基于生命周期规划的无线传感器网络QoS改进

Mohamed Abdelaal, Peilin Zhang, Oliver E. Theel
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引用次数: 2

摘要

能源效率是无线传感器网络(WSN)设计者的一个重要目标。然而,这种网络的成功实现高度依赖于使能技术,以及网络中服务质量(QoS)的提供。在本文中,我们提出了一种新的策略,称为生命周期规划,以实现最佳努力的QoS。同时,达到了完成指定任务所需的足够的生命周期。其核心思想是避开寿命最大化策略,即传感器节点即使在完成所需任务后仍能继续工作。在这些情况下,我们可以有意地将操作生命周期绑定到预期的任务生命周期。因此,在整个任务生命周期中可以花费更多的精力来提高所提供的服务质量。设计了一个分析QoS模型来验证QoS生命周期规划的“无冲突”性质。通过在设计时设计QoS边界,该策略是可行的。在运行过程中,通过主动自适应机制对可控参数进行调节。为了证明我们设计的有效性,我们在集群树WSN拓扑中使用办公室监控场景进行了密集的性能评估。该场景是在Contiki网络模拟器Cooja中使用Tmote天空模式设计的。此外,我们还考察了采用我们的策略相对于固定启发式和盲目适应的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
QoS Improvement with Lifetime Planning in Wireless Sensor Networks
Energy efficiency is an important goal for Wireless Sensor Network (WSN) designers. However, successful implementations of such networks are highly dependent on the enabling technologies, as well as on the provisioning of Quality of Service (QoS) in the network. In this paper, we propose a novel strategy, referred to as the lifetime planning for achieving best-effort QoS. Simultaneously, an adequate lifetime required to complete the assigned task is reached. The core idea is to sidestep lifetime maximization strategies in which sensor nodes continue functioning even after the fulfillment of the required task. In these cases, we could deliberately bound the operational lifetime to the expected task lifetime. As a result, more energy can be spent throughout the entire task lifetime for enhancing the provided service qualities. An analytical QoS model is engineered to validate the QoS's "conflicts-free" nature of lifetime planning. The proposed strategy is feasible via the design of QoS boundaries at design-time. During run-time, the controllable parameters are modulated by a proactive adaptation mechanism. To demonstrate the effectiveness of our design, we conduct an intensive performance evaluation using an office monitoring scenario in a cluster-tree WSN topology. The scenario has been designed in the Contiki network simulator Cooja using Tmote sky motes. Furthermore, we examine the profit of adopting our strategy relative to fixed heuristics and blind adaptation.
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