无线传感器网络中的节能目标监测

D. Jain, V. Vokkarane
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引用次数: 11

摘要

传感信息的基本目的之一是对任何异常情况立即作出反应。无线传感器网络(WSN)是一种低成本、低功耗的节点网络,具有嵌入式处理器、无线电、传感器和执行器,通常集成在单个芯片上,用于在安全和监视、智能教室、自然栖息地监测和医疗监测等应用中与物理世界通信。wsn与当前的网络和嵌入式系统有很大的不同,由于其极端的能量限制,它的设计需要正确理解网络协议、能量感知设计、信号处理算法和分布式编程之间的相互作用。虽然传感器节点的小尺寸使它们在监控应用中很有吸引力,但同时它们的小尺寸也会影响能源、计算能力和存储等资源。因此,即兴发挥无线传感器网络的能量约束是至关重要的。我们提出了两种基站迁移策略,旨在最大限度地减少向基站传输数据所消耗的能量。这两种策略都涉及移动基站,重点是将基站移动到更靠近探测目标的主动传感器的位置。我们的第一个策略包括有一个移动基站,并将其重新定位到所有检测目标的传感器的几何质心上。这种方法显著降低了从传感器向基站传输数据所需的能量开销。我们执行网络生命周期优化的第二个策略是将基站移动到几个时间段内获得的基站位置的几何质心。然而,在每种情况下,在每个时间段移动基站都涉及相当大的开销,因此我们观察到在特定时间段之后移动基站的效果,而不是在每个时间段之后移动基站。我们在不同的网络场景中评估了这两种建议策略的网络生命周期性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy-Efficient Target Monitoring in Wireless Sensor Networks
One of the fundamental purpose of sensing information is to immediately respond to any anomalies. Wireless sensor network (WSN) is a network of inexpensive, low-power nodes with embedded processors, radios, sensors, and actuators, often integrated on a single chip, to communicate with the physical world in applications, such as security and surveillance, smart classroom, monitoring of natural habitats, and medical monitoring. WSNs differ considerably from current networked and embedded systems and due to its extreme energy constraints its design requires a proper understanding of the interplay between network protocols, energy-aware design, signal-processing algorithms, and distributed programming. Though the small form-factor of sensor nodes makes them attractable for use in monitoring applications, at the same time their small size affects resources such as the energy, computational power, and storage. Therefore, improvising on the energy constraints of wireless sensor networks is crucial. We propose two base-station relocation policies that aim to minimize the energy consumed for transmitting the data to base station. Both the policies involve a mobile base station, and focus on moving the base station closer to the active sensors that detect the target. Our first policy involves having a mobile base station and relocating it to the geometric centroid of all the sensors detecting the target. This approach significantly reduces the energy overhead required for transmitting data from the sensors to the base station. Our second policy for performing network lifetime optimization is to move the base station to geometric centroid of the base station locations obtained over several time periods. However, in each case, moving the base station at each time period involves a considerable overhead and therefore we observe the effects of moving the base station after a specific number of time periods as opposed to moving after every time period. We evaluate the network lifetime performance of these two proposed policies over different network scenarios.
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