Tracking Coordinated and Adaptive Information using Security from Target Wireless sensor network by GRD Journals

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Tracking Coordinated and Adaptive Information using Security from Target Wireless Sensor Network 1P.

Ramya 2M. Satheesh Kumar 3M. Kaveri 1,3 P.G Student 2Assistant Professor 1,2,3 Department of Information Technology 1,2,3 K.L.N. College of Engineering, Pottapalayam, Sivagangai 630612, India Abstract Wireless sensor network (WSNs) consist of sensor node with sensing and communication capabilities. The important problem of WSNs applications is Energy conservation. Clustered sensor network uses cluster heads (CHs) for data gathering. In Target tracking applications, sensor nodes in multiple clusters do not uses a single target path, which leads to redundant data transmissions from CHs to the sink so much energy is to waste. In this paper we propose coordinated and adaptive information collecting strategy (CAICS) for Target tracking in clustered WSNs. In CAICS a coordinated node selection method considering the spatial correlation of sensing nodes is proposed to dynamically select the best set of nodes for tracking missions. And an adaptive aggregation node is selected for data collection to reduce the redundant data transmission and balance the energy cost. Experimental results exhibit that the proposed approach reduces the computational complexity of node selections without degrading tracking accuracy and achieves a significant energy consumption reduction and network lifetime extension compared with the state-of-the-art approaches. Keyword- Energy Conservation, WSNs, Clustered sensor network, Target tracking, Adaptive, CAICS __________________________________________________________________________________________________

I. INTRODUCTION WIRELESS sensor networks (WSNs) have emerged as an gorgeous technology which can consist of large number of cerebral sensor nodes. With sensing, processing and wireless communicating capabilities.[2] In WSNs, one of important applications is target tracking, such as vehicle tracking and migration tracking of animals. [3] The sensor nodes collectively monitor the roaming path of moving targets in the deployed area. Since the sensor nodes are always deployed in an unattended environment, it is very difficult to replace their battery after the deployment. As a result, energy efficiency is the most critical design issue for WSNs. In a target tracking application, the sensor nodes which can sense the target at a particular time are kept in active mode while the remaining nodes are to be retained in inactive mode so as to conserve energy until the target approaches them. Clustering is an important approach to organize a closely deployed network. The clustering algorithms keep only a portion of nodes (CHs) active and save the energy for the rest of the nodes. [1].The target will often be detected by the nodes in multiple clusters at the same time. When all the nodes report their data to the respective CHs, each involved CH has to send the data to the sink. This way may produce much redundant data that causes unnecessary energy consumption. The data gathering approaches for clustered WSNs can be categorized into: DL-cluster [5] and MH-cluster [13].In DL-cluster each CH aggregates the sensed data from its Cluster Members (CMs) and directly transmits it to the sink, while in MH-cluster a hierarchical CHs model is built and the data can be aggregated hop by hop through multiple intermediate CHs. These approaches are not efficient for target tracking because of redundant data transmissions. A simple 5-cluster sensor network is taken as an example in Fig. 1. In Fig. 1(a), CH1, CH2 and CH3 obtain the sensed data from their CMs and send them to the sink respectively. We can see that there are 3 data flows from CHs to the sink. In Fig. 1(b), CH1 and CH3 send the aggregated data to CH2 which further aggregates the data. Therefore, there is only one data flow from CH2 to the sink. However, when the target moves to the position shown in Fig. 1(c), two data flows are established from CH2 and CH4 to the sink, because CH3 always transmits its data to CH2 and CH5 to CH4. If we want to change their transmission routes dynamically according to the position of the moving target, a lot of communications among CHs are needed. It will cause more energy cost and time delay. With these motivations, we propose a coordinated and adaptive information collecting strategy (CAICS) for target tracking in clustered WSNs. The major objective of CAICS is to reduce the redundant data transmission and keep a reliable object tracking with minimum energy consumption.

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A. Novel Spatial-Correlated Node Selection We proposed a novel coordinated node selection approach considering the spatial correlation of sensor nodes. Moreover, a joint distance weighted information utility measurement is proposed to select the optimal set of nodes for tracking. B. Improved the Aggregation Node (AN) Selection to avoid the different starting point of waiting executed by each node, the time flag was added to the waiting time of the nodes which competed for AN. C. Parameters Selection Discussing The waiting time for AN election is appropriately selected to reduce the delay and make the node which has the most residual energy as AN so that the energy consumption in each node is balanced. D. Strengthening Experimental Results We added the comparisons of mean square errors, energy consumption (Îą = 3.5), and transmitting delay in different cases, to further illustrate the effectiveness of our approach. The rest of this paper is organized as follows: Section II reviews related works. Section III specifies proposed CAICS is described in detail. Then, Section III presents the experimental results. Finally, we conclude the paper in Section IV.

II. RELATED WORK In [16] they proposed a weighted distance based node selection method for bearings-only sensors. The sensor with the minimum weighted distance is activated for tracking mission. Although weighted distance method needs less computation comparing with entropy-based method, it chooses only one sensor each time and does not consider the spatial correlations of sensor nodes. The data gathering techniques for WSNs vary with different network architectures. In [5] an adaptive scheme is proposed to control the degree of data aggregation with respect to the reliability requirement. In [9] a dynamic clustering scheme is proposed for target tracking applications. The idea is to dynamically construct a cluster around the target. The sensor nodes in the dynamic cluster perform the tracking task. When the target moves out of the range of the cluster, the cluster is deconstructed and a new cluster is built. However, such a dynamic cluster must be constructed before the target moves into the range.

Fig. 1: State-of-the-art data gathering approach for cluster-based WSNs (a) DL-cluster (b) and (c) ML-cluster

III. COORDINATED AND ADAPTIVE INFORMATION COLLECTING STRATEGY In a cluster-based target tracking WSN, each Cluster Member (CM) of a cluster has the detecting and tracking states. In the detecting state, it is in the sleep mode most of the time and wakes up for a fraction of time to process and to react to the control messages from its CH. In the tracking state, a CM keeps active to track the target until the target is out of its sensing range. Each involved CH performs data aggregation and transmits the aggregated data either directly (DL-cluster) or hop by hop (MLcluster) to the sink. These approaches do not work efficiently for target tracking due to the changing position of the randomly moving target. To overcome this problem, we propose an coordinate and adaptive information collecting strategy CAICS Our novel idea is to utilize an Aggregation Node (AN) for data gathering. The AN is selected from the nodes in the tracking state that are all close to the target. As the target moves, the AN will be adaptively re-selected. Therefore, the position of the AN is dynamically changing and is always close to the target. Hence, the proposed approach consists of 1) coordinated tracking nodes selection 2) adaptive AN (re-)selection and 3) data aggregation and transmission.

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Tracking Coordinated and Adaptive Information using Security from Target Wireless Sensor Network (GRDJE / CONFERENCE / ICIET - 2016 / 028)

A. Coordinated Tracking Nodes Selection Dynamically choose the best set of sensor nodes for tracking task can reduce the network energy consumption and improve tracking accuracy. Generally, CHs should select their members which can bring more information among the candidate nodes in the sensing area. For information utility measure, the entropy is used in the entropy-based method. The information utility of node Ni is calculated by,

where Dx_ and Dy_ are deviations along axes X_ and Y _, respectively, and r is the correlation coefficient. Then a new coordinate system, whose origin is at (x_, y_) and axes are along the direction of the eigenvectors Cov_, is established [16]. In the new coordinate system, the predicted belief is represented by zero-mean Gaussian density function with covariance,

where σ2x and σ2y are the largest and smallest eigenvalue of Cov_, respectively. Then, the state uncertainty of the target can be represented by an ellipse whose major axis and minor axis are 3σ2 x and 3σ2 y [16], respectively, as Fig. 2 shows. Assuming the measurement error _δi is known, and the location (xi , yi ) of node Ni can be denoted by the polar coordinates (δi , Ri ). Then, the information utility in weighted distance method is defined as Utility(Ni ) = −Areaabcd. And we proposed a joint distance weightedinformation utility measurement to effectively select nodes for tracking mission. It can be represented as follow,

Where C_AreaNi is the certainty area enclosed by the sight lines of node Ni and the ellipse. C_AreaNi ∩ C_AreaNj is the overlapped area of the certainty area of node Ni and Nj . The smaller the area C_AreaNi ∩ C_AreaNj is, the more certainty by using the node’s information there will be. In order to obtain the joint distance weighted information utility, we just need to calculate the equation of the sight lines of the sensor node and calculate the certainty area. In this work, each CM has detecting and tracking states. In the detecting state, it is in the sleep mode most of the time and wakes up for a fraction of time to react to the control messages from its CH. In the tracking state, a CM keeps active to track the target until the target is out of its sensing range and the node that has the largest joint information utility is chosen as tracking node. The procedure is continued until enough tracking nodes are selected. B. Selection of Aggregation Node When a target is detected, the sensor nodes around it will be woken up by their CHs and get into the tracking state. All sensor nodes in the tracking state form a tracking node set D={N 1D,N2D,N3D…..NmD}where m is the number of nodes in the tracking state. Since all nodes in D are in a vicinity area, they can directly communicate with each other. Therefore, AN can be elected by the internal negotiation of the nodes in D without the participation of any CHs. To achieve load balance the node that has the most residual energy and consumes the least energy for communication with its CH as well as the other nodes in D is selected as AN. The pseudo code of the AN selection is shown in Algorithm 1. For each ND i , the residual energy Ei res and communication cost Eic are converted to a waiting time ti (line 3). More residual energy and less communication cost lead to a shorter ti. Therefore, the node with the most Ei res and least Eic waits for the shortest time in D. This node first finishes waiting and is elected as the AN (line 8). It then broadcasts a message “finish election” to the other nodes that are still waiting (line 9). These nodes stop waiting and give up the AN election as soon as they receive the message, and send their sensed data to the AN (line 5 and 6). ti is calculated by the following equation:

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where tmax a design parameter, used to control the waiting time in a reasonable range (0 <ti <tmax). Ei ini is the initial energy of node ND i .Two nodes might have the same residual energy and communication cost and therefore have the same ti. To avoid this, a random time tran is added. tran is an order of magnitude smaller than tmax, so that it will not have a large impact on the value of ti. The AN is dynamically selected according to the changing position of the target. It is assumed that the AN can obtain the distance to the target (line 17 in Algorithm 1), using some technique, for example, distance measurement by means of the strength of the detecting signal. If the distance between the AN and the target is larger than a defined threshold dth, the AN will broadcast a message “reselect AN” to the other nodes to start the next round of AN election (line 21). In the whole process, all tracking nodes in D are kept under the control of respective CHs. When they finish the tracking task, their CHs will get them back to the detecting state. C. Data Aggregation and Transmission After AN is selected, it will periodically broadcast the flag message “gather_data” to the other nodes in D to request sensed data. When the nodes receive this message, they will wait for a random time before reporting their sensed data toCthe AN, in order to avoid data collision. Although this random waiting time reduces the possibility of collision, it cannot totally eliminate it. This is mainly due to the transmission delay of the data packets. An underlying carrier-sense MAC protocol is still needed to mitigate collision at the MAC level [9]. The sensed data in local area have strong correlations and therefore can be effectively aggregated by AN.

Fig. 2: Data Collection in CAICS

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Tracking Coordinated and Adaptive Information using Security from Target Wireless Sensor Network (GRDJE / CONFERENCE / ICIET - 2016 / 028)

In addition, CAICS utilizes local communications with low cost to accelerate data aggregations and to reduce the number of data packets transmitted over long distances. Fig. 2 illustrates CAICS using the same example as in Fig. 1. Nd is selected as the AN of the tracking node set D = {Na, Nb, Nc, Nd }. Then, Na , Nb and Nc send their sensed data to Nd instead of their own CHs. Nd aggregates the received data as well as its own data into one packet and sends it to CH2. Then, CH2 forwards the aggregated data to the sink. In this example, there is only one long-distance transmission (from CH2 to the sink). Comparing this example with the aforementioned approach, it is seen that the nearer to the source node the sensed data can be aggregated, the less long-distance transmission is needed to transmit to the sink so that both the transmission cost and data collision can be significantly reduced in our approach. D. Communication among Sensor Node

IV. EXPERIMENTAL EVALUATION In the experiments, we demonstrate the energy efficiency of CAICS by comparing to the state-of-the-art approaches, DL cluster and MH cluster, by means of simulations carried out on NS 2.34. The performance metrics include the network load (the number of packets transmitted), the energy consumption for data transmission and the network lifetime. A. Simulation Setup Table I shows the simulation parameters. Although the number of sensor nodes and the number of clusters are fixed, the distribution of sensor nodes varies for each simulation run and each result is averaged over five runs. The selection of the cluster head is periodically performed. The target moves randomly in the field. The AN is reselected when the target moves out of its sensing range, so the threshold of the distance between the AN and the target is set to dth = 12m, the same as the sensing range. The listening power consumption is set to 12.5 mw, while the power consumption in the sleep mode is ignored. The location of the sink can also affect the network performance, so we simulated two scenarios: (1) Scenario 1 (s1): The sink is located at the edge of the field and its coordinate is (0,0); (2) Scenario 2 (s2): The sink is located in the center of the field and its coordinate is (225, 225). In order to examine the scalability of CAICS and study the impact of the velocity of the target, we also simulate different network sizes and different velocities of the target. B. Simulation Results Fig. 3 shows the total number of transmitted data packets after 300s simulation time. Using DL-cluster, since all CHs directly transmit the data to the sink, the number of transmitted data packets is not affected by the location of the sink. Therefore, DLcluster has the same number of transmitted data packets for both scenarios. Whereas, using MH-cluster, each CH transmits data hop by hop to the sink. The larger distance between the data source and the sink, the more hops are needed, leading to more data packets transmitted between CHs. Therefore, the number of data packets transmitted in scenario 1 is larger than that in scenario 2. Due to the hop-by-hop data transmission between CHs, MH-cluster transmits more data packets than DL-cluster. As shown, CAICS can significantly reduce the network load in both scenarios, compared to DL-cluster and MH-cluster, because, using CAICS, all the sensed data can be completely aggregated on a single AN. Fig. 4,5 shows the average energy consumption of each sensor node versus simulation time. As shown, all three approaches consume more energy in scenario 1 because the sink is located at the edge of the field so that many data packets are transmitted over long distances. Compared to DL-cluster, CAICS achieves 43.7% energy saving for scenario 1 and 44.6% for scenario 2 after 2000s simulation time. Although MH-cluster provides a mechanism for data aggregation, the most data are aggregated after multiple hops of transmission. In contrast, in our CAICS, all data can be aggregated on the AN before being delivered to the sink. Therefore, CAICS achives 20.5% energy saving for both scenarios compared to MH-cluster.

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The energy saving is highly related to the number clusters involved in the tracking. If the target is detected by sensor nodes in n clusters, using CAICS there is always one data flow from the AN to the sink, while using DL-cluster and MH-cluster, in the worst case, there might be n data flows from the cluster heads to the sink. In the simulation, we traced the value of n. Fig. 6

Table 1: Simulation Parameters

Fig. 3: Number of packet transmitted

Illustrates the distribution of the value n in the whole tracking process. As shown, in most cases (_ 93%) the target is detected by sensor nodes in more than one cluster. CAICS can significantly prolong the network lifetime in all cases. For example, if the lifetime is defined as the time when 10% of nodes die, CAICS achieves lifetime extensions by 857.6% and 85.8% compared to DL-cluster and MH-cluster, respectively, for scenario 1. Similar lifetime extensions are achieved for the other cases. The main reasons for the large lifetime extension are twofold: 1) CAICS avoids redundant long-distance transmissions and saves energy, as shown in Fig. 5; 2) The energy consumption is better balanced because the node with the highest residual energy and the lowest communication cost is selected as AN. We further investigated the average data transmission delay, which is defined as the time between the moment a source transmits a packet and the moment the sink receives the packet, averaged over all source-sink pairs in the two scenarios. The results after 3000s simulation time are shown in Table II. We can see that CAICS has the lowest data transmission delay because the AN can send the data to its CH without waiting for the time slot. The delay of waiting for the time slots in DL-cluster and MH-cluster is larger than the delay of AN selection in CAICS. DL-cluster has longer data aggregation delay than CAICS but shorter transmission delay because of its end-to-end transmission. Therefore, the overall data transmission delay of DL-cluster and CAICS are close. Now, we can conclude that CAICS can reduce the energy consumption and extend the lifetime of the cluster based WSNs for target tracking, without sacrificing the system performance. We also examined the impact of the moving speed of the target, while fixing the sink at (0, 0).

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Fig. 4:

Fig. 5:

Table 2: Transmission Delay

V. CONCLUSION This paper proposed a novel coordinated and adaptive information collecting strategy (CAICS) for target tracking WSNs. In CAICS, a coordinated spatial-correlated node selection algorithm is proposed. It uses a joint distance weighted measurement to estimate the information utility of sensing nodes. Moreover, the AN is dynamically selected to gather and aggregate the sensed data. Simulation results proved that the coordinated node selection approach outperformed the existing approaches by reducing computational complexity and guaranteeing the tracking accuracy and achieved a significant energy consumption reduction and network lifetime extension. Future work will explore and extend the coordinated and adaptive information collecting strategy to other application domains.

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