Adaptive Sampling In Wireless Sensor Networks For Air Monitoring System

Figure 2.1: GreenIoT architecture[10]This sensor node has a Cortex M3 processor and local sensors for 3D acceleration, 3D gyro, 3D magnetic field/compass, humidity, temperature, noise/audio,barometric pressure, light, N O2 , CO and IR/proximity. This sensor is poweredby external 4.1V LiPo cell which can be charged by USB or 5V, and the onboard RF transceiver and PCB antenna is for 2.4GHz.Figure 2.4 is showing the block diagram of U115 and Table 2.1, 2.2 shows atechnical specification of U115 sensor node.2.36LoWPAN and MQTT6LoWPAN (IPv6 over Low power Wireless Personal Area Network) is a protocol which enables IPv6 packets to be carried on top of IEEE 802.15.4 networks.6LoWPAN is used to apply Internet Protocol to smallest devices and to let lowpower devices such as sensors take part in Internet of Things [11]. Since IoTshould reach an efficient support for global communications, access to servicesand information, discovery and look-up, it is necessary to enable a communication mechanism which allows global access to devices, sensors and smart objects[12]. Thus 6LoWPAN is the answer to the technological requirements due to itsown advantages and attributes.MQTT (Message Queuing Telemetry Transport) is an open publish-subscribe7

Table 2.1: CPU board module of U115ParametersTechnical specificationCPUCPU speedFLASHRAMSerial InterfaceFlash UpdateSupply Voltage Charging PowerSupply Voltage LiPo CellCurrent Consumption LiPo CellST STM32L151VC 100pin LQFPMax 72MHz, Min 32KHz or stopped128k32k3 serial ports, 3 SPI ports, I 2 Cvia JTAG or Serial Port/Radio 5V DC, 200mA 5% from USB 2.7V to 4.1V DC72MHz : 0.06A typ32KHz : 2mA typ0KHz : 10uAtypOperation and Storage 40o to 85o COperation : 10% to 90%Storage : 5% to 95% (non-condesing)56 X 44mm 10mm with CPUTemperatureHumidityMechanical DimensionsThickness (Bottom Top)Table 2.2: On-board Sensors of U115Sensor typesModelAcceleration SensorMagnetic SensorGyro SensorLight SensorHumidity SensorPressure SensorTemperature SensorProximity SensorIR Image SensorGas Sensor N O2Gas Sensor G8831AMG8831MICS-4514MICS-45148

Figure 2.2: Sensor data collected from different data sources are necessary tosupport smart city development.[10]protocol designed for constrained devices used in telemetry applications [13].MQTT supports the connections with remote locations with limited networkbandwidth or with low data rate. Since publish-subscribe messaging patternrequires a message broker which distributes messages to clients based on thetopic of a message, MQTT also needs a MQTT broker. MQTT is widely usedin wireless networks that has some latency which is caused by bandwidth constraints or unreliable connections.In GreenIoT project, sensors monitor the environment and send the measurements to a server via 6LoWPAN Router by using MQTT protocol.9

Figure 2.3: Upwis sensor node : U115Figure 2.4: Block diagram of U11510

Chapter 3Related worksIn wireless sensor networks, sensor nodes are generally powered by battery witha limited energy budget. Moreover, since too many nodes could be deployedover a wide area or deployed in a hostile or harsh environment such as aroundthe volcanoes and in the deep sea, it could be impossible or inconvenient torecharge or replace the battery when they run out. Therefore, how to reducethe energy consumption of sensor nodes has been a critical issue in order toextend the lifetime of the network to fulfill the application requirements [14].Many researchers proposed new MAC protocols for WSNs to achieve energyconservation [15, 16, 17]. In [15], an adaptive MAC protocol is presented toguarantee the pre-configured network lifetime while reducing the end-to-end latency. In order to accomplish this, the protocol introduces the adaptive dutycycle which is adjusted based on the ratio of the remaining energy to the initialenergy and the pre-configured lifetime. Thus if a sensor node has a lot of energy,it wakes up frequently so that relaying data can be fastened, and on the otherhand, a sensor node with low energy has a long sleep-mode. This way the sensornode can run out of energy around the pre-configured lifetime.There are other studies to save energy of sensor network by using good routingprotocols [18, 19, 20, 21, 22]. [18] argued that it is impossible to derive optimalrouting in wireless sensor network, and proposed a new routing method whichworks based on energy histogram and traffic flow. Finally several techniques arecompared and it was found that good routing can increase the network lifetime.Ambient backscatter method proposed in [23] let sensors use TV signals andother source of RF signals as both the source of power and the means of communication. This design can avoid the expensive process of generating radiowaves by using backscatter communication which is more power-efficient thantraditional radio communication. Although this method does not require a dedicated power infrastructure and batteries, it is only available where ambient RFsignals exist.There has been studies about participatory sensing which coordinates mobilephone-based sensing with stationary sensors [24, 25, 26, 27]. In [24], a collaborative sensing paradigm is proposed which exploits mobile phones and station11

ary sensors to optimize the sensing. In order to obtain better sensing qualitywhile reducing the energy consumption in stationary sensors, it enables the mobile phones and stationary sensors to complement each other. It gives mobilephones a higher priority to perform sensing which means stationary sensorsperform sensing if the required sensing rate is not reached after mobile phonesensing, so in the area where many mobile phones exist we can save energy ofwireless sensors as well as provide good quality of service.In [28], on-demand network flooding method is proposed which supports ondemand multi-hop flooding with end-to-end latencies of tens of milliseconds,while dissipating less than ten microwatts during periods of inactivity. This paper argues that although previous studies are for disseminating periodic eventsthrough a network in a multihop topology, in reality, many events are not periodic but occur only on rare events. Thus they demonstrate a new low-powerprotocol design which performs on-demand wake-up of a multi-hop network using low-complexity radio hardware.In addition to studies above, there has been a lot of researches about adaptive sampling in sensor networks. The use of adaptive sampling and bandwidthmanagement in sensor networks has been well-motivated in [29, 30, 31, 7, 32].In [30], they propose a method called “Backcasting” which consists of two steps.In the first step, called “preview” step, they use only subset of sensors to estimate the environment. According to the result of the first step, it is determinedthat how many extra sensors are needed in order to achieve a desired level ofaccuracy. Then in the second step, called “refinement” step, additional sensorsare activated. Thus fewer sensors are used to obtain enough sensing accuracyso energy is saved compared to dense, non-adaptive sampling. However, thismethod is available when a sensor network covers a wide area and consists ofa lot of sensors. In addition, this method tries to save energy by reducing thenumber of sensors activated, not by decreasing the energy consumption in eachsensor.One adaptive sampling method which uses Kalman filter is described in [7].Here they use Kalman filter to estimate the environment in video surveillanceapplication and if estimation and measurement is quite different, it increasesthe sampling rate. This method is good but it can change the sampling rateonly when the Kalman filter estimation is much different from the measurement.Thus, it could be a good way for applying to tracking systems but not for airmonitoring.12

Chapter 4Problem formulation4.1MotivationIn WSN, energy conservation is very important since sensor nodes are operatingwith their own limited battery and if large number of sensors are deployed overwide area, it is very difficult to replace the batteries of all sensors. In order tomaintain a sensor network for a long time, we have to maximize battery lifetimeof sensor nodes.In sensor nodes, energy is consumed in many ways such as transmitting andreceiving the data, sensing, data processing, etc. Among all these, data transmission is very expensive in terms of energy consumption, while data processingconsumes significantly less [33]. Therefore, we can save considerable energy byreducing the number of transmission in sensor networks.In GreenIoT project, sensors monitor air pollutants and send the data to thesink node periodically all the time. If the air quality is same for certain period,transmission during that time can be omitted, and from historical data, wefound that there are some periods when air pollutants have almost same or atleast, no radical change. The figure (4.1), (4.2), (4.3) show the nitrogen dioxide(N O2 ) in several areas in Sweden on Feb 2, March 4, April 7 in 2015.As we can see in the graphs, the amount of N O2 in the air is quite stable between 0.am and 5.am and has no big variance from 9.pm to 12.pm. This meanswe do not have to send the data as often as we do from 16.pm to 20.pm duringwhich we have a lot of large fluctuations, and thus we can save energy for thetransmissions we can omit in those time periods.4.2ChallengesAdaptive sampling has been developed by several researchers so far and gaineda lot of progress in theoretical and practical field. However, adaptive samplingbased on input characteristic in air monitoring is still not that sophisticatedsince it has some challenging problems.13

Figure 4.1: NO2 in several cities in Sweden, 2015-2-17One challenge is the accuracy of raw sensor measurements. Adaptive samplingdetermines the sampling interval based on the measurement values from thesensors so we can easily fall into wrong results if sensors are not quite accurate.Another challenge is that air pollutants such as nitrogen dioxide or ozone havedifferent properties. Our method adjusts the sampling interval based on inputcharacteristic which is the characteristic of the air pollutants. Since every airpollutant has its own attribute, the adaptive sampling method should workdifferently with different air pollutants in order to achieve our goal.4.3SolutionThe goal of this project is to save energy of the sensors while not degradingthe sensing quality by adjusting the sampling rate. There are several ways todo this but what we study here is an adaptive sampling based on the inputcharacteristics. Specifically, if the input measurement is changing rapidly(bothincreasing and decreasing), we raise the sampling rate to observe the change ofair pollutants more correctly. If input is same or changed a little, we keep thelow sampling rate.In our project, sensors have “sampling interval (SI)” and “sampling intervalrange (SIR)” and they can modify the SI independently only within the SIR.If modified SI is out of the SIR, the sensor requests the new SIR from theserver. Here is a detailed description of the method. First, we calculate thedifference between the present measurement and the previous measurements tofind whether the air pollutants have large fluctuations or quite stable. Smalldifference means that there is no big change in air pollutants while big differ-14

Figure 4.2: NO2 in several cities in Sweden, 2015-3-4ence shows that there must be some unexpected events such as heavy traffic andfire which we have to observe carefully. Next, we adjust the SI with a certainformula which represents the relationship between difference and SI. Then, wecheck if modified SI is within the SIR. If the new SI belongs to the current SIR,we use that SI and if not, sensor asks the server to assign a new proper SIR andSI. When the server gets the request from the sensor, it allocates a new SIR tothe requesting sensor according to the available bandwidth, network contentionand sensor priority. This way, we can detect the moments when there are unexpected events, and monitor them more carefully than usual by adjusting thesampling rate based on input characteristics which leads to reduced transmission. Therefore, we can save energy as well as achieve high quality of service.However, there is one more problem - “sensor measurement noise”. Sensors havecertain noises and especially noise of gas sensor measurements is not negligible.Since our adaptive sampling method can be very sensitive to the input values,noises above certain amount can result in wrong SIR request to the server whichis energy-consuming transmission. Thus we have to eliminate the noise as muchas possible before we calculate the difference between the present and the pastmeasurements. As a solution for this, we choose Kalman filter to remove thenoise from the sensor measurements.15

Figure 4.3: NO2 in several cities in Sweden, 2015-4-716

Chapter 5Approach5.1Workflow of adaptive sampling methodFigure 5.1 shows the work flow of our adaptive sampling method.After every sampling interval, the sensor reads the measurement which includesFigure 5.1: The work flow of our adaptive sampling method in sensorsome noise. Then we use extended Kalman filter to remove the noise from themeasurement so as a result, we can get less noisy measurement. With thesemeasurements, we calculate a new sampling interval for next measurement. Ifthe new sampling interval belongs to current sampling interval range, we use itbut if not, the sensor requests the new sampling interval range from the server.Then the server gives a proper sampling interval range to the requesting sensorconsidering the sensor priority, the available bandwidth and network contention.17

5.2Kalman filterIn 1960, R. E. Kalman developed the Kalman filter as a recursive solution tothe discrete-data linear filtering problem [34]. Since then, it has been appliedin many fields such as data smoothing, object tracking and process estimation.The traditional Kalman filter is a linear algorithm that estimates the internalstate of a system based on a prediction/correction paradigm. Here is a briefintroduction to the Kalman filter’s mathematical basis, for more details refer[35].Kalman filter tries to estimate the state x of a discrete-time controlled processwhose system model is represented in the form of the following equationsxk Axk 1 Buk wk 1 ,(5.1)with a measurement z that iszk Hxk vk .(5.2)wherewk : the process noisevk : the measurement noiseuk : the zero-mean white random noise processA : state transition matrix relating xk 1 to xkB : coefficient matrix of ukH : matrix relating system state and measurement vectork : discrete time indexNow we define xk as a priori state estimate at step k provided that we knowthe process prior to step k, and also define xk to be a posteriori state estimateat step k given measurement zk . Then a priori and a posteriori estimate errorcan be defined as ek xk xk and ek xk xk .The a priori and a posteriori estimate error covariances are thenPk E[ek eTk ],(5.3)Pk E[ek eTk ].(5.4)Here, we describe xk as a linear combination of an a priori estimate xk and18

a weighted difference between an actual measurement zk and a measurementprediction Hxk like below.xk xk K(zk Hxk )(5.5)K in the above equation is “innovation” or the “residual” and can be calculatedbyKk Pk H T (HPk H T R) 1(5.6)The Kalman filter operates by using feedback control, that is, first the filterestimates a process at some time and then use real measurements as a feedback.Thus Kalman filter is divided into two, one is “time update equations” andanother one is “measurement update equations”. In time update equations,the filter estimates the future state and error covariance. On the other hand,measurement update equations provide time update equations with feedback.The algorithm is described in the figure 5.2.Figure 5.3 shows the complete figure of the Kalman filter algorithm.Figure 5.2: The Kalman filter cycle[35]5.2.1Extended Kalman filterSensor measurements are noisy data. Our adaptive sampling method determinesthe next sampling rate based on the difference between the present measurementand the previous ones. Thus if there is a large noise, it will affect the result, andsince the method is very sensitive to the small change of the measurements, wecannot ignore the noise. Therefore, the noise must be eliminated, and Kalmanfilter is very powerful when it comes to the noisy systems. In this project, weapply the extended Kalman filter to eliminate the noise from the raw sensormeasurement data because air pollution is non-linear.Here is a brief mathematical overview of extended Kalman filter. First of all,19

Figure 5.3: Complete picture of the Kalman filter operation[35]we have to remove the noise from the raw sensor measurements by using extended Kalman filter. Extended Kalman Filter is same as Kalman filter but itis applied to non-linear systems, and since air pollution is non-linear, we useextended Kalman filter in this project. Extended Kalman filter has two steps:the prediction step (where the next state of the system is predicted given theprevious measurement data) and the update step (where the current state ofthe system is estimated given the measurement at that time).Prediction: x (5.7)k f (xk 1 , uk , 0)Update:Pk Ak Pk 1 ATk Wk Qk 1 WkT(5.8)Kk Pk HkT (Hk Pk HkT Vk Pk VkT ) 1(5.9)xk x k Kk (Zk Pk (1 h(x k , 0))Kk Hk )Pk (5.10)(5.11)xk : the stateZk : the measurementuk : the zero-mean white random noise processf () : the dynamic model functionh() : the measurement model functionKk : Kalman gainIf you want more detail, please refer [34].In this project, we model the air pollutant by using AR method. AR (autoregressive) approach has been applied to modeling the time series of air pollutants20

in several cities and the AR model presented how the current measurement depended on the previous measurements [36]. We assume that air pollutant isthe Markov process which is a particular type of stochastic process where onlythe present value of a variable is relevant for predicting the future and the pasthistory of the variable is irrelevant. Environmental data profiles are usuallyassumed to follow a Markov process [37]. We use AR(1) model and by using Yule-walker method we calculate the parameters for AR(1) model for thisproject.5.3Calculating the new sampling intervalAfter Kalman filtering, we should calculate the new sampling interval basedon input characteristic, specifically the difference between current measurementand previous measurements.The variance of the air pollutants measurement is calculated asdif (k) sqrt((m(k) m(k 1))2 )(5.12)dif (k) : the difference between kth and the (k-1)th measurementm(k) : the kth measurementThe reason we take square of the difference is to eliminate the negative valuesso that we can increase the sampling rate when the air pollutant is increasin

1.2 Wireless sensor network Wireless sensor network (WSN) is a set of sensors which are spatially distributed to monitor physical environments, such as temperature, humidity, air quality, etc., and configured to form a network [3]. WSNs consist of sensor nodes, sink node (gateway) and software. Sensors in the WSNs are called a "sensor node .