (this is referred to as group information) is determined in advance and stored, together with the predetermined weightings of each sensor in each group at each measurement time, in the storage medium <NUM> of the device <NUM>. This allows the processor <NUM> to calculate the value of the parameter (e.g. temperature) for the group sensor of each group at each measurement time of the measurement cycle of that group, in the way as previously described.

In an embodiment, the group information and weightings are configured using a suitable user interface (e.g. keyboard or touch screen, not shown) of the device <NUM>. For example, once the sensors <NUM> have been distributed across the environment to be monitored, a user controls the device <NUM> to broadcast a discovery signal detectable by all distributed sensors. Each sensor <NUM> then transmits a response signal, the response signal of each sensor comprising the unique identifier of that sensor and, optionally, location data (e.g. Global Navigation Satellite System (GNSS) data generated by a GNSS receiver of the sensor included as part of the sensor controller <NUM>) indicative of the geographical location of that sensor. In the case that location data is not included in the response signal, then the user may manually enter location data indicative of the geographical location of each sensor. The unique identifier and location data of each sensor are then stored in a sensor location lookup table stored in the storage medium <NUM>, for example. Alternatively, each sensor may transmit location data together with each instance of sensor data that it transmits. In this case, such a sensor location lookup table is not required. The location data of each sensor (if present) may be used by the user to help determine which sensors should be in each group and/or to help with the generation of information to be output for display on the display device <NUM>. The information to be displayed may take the form of a computer generated image of the environment over which the sensors are distributed together with information (e.g. overlaid numerical values, different coloration, or the like) indicative of the calculated value of the one or more measured parameters at each location within the environment at which a sensor is present, for example.

The user then defines a group of sensors for each sensor by identifying each sensor which is in that group. For example, the user would identify the sensors Sn-<NUM>, Sn-<NUM>, Sn, Sn+<NUM> and Sn+<NUM> as being in the sensor group of sensor Sn in <FIG>. The user does this based on the unique identifier and location data of each sensor for which a response signal is received. Appropriate further sensor variables, such as the time during the measurement cycle at which each sensor in each group transmits sensor data and the weightings for each sensor in the group at each of those times, are then defined. In one example, a timing signal is transmitted to each sensor. The timing signal comprises, for example, the unique identifier of the sensor it is intended for and information indicative of the time during the measurement cycle (e.g. t<NUM>, t<NUM>, t<NUM>, t<NUM> or t<NUM>) at which that sensor should transmit sensor data. The time assigned to each sensor may be defined relative to the start of an active time period of that sensor, for example. Once the sensor measurement times have been defined, the setup is complete and the device <NUM> will begin to receive sensor data from each of the sensors at the time defined for that sensor. The value of the measured one or more parameters of each group sensor can then be calculated at each measurement time during the measurement cycle in the way as previously described.

In an alternative embodiment, each sensor <NUM> may be configured to transmit but not receive wireless signals. In this case, when each sensor <NUM> is placed at its intended location within the environment to be monitored, it is preconfigured with a measurement time at which to take a measurement and transmit sensor data to the device <NUM>. Again, the measurement time (e.g. t<NUM>, t<NUM>, t<NUM>, t<NUM> or t<NUM>) of each sensor is defined relative to the start of an active time period of that sensor. Upon successful placement of the sensor, it starts periodically transmitting sensor data to the device <NUM> at the preconfigured measurement time. The device <NUM> thus uses the signals comprising the sensor data (with which the unique identifier and, optionally, location data of the sensor is also included) to allow the groups of sensors to be defined by the user. In an embodiment, the time of the measurement cycle at which each sensor transmits sensor data is known by the user in advance and used when defining the groups of sensors (e.g. so that each sensor in a given group takes a measurement and transmits sensor data at a different respective time). Alternatively, upon receiving the sensor data, the device <NUM> is able to determine the measurement time of each sensor based the time at which sensor data is received from that sensor relative to time at which sensor data is received from the other sensors. This information is then presented to the user to aid them in defining the groups of sensors.

It will be appreciated that the sensors in each group, the weightings and/or any other sensor variables (e.g. the measurement time of each sensor during the measurement cycle) may be determined automatically using a suitable computer algorithm rather than being determined manually by the user. Any suitable computer algorithm may be used. Such an algorithm is executed by the processor <NUM> of the data processing device <NUM>, for example. In one example, a machine learning algorithm is used. The machine learning algorithm is trained by providing data from every sensor at every measurement time for a finite, initial, training time period. For example, during this training time period, each of Sn-<NUM>, Sn-<NUM>, Sn, Sn+<NUM> and Sn+<NUM> would provide a measurement at every one of times t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> (rather than only Sn providing a measurement at t<NUM>, only Sn+<NUM> providing a measurement at time t<NUM>, etc.) This allows a true, measured value of the parameter at every sensor location at every measurement time over the training time period to be known. The machine learning algorithm then performs an optimisation process to determine the sensor groupings, weightings and/or other sensor variables which produce the best estimates of the measured parameter values. For example, the machine learning algorithm may be a suitable genetic algorithm (various genetic algorithms are known in the art and are therefore not described here). Once the sensor groupings, weightings and/or other sensor variables have been optimised in this way, the sensors (e.g. in response to a trigger signal transmitted by the device <NUM>) then revert back to the normal operation as previously described (in which only one sensor provides sensor data at each measurement time during the measurement cycle and in which the parameter values of the other sensors at that measurement time are estimated). Because of the optimisation, however, the accuracy of the estimated parameter values at each sensor location is improved.

In general, it will thus be appreciated that, in order for the device <NUM> to calculate the value of the one or more parameters at the location of the group sensor at each measurement time of the measurement cycle of that group, the sensor which transmits each instance of sensor data must be identifiable (e.g. based on a unique identifier of the sensor provided with the sensor data or, more generally, a characteristic of the wireless signal comprising the sensor data from which the transmitting sensor is identifiable) and the sensors in each group (i.e. the group information) and the weightings of the sensors in each group at each measurement time of the measurement cycle of that group must be known. The device <NUM> must also know which measurement time of the measurement cycle it currently is (e.g. whether it is time t<NUM>, t<NUM>, t<NUM>, t<NUM> or t<NUM>) in order to select the correct weightings (e.g. according to a table such as Table <NUM>). The device <NUM> may know the measurement time based on a predetermined measurement time associated with each uniquely defined sensor (e.g. so that, when sensor data is received from sensor Sn, the device <NUM> knows that it must be measurement time t<NUM> of the measurement cycle, when sensor data is received from sensor Sn+<NUM>, the device <NUM> knows that it must be the measurement time t<NUM> of the measurement cycle, and so on). Alternatively, the device <NUM> may establish timing synchronisation with each of the sensors <NUM> (e.g. so that both the devices <NUM> and sensors <NUM> determine it to be time t<NUM>, t<NUM>, t<NUM>, etc. at the same time). This is achieved using any suitable technique for achieving timing synchronisation in a wireless communications system.

In embodiments, associations between instances of stored information (e.g. the unique identifier of the group sensor and the unique identifiers of the one or more other sensors in each group, the weightings of each sensor in each group at each measurement time of the measurement cycle of that group, the location data of each sensor (if present) and the time at which each sensor makes a measurement (if present)) are implemented by way of lookup tables, databases or the like stored in the storage medium <NUM>. When a portion of this information is different for different time periods (e.g. if it is different during the day and night, as exemplified above), then separate sets of this information may be stored for each of these time periods. The appropriate set of information is then used according to the current time.

In an embodiment, in order to reduce the amount of sensor data which each sensor must transmit at its associated measurement time of the measurement cycle, if the measured value of the parameter of a particular sensor has not changed since the last time a measurement was taken (or has not changed by more than a predetermined threshold), the sensor transmits, as the sensor data, data indicative that there has been no change in the measured parameter value rather than retransmitting the same value of the parameter. The data indicative that there has been no change (e.g. a flag such as a single bit in a predetermined field of a transmitted data packet) is less than the amount of data which must be transmitted to indicate a particular parameter value. Transmitting less data in this way reduces the network overhead required to transmit the sensor data and reduces the power consumption of the transmitting sensor.

In the above embodiments, the parameter measured by the sensors <NUM> distributed along the road <NUM> is temperature. Other parameters may be measured instead of or in addition to the temperature. For example, the humidity, an amount of an air pollutant (such as CO<NUM>, NOx or particulate pollutants) and/or a vehicle count may be measured. The sensor data transmitted to the device <NUM> comprises data indicative of the measured value of each of the parameters monitored by the sensor.

The present technique is not limited to road sensors, but may be used with sensors distributed across any conceivable environment in which it is desirable for the sensor battery life to be improved. Some further example environments are discussed with reference to <FIG>.

<FIG> shows a fluid transport conduit <NUM> (e.g. a gas or oil pipeline) along which sensors <NUM> are distributed. For example, the sensors may be attached to or embedded within the inner surface <NUM> of the conduit <NUM> so as to measure one or more parameters associated with fluid flowing through the conduit. The one or more parameters measured by the sensors include, for example, temperature, vibration, pressure, fluid flow rate, a parameter indicative of degradation of the inner surface of the conduit (e.g. by corrosion, especially at inflection points, elbows, valves or inlets of the conduit) and/or a parameter indicative of a build up of a substance on the inner surface of the conduit (e.g. fat build up on the inner surface of a sewage pipe). The measured parameters are chosen to allow fluid flow through the conduit and/or the structural integrity of the conduit to be monitored and any problems (e.g. blockages, leaks, degradation or the like) to be detected. The distribution of the sensors is similar to that for the road <NUM> in that the sensors are distributed along the length of the conduit in the direction of fluid flow (for the road <NUM>, the sensors are distributed along the length of the road in the direction of traffic flow). Replacing the batteries of the sensors distributed along the length of the conduit requires either taking apart the conduit (which is time consuming and requires the flow of fluid to be temporarily suspended) and/or a person to enter the conduit (which is time consuming and hazardous). The lower sensor power consumption enabled by the present technique reduces how often batteries need to be replaced, thereby alleviating these problems.

<FIG> shows an agricultural region (in this example, a vineyard) over which sensors <NUM> are distributed. The sensors are embedded in the soil <NUM> in which crops <NUM> (in this case, grapevines) are grown so as to measure one or more parameters associated with the soil. The one or more parameters measured by the sensors include, for example, temperature, humidity (which can be used to determine soil permeability, for example, based on the humidity measured by sensors at different depths within the soil), soil acidity / alkalinity, nutrient (e.g. nitrate) concentration or toxin concentration. It will be appreciated that the agricultural region may make use of a hydroponic system (over which at least a portion of the sensors are distributed) instead of or in addition to the use of soil. The measured parameters are chosen to allow the growing conditions of the crops to be monitored so as to allow more effective cultivation of the crops. For example, if humidity is too low or too high, then it can be concluded that more or less water should be provided to the crops. If the temperature is too low or too high, then it can be concluded that the crops should be protected from the cold or should be shaded from the sun. If the soil acidity / alkalinity is too low or too high, then appropriate agricultural products can be added to the soil to correct the acidity / alkalinity. The distribution of the sensors is different to that of the road <NUM> or conduit <NUM> in that the sensors are distributed over a wide area of agricultural land rather than along a length in the direction of traffic or fluid flow (that is, the sensors of <FIG> are distributed over an approximate 2D area rather than along an approximate 1D length as in <FIG> and <FIG>). However, the present technique applies in the same way. In this case, the groups of sensors are determined by determining all sensors within a predetermined distance of a group sensor to be within that group, for example. Replacing the batteries of the sensors distributed over the agricultural region requires a person to travel over the entire agricultural region (which is time consuming and may result in plants becoming disturbed). The lower sensor power consumption enabled by the present technique reduces how often batteries need to be replaced, thereby alleviating these problems.

In a variation of <FIG>, the agricultural region may comprise a volume of water (e.g. in a tank or in a delimited area of open water) containing aquatic animals. This occurs for a fish farm, for example. In this case, the one or more parameters measured by the sensors include, for example, temperature, oxygen concentration, algae concentration (e.g. based on CO<NUM> concentration, with higher levels of algae being associated with higher levels of dissolved CO<NUM>), toxin concentration and sediment build up.

<FIG> shows a pedestrianised region (in this example, a shopping mall comprising various shops <NUM> with their own entrances <NUM>, decorative features <NUM> (e.g. plants, fountains or the like) and entrance / exit doors <NUM>) over which sensors <NUM> are distributed. The sensors are embedded in the floor <NUM> so as to measure one or more parameters associated with the pedestrianised region. The one or more parameters measured by the sensors include, for example, temperature, humidity, an amount of an air pollutant (such as CO<NUM>, NOx or particulate pollutants) and/or a pedestrian count. The measured parameters are chosen to allow conditions of the pedestrianised area to be monitored so as to ensure that it is comfortable and safe for pedestrians. For example, if the temperature or humidity is too low or too high, then the air ventilation and cooling / heating system of the shopping mall can be adjusted accordingly. If an amount of a pollutant is too high, then the source of that pollutant can be investigated (e.g. there may be a road with heavy, polluting traffic nearby) and appropriate action taken (e.g. installing air filters in the air ventilation system of the shopping mall and/or asking the local authority to take action on pollution generated by nearby traffic). The pedestrian count at each of the sensors can be used to determine the distribution of pedestrians over the pedestrianised area at various times, thereby enabling the layout of the pedestrianised area to be adjusted so as to improve the flow of pedestrians. This improves the experience of pedestrians and also facilitates pedestrian safety in the case of an emergency evacuation, for example.

The distribution of the sensors is similar to that of the agricultural region of <FIG> in that the sensors are distributed over a wide area of floor space rather than along a length in the direction of traffic or fluid flow. In this case, the groups of sensors may be determined differently depending on the one or more parameters which are measured.

For example, for environmental parameters such as temperature, humidity and pollution, the groups may be determined by determining all sensors within a predetermined distance of a group sensor to be within that group.

On the other hand, for parameters related to the pedestrians themselves such as the pedestrian count, the groups may be determined in a bespoke manner depending on the layout of the pedestrianised area. In <FIG>, two example sensor groups <NUM> and <NUM> are shown. Each sensor in each group periodically measures and reports a pedestrian count (e.g. the number of pedestrians detected by that sensor since it last took a measurement). All sensors in the group <NUM> are located close to the south side of the shopping mall. The sensor data generated by each of these sensors can therefore be combined to give a reliable estimate of the pedestrian count at the south side of the shopping mall. Similarly, all sensors in the group <NUM> are located close to the east side of the shopping mall. The sensor data generated by each of these sensors can therefore be combined to give a reliable estimate of the pedestrian count at the east side of the shopping mall. Groups of sensors located at other predetermined locations within the shopping mall (e.g. the north side and west side or outside certain shops) can be selected in a similar way. This allows the pedestrian distribution throughout the shopping mall to be monitored and appropriate improvements to be made. For example, if it is seen that the pedestrian count at the south side of the shopping mall is consistently higher than that at the north side of the shopping mall, then it may be concluded that the decorative feature 602A at the south side should be removed in order to provide more space for pedestrians.

Replacing the batteries of the sensors distributed over the pedestrianised region requires a person to travel over the entire agricultural region (which is time consuming and may require certain parts of the pedestrianised area to be temporarily made off limits to pedestrians whilst the sensors batteries are removed and replaced). The lower sensor power consumption enabled by the present technique reduces how often batteries need to be replaced, thereby alleviating these problems.

In some embodiments, sensors which measure different parameters in the same environment (e.g. sensors which measure temperature and sensors which measure a pedestrian count in a shopping mall or sensors which measure CO<NUM> pollution along a road and sensors which measure particulate pollution along a road) may be grouped differently and make and report measurements at different times and/or at different intervals. This allows an appropriate balance to be achieved between reduced sensor power consumption (the measurement of different parameters may required different amounts of power) and the reliability of estimated measurements (different parameters may have a different spatial and/or temporal variation).

An example of this is shown in <FIG>, in which there is a first set of sensors 102A which measure a first parameter and a second set of sensors 102B which measure a second, different, parameter. The sensors 102A and 10B are located along the same road <NUM>. Each sensor 102A and 102B may be a physically separate sensor (each with its own communication interface <NUM>, sensor hardware <NUM>, controller <NUM> and battery <NUM>). Alternatively, at least some of the sensors 102A may each be combined with a sensor 10B so as to form a single sensor (with a single shared communication interface <NUM>, controller <NUM> and battery <NUM> connected to two different types of sensor hardware <NUM>, one type for measuring the first parameter and one type for measuring the second parameter). In the latter case, the second set of sensors is part of the first set of sensors. The first set of sensors 102A comprises a different number of sensors than the second set of sensors 102B and the groups of sensors 102A each comprise a different number of sensors than the groups of sensors 102B. In this case, there are more sensors 102A than there are sensors 101B and the number of sensors 102A in each group is greater than the number of sensors 102B in each group. This is illustrated in <FIG>, in which the group of sensor S(<NUM>)n (which belongs to the first set of sensors 102A) comprises sensors S(<NUM>)n-<NUM>, S(<NUM>)n-<NUM>, S(<NUM>)n, S(<NUM>)n+<NUM> and S(<NUM>)n+<NUM> (a total of <NUM> sensors) whereas the group of sensor S(<NUM>)n (which belongs to the second set of sensors 102B) comprises sensors S(<NUM>)n-<NUM>, S(<NUM>)n, S(<NUM>)n+<NUM> (a total of <NUM> sensors). The measurement cycle of each group of sensors 102A thus comprises a larger number of measurement times than the measurement cycle of each group of sensors 102B (since there are more sensors in each group to give a measurement). The length of the measurement cycle of each group of sensors 102A may be different to that of each group of sensors 102B. For example, the measurement cycle of each group of sensors 102B may be longer than that of each group of sensors 102A, thereby further reducing the rate at which measurements are made in the second group. In the example of <FIG>, the first parameter may be a parameter with a greater spatial and/or temporal variation and/or a parameter for which each measurement and transmission requires a lower amount of power and the second parameter may be a parameter with a lower spatial and/or temporal variation and/or a parameter which requires a higher amount of power to measure. Having the first and second sets of sensors to respectively measure each of these parameters (with appropriately different timings and groupings) thus provides a balance between sensor power consumption and parameter calculation accuracy which is tailored to the nature of the parameter which is measured.

In embodiments, the sensor hardware <NUM> may be any suitable sensor hardware known in the art to measure the parameter concerned. When the measured parameter is temperature, the sensor hardware <NUM> may comprise a thermistor or the like, for example. When the measured parameter is humidity / moisture level, the sensor hardware <NUM> may comprise a capacitive hygrometer or the like, for example. When the measured parameter is an amount of an air pollutant, the sensor hardware <NUM> may comprise a nondispersive infrared (NDIR) sensor or the like (e.g. to measure CO<NUM> or NOx levels) or a tapered element oscillating microbalance (TEOM) or the like (e.g. to measure the amount of particulate matter in the air), for example. When the measured parameter is vibration or pressure, the sensor hardware <NUM> may comprise a piezoelectric sensor or the like, for example. Measured pressure may also be used to determine fluid flow rate (e.g. based on a predefined relationship between flow rate and pressure), a depth of water or snow (for example, for a sensor at the bottom of a puddle of water, the pressure will be higher the deeper the puddle) or a vehicle or pedestrian count (e.g. based the measured pressure exceeding a predetermined threshold corresponding to the weight of a vehicle or pedestrian). When the measured parameter is acidity / alkalinity, the sensor hardware <NUM> may comprise a pH meter or the like, for example. When the measured parameter is an amount of oil, the sensor hardware <NUM> comprises suitable oil detection hardware such as a nephelometer. When the measured parameter is an amount of corrosion, the sensor hardware <NUM> comprises suitable ultrasonic, radiographic, guided wave or electromagnetic corrosion detection hardware, for example. When the measured parameter is an amount of substance build up (e.g. build up of a substance adhered to an inner surface of a conduit or of sediment in a volume of water), the sensor hardware <NUM> comprises suitable build up detection hardware (e.g. hardware which uses laser measurements to detect a change in width of a conduit due to build up or a change in depth of a volume of water due to build up or which uses a change in electrical conductivity due to the build up a foreign substance on the inner surface of a conduit or on the floor of a volume of water), for example. When the measured parameter is a chemical substance such as a nutrient (e.g. nitrate), a toxin, oxygen or CO<NUM>, the sensor hardware <NUM> comprises suitable chemical detection hardware for detecting characteristic chemicals of the substance concerned, for example. Various electronic detection methods for detecting the presence of particular chemicals in air or water are known in the art and are therefore not discussed in detail here. It will be appreciated that these types of sensor hardware are merely examples and that sensor hardware appropriate to the parameter to be measured and the environment within which the parameter is to be measured may be selected on a case by case basis by one skilled in the art.

<FIG> shows a method carried out by the device <NUM>, according to an embodiment. The method starts at step <NUM>. At step <NUM>, a signal is received from each of a plurality of sensors (e.g. sensors Sn-<NUM>, Sn-<NUM>, Sn, Sn+<NUM> and Sn+<NUM>) each located at a respective location. Each signal is indicative of a respective value of a parameter measured by that sensor at a respective one of a plurality of successive times (e.g. times t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM>). At step <NUM>, based on the value of the parameter measured by one or more first sensors of the plurality of sensors at a respective one or more of the plurality of successive times, a value of the parameter at the location of a second sensor of the plurality of sensors at one of the one or more of the plurality of successive times is determined (for example, the parameter value measured by one or more of the sensors Sn-<NUM>, Sn-<NUM>, Sn, Sn+<NUM> and Sn+<NUM> at times t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM>, respectively, is used to determine the parameter value at the location of Sn at one or more of the times t<NUM>, t<NUM>, t<NUM>, t<NUM> and t<NUM> using a suitable weighted sum, as previously described). The method ends at step <NUM>.

<FIG> shows a method carried out by a sensor <NUM>, according to an embodiment. The method starts at step <NUM>. At step <NUM>, the sensor periodically measures a value of a parameter of its environment (e.g. temperature, humidity or the like, as previously described). At step <NUM>, the sensor <NUM> transmits, to the device <NUM>, signals each indicative of a respective one of the measured parameter values. The signals are periodically transmitted over a low-power wide-area network, LPWAN, during a subset of periodic duty cycle-constrained active time periods of the LPWAN. The method ends at step <NUM>.

Claim 1:
A data processing method comprising:
receiving (<NUM>), from each of a plurality of sensors (<NUM>),
one of the sensors being a group sensor,
the sensors being configured to measure a parameter,
the sensors being located at respective locations distributed over an environment,
each sensor being configured to measure a value of the parameter at a respective one of a plurality of successive times of a repeating measurement cycle,
a signal indicative of the value of the parameter measured by that sensor during the repeating measurement cycle; and
determining (<NUM>), based on the values of the parameter measured by the plurality of sensors during the repeating measurement cycle, a value of the parameter at the location of the group sensor at each of the plurality of successive times of the repeating measurement cycle.