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A Utility-Based Adaptive Sensing and Multihop Communication Protocol for Wireless Sensor Networks

机译:基于实用程序的无线传感器网络自适应传感和多跳通信协议

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This article reports on the development of a utility-based mechanism for managing sensing and communication in cooperative multisensor networks. The specific application on which we illustrate our mechanism is that of GlacsWeb. This is a deployed system that uses battery-powered sensors to collect environmental data related to glaciers which it transmits back to a base station so that it can be made available world-wide to researchers. In this context, we first develop a sensing protocol in which each sensor locally adjusts its sensing rate based on the value of the data it believes it will observe. The sensors employ a Bayesian linear model to decide their sampling rate and exploit the properties of the Kullback-Leibler divergence to place an appropriate value on the data. Then, we detail a communication protocol that finds optimal routes for relaying this data back to the base station based on the cost of communicating it (derived from the opportunity cost of using the battery power for relaying data). Finally, we empirically evaluate our protocol by examining the impact on efficiency of a static network topology, a dynamic network topology, the size of the network, the degree of dynamism of the environment, and the mobility of the nodes. In so doing, we demonstrate that the efficiency gains of our new protocol, over the currently implemented method over a 6 month period, are 78%, 133%, 100%, and 93%, respectively. Furthermore, we show that our system performs at 65%, 70%, 63%, and 70% of the theoretical optimal, respectively, despite being a distributed protocol that operates with incomplete knowledge of the environment.
机译:本文报告了基于实用程序的机制的发展,该机制用于管理协作式多传感器网络中的传感和通信。我们说明其机制的特定应用程序是GlacsWeb。这是一个已部署的系统,它使用电池供电的传感器来收集与冰川相关的环境数据,并将其传输回基站,以便研究人员可以在全球范围内使用。在这种情况下,我们首先开发一种传感协议,其中每个传感器根据其认为将观察到的数据的值在本地调整其传感速率。传感器采用贝叶斯线性模型来确定其采样率,并利用Kullback-Leibler散度的特性在数据上放置适当的值。然后,我们详细介绍一种通信协议,该协议根据通信数据的成本(从使用电池电量中继数据的机会成本中得出)找到用于将该数据中继回基站的最佳路由。最后,我们通过检查对静态网络拓扑,动态网络拓扑,网络的大小,环境的动态程度以及节点的移动性的影响来对协议进行经验评估。通过这样做,我们证明了在当前实施的方法中,在六个月的时间内,新协议的效率分别提高了78%,133%,100%和93%。此外,我们证明了我们的系统尽管是分布式协议,并且在不完全了解环境的情况下运行,但它们的性能分别达到理论最优值的65%,70%,63%和70%。

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