Wireless Sensor Network (WSN) is accumulation of sensor nodes. Number of sensor nodes connected to each other to form sensor network. As the sensor nodes are of small size, consumes low power, they are available at low cost. Similarly, they are capable of providing solutions for wide range of applications. Therefore, numbers of application are proposed for such sensor network. Initially, WSN was used only for military purpose for surveillance in the battleground later it was developed for number of other applications such as target tracking, health, environmental monitoring and many other applications [1]. For tracking application in wireless sensor network energy efficiency and tracking accuracy are major challenges. As WSNs can be deployed in hostile environment providing location information with high accuracy is of utmost importance. Sensor nodes and their location information is necessary part of target tracking in WSNs. There are many practical difficulties arise such as failure of single node, delays and many more. Inaccurate localization can create problem for target and then target missing brings lot of energy consumption in the network. The idea of Face Track overcomes the above concern and difficulties [2]. In Face Tracking, edge detection of polygons helps to track target in timely as it informs the WSN slightly earlier as it enters in polygon. By selecting couple of nodes from target current path optimal node selection algorithm keep the number of active nodes at minimum numbers. So in the absence of target, nodes of faces need not perform the tracking so as to save energy. Network works in such cooperative way to overcome the failure of nodes and for target missing action. As location is important factor in target tracking, for prediction of the next location Kalman filter (KF) and Extended Kalman filter (EKF) can be considered. Prediction and updating are the two states of KF. Priory estimated state calculates the present state using time update equations. In the second state the measurement equations sum up the present and prior values to find future value so that improves the estimation. EKF also use some set of nonlinear equations to reach at the state of the system [3]. RELATED WORKMany wireless applications require the real time event detection for corresponding network. When any physical event appears in the network multiple messages are forwarded by sensor nodes. For timely delivery of the packets certain numbers of sensor nodes are needed. A spatio-temporal fluid model is proposed to evaluate the delay in the event detection of WSNs [4]. Tracking unreliable node sequences in the field where factors such as environmental noise and sensing disturbance create difficulty in the tracking. Using these node sequences robust framework is developed which, optimally matches the node of sequences to find the shortest path from the present path of tracking [5]. Here, the concept is to form dynamic convoy tree of sensor nodes that detect the target and that nodes collaborates with each other to find root. So, dynamic convoy tree-based collaboration finds the convoy tree sequence from large scale of tree network with optimal solution to lowers the energy consumption of the network [6].
Methods like tree and cluster based target tracking require huge interaction among nodes. It is so often that each sensor communicate with next node and then to sink. During this multi hop communication delay is induced in target detection. Unlike clustering, the nodes are organized in polygon without overlapping. Target is tracked using face prediction instead of location prediction in face [7]. Polygonal tracking framework for nodes is organized which detects the movement of target using brink called critical region instead of predicting future movement of the target. Edge detection algorithms bring reliable interconnection among nodes. Optimal node selection algorithm is to select node and to guide target information to sink [8]. III.SYSTEM MODELConsider moving target is to be observed by wireless sensor network.
. A moving target always transmits signal this transmission from the target are detected by nodes in its sensing range. Using standard estimation theory node sense the presence of target by recording the signal strength and noise energy of the signal as follows: es(t)=Si(t) + Es(t) (1) es(t) is the average signal energy strength in term of received signal Si(t) and noise energy Es(t) at time t. Position of the target is defined by the velocity and the intersection of the x and y point in x-y plane in time t is as follows: Pk = [ Xk Vx(k) Yk Vy(k)]t (2) A.Assumption •Moving target to be observed and detected by WSN is single target. •All sensor nodes are synchronized. •Target detected by nodes in different time span is assumed to be the same target. •Faulty nodes are avoided to keep on tracking B.Face Track with Kalman Filter Localization of polygons, edge detection and optimal node selection algorithm are main factors in the design of the Face track system. Polygons are used to describe the moving path for target. Nodes of the polygon is given as PNwhere N is number of nodes, PN = (v1,v2,...vp). Suppose target is detected by some nodes and suppose that nodes are from polygon P2then this polygon is said to be active polygon. Suppose node v2is active node in P2polygon (Fig. 1). Active node is aware of its position, its adjacent node and adjacent polygons. Edge detection constructs the next polygon by connecting edges of the active polygon. Measurement data obtained from the sensor nodes processed for Kalman filter to arrive at final state of the system. Kalman filter is so powerful that its equations supports to calculate the past, present and future value of the state. Kalman filter is library of set of mathematical equations that provide efficient computational of two steps: first is predicting and second is correction. In prediction step, using time update equations of one step ahead of observation is calculated. In correction step, using measurement update equations correction for estimate of present state is calculated. In time update equation step the error covariance and the present state estimates is used to calculate prior estimate. In measurement update equations step prior estimate is used to calculate posterior estimates. The process is repeated as to calculate new prior state again and again with time and measurement update equations. Fig. 1, polygon network with nodes is typical scenario of Face track framework. Time update equation of KF xˆk ̄ = A. xk-1 (3) Pk ̄ = A. Pk-1 AT + Q (4) A is n×n matrix which gives the state at time from k-1 to current time k. Q is the process noise covariance which might be changed or kept constant. Time update equation (3) calculates the posterior state for projecting state ahead and equation (4) calculates the priori estimate error covariance for the next time. Measurement update equation of KF Kk = Pk ̄. HT (H Pk ̄. HT + R)-1 (5) xˆk = xˆk ̄ + Kk (zk - H xˆk ̄ ) (6) Pk = (I - KkH) Pk ̄ (7) K is the n×m matrix calculates gain which reduces the posteriori error covariance. A H m×n matrix relates to the actual measurement zkand predicted measurement Hxˆk ̄. The posteriori state estimate in equation (6) is updated with zk. Equation (7) updates the error covariance Pk. C.Edge Detection In edge detection algorithm, when target moves from one polygon to other polygon edge is detected between couple of nodes so as to confirm that target has moved from one polygon to other polygon. Detection of the edge allow target to track in timely fashion. While leaving the current polygon target may come across three phases that are: first is to target detected the edge but it will not cross it, second is that, it will cross the edge and third is that, it is about to cross the edge. Considering this three phase target tracking process proceeds further for next polygon
COURIER MANAGEMENT
Face track network is mapped to develop the courier management service in which optimal node selection algorithm will selects the minimum number of courier places between source and destination. Active nodes show the places to refer while moving from starting point to end point. Following this places help to reach at destination early without consuming time. Optimal node selection function selects the nodes in polygon depending upon the distance calculated between polygons. Fig. 3, shows the face track for courier management, source node and destination node is shown in face track which mapped for courier places. Fig. 3, shows that to start from node 1 to reach at node 3 node 2 has to be referred.
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