A Data Fusion Methodology for Wireless Sensor Systems

Joy Iong-Zong Chen, Yi-Nung Chung
2012 International Journal of Computers Communications & Control  
An efficient DFA (data fusion algorithm) plays an important role in tracking for moving objects over WSS (wireless sensor system) deployments in order to track the objects accurately. Accuracy in object tracking is mainly dominated by the prediction for those moving targets by filtering and refining the results from wireless mobile sensors deployed in WSS environment. A DFA based on CHHN (competitive Hopfield neural network) technique for obtaining the relationship between measurements results
more » ... asurements results from wireless mobile sensors and estimation of existing tracks over WSS (wireless sensor system) is proposed in this paper. Embedded within the CHNN is also a competitive learning mechanism which creatively removes the dilemma of occasional irrational solutions in traditional HNN (Hopfield neural networks). In this research, except the proposed approach is established with CHNN, the methodology of data fusion over WSS is guaranteed to converge into a stable state when performing a data association. In words, the CHNN-based DFA is combined with wireless mobile sensors in a WSS environment to demonstrate the target tracking capabilities. Computer simulation results illustrate that the new methodology of data fusion based on CHNN is not only successfully able to solve the data association problems addressed over WSS environments, but the specified simulated targets can also be tracked without large scale missing. data association consists of the three aforementioned steps. A DFA (data fusion algorithm) is the most important technique for maintaining tracking procedures. Mobile-sensor tracking with a DFA is a prerequisite step for mobile-sensor surveillance systems in WSS deployments. Once tracks are formed and confirmed, the number of targets can be estimated and information, such as the target position and velocity, computed for each track [3] . In the literature, several DFAs for MTT (multiple target tracking) have been proposed and discussed. It is well known that the JPDA (joint probabilistic data association) DFA technique, discussed in References [4] [5] [6] [7] [8] focusing mainly on MTT deployment, is appropriate for a high false-target density environment. However, these techniques for solving MTT problems may cause some unreliability (latency) because in a nearest-neighbor or all-neighbors-based environment, the relationships between sensor measurements and existing target tracks are usually considered independently [9] . Thus, currently a traditional HNN (Hopfield neural network), which incorporates weighted objective costs and constraints into an overall energy function, is employed to combine with the neural network approaches for achieving good tracking results. [10] Then, through minimizing the overall energy function, superior performance results can be obtained. [11] The CHNN (competitive Hopfield neural network) algorithm has been applied in image processing. [12] Moreover, Soujeri and Bilgekul [13] adopted a conventional HNN to solve the problems of multiuser reception for asynchronous MC-CDMA (multi-carrier coded-division multiple-access) systems in multipath fading channels. Since this approach is defective in that the weighting values are very difficult to properly determine, frequently the solution obtains an irrational result, as reported by Zhou. [14] Thereafter, conventional HNN schemes for tracking maneuvering or non-maneuvering targets with mobile sensors over WSS deployments are very sparse. However, Wang et al. [15] , recently, combined an HNN with a genetic algorithm, designated as HNN-GA, for proposing a mobile agent-based strategy utilizing a low network load and cooperation of mobile agents, to dynamically optimize the combination of nodes and deploy tasks among nodes. Based on HNN, the selection method investigated by Liang et al. [16], in which the sensor node having the lowest cost and satisfying the distance requirement of a MIMO (multi-input multi-output) system, is selected to function as the best transmitting and receiving antenna in WSN environments. By extending the idea expressed in [17] , Wang et al. [18] proposed a new dynamic sensor node selection strategy to implement global searching to reduce the search space of a GA and ensure the validity of each chromosome in WSN applications. Deploying with mobile sensors in WSS environment is increasingly becoming integral to targets tracking, mainly due to its convenient deployment, small size, real-time characteristic, and flexibility to support integrated applications. The technique of WSS is applied to the traffic monitoring and control in [19] by the authors Semertzidis et al. The information is fused and used to provide real-time analytical. However, it is necessary for the important issue of energy supply to the operation of sensor nodes over WSS. Yen et al. in [20] proposed CLD (controlled layer deployment) protocol to guarantee coverage and energy efficiency for sensor nodes deployed in a WSS. In order to promote the operation efficiency of the data fusion for the sensor nodes deployed in WSS environment. Multi-layer clustering routing algorithm is presented by Liu et al. in [21] where the WSS techniques is developed to track the moving vehicles, and the new scheme efficiently overcomes the hot spot problem in WSS environment. Furthermore, the authors, Shi et al., propose a structure that represents the sensor communications with the fusion centre, obtain the optimal estimation algorithm at the fusion centre, and provide a theoretical closed-form for the steady-state error covariance matrix which has low energy consumption and guarantees a desired level of estimation quality at the fusion centre. [22] However, to apply the advantages of the HNN technique in this research the improved CHNN method, which can by artfully arranging the updating function and the cost measurement properly eliminate the aforementioned dilemma, is adopted for solving target tracking with mobile sensors in WSS deployments. The CHNN is an improved HNN wherein a
doi:10.15837/ijccc.2012.1.1421 fatcat:htdfw5ioxzbl3jyvvjysewyrti