基于路径优化策略的物联网感知层数据收集研究

Optimizing Path Selection for Data Collection of the Perception Layer of Internet of Things

针对物联网感知层移动无线传感器网络在数据收集过程中节点能量有限、能耗不均衡和存在不可靠性、时延较长等问题,经过数学推导和理论证明这是一个典型多目标优化问题。文章将优化目标规约为时延受限下的能耗最小化和可靠性最大化问题,提出基于改进萤火虫算法优化移动无线传感器网络移动Sink路径优化机制,该算法充分利用移动Sink的存储空间充裕、能量充足和计算能力强的优势,保证网络的连通性,提高网络通信效率。通过仿真对比分析,与随机移动方法、蚁群算法和粒子群算法对比,簇头节点能耗均衡性减少了40%、52%和56%,可靠性提高了13%、8%和7%,时延减低了92.9%、82.8%和56.4%,提出的算法均衡了节点能耗,满足网络服务质量,提高网络可靠性。

In the process of data collection of the perception layer of the Internet of things, there are many problems, such as limited energy, unbalanced energy consumption, unreliability, and long delay, etc. It is proved that it is a typical multi-objective optimization problem through mathematical derivation and theory. In this paper, the problem of minimizing the energy consumption and maximizing the reliability under the delay-constrained optimization is proposed. The optimization of the mobile Sink path in the mobile wireless sensor network is proposed based on the improved firefly algorithm. The proposed algorithm makes full use of the advantages of abundant storage space of mobile Sink, abundant energy and computing power, which can ensure the connectivity of the network and improve the efficiency of network communication. Through simulation and comparative analysis, compared with the random migration method, ant colony algorithm and particle swarm optimization algorithm, the energy balance of cluster head nodes is reduced by 40%, 52% and 56%, the reliability is increased by 13%, 8% and 7%, the latency is reduced by 92.9%, 82.8% and 56.4%, respectively, the proposed algorithm balances the node energy consumption, satisfies the network service quality and improves the network reliability.