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A Prediction Algorithm based on Markov Chains for finding the Minimum Cost Path in a Mobile WSNs

机译:基于马尔可夫链的移动无线传感器网络最小成本路径预测算法

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In this paper we propose the usage of a prediction technique based on Markov Chains to predict nodes positions with the aim of obtain short paths at minimum energy consumption. Specifically, the valuable information from the mobility prediction method is provided to our distributed routing algorithm in order to take the best network decisions considering future states of network resources. In this sense, in each network node, the mobility method employed is based on a Markov model to forecast future RSSI states of neighboring nodes for determining if they will be farther or closer within the next steps. The approach is evaluated considering different algorithms such as: Distance algorithm, Distance Away algorithm and Random algorithm. In addition, with the aim of performing comparisons against optimal values, we present a mathematical optimization model for finding the minimum cost path between a source and a destination node considering all network nodes are mobile. This paper is an extended variant of [8].
机译:在本文中,我们提出了一种基于马尔可夫链的预测技术来预测节点位置,目的是以最小的能耗获得短路径。具体而言,来自移动性预测方法的宝贵信息将提供给我们的分布式路由算法,以便考虑网络资源的未来状态来做出最佳网络决策。从这个意义上说,在每个网络节点中,所采用的移动性方法基于马尔可夫模型,以预测相邻节点的未来RSSI状态,以确定它们在下一步中是否会更远或更近。在考虑不同算法的情况下评估该方法,例如:距离算法,距离算法和随机算法。此外,出于与最佳值进行比较的目的,我们提出了一种数学优化模型,用于考虑所有网络节点都是可移动的情况,以找到源节点和目标节点之间的最小成本路径。本文是[8]的扩展变体。

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