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首页> 外文期刊>Journal of Sensors >Optimization-Based Artificial Bee Colony Algorithm for Data Collection in Large-Scale Mobile Wireless Sensor Networks
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Optimization-Based Artificial Bee Colony Algorithm for Data Collection in Large-Scale Mobile Wireless Sensor Networks

机译:大规模移动无线传感器网络中基于优化的人工蜂群数据收集算法

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摘要

Data collection is a fundamental operation in various mobile wireless sensor networks (MWSN) applications. The energy of nodes around the Sink can be untimely depleted owing to the fact that sensor nodes must transmit vast amounts of data, readily forming a bottleneck in energy consumption; mobile wireless sensor networks have been designed to address this issue. In this study, we focused on a large-scale and intensive MWSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. We also derived a corresponding formula to represent the MWSN and proved that it represents an NP-hard problem. Traditional data collection methods only focus on increasing the amount data collection or reducing the overall network energy consumption, which is why we designed the proposed heuristic algorithm to jointly consider cluster head selection, the routing path from ordinary nodes to the cluster head node, and mobile Sink path planning optimization. The proposed data collection algorithm for mobile Sinks is, in effect, based on artificial bee colony. Simulation results show that, in comparison with other algorithms, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime.
机译:数据收集是各种移动无线传感器网络(MWSN)应用程序中的基本操作。由于传感器节点必须传输大量数据,很容易形成能耗瓶颈,因此接收器周围节点的能量可能会不合时宜地耗尽。移动无线传感器网络已设计为解决此问题。在本研究中,我们集中于大规模密集型MWSN,它通过从三个方面调查移动接收器平衡来允许一定量的数据延迟:数据收集最大化,移动路径长度最小化和网络可靠性优化。我们还推导了代表MWSN的相应公式,并证明它代表了NP难题。传统的数据收集方法仅关注于增加数据收集量或减少整体网络能耗,这就是为什么我们设计提出的启发式算法来共同考虑簇头选择,从普通节点到簇头节点的路由路径以及移动节点的原因。下沉路径规划优化。所提出的用于移动水槽的数据收集算法实际上是基于人工蜂群。仿真结果表明,与其他算法相比,该算法可以有效减少数据传输,节约能源,提高网络数据采集效率和可靠性,延长网络寿命。

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