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An Energy Efficient Routing Based on Swarm Intelligence for Wireless Sensor Networks

机译：基于群体智能的无线传感器网络节能路由

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Wireless Sensor Networks are characterized by having specific requirements such as limited power, memory and functionality to support communications. In sensor networks, minimization of energy consumption is considered a major performance criterion to provide maximum network lifetime. Traditional routing protocols do not take into account that a node contains only a limited energy supply. In this paper, we describe a novel energy efficient routing approach which combines swarm intelligence, especially the Ant colony based meta heuristic, with a novel variation of Reinforcement learning for Wireless Sensor Networks (ARNet). The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using an improved swarm intelligence based optimization. ARNet balances the energy consumption of nodes in the network and extends the network lifetime. Simulation results show that ARNet can obviously improve adaptability and effectively reduce the average energy consumption compared with the traditional EEABR algorithm.
机译：无线传感器网络的特点是具有特定要求，例如有限的功率，内存和支持通信的功能。在传感器网络中，最小化能耗被认为是提供最长网络寿命的主要性能标准。传统的路由协议没有考虑到节点仅包含有限的能量供应。在本文中，我们描述了一种新颖的节能路由方法，该方法结合了群体智能（尤其是基于蚁群的元启发式算法）和无线传感器网络（ARNet）的强化学习的新变化。我们研究的主要目标是最大程度地保持网络寿命，同时使用改进的基于群体智能的优化方法发现从源节点到宿节点的最短路径。 ARNet平衡了网络中节点的能耗并延长了网络寿命。仿真结果表明，与传统的EEABR算法相比，ARNet可以明显提高适应性，并有效降低平均能耗。

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