A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

Adarsh Ghimire,Selina Shrestha,Hadi Otrok

doi:10.1109/access.2021.3074249

Adarsh Ghimire, Selina Shrestha + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3074249

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 31	License type: CC BY 4.0

Affiliation: Khalifa University of Science and Technology

Abstract

In this paper, an efficient and fault-proof active node selection approach for localization tasks in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems is proposed. The proposed approach is resilient to the presence of anomalous nodes. Localization is the process of fusing data readings from multiple sensing nodes with the aim of finding the location of a specific target, such as radiation source, forest fires and noisy areas. Current active node selection systems for localization tasks perform algorithms like greedy and genetic methods over the whole Area of Interest (AoI). As such, a system that considers anomalous data is required to detect anomalies and perform localization over a large number of active nodes, which usually takes multiple iterations and is computationally costly. To overcome this, we propose a resilient localization approach which a) uses the median filter based image filtering technique to level out anomalous readings, b) uses the filtered readings to reduce the AoI to be around the target location without being influenced by anomalous nodes, c) detects and eliminates anomalies in the new AoI based on the deviation between filtered readings and original readings, and d) selects remaining nodes in new AoI for localization. As a result, there is a huge reduction in the complexity of active node selection and thus reduction in time taken by the system to perform the task of source localization. The efficacy of the proposed system is evaluated for radiation source localization tasks using simulated radiation dataset, by performing experiments for several test scenarios. The results demonstrate that the system is able to perform localization tasks in significantly reduced time and therefore generate near real-time results while also maintaining low localization error.

Highlights

The Internet of Things (IoT) allows the connection and interaction among different objects like sensors, RFIDs, actuators, and mobile phones, due to which it is widely being considered as the big technological revolution [1], [2]
× Ai ×2 ηi where CPMi is the photons counts per minute at node i due to source with intensity Is, Ai is the detector surface area, dis is the distance between node i and the source S, and ηi is detector efficiency which is given by:
The system targets to reduce system complexity by providing an efficient mechanism of active node selection by narrowing the AoI to be around the target location in a way that is resilient to the presence of anomalous nodes

Summary

Introduction

The Internet of Things (IoT) allows the connection and interaction among different objects like sensors, RFIDs, actuators, and mobile phones, due to which it is widely being considered as the big technological revolution [1], [2]. Localization tasks integrate and analyze data readings from multiple sensing nodes to identify the location of a particular event or phenomenon within a certain AoI. These can be used to localize events or phenomena like radiation, forest fires, noise pollution, water pollution, air pollution, etc [8]–[11]. The sensing nodes, comprise of small battery powered devices with limited residual energy, resulting in a need to minimize the number of nodes used to perform localization effectively. Optimum localization and optimum node selection/ placement are both active areas of research in localization tasks These networks may comprise faulty nodes, which if used may deteriorate the system’s localization accuracy.

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