Abstract
The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks.
Highlights
Research related to Internet of Things (IoT) is a hot area where many people from diverse fields are working due to the demand of smart systems that enable the ability to transfer data over a network without requiring human interactions by using smart devices, such as smartphones [1,2,3,4].In this smart system, a sensor network is formed to interconnect densely populated sensors for data communications and processing among the sensors
We proposed the minimax particle filtering (PF) for the problem of tracking a target based on received signal strength (RSS)
We verified the validity of the proposed minimax strategy in various scenarios in terms of M and σθ2 by applying to various variants of PF
Summary
Research related to Internet of Things (IoT) is a hot area where many people from diverse fields are working due to the demand of smart systems that enable the ability to transfer data over a network without requiring human interactions by using smart devices, such as smartphones [1,2,3,4]. We propose minimax-PF (MPF) for the target tracking based on multi-RSS-measurements at up to ten sensors. The minimax PF approach was initially introduced in Reference [34] recently, where a maneuvering target is tracked based on the range and bearing, which are only two measurements; the methodology was not fully investigated in terms of the number of measurements, clarity of the derivation of the approach, analysis of risk and weight concerning to all compared variants of PFs. The investigation of the method in the application of networked sensor measurements where a large number of sensors are available is more appropriate for the verification of the approach. We verify with up to ten RSS measurements in wireless sensor networks to generalize the validity of the proposed approach with extensive experimental results and analysis. Matrices, vectors, and scalars are denoted by bold uppercase letters, bold lowercase letters, and lowercase letters, respectively, and > denotes the transpose of a matrix
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