Localization Bias Reduction in Wireless Sensor Networks

Abstract

In this paper, a novel bias-reduction method is proposed to analytically express and reduce the bias arising in sensor network localization problems, thereby improving the localization accuracy. The proposed bias-reduction method mixes Taylor series and a maximum-likelihood estimate and leads to an easily calculated analytical bias expression in terms of a known maximumlikelihood cost function. In contrast to existing contributions, this paper considers a sensor network as a whole when the bias is investigated by introducing the geometric structure of the sensor network into the proposed biasreduction method via the rigidity matrix, a concept drawn from graph theory. The maximum-likelihood cost function is related to the rigidity matrix resulting in the final analytical expression of localization bias in terms of the rigidity matrix. Another important contribution of this paper is that, in addition to the presented simulation results, experimental results obtained by using a wireless localization system, which is developed based on reconfigurable software-defined radios (SDRs), are also provided to verify the performance of the proposed bias-reduction method. Both the simulation and experimental results demonstrate that the proposed method can reduce the bias, thereby improving the localization accuracy in different scenarios.

Efficient Energy Supply Using Mobile Charger for Solar-Powered Wireless Sensor Networks

“…The sensor nodes determine their role by communicating with the drone and transmit data forward the drone because the drone determines their path in consideration of the size of the network and Since the drone determines its own path considering the size of the network, and selects anchor nodes by traversing the path, other sensor nodes can determine their routes by communicating with the anchors and transmit the data along the routes. In this case, the drones can use the methods proposed by Oliva et al [39] and Ji et al [40] to obtain more precise location of the nodes to which the drone should transmit energy. Alternatively, the location and routing of nodes can be performed simultaneously using the scheme proposed by Oliva et al [41].…”

Bias reduction for TDOA localization in the presence of receiver position errors and synchronization clock bias

“…methods [38][39][40][41][42][43][44][45], the proposed method considers both receiver position errors and synchronization clock bias. 2.…”

“…3. All previous studies [38][39][40][41][42][43][44][45] aim at reducing the bias of the source position. We develop a bias-reduced method that effectively reduces not only the bias of the source position but also the bias of refined receiver positions and estimated clock bias vector.…”

“…It is therefore necessary to reduce the bias to improve the localization performance. Over the years, many studies have reduced the bias of an estimator using TDOAs [38][39][40][41][42][43][44][45]. Two methods of reducing the bias of the closed-form solution using TDOAs have been proposed [38], but the receiver position errors and synchronization clock bias were not taken into account.…”

“…Inspired by previous works [38][39][40][41][42][43][44][45], this paper proposes a bias-reduced method of reducing the bias of estimates from [36] using TDOAs in the presence of receiver position errors and synchronization clock bias. The study begins with a bias analysis for the original TSWLS solution.…”