Wireless Networks in the Rural Areas

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Energy Efficient Target Tracking in Wireless Sensor Actor Network

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Iqra Khalid

Presented By

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ABSTRACT

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Wireless Sensor Actor Networks (WSANs)

Abstract Wireless sensorand actor networks (WSANs) refer to a group of sensors and actors linked by wireless medium to performdistributed sensing and acting tasks. In such a network, sensors gather information about the physical world, while actors take decisions and then perform appropriate actions upon the environment.

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Awireless sensor network(WSN) has spatially distributed autonomoussensorsto monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the networkto a main location.

Wireless Sensor Network (WSN)

Target Tracking :

Target Tracking is the prediction of the future location of a dynamical system based on its estimates and measurements.

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Importance of Target Tracking :

Target Tracking is an important problem in sensor networks, where it dictates how accurate a target position can be measured.

Energy Efficient :

Energy saving is one of the main challenges in WSNs. It is because of the energy of a sensor node is limited and replenishment of its battery is usually impossible. So the lifetime of a sensor node is strongly dependent on its battery lifetime and the lifetime of a WSN is directly related to the lifetime of its sensor nodes. Thus, if we can preserve sensor nodes more time in the network, we will increase the lifetime of the network.

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In this paper

In this paper we analyse the different methods which are designed to improve the target tracking and minimizing the total energy and pave the way to the future research, a detail discussion on Energy Efficient Target Tracking in Wireless Sensor Actor Network is discussed in this paper.

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Applications of WSANs

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Wireless Sensor Network

Target tracking

Energy-efficient

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Wireless Sensor Actor Networks (WSANs)

Sensor :

Passive elements sensing from the environment.

Limited energy, processing and communication capabilities.

Inexpensive device with limited sensing.

Actors :

Active elements acting on the environment.

Higher processing and communication capabilities.

Long battery Life

Sensor + Actors -------> WSANs

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Architecture of WSN :

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WSN classify into two categories :

Hierarchical Network

Peer to Peer Network

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Tracking:

Tracking is a special case of event detection applications, where the event (the target) moves and its position needs to be tracked.

Localisation is an important feature.

Time synchronisation is also very relevant.

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Importance of Tracking

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Target tracking, as one of the vital application of WSN, has been used intensively in environmental detecting, industrial monitoring, disaster alerting, and healthcare.

Target Tracking in WSNs

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Applications of WSANs

Health Care Monitoring :

The medical applications can be of two types: wearable and implanted. Wearable devices are used on the body surface of a human or just at close proximity of the user. The implantable medical devices are those that are inserted inside human body. There are many other applications too e.g. body position measurement and location of the person, overall monitoring of ill patients in hospitals and at homes. Body-area networks can collect information about an individual's health, fitness, and energy expenditure.

Environmental Monitoring :

There are many applications in monitoring environmental parameters,examples of which are given below. They share the extra challenges of harsh environments and reduced power supply.

Area Monitoring :

Area monitoring is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. A military example is the use of sensors detect enemy intrusion; a civilian example is thegeo-fencingof gas or oil pipelines.

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Structure of Paper

In Section 2 :

Problem formulation including target dynamic model and different algorithms briefly mentioned.

In Section 3 :

Target tracking scheme is described, which include the sensing and communication model, energy model and our proposed sensor selection method.

In Section 4 :

Simulation results are proposed.

In Section 5 :

Conclusion & Comparison .

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Literature Review 1/7

Author adopted the Target Model & Kalman-Consensus Filter Algorithm for target tracking & Sensing model and Communication model.

Kalman-Consensus Algorithm is used to calculate the state estimation of the target.

This algorithm gives some initial parameters and then calculatesS measurement model and message send to active neighbours.

After receiving messages from active nodes it computes the Kalman consensus and update the estimated state of the target.

Each target sensor sends its state to cluster header.

A threshold is defined for tracking accuracy. If the predicted target state uncertainty is less than the threshold, the tracking accuracy is supposed to be satisfied. If not, it is deemed to be unsatisfied.

In sensing and communication model, a binary detection model is assumed that if the target is within the sensing zone of a node, the detection to the target is successful.

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Energy Model & Target Tracking Scheme :

All of the sensors in sensing zone can be selected to detect the target.

First, cooperate between each other to generate an estimation of target position using Kalman-Consensus Filter algorithm. And then send their estimation to cluster header for further mixture so that an accurate estimation of target position is attained.

At the start of detecting, all sensors are initially in a sleep state, except sensors that are on the borders of the sensor field.

The sensor on borders first detect the target tracking & compute the initial target state estimation,

Select and activate the next tasking sensor(including a cluster header) for the next sample interval, send its state prediction to the next tasking cluster. The cluster header needs to perform some additional jobs as well i.e. to fusion the state estimation so that an accurate estimation of target position is achieved.

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Advantage :

The advantage of using this approach are evident as the energy efficient scheduling scheme will not compromise the tracking accuracy and a dynamic sampling interval is also achieved resulting in superior reliability for target tracking.

Disadvantage :

Always uncertainty when sensors perform the operation of detection.

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Extended Kalman Filter (EKF)

One sensor can be used as the tasking sensor at each time step.

Tasking sensor is responsible for sensing and estimation update, sensor scheduling, and transmitting the sensor scheduling result and target estimate to the next selected tasking sensor at each time step.

Linear target motion model and non-linear measurement model are adopted, both with Gaussian noise distributions.

EKF is used as the estimation algorithm.

i. In extended kalman filter, given the state of the target at kth time step, i.e. X(k|k) with covariance value P(k|k) and assuming a sensor j is used for measurement in the next time step, the predicted state of sensor j i.e. X(k+1|k) in next time step, can be calculated as:

ii. X(k+1|k)=A* X(k|k) .

The EKF operations are used for the estimation update when a tasking sensor gets an actual measurement, and for prediction of the tracking accuracy in sensor scheduling without an actual measurement being taken.

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Literature Review 2/7

For target tracking, there are three steps which include

Initialization, Estimation update, and Sensor scheduling in target tracking mechanism.

It is assumed that the first tasking sensor is activated by a PIR (Passive infrared) detector installing on the sensor node when the target appears in the sensing field.

Initialization where all sensors are at the sleeping state when there is no target in the sensing field. The first tasking sensor is activated by PIR detectors to detect the target when the target enters the sensing field. It generates the track of the target, sets the initial state estimation, and selects the tasking sensor of the next time step by MPBB(Multi-step Prediction Based Branch & Bound Algorithm ).

Estimation update, When the next tasking sensor receives the information from the previous tasking sensor, it becomes the tasking sensor in the current time step and gets new estimation information by EKF using its measurement.

Sensor scheduling, The current tasking sensor uses MPBB to select the tasking sensor for the next time step.

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Literature Review 3/7

Proposed Algorithm:

Proposed algorithm is splitinto2stages

clusterandtargeting.

Clusterstage iswithin thebeginof networkpreparing. To doingclustertask,everyCH broadcasts amessage hello.Everynode that receives the message from a CH, node reply back a message to CH to insert the ID, location and energy of itself in it. If a node receives this message from more than one CH,then node reply the first one received message and discards differentmessages.

Network is divided into cluster and then move to tracking stage. when we start tracking only the CHs are awake and all other nodes are in sleep mode. Oncea target enters the network,the primaryCH that detects the target becomes active.

This CH selects three sensor nodes for tracking. These nodes sense the target and current location of target is calculated from thisperceivedinformation.

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Prediction mechanism :

Prediction based algorithm predicts the next location of target and after predict the location activate specific nodes for tracking and others nodes are still on sleep mode.

Prediction mechanism we can predict the next location by using current location of target & previous location of target.

If the location is placedwithin thecurrent cluster, active CH selects3 sensor nodes for target trackingwithin thenext interval time, with the tracking sensor node and wakes up them withsending a message.

If location of target is placed out ofthiscluster, active CH selects nearest CHthere tolocation as next active CH and with sending a message informs it from arriving the target and gives the tracking task to the new active CH.

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Literature Review 4/7

Cluster based prediction algorithm used for target tracking.

Network is divided into grids. A Cluster head is made based on basis of having higher energy and longer range.

In this method, the localization of target is done locally in sensor nodes not in CHs.

thr: user-defined error threshold.

PL: Predicted next location of target.

CL: Current location of target.

In the algo, the active CH predicts targets next location via a prediction mechanism.

If PL Is in current cluster, active CH selects 3 nearest nodes to PL and selects one of them as leader and sends PL to leader, and inserts leader node ID to other selected nodes

Each selected node except leader calculates its distance from target and sends calculated distance and coordinates to leader node.

The leader node after calculating its own distance from Target, calculates CL using trilateration algo.

If CL-PL is greater than threshold then it sends a message to CH containing CL.

If PL is not in current cluster, then a message is sent to nearest CH which becomes active.

Incase active CH receives a message from CL node, then it sends CL located inside this message to the sink otherwise it sends PL to the sink.

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Prediction Mechanism:

It is used to predict next location of target. A linear prediction algorithm is used where from current and previous location, next location is determined.

Trilateration Algorithm:

It is used for localization as explained earlier. This algorithm forms relation 5 for three nodes and from solving three formed relations, obtains target coordinates.

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Literature Review 5/7

This paper proposes a modified adaptive sleep time management scheme called Modified Predict and Mesh (MPaM) to adapt tracking time interval such that it minimizes power consumption while keeping an acceptable tracking accuracy.

Adaptive Prediction based tracking scheme (PAM):

The PaM scheme consists of two prediction models: n-step and collaborative predictions and a prediction failure recovery process called Mesh.

Prediction Module:

Once the target is detected, the active sensor (current node) will predict the next location of the object using a simple averaging prediction model, which is called n-step prediction.

Then it will activate another sensor node as the next current node.

To reduce the power consumption, current node estimates the time needed for the object to move out of its sensing range (escape period).

Using this knowledge, it is able to modify the tracking time interval to avoid unnecessary activations while not losing the object.

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Mesh Module:

The predictions are not always exact due to the process noise and blind coverage areas, which can make the tracking system to lose the object. Hence, a recovery module is required to compensate for this loss. If the predicted location is very different from the actual location of the object sensed, the object may be out of coverage of the next (predicted) current node. In this case, the object is lost. Therefore, some other surrounding nodes should be awakened to recapture the lost object (Mesh process). This process can be done in two or more stages. In the first stage (1st_Mesh), some of the nearest neighboring sensor nodes are awakened to find the actual location of the object. If the object is found then the task of the Mesh module completes and no extra sensor nodes are required for localizing the object. If the object is not found, then a more assertive approach happens and in this stage (2nd_Mesh) a lot of other neighboring sensor nodes should be awakened and with a high probability, the object will be found.

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Modified PAM Scheme:

The PAM scheme can be further modified to achieve a better performance in power conservation and tracking accuracy. Meanwhile, the modifications can be applied in both of its main modules.

Modified Prediction Module: The main drawback of the conventional PaM is due to its inexact calculation of the escape period. We propose to modify this formula by considering the relative movement direction of the object with respect to the current node. To optimize the power consumption by avoiding unnecessary activation of sensor nodes, we can use the maximum of t as the best escape period for modifying tracking time interval.

Modified Mesh Module: The improvement can also be achieved by considering the effect of objects movement direction in ouranalysis. Virtual nodes are activated instead of all of the nodes to find the lost object. However, in MPaM we are using some extra information (i.e. direction of the movement), which can be employed again to simplify the mesh activating process. In the case of losing the object with a high probability it will be located in somewhere around its predicted location. Thus, activating all of the virtual nodes seems to be unnecessary. It is proposed to activate only Selected Virtual Nodes, which are located inside the Selected Sector.

Quantitative Analysis of Power Consumption:

The power efficiency of MpaM turns out to be:

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Mpam is thus 23 % better than conventional PaM based on power consumption.

Literature Review 6/7

Proposed Distributive, Adaptive Multi-sensor Scheduling scheme:

The goal is to jointly select the sensors in each time interval and the time instant for them to perform the sensing tasks with target tracking accuracy and energy efficiency.

Initially the first sensor detects the target and broadcasts its location info as the initial target state with an initial state error covariance.

At the kth time step, a cluster of sensing nodes, called tasking cluster are activated to perform sensing task, and one of them is current cluster head.

Cluster head, in addition to the same tasks, combines innovations from all tasking sensors and itself into a vector form for estimating the target state using EKF algorithm.

It chooses the sampling interval, tasking sensors and a new cluster head for the next time step and passes predicted target state and its error covariance to the next tasking cluster.

Sensor Detection Model:

It is assumed that a sensor has a probability to detect a target if the target is within its detection region.

Detection probability is given and based on statistics theory; Predicted Detection Probability (PDP) of sensor and Joint Detection Probability (JDP) for m tasking sensors is calculated.

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Energy Model:

The energy for transmission of b bits from 1 sensor node to other is calculated and the energy consumption for receiving data of b bits is also calculated.

Selection of Sampling Interval:

The optimal sampling interval is calculated by means of time update. Two operation modes are used

Fast tracking

Tracking maintenance.

The fast tracking mode is operated when the position uncertainty threshold cannot be satisfied.. On the other hand, the tracking maintenance mode is operated when the current and predicted tracking accuracy can be satisfactory. In this case, the calculated sampling interval is bigger than Tmin.

Selection of Tasking Sensors

After the selection of Sampling Interval, tasking sensors will be selected. It is assumed that a sensor node keeps necessary sensing characteristic information of its neighboring node, such as its location and its detection region. The current cluster head calculates the PDP of each neighbor sensor, based on the predicted target location and associated covariance matrix. Denoting the set of neighbor sensors as SC, then the sensor with the maximum

PDP at the next time step is selected as the as the first member of the tasking cluster at time step k + 1.

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Comparison

Advantages Disadvantages1. The advantage of using this approach are evident as the energy efficient scheduling scheme will not compromise the tracking accuracy and a dynamic sampling interval is also achieved resulting in superior reliability for target tracking. 1. Always uncertainty when sensors perform the operation of detection.2. multi-step prediction based branch-and-bound sensor scheduling scheme for target tracking in wireless sensor networks was presented. The performance was improved3. By using this approach are evident as the energy efficient scheduling scheme will not compromise the tracking accuracy and a dynamic sampling interval is also achieved resulting in superior reliability for target tracking.

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4. 5. One of the main advantage of proposed algorithm is that localization of target is done locally in sensor nodes not centrally in CHs & Transmission power is directly related to the distance, by using this method energy consumption of network will be decreased.6. MPaM is capable of providing better power consumption when compared with the conventional PaM (up to 22%) along with a better tracking accuracy.

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Section 5:Conclusion

In this paper, we have suggested a target tracking pattern for WSNs , which holds two parts:

The target sensing based on the Kalman-Consensus Filter that active sensors receive/broadcast information with its active neighbors and estimate the position of target, the sensors selection scheme that selecting the tasking sensors by a predicted objective function. The tradeoff between tracking accuracy and energy effective is attained.

In addition, our future work will focus on the scenario that the sensing range of a sensor will decay as energy consumption.

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References

F. Akyildiz and I.H. Kasimoglu,"Wireless Sensor and Actor Networks: Research Challenges,"; Ad Hoc Networks, vol. 2, no. 4, pp. 351-367, Oct. 2004.

Prof. Jianxun Li , Book Chapter|Target Tracking in Wireless Sensor Networks.

http://en.wikipedia.org/wiki/Wireless_sensor_network#cite_note-1

http://code.eng.buffalo.edu/tracking/index_frames.html

Jain K, Bahuguna U. Servey on Wireless Sensor Network. International Journal of Science Technology & Management. 3(2): 83-90, 2012.

Ramya K, Kumar K P, Rao V S. A Survey on Target Tracking Techniques in Wireless Sensor Networks. International Journal of Computer Science and Engineering Survey, 3(4): 93-108, 2012.

Jianming Xiao ,Weirong Liu , Energy-Efficient Sensor Scheduling Scheme for Target Tracking in Wireless Sensor Networks , 2014.

Biao Song, Wendong Xiao, Zhaohui Zhang, Multi-Step Sensor Scheduling for Energy-Efficient High-Accuracy Collaborative Target Tracking in Wireless Sensor Networks, 2013.

Fatemeh Deldar , Mohammad Hossien Yaghmaee ,Designing an Energy Efficient Prediction-based Algorithm for Target Tracking in Wireless Sensor Networks ,2011.

Mohammad Hossien Yaghmaee , Energy Efficient Prediction-based Clustering Algorithm for Target Tracking in Wireless Sensor Networks, 2010.

Hadi Jamali Rad, An Energy Efficient Target Tracking Scheme for Distributed Wireless Sensor Networks , 2009.

Jianyong Lin, Wendong Xiao , Energy-Efcient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks, 2008.

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