RFLS - Resilient Fault-proof Localization System in IoT and Crowd-based Sensing Applications

Alagha, Ahmed; Singh, Shakti; Otrok, Hadi; Mizouni, Rabeb

doi:10.1016/j.jnca.2020.102783

Cited by 25 publications

(9 citation statements)

References 38 publications

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“…Many researches have taken place in literature which are aimed towards developing algorithms to solve the problem of localization using information from a network of sensing devices that is distributed in a particular area. Some works from literature [8], [12], [21], [22], [23], [24] are mainly concerned with just computing the mathematical estimation of the target location based on the data from sensing nodes, while other works [12], [13], [14], [15], [16], [25] focus on development of full localization system which addresses the optimal node selection for the purpose of optimizing the task of localization.…”

Section: Related Workmentioning

confidence: 99%

“…[21], [23], [24], [32] use Time Difference of Arrival(TDoA) and Direction of Arrival(DoA) on readings to find the source location. Likewise, Bayesian method is used by [8], [12], [15], [16], [31], [33], in which prior belief about the target location are updated based on the fusion of the current data readings.…”

Section: Related Workmentioning

confidence: 99%

“…More recent works have thus focused on selecting a number of nodes from the whole population to use for localization. A data-driven active node selection mechanism while considering parameters including residual energy, data confidence, area coverage, and cost is used in [15], [16], [19]. Likewise, [17], [18] perform an initial estimation of the target to generate the distance weight measurements for active node selection.…”

Section: Related Workmentioning

confidence: 99%

“…Current works fuse data from multiple sensors for reliable outcomes. Sensor optimization methods in the literature primarily consist of 2 types: node placement optimization [12], [13], [14] and active node selection [15], [16], [17], [18], [19]. The node placement optimization methods are concerned with placing the sensing nodes in a certain AoI with the objective of minimizing the localization time and the number of deployed nodes, as well as maximizing the localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these methods are not effective in localization tasks. A data-based centroiding technique for the anomaly detection for localization tasks was introduced in [16]. However in this work, the anomaly detection as well as localization is done over the whole AoI and therefore takes multiple iterations for completion.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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