Energy Efficient Communication Framework for Target Coverage using Trust Concepts
Pooja Chaturvedi1, A. K. Daniel2
1Pooja Chaturvedi*, Department of Computer Science, School of Management Sciences, Varanasi, India.
2A. K. Daniel, M. M. M. University of Technology, Gorakhpur, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 247-257 | Volume-9 Issue-3, February 2020. | Retrieval Number: A9858109119/2020©BEIESP | DOI: 10.35940/ijeat.A9858.029320
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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: Target coverage is a challenging task in the field of wireless sensor networks aiming to observe a set of critical targets while considering the limited resources and the network lifetime is improved. The paper proposes an approach to : i) determine the strategy according to which the critical targets can be monitored while satisfying a certain confidence level. ii) determine the maximum and minimum number of nodes which can guarantee the coverage, iii) determine the optimal number of active nodes for various deployment strategies, iv) to determine a routing mechanism using either single hop/multi hop communication based on the reachability of the node to the base station and iv) to develop an aggregation protocol which can reduce the redundancy and number of packet transmissions. The proposed protocol is based on the two level aggregation at the set cover level and at the cluster level using the concept of Locality Sensitive Hashing (LSH) and Jaccard Similarity measure. The efficiency of the proposed aggregation mechanism is determined for various data sets of multiple dimensions. The results obtained through the simulations show the improvement in the network performance with respect to the network longevity, coverage, reliability and of the data transmission as compared to the Boolean coverage model
Keywords: Clustering, Network Lifetime, Target Coverage, Fuzzy Inference, Aggregation, Routing, Deployment