%0 Journal Article
%T Random Walk Based Location Prediction in Wireless Sensor Networks
%A Zhaoyan Jin
%A Dianxi Shi
%A Quanyuan Wu
%A Huining Yan
%J International Journal of Distributed Sensor Networks
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/691042
%X With the development of wireless sensor network (WSN) technologies, WSNs have been applied in many areas. In all WSN technologies, localization is a crucial problem. Traditional localization approaches in WSNs mainly focus on calculating the current location of sensor nodes or mobile objects. In this paper, we study the problem of future location prediction in WSNs. We assume the location histories of mobile objects as a rating matrix and then use a random walk based social recommender algorithm to predict the future locations of mobile objects. Experiments show that the proposed algorithm has better prediction accuracy and can solve the rating matrix sparsity problem more effectively than related works. 1. Introduction Recently, WSNs have been applied in many areas, such as environmental monitoring [1], target tracking [2], and intrusion detection [3]. However, in all of these areas, location is very important, for data without location in WSNs will be useless. Research of localization is a hot topic in WSN, and it includes two kinds, that is, localization of sensor nodes in the WSN itself and localization of mobile objects using a WSN. For localization of sensor nodes in the WSN itself, several anchor nodes with known location are selected, and locations of other sensor nodes can be calculated with predefined anchor nodes [4]. For localization of mobile objects using a WSN, all sensor nodes are assumed to be anchor nodes, and the location of mobile objects can be calculated with observed anchor nodes [5]. The most popular localization system of this kind is the global position system (GPS). Given a WSN, where all sensor nodes are anchor nodes, the locations of mobile objects can be calculated with the WSN. Furthermore, if we save all location records of mobile objects, then we can predict future locations of mobile objects with the observed location histories and the WSN. In this paper, we study the problem of future location prediction in WSNs. Here, we assume a WSN as a connected network, where each sensor node has connections with all its neighbors. The reason is that if a mobile object visits location , it will probably visit ¡¯s neighborhood. In a WSN, each node records the number of visits of all mobile objects to itself. Hence, we have a network of sensor nodes and a rating matrix of sensor nodes on all mobile objects. The purpose of this paper is to predict which mobile object will probably visit the desired locations or sensor nodes in the future. 2. Related Works If we assume each sensor node as a user and each mobile object as an item or a
%U http://www.hindawi.com/journals/ijdsn/2013/691042/