Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks
Deepika Lokesh1, N V Uma Reddy2
1Deepika Lokesh*, Research scholar , Dept of Electronics and Communication Engineering, AMC Engineering College, Bangalore.
2Dr N V Uma Reddy, Professor and Head, Dept. Electronics and Communication Engineering , AMC Engineering College, Bangalore.
Manuscript received on December 12, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 1638-1644 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8529019320/2020©BEIESP | DOI: 10.35940/ijitee.C8529.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data collection utilizing wireless sensors networks (WSNs) has been utilized for surveillance, monitoring environment, animal etc. Target tracking of maneuvering objects is an essential need of modern life. Nonetheless, because of diverse nature of sensor and complex environment, sensors measurement errors need to be minimized considering diverse motion states in process of tracking (sensing) operation. Enhancing network lifetime (i.e., reducing energy dissipation of sensor nodes) and improving tracking quality are major concern of target tracking using WSN. Form improving network energy efficiency, multi-sensory target tracking method has been modelled using Kalman Filter (KF) by existing target tracking method. The KF based model are affected due to presence of noise or missing data. For overcoming research issues this paper present an H-infinity filter (HF) to evaluate fusion for maneuvering target tracking in WSN. Further, to minimize the estimation errors and reduces/controlling the effects of outliers fuzzy H-infinity (FHF) filter for target tracking WSN is presented. Experiment outcome shows proposed HF and FHF fusion model attain better performance than existing KF based method for clustered based WSN in terms of positional and velocity root mean square error and energy dissipation.
Keywords: Energy Efficiency, Fuzzy Computing, H-Infinity Filter, Kalman Filter, Network lifetime, Target Tracking, Wireless Sensor Network.
Scope of the Article: Autonomic computing