移动机器人无线传感器网络的有效能量恢复

P. Flocchini, Eman Omar, N. Santoro
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引用次数: 1

摘要

由于大多数现有的传感器都是由电池供电的,传感器网络提供的覆盖范围会随着时间的推移而降低,如果不能恢复能量,最终会消失。一种流行的能量恢复方法是使用机器人作为移动电池充电器/更换器。机器人根据预先设定的在线能量恢复策略决定下一步的移动方向。这种策略的有效性取决于它在任何给定时间能够维持运行的节点的数量,以及电池耗尽的节点保持不可运行的时间。理想的最优在线策略(optimal)发生在机器人随时知道所有传感器的当前状态,并根据这些信息计算出下一步要满足的最佳请求。这一集中战略虽然在效力方面是最佳的,但将不断需要最新的全球资料;因此,它的高计算和通信成本使其不可行。我们考虑了一种完全不同的在线策略(称为LIC),它简单且完全分散,仅使用本地通信,不需要计算,并且具有高度可扩展性。在我们的策略中,机器人以预定义的圆形顺序访问传感器,在“顺时针”方向移动,并且只有在意识到待处理的请求时才移动。电池即将耗尽的传感器发出充电请求并等待机器人;请求按照圆形顺序以“逆时针”方向转发,直到它到达机器人或等待机器人的另一个传感器。我们展示了可能意想不到的结果,即一旦系统变得稳定,在大多数网络中,LIC的有效性等同于OPTIMAL的有效性。换句话说,在大多数情况下,尽管分散策略简单且(通信和计算)成本极小,但所提出的分散策略与最优集中策略一样有效。我们用实验分析增强了我们的理论结果,证实了所有的分析结果,并显示出系统稳定得非常快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effective Energy Restoration of Wireless Sensor Networks by a Mobile Robot
As most existing sensors are powered by batteries, the coverage provided by a sensor network degrades over time and eventually disappears if energy is not restored. A popular approach to energy restoration is to use a robot acting as a mobile battery charger/changer. The robot decides where to move next according to a predefined on-line energy restoration strategy. The effectiveness of such a strategy depends on the number of nodes it is able to maintain operational at any given time, as well as on for how long a node whose battery is depleted remains non-operational. The ideal optimal on-line strategy (called OPTIMAL) occurs when the robot knows at any time the current status of all sensors, and it computes the best request to satisfy next, based on this information. Although optimal in terms of effectiveness, this centralized strategy would constantly require up-to-date global information; hence its high computational and communication costs make it not feasible. We consider a drastically different on-line strategy (called LIC), which is simple and fully decentralized, uses only local communication, requires no computations, and is highly scalable. In our strategy, the robot visits the sensors in a predefined circular order, moving in a "clockwise" direction and only when aware of a pending request. A sensor whose battery is about to become depleted originates a recharging request and waits for the robot; the request is forwarded according to the circular order in a "counter-clockwise" direction until it reaches either the robot or another sensor waiting for the robot. We show the perhaps unexpected result that, once the system becomes stable, in most networks the effectiveness of LIC is equivalent to that of OPTIMAL. In other words, in most cases, in spite of its simplicity and its extremely small (communication and computation) costs, the proposed decentralized strategy is as effective as the optimal centralized one. We augment our theoretical results with experimental analysis, confirming all the analytical results and showing among other things that the system stabilizes very quickly.
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