Open AccessBayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking
Author(s) -
P. Sumathy*,
S. Alonshia
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l3537.1081219
Subject(s) - wireless sensor network , node (physics) , computer science , sensor node , key distribution in wireless sensor networks , energy consumption , efficient energy use , tracking (education) , energy (signal processing) , real time computing , transmission (telecommunications) , computer network , engineering , wireless , wireless network , mathematics , telecommunications , statistics , electrical engineering , psychology , pedagogy , structural engineering
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.