Selective versus Non-Selective Acquisition of Crowd-Solicited IoT Data and Its Dependability

Dasari, Venkat Surya; Pouryazdan, Maryam; Kantarcı, Burak

doi:10.1109/iccw.2018.8403742

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Some papers propose a framework for resource management in fog/cloud [71], other propose a framework for trust management for IoT devices [51], [72], [73], and fog nodes [74]. Also, some papers focus on security issues [10], [75][76][77] or fault recovery [78].…”

Section: ) Framework Designmentioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

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Section: ) Framework Designmentioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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“…Nevertheless, to prevent MCS campaigns from vulnerabilities, effective user recruitment is the only tool that an MCS platform can use. More specifically, we consider that some possible solutions to prevent the lack of dependability [12][13][14] are: to design systems able to manage and update the reputation of the involved users by adopting optimal user's recruiting policies; to dynamically assess and keep updated the trustworthiness of the involved users; and to adopt recruitment policies able to detect or event to predict the existence of non-cooperative users, that is, users not providing useful data for the MCS system. Reputation based selection schemes are widely used by researchers in such a way that reputation level became an added attribute/constraint for recruiting a participant [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Dasari

Kantarcı

Pouryazdan³

et al. 2020

Sensors

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Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research.

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