Range-Free Monte Carlo Localization for Mobile Wireless Sensor Networks

Abstract

The position information is usually needed when wireless sensor networks are applied for surveillance within certain region. Thus the localization of nodes is critical for WSNs. However, the present localization schemes mostly focus on static sensor networks, and in mobile WSNs, these schemes will not work well. This paper proposes a range-free Monte Carlo localization algorithm TSBMCL, which is extended from the Monte Carlo Boxed (MCB) scheme [1] . In this scheme, the well localized nodes are applied to aid other nodes for localization based on the sequential Monte Carlo method. The scheme could further improve the localization accuracy of the WSNs. We evaluate the performance of the scheme with comparison to MCB using MATLAB. The simulation results demonstrate that the localization scheme outperforms the MCB scheme.

Mobile geolocation techniques for indoor environment monitoring

“…Most sensor-network-based localization techniques use RSS measurements [37,38]. Four sensor-based localization configurations exist: (1) static sensor nodes and static anchor nodes [39], (2) mobile sensor nodes and static anchor nodes [40], (3) static sensor nodes and mobile anchor nodes [41], and (4) mobile sensor nodes and mobile anchor nodes [42]. The study in [43] surveys recent localization techniques considering wireless sensor networks and their fundamental limits, challenges, and applications.…”

Model for autonomous agents in machine‐to‐machine navigation networks

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Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

“…That is, when the weight difference is small between two nodes, the phenomenon of swapping weight ordering would happen frequently, due to slight weight jitter. In this case, it is reasonable to remain the CH unchanged in a larger probability Monte Carlo is a method obtained from the drop rule of local search algorithm [8] , in which the basic idea is to accept a relatively worse state in a certain probability. The Monte Carlo method in cluster maintenance is as follows [9] : when CH-i encounter competition of cluster-member (CM)-v (W i >W v ),, the CH will be replaced in probability (1-p); the accepting probability is defined as…”