Anchor‐free distance estimation: A new approach to distance estimation for multihop ad hoc wireless networks

Abstract

In ad hoc wireless networks, devices that normally cannot directly communicate route their messages through intermediate nodes. The number of those nodes is called hop count, a useful metric in estimating the distance between 2 nodes. Current methods usually depend on special nodes, called anchors, that need accurate localization information, in order to calculate an estimate for the average distance traversed per hop. The drawback of this approach is that anchor nodes increase the overall cost and complexity of the system. To address this problem, this letter proposes a novel, anchor node-free algorithm that can achieve a useful estimate for actual distance between 2 nodes, by analytically finding an estimate for the average maximum distance traveled per hop and multiplying with the hop count. The only requirement is the a priori knowledge of the networks' node density and the node range. The performance of our method is compared with a recent anchor node-based method and is shown to yield similar location estimation accuracy, despite the fact that it does not use anchor nodes.

A weighted range‐free localization algorithm for irregular multihop networks

“…where hd is the general expression of hop-distance and S b is the corresponding smallest segment area. Mavridopoulos et al 28 deduced that the area of S is inversely proportional to the density of nodes where nodes are uniformly and densely distributed in convex networks. However, in practical scenarios, the network may be irregular, making the density of nodes in different local areas vary significantly.…”

“…Therefore, these algorithms that relied solely on anchor nodes to obtain the estimated hop‐distance would not fully exploit other useful prior information to improve localization accuracy. Recently, anchor‐free distance estimation (AFDE) algorithm was presented in 28 . In this algorithm, a global estimated hop‐distance could be derived by employing the network node density and the node range.…”

“…We know from Mavridopoulos et al 28 that is inversely proportional to the distribution density of nodes. We can approximate by ,where and are constant.…”

“…Therefore, in some applications, range-free technology can be more practical than range-based technology. The basic idea behind range-free localization is using connectivity information such as average hop-distance, 25,26 expected hop progress, 27 density, 28 or neighbor information 29 to build up the one-hop measured distance. The measured distance between any two nodes can be estimated without ranging hardware by accumulating several one-hop measured distances.…”

“…Recently, anchor-free distance estimation (AFDE) algorithm was presented in. 28 In this algorithm, a global estimated hop-distance could be derived by employing the network node density and the node range. In other words, AFDE did not rely on anchor nodes number to compute the distance between nodes.…”