Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking
P. Sumathy1, S.Alonshia2
1Dr. P. Sumathy *, Assistant Professor, Department of Computer Science & Engineering, Bharathidasan University, Tiruchirappalli, India.
2S.Alonshia, Research Scholar, Department of Computer Science & Engineering, Bharathidasan University, Tiruchirappalli, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4517-4524 | Volume-8 Issue-12, October 2019. | Retrieval Number: L35371081219/2019©BEIESP | DOI: 10.35940/ijitee.L3537.1081219
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Abstract: In wireless sensor network application, the localization of nodes are carried out for extended life time of the node. Many applications in wireless sensor network perform localization of nodes over an extended period of time with energy variance. However, optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this work, it is proposed to develop a Bayesian Localized Energy Optimized Sensor Distribution (BLEOSD) scheme for efficient target tracking in Wireless Sensor Network. The sensor node localized with Bayesian average scheme thatestimates the sensor node’s energy are optimized as per data transfer capacity verification. The Bayesian average energy level of the sensor network is compared with the energy of each sensor node. The sensor nodes are localized and energy distribution based on the Bayesian energy estimate for efficient target tracking. The sensor node distribution strategy improves the accuracyto identify the targets effectively. Experiments are conducted using simulation of WSN by varying number of nodes, energy levels of the node and target object density using the Network Simulator Tool (NS2) The proposed BLEOSD technique is compared with various recent methods by evaluating accuracy of target tracking, energy consumption rate, localized node density and time for target tracking. The experimental results shows that the performance of BLESOD is more encouraging compared to contemporary methods.
Keywords: Wireless Sensor Network, Localization, Bayesian, Energy optimization
Scope of the Article: Wireless Sensor Network