User recruitment for mobile crowdsensing over opportunistic networks

Karaliopoulos, Merkourios; Telelis, Orestis; Koutsopoulos, Iordanis

doi:10.1109/infocom.2015.7218612

Cited by 192 publications

(64 citation statements)

References 13 publications

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“…Reddy et al have proposed a recruitment mechanism in a participatory sensing platform considering some core attributes such as geographic and temporal coverage and user behaviors for defining participant profiles comprising of availability, reputation and cost in their recruitment policy [19]. Standing on these attributes, Karaliopoulos et al have come up with a deterministic and stochastic mobility model for solving an optimization problem on cost minimization and user location in their recruitment policy [20]. Lately, other researchers have employed piggyback crowd-sensing techniques for gathering more information from mobile-device owners such as phone call, GPS coordination, and mobile application usages.…”

Section: B Related Workmentioning

confidence: 99%

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Truong

Lee

et al. 2019

IEEE Trans.Inform.Forensic Secur.

101

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Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting highquality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.

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Section: B Related Workmentioning

confidence: 99%

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Truong

Lee

et al. 2019

IEEE Trans.Inform.Forensic Secur.

101

View full text Add to dashboard Cite

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“…Fig. 3: Performance evaluation of CSOPT and comparison with GSSUM algorithm that was proposed by the authors of [28]. to the M U s) of CSOPT is decreasing as the number of M U s is increasing.…”

Section: Performance Evaluationmentioning

confidence: 99%

Privacy Preserving and Cost Optimal Mobile Crowdsensing Using Smart Contracts on Blockchain

Chatzopoulos

Gujar

Faltings

et al. 2018

2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who contribute their resources and preserve their privacy. Blockchain technologies enable semi-anonymous multi-party interactions and can be utilized in crowdsensing applications to maintain the privacy of the mobile users while ensuring first-rate crowdsensed data. In this work, we propose to use blockchain technologies and smart contracts to orchestrate the interactions between mobile crowdsensing providers and mobile users for the case of spatial crowdsensing, where mobile users need to be at specific locations to perform the tasks. Smart contracts, by operating as processes that are executed on the blockchain, are used to preserve users' privacy and make payments. Furthermore, for the assignment of the crowdsensing tasks to the mobile users, we design a truthful, cost-optimal auction that minimizes the payments from the crowdsensing providers to the mobile users. Extensive experimental results show that the proposed privacy preserving auction outperforms state-of-the-art proposals regarding cost by ten times for high numbers of mobile users and tasks.

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“…Several researchers considered selecting participants to minimize cost while guaranteeing data quality. Karaliopoulos et al [10] studied the manner for selecting mobile users to minimize cost while ensuring the coverage of points of interest. Zhang et al [11] predicted the mobility of participants and then selected minimum number of participants to meet the predefined temporalspatial coverage.…”

Section: Related Workmentioning

confidence: 99%

Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing

Zhu

Guo

et al. 2018

Wireless Communications and Mobile Computing

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Task allocation is a key problem in Mobile Crowd Sensing (MCS). Prior works have mainly assumed that participants can complete tasks once they arrive at the location of tasks. However, this assumption may lead to poor reliability in sensing data because the heterogeneity among participants is disregarded. In this study, we investigate a multitask allocation problem that considers the heterogeneity of participants (i.e., different participants carry various devices and accomplish different tasks). A greedy discrete particle swarm optimization with genetic algorithm operation is proposed in this study to address the abovementioned problem. This study is aimed at maximizing the number of completed tasks while satisfying certain constraints. Simulations over a real-life mobile dataset verify that the proposed algorithm outperforms baseline methods under different settings.

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User recruitment for mobile crowdsensing over opportunistic networks

Cited by 192 publications

References 13 publications

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

Privacy Preserving and Cost Optimal Mobile Crowdsensing Using Smart Contracts on Blockchain

Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing

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