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Swarm Intelligent Data Aggregation in Wireless Sensor Network

机译:无线传感器网络中的集群智能数据聚合

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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.
机译:WSN中的数据聚合是一个有趣的问题,其中传感器感测的数据以有效的方式路由到聚合节点。由于传感器是电池供电的,因此路由协议节约能源并确保负载平衡和更快的交付非常重要。在这项研究中,针对此问题开发了一个多目标线性规划模型,并使用应用优势原理的精确算法对其进行了求解。为了确保更快的收敛,采用了结合了诸如蚂蚁,蜜蜂和萤火虫之类的自然策略的路由算法。在仿真研究中,从收敛特性可以很明显地看出,群体智能算法可以比精确算法更早地收敛,收敛时间要短90%。而且,当精确算法可以解决较小的网络时,群智能算法可以解决更大的网络实例。 Firefly算法能够产生近似的pareto-优于蚁群优化和蜂群优化算法的最佳路线。

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