A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor Networks

Abstract

The wide availability of sensing modules and computing capabilities in modern mobile devices (smartphones, smart watches, in-vehicle sensors, etc.) is driving the shift from mote-class wireless sensor networks (WSNs) to the new era of crowdsensing WSNs. In this emerging paradigm sensors are no longer static and homogeneous, but are rather worn/carried by people or cars. This results in a new type of wide-area WSN—crowd-based and overlaid on top of heterogeneous communication technologies—that paves the way for very innovative applications. To this aim, the positioning of mobile devices operating in the network becomes crucial. Indeed, the pervasive, almost ubiquitous availability of smart devices brings unprecedented opportunities but also poses new research challenges in their precise location under mobility and dense-multipath environments typical of urban and indoor scenarios. In this paper, we review recent advances in the field of wireless positioning with focus on cooperation, mobility, and advanced array processing, which are key enablers for the design of novel localization solutions for crowdsensing WSNs.

Localization Based on Probabilistic Multilateration Approach for Mobile Wireless Sensor Networks

“…Finally, in the filtering phase, the samples from one or two hops of the RNs are used in order to estimate the position of the NOI. In [12], [37], authors propose the algorithm Weighted Monte Carlos Localization (WMCL), which is based on the method SMC [36]. This proposal improves the accuracy on the localization of the NOI in comparison with the DV-Hop [31] and SMC [36].…”

“…As seen in (11), we get the pdf of the separation distanceδ, using the following process δ = 10 Equation (12) shows that the term Y = ln (d) + χ σ ln (10) 10η is a random variable, since χ σ ∼ N(0, σ ). Thus, we may conclude that Y ∼ N(m Y , σ Y ).…”

“…Then, Y ∼ N(m Y , σ Y ), where the statistical parameters are given by m Y = ln (d) and σ Y = σ ln(10) 10η. Equation (12) indicates that the estimated separation distance between two nodes has a pdf with log-normal distribution with parameters µ d = ln(d) and…”

“…Nowadays, the Mobile Wireless Sensor Networks (MWSNs) play a fundamental role in the communication networks [1], since they are used in many applications, such as tracking [2], [3], real-time localization [4], [5], search and preservation of natural resources [6], power consumption systems [7], Internet of the Things (IoT) [8], physical environments monitoring [5], [9]- [12], traffic monitoring [13], industry and agriculture [6], health care [5], natural disaster prevention [12], etc. MWSNs are composed of nodes spatially distributed over a geographic area of interest; where nodes are equipped into vehicles or mobile robots that move in certain environment [14].…”

Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

“…Several approaches have been proposed in the literature to tackle the problem of node localization by leveraging information extracted from existing terrestrial technologies [1][2][3][4][5][6]. Among others, range-based solutions have been widely employed in popular terrestrial localization systems, especially in WSNs, where they are preferred to range-free techniques [7,8] thanks to their reduced complexity.…”