Decentralized localization using radio-fingerprints and accelerometer in WSNs

Abstract

This paper considers the problem of decentralized localization in wireless sensor networks (WSNs). The problem is set by combining both radio-fingerprints and accelerometer information. It is then resolved in a decentralized manner where the surveillance area is divided into several zones equipped with calculators. The proposed algorithm consists of computing first estimates using received signal strength indicator (RSSI)-fingerprints, then correcting them using accelerometer information leading to more accurate estimates. Computations are performed using the interval analysis and the Kalman filter in a simulated environment.

A Hybrid Mobile Node Localization Algorithm Based on Adaptive MCB-PSO Approach in Wireless Sensor Networks

“…Each unknown node i at time unit k establishes the neighbor sets based on the hop counts. One-hop and two-hop sets are denoted as S k i1 and S k i2 , respectively, as in equation (8). Finally, all the neighbor anchors can be obtained using equation (9).…”

“…Moreover, GPS receivers are energy consuming, costly, and bulky for tiny sensors. Hence, to extend the lifetime of the network, hardware, communication protocols, and processing algorithms should all be designed in a way with low energy consumption [8]. Alternative solutions have been proposed in the literature, considering some sensors, called anchor nodes, having known positions, and the others, called unknown nodes, requiring to be localized.…”

“…Salari et al use mobility information of sensor nodes to improve the accuracy of the localization algorithm. In another one [8], both fingerprint and accelerometer information are incorporated for localization, which is proven to be better in localization accuracy. An algorithm is proposed using basic directional and meandering mobility models for the localization of moving sensor nodes [16].…”

An Evidential Framework for Localization of Sensors in Indoor Environments

“…In the online step, the estimated signatures at each AN are compared with the database fingerprints. This can be formulated as a regression problem [19], where the idea is to construct a model that takes the signature as an input and outputs the position of the MN [20]. The advantage of such an approach is that there is no need for a geometrical model that relates the signal strengths to traveled distances.…”

Stochastic stability of decentralized Kalman filter for nonlinear systems

“…Multiple variants of the DKF [2][3][4][5][6][7][8] have been studied for a large sensor network to handle scalability, and consensus under different types of topologies using diffusion and weighted averaging. DKF has been used for multi-platform navigation, 9 localization using accelerometer and radio-fingerprints, 10 spacecraft relative motion estimation, 11 etc. However, despite its applicability in a sensor network, its stochastic stability and the bounds on the estimation errors have not been analyzed explicitly.…”