Grid Partition: an Efficient Greedy Approach for Outdoor Camera IoT Deployments in 2.5D Terrain

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI:10.1109/ICCCN49398.2020.9209624

K. Veenstra, K. Obraczka

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引用次数: 2

Abstract

In this paper, we introduce Distributed Grid Partition, a distributed greedy deployment algorithm for outdoor IoT camera networks. The proposed algorithm optimizes visual network coverage over 2.5D terrain. The main idea behind Distributed Grid Partition is that each deployment node tries to find the best vantage point in its neighborhood that will maximize the network’s overall visual coverage. It does so by using information from its immediate neighbors. In order to achieve a favorable cost-performance trade-off, Distributed Grid Partition uses height as a proxy for visual coverage, or fitness, avoiding expensive fitness computations. In addition, each node’s contribution to network fitness is determined without knowledge of the overall network using the concept of "Wonderful Life Utility". Our experimental results show that Distributed Grid Partition results in deployments with superior coverage-cost performance when compared to other distributed optimization algorithms as well as a centralized greedy set cover heuristic.

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网格划分:2.5D地形下户外摄像头物联网部署的一种高效贪婪方法

本文介绍了一种用于室外物联网摄像机网络的分布式贪婪部署算法——分布式网格划分。该算法优化了2.5D地形上的视觉网络覆盖。分布式网格分区背后的主要思想是，每个部署节点都试图在其邻居中找到最佳有利位置，从而最大化网络的整体视觉覆盖。它通过使用来自近邻的信息来做到这一点。为了实现有利的成本-性能权衡，分布式网格分区使用高度作为视觉覆盖或适应度的代理，避免了昂贵的适应度计算。此外，每个节点对网络适应度的贡献是在不了解整个网络的情况下，使用“奇妙生活效用”的概念来确定的。我们的实验结果表明，与其他分布式优化算法以及集中式贪婪集覆盖启发式算法相比，分布式网格分区的部署具有更好的覆盖成本性能。

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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2020 29th International Conference on Computer Communications and Networks (ICCCN)

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