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Swarm Intelligent Data Aggregation in Wireless Sensor Network
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DOI：10.4018/IJSIR.2020040101.png)
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Abstract
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Data aggregation in WSNs is an interesting problem wherein data sensed by the sensors is routed to an aggregation node in an efficient way. Since the sensors are battery operated, it is very important for a routing protocol to conserve energy and also ensure load balancing and faster delivery. In this study, a multi-objective linear programming model is developed for this problem and solved using an exact algorithm applying dominance principle. In order to ensure faster convergence, routing algorithms incorporating strategies of swarms in nature such as Ants, Bees and Fireflies are adapted. In the simulation study, it is quite evident from the convergence characteristics, swarm intelligent algorithms could converge earlier than the exact algorithm with convergence time lesser by 90%. Moreover, when exact algorithm could solve smaller networks, the swarm intelligent algorithms could solve even larger network instances. Firefly algorithm is able to yield approximated pareto - optimal routes which outperforms ant colony optimization and bee colony optimization algorithms.
Keywords:
Ant Colony Optimization
Bee Colony Optimization
Data Aggregation
Firefly Algorithm
Multi-Objective Optimization
Swarm Intelligence
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