Minimizing age of correlated information for wireless camera networks

He, Qinming; Dán, György; Fodor, Viktória

doi:10.1109/infcomw.2018.8406914

Cited by 38 publications

(18 citation statements)

References 13 publications

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“…Now we introduce the definition of age of data. Inspired by [14], [16], [21], we define the age of data as the amount of time elapsed since the generation of the data. Naturally, it captures the freshness of data.…”

Section: B Age Of Datamentioning

confidence: 99%

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

Deng

Wang

et al. 2020

IEEE Access

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As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism. INDEX TERMS Crowdsensing, incentive mechanism, privacy-preserving, age of data.

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Section: B Age Of Datamentioning

confidence: 99%

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

Deng

Wang

et al. 2020

IEEE Access

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“…In particular, sensor nodes capturing velocity, acceleration, parking radars measurements etc., disseminate this timecritical content to interconnected vehicles across the network to improve road safety and transportation efficiency [4]. Maintaining data freshness is a requirement in numerous other applications like wireless sensor networks (WSN) for healthcare and environmental monitoring [5,6], active data warehousing [7], content caching [8,9,10,11], realtime databases [12], ad hoc networks [13,14], wireless smart camera networks [15,16], unmanned aerial vehicle (UAV)-assisted IoT networks [17,18], broadcast wireless networks [19,20,21], and the efficient design of freshness-aware IoT [22].…”

Section: Motivationmentioning

confidence: 99%

Age of Information Aware Communication Systems : Modeling and Performance Analysis

Kosta

2020

Linköping Studies in Science and Technology. Dissertations

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“…Since σ nk is the only state parameter that characterizes the probability and the age of a new observation (see Eqs. (8) and (9)), we simply define x Λ,(n,k) = [1, σ nk ] T , where the first entry represents the bias element that allows the function to be shifted from the origin. While more complex features could be included, such as non-linear transformations of σ nk , such features have shown not to give better performance in the scenario considered in Section V.…”

Section: Policies With Unknown Parametersmentioning

confidence: 99%

Minimizing the Age of Information From Sensors With Common Observations

Kalør

Popovski

2019

IEEE Wireless Commun. Lett.

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We study the average Age of Information (AoI) in a system where physical sources produce independent discretetime updates that are each observed by several sensors. We devise a model that is simple, but still capable to capture the main tradeoffs. Two sensor scheduling policies are proposed to minimize the AoI of the sources; one in which the system parameters are assumed known, and one in which they are learned. Both policies are able to exploit the common sensor information to reduce the AoI, resulting in large reductions in AoI compared to common schedules.

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Minimizing age of correlated information for wireless camera networks

Cited by 38 publications

References 13 publications

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

Age of Information Aware Communication Systems : Modeling and Performance Analysis

Minimizing the Age of Information From Sensors With Common Observations

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