Data-Driven Dynamic Active Node Selection for Event Localization in IoT Applications - A Case Study of Radiation Localization

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

doi:10.1109/access.2019.2894956

Cited by 37 publications

(18 citation statements)

References 25 publications

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“…Workers allocation is one of the main challenging aspects in MCS since different tasks have different requirements, such as acceptable QoS, reputation, and execution time [3], [17]. In addition, workers in an MCS system have different capabilities such as the devices they are carrying, acceptable traveling distance [18], and reputation [19], [20].…”

Section: ) Multitask Allocationmentioning

confidence: 99%

“…The data collected by the workers depends on the nature of the task. If the task is objective, such as environmental monitoring including noise pollution and nuclear source localization [3], the data is in the form of sensory readings. On the other hand, if the task is subjective, the crowd's opinions regarding a certain phenomenon are requested [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

et al. 2020

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With the tremendous advances in ubiquitous computing, mobile crowd sourcing (MCS) has become an appealing part of the Internet of Things (IoT). In MCS systems, workers collect data with a certain quality, and get incentivized in return. However, MCS systems are vulnerable to misbehaving acts such as workers submitting multiple false or fake reports using multiple devices to affect the majority vote of the task. In addition, workers may try to maximize their profit by submitting multiple truthful data, a behavior that may prevent other potential workers to participate. The selection of such workers for a task has a negative impact on the decision making or the payoff of the task. Most of the current approaches aim to maximize the completion of tasks based on the reputation or the credibility of workers, but without consideration of tasks' payoff and the threat of misbehavior. In literature, a misbehaving act, where workers impersonate multiple identities using multiple devices to maliciously change the majority votes or selfishly increase their payment, has not been addressed during MCS recruitment. In this paper, a two-layer selection approach is proposed based on game-theory in which the payoff of the tasks is maximized based on the individual contributions of the workers. In addition, the proposed model detects and eliminates the misbehaving act where workers submit multiple reports using multiple devices, during the recruitment phase. Simulations using real-life datasets show that the proposed approach succeeds in detecting and eliminating misbehaving devices, and outperforms the benchmarks in terms of the payoff of the tasks.

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Section: ) Multitask Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

et al. 2020

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“…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%

“…Thus, detection and removal of such node readings is very important for accurate processing of such information. Most of the existing localization systems do not take anomalous readings into account and thus are less applicable to real life scenarios [8], [9], [10], [11], [12], [15], [20], [21], [22], [23], [24], [25].…”

Section: Introductionmentioning

confidence: 99%

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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“…The algorithm focuses on composite event monitoring. An Internet-of-Things (IoT) Cooperative System (IoT-CS) based on local Event-Driven response is developed in [13]. The collaboration here is based on the service cooperative of the upper application.…”

Section: Introductionmentioning

confidence: 99%

Efficient Matching of Multi-Modal Sensing Nodes for Collaborative Sense Optimization of Composite Events

2019

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Composite events are sense maximizes collaboration through multiple sensors. Efficient matching of multi-modal sensing nodes in multi-composite events is always a thorny problem. In this paper, the composite event sensing model is first proposed, and then the collaborative-sense problem of multi-modal sensing nodes is translated into a binary matching problem. For these multi-class sensors and multi-class compound events scene, a pruning-grafting and parallel strategy be adopted, which can speed up the traversal speed and find the maximum matching edge quickly. For multi-nodes selection, the distance of the composite event constraints into binarily weighted matching. A collaborative-sense intelligent matching algorithm is suggested. It takes collaborative in various kinds of nodes matching combining with the distribution of the composite event itself around the nodes. Combined with the random distribution of various sensor nodes and composite events, the matching rate of some sensor nodes is sacrificed for the overall event efficiency. Compare to parallel algorithms, it has another effect on perceived efficiency. Finally, by comparing with other algorithms, CSSMA and other proposed algorithms have a certain advantage in the inclusive sense efficiency. In terms of composite events collaborative-sense, this work has nice theoretical significance and practical value.

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Data-Driven Dynamic Active Node Selection for Event Localization in IoT Applications - A Case Study of Radiation Localization

Cited by 37 publications

References 25 publications

A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

A Misbehaving-Proof Game Theoretical Selection Approach for Mobile Crowd Sourcing

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

Efficient Matching of Multi-Modal Sensing Nodes for Collaborative Sense Optimization of Composite Events

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