Minimizing aggregation latency under the physical interference model in Wireless Sensor Networks

Baobing Wang, J. Baras
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引用次数: 12

Abstract

Wireless Sensor Networks (WSNs) have been widely recognized as a promising technology that can enhance various aspects of today's electric power systems, making them a vital component of the smart grid. Efficient aggregation of data collected by sensors is crucial for a successful WSN-based smart grid application. Existing works on the Minimum Latency Aggregation Scheduling (MLAS) problem in WSNs usually adopt the protocol interference model, which is a tremendous simplification of the physical reality faced in wireless networks. In contrast, the more realistic physical interference model has been proved to have the potential to increase the network capacity. In this paper, we propose a distributed algorithm to minimize the data aggregation latency under the physical interference model, which jointly considers routing, power assignment and transmission scheduling. We theoretically prove that our algorithm solves the MLAS problem correctly and the latency is bounded by √ 3(K + 1)2(Δ + log √2/K+1) + 6K2 + 4K + 2, where K is a model-specific 2 constant and Δ is the logarithm of the ratio between the lengths of the longest and shortest links in the network. Simulation results demonstrate that our algorithm can significantly reduce the aggregation latency compared to other schemes under the physical interference model. In networks where n nodes are uniformly distributed, our algorithm achieves an average latency between O(log3 n) and O(log4 n). We also discuss how to improve the energy efficiency through load-balancing techniques.
无线传感器网络物理干扰模型下的最小聚合延迟
无线传感器网络(WSNs)已被广泛认为是一项有前途的技术,可以增强当今电力系统的各个方面,使其成为智能电网的重要组成部分。传感器收集的数据的有效聚合对于基于wsn的智能电网应用的成功至关重要。现有研究WSNs最小时延聚合调度(MLAS)问题的工作通常采用协议干扰模型,这极大地简化了无线网络所面临的物理现实。相比之下,更现实的物理干扰模型已被证明具有增加网络容量的潜力。本文提出了一种在物理干扰模型下,综合考虑路由、功率分配和传输调度的分布式数据汇聚延迟最小化算法。我们从理论上证明了我们的算法正确地解决了MLAS问题,并且延迟的边界为√3(K +1) 2(Δ + log√2/K+1) + 6K2 + 4K + 2,其中K是特定于模型的2常数,Δ是网络中最长和最短链路长度之比的对数。仿真结果表明,在物理干扰模型下,与其他方案相比,该算法可以显著降低聚合延迟。在n个节点均匀分布的网络中,我们的算法实现了O(log3n)和O(log4n)之间的平均延迟。我们还讨论了如何通过负载平衡技术提高能源效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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