Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model
Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model
复制标题
使用马尔可夫随机场模型的无线传感器网络节能节点调度算法
DOI：
10.1016/j.ins.2015.09.039
复制
发表时间：
2016-02-01
影响因子：
8.1
通讯作者：
Xiao, Yang
中科院分区：
文献类型：
--
作者：
Cheng, Hongju;Su, Zhihuang;Xiao, Yang
无线传感器网络的密集部署是延长无线传感器网络寿命的重要途径之一。感知数据之间的冗余性表明，如果我们进一步考虑数据通常是空间相关的，那么从网络中所有节点收集原始数据的效率并不高。节点调度策略旨在选择一组有代表性的节点，在保证准确性的前提下，周期性地提供所需的数据服务。这种策略可以有效地减少网络中的活动节点数量和消息量，从而延长网络的生命周期。本文首先介绍了如何利用马尔可夫随机场（MRF）模型对遥感数据间的空间相关性进行建模。其次，我们制定了问题的定义，即数据修正问题，即通过修正来自邻居的原始噪声损坏数据来最大化给定节点的数据覆盖范围；代表节点选择问题侧重于减少代表节点的数量并覆盖网络中的所有节点；节点调度问题旨在最大化网络生命周期。针对上述问题，我们分别提出了一种新的数据修正算法（DAP）、代表性节点选择算法（RSP）和节能节点调度算法（NSA）。最后，大量的实验表明，与相关工作相比，所提出的节点调度算法可以显著提高网络生存期，平均提高约80%。(C) 2015爱思唯尔公司版权所有。
One important way to extend the lifetime of wireless sensor networks is to deploy the sensors in a dense manner. The redundancy among the sensed data demonstrates that it is not efficient to collect raw data from all nodes in the network if we further consider that the data is generally spatial-correlated. The node scheduling strategy aims at selecting a set of representative nodes to provide the required data service in a periodic manner with accuracy guarantee. This strategy can effectively reduce the number of active nodes and the amount of messages in the network, and extend the network lifetime accordingly. In this paper, we firstly introduce how to model the spatial correlation among sensed data by Markov Random Field (MRF) model. Secondly, we formulate the problem definitions, namely, the data amendment problem which maximizes the data coverage range for a given node by amending the raw noise-corrupted data from the neighbors, while the representative nodes selection problem focuses on reducing the number of representative nodes and covering all nodes in the network, and the node scheduling problem aims at maximizing the network lifetime. Thirdly, we propose a novel Data Amendment Procedure (DAP), Representative node Selection Procedure (RSP) and energy-efficient Node Scheduling Algorithm (NSA) respectively for these above problems. Finally, extensive experiments demonstrate that the proposed node scheduling algorithm can significantly improve the network lifetime compared with related works with an average increment of about 80%. (C) 2015 Elsevier Inc. All rights reserved.