Cluster Based Optimization of Routing in Distributed Sensor Networks Using Bayesian Networks with Tabu Search
Abstract
This paper proposes a cluster based optimizat ion of routing in Distributed Sensor Network (DSN) by employing aBayesian network with Tabu search approach. Bayesian Network based approach is used to select efficient cluster
heads, as well as construction of Bayesian Networks for the proposed scheme. This approach incorporates energy
level of each node, bandwidth and link efficiency. Simulations have been conducted to compare the performance of
the proposed approach and LEACH.
The optimization of routing is considered as a design issue in DSNs due to lack of energy consumption, delay
and maximum time required for data transmission between source nodes (cluster heads) to sink node. In this work,
optimization of routing takes place through cluster head nodes by using Tabu search. This meta - heuristic technique
is used to optimize the routing in the DSN environment that guides a local search procedure to explore the solution
space beyond local optimality. The objective of the proposed work is to improve the performance of network in
terms of energy consumption, throughput, packet delivery ratio, and time efficiency of optimizat ion of routing. The
results shows that the proposed approach perform better than LEACH protocol and proposed protocol utilizes
minimum energy and latency for cluster formation, thereby reducing the overhead of the protocol.
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