Autonomic approaches for enhancing communication QoS in dense Wireless Sensor Networks with real time requirements

2010 IEEE International Test Conference Pub Date : 2010-11-01 DOI:10.1109/TEST.2010.5699288

A. R. Pinto, C. Montez

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引用次数: 11

Abstract

Wireless Sensor Networks (WSN) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) in each node can not be easily replaced. One solution is to deploy a large number of sensor nodes, since the lifetime and dependability of the network can be increased through cooperation among nodes. In addition to energy consumption, applications for WSN may also have other concerns, such as, meeting deadlines and maximizing the quality of information. In this paper, two autonomic approaches for dense WSN are presented. The first approach is a Genetic Machine Learning algorithm aimed at applications that make use of trade-offs between different metrics. Simulations were performed on random topologies assuming different levels of faults. GMLA showed a significant improvement when compared with the use of IEEE 802.15.4 protocol. Moreover, an approach that autonomically provides QoS for dense WSN called VOA ( Variable Offset Algorithm) is presented. Experimental results had showed that VOA can significantly improve communication efficiency in dense WSN.

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具有实时性要求的密集无线传感器网络中增强通信QoS的自主方法

无线传感器网络(WSN)可用于监测危险和难以进入的区域。在这种情况下，每个节点的电源(如电池)都不容易更换。一种解决方案是部署大量的传感器节点，因为通过节点之间的合作可以增加网络的生命周期和可靠性。除了能源消耗之外，WSN的应用程序还可能有其他问题，例如，满足最后期限和最大限度地提高信息质量。本文提出了两种用于密集无线传感器网络的自治方法。第一种方法是遗传机器学习算法，旨在利用不同指标之间的权衡。在假设不同故障级别的随机拓扑结构上进行了仿真。与使用IEEE 802.15.4协议相比，GMLA显示出显著的改进。在此基础上，提出了一种为密集WSN自动提供QoS的方法——可变偏移算法(VOA)。实验结果表明，VOA可以显著提高密集WSN的通信效率。

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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2010 IEEE International Test Conference

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