The Error Propagation Analysis of the Received Signal Strength-Based Simultaneous Localization and Tracking in Wireless Sensor Networks

Abstract

Abstract-Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, a localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatialtemporal-domain doubly random due to the target mobility, environment dynamics and different surroundings at different reference nodes. Firstly, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Secondly, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Thirdly, the convergence conditions, convergence properties and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme. Finally, numerical simulations are presented to corroborate the EP analysis.

Location estimation and detection in wireless sensor networks in the presence of fading

“…When there is no CSI available at any anchor, the CRLB is the highest. In addition, one can see that ratio between CRLB 2-D (z) in (27) is about k = 10 3 higher than the CRLB in the absence of fading, and the CRLB in the presence of fading without knowing the phase at any anchor is a factor of k = 4 higher than the CRLB in the absence of fading, which corroborates (40).…”

“…Consider now a sensor network with M = 4 anchors at the corners of a 1m × 1m square, and one node within the square, and Rayleigh fading. In Figure 5, we compare the CRLB for the AWGN case in [12], the CRLB in (27), in which the phase information is known at each anchor, and the CRLB in Section 2.5 when no CSI is available. We observe that the CRLB for the AWGN case is the lowest, followed by the CRLB when the phase of the fading coefficients is known.…”

“…The FIM in the absence of fading for the 2-D case is given in [12], and can be written the same as (26) except without the Y (m) and X(d i ) terms. Comparing (27) with the CRLB in the absence of fading in [12], both CRLBs in 2-D depend on the true location of the node. When d i c ≈ 0, similar to the 1-D case, X(d i ) in (20) can be simplified.…”

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

“…The simulation is performed in a machine having Intel Core i7-2500S 2.70 GHz processor, 16GB RAM, 64bit Linux OS. The other basic setting of parameters in simulation is approximately similar the parameter table and setting considered in [28,29]. Three different sets of network with 100, 500 and 1000, 1500, 2000 nodes are generated following Poisson process.…”

RFID 3D-LANDMARC Localization Algorithm Based on Quantum Particle Swarm Optimization

“…The indoor localization with low complexity and high accuracy is one of the main challenges in today's wireless world [2]. In order to provide positioning and navigation in the indoor environment, various methods based on different technologies such as WSN-based networks [3,4], WIFI network [5,6], and RFID localization technology [7,8] have been proposed and developed. Among various indoor localization schemes, RFID technology has obtained more and more interest in localization systems development for its low cost, easy deployment, and successful utility in harsh environments in recent years [9].…”