Optimize Multiple Mobile Elements Touring in Wireless Sensor Networks

Liang He, Jingdong Xu, Yuntao Yu
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引用次数: 6

Abstract

Integrating mobility into WSNs can significantly reduce the energy consumption of sensor nodes. However, this may lead to unacceptable data collection latency at the same time. In our previous work, we alleviated the problem under the assumption of a mobile base station (BS). In this paper, we discuss how the problem can be solved when the BS itself is not capable of moving, but it can instead employ some mobile elements (MEs). The data collection latency is mainly determined by the longest tour of the MEs in this case. Each ME should be assigned a similar workload to reduce the latency. Furthermore, the total length of the tours should be minimized to decrease the working cost of MEs. We propose three methods to solve the problem with these two-fold objectives. In the first two methods, we cluster the network according to some criteria, and then construct the data collection tour for each ME. We apply a heuristic operator based on the genetic algorithm in the third method, whose fitness function is defined according to the two-fold objectives. These methods are evaluated by comprehensive experiments. The results show that the genetic method can provide us more steady solutions in term of data collection latency. We also compare the mobile BS model and the multiple MEs model, whose results show that the latter can get us better solutions when the number of MEs gets larger.
无线传感器网络中多移动元素巡回优化
将移动性集成到WSNs中可以显著降低传感器节点的能量消耗。但是,这可能同时导致不可接受的数据收集延迟。在我们之前的工作中,我们在移动基站(BS)的假设下缓解了这个问题。在本文中,我们讨论了当BS本身不能移动时,如何解决这个问题,但它可以使用一些移动元素(MEs)来代替。在这种情况下,数据收集延迟主要由MEs的最长行程决定。应该为每个ME分配类似的工作负载,以减少延迟。此外,应尽量减少旅行的总长度,以减少MEs的工作成本。我们提出了三种方法来解决这一双重目标的问题。在前两种方法中,我们根据一定的标准对网络进行聚类,然后为每个ME构造数据收集之旅。第三种方法采用了一种基于遗传算法的启发式算子,其适应度函数根据双重目标定义。通过综合实验对这些方法进行了评价。结果表明,遗传方法在数据采集延迟方面可以提供更稳定的解。我们还比较了移动BS模型和多个微商户模型,结果表明,当微商户数量较大时,后者可以得到更好的解决方案。
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