2017
DOI: 10.1109/jsyst.2015.2430362
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Energy-Aware Participant Selection for Smartphone-Enabled Mobile Crowd Sensing

Abstract: Mobile crowd sensing systems have been widely used in various domains but are currently facing new challenges. On one hand, the increasingly complex services need a large number of participants to satisfy their demand for sensory data with multidimensional high quality-of-information (QoI) requirements. On the other hand, the willingness of their participation is not always at a high level due to the energy consumption and its impacts on their regular activities. In this paper, we introduce a new metric, calle… Show more

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Cited by 89 publications
(46 citation statements)
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“…Hence, the goal of the incentive strategy design is to select the participants with highest level of performance assessment (e.g., QCS and QoI) from these users. At the time, these studies focus on minimizing the overall cost, profit, or energy consumption while selecting the high-quality participants, shown by Liu et al [49][50][51][52].…”
Section: Related Workmentioning
confidence: 99%
“…Hence, the goal of the incentive strategy design is to select the participants with highest level of performance assessment (e.g., QCS and QoI) from these users. At the time, these studies focus on minimizing the overall cost, profit, or energy consumption while selecting the high-quality participants, shown by Liu et al [49][50][51][52].…”
Section: Related Workmentioning
confidence: 99%
“…Individuals are currently the bearers of sensing devices and the sources and consumers about the sensed events [7]. Mobile Crowdsensing plays an analogous role with the one played through Amazons Mechanical Turk (MTurk) or ChaCha in crowdsourcing [8].…”
Section: Related Workmentioning
confidence: 99%
“…Considering different incentive requirements, associated sensing capabilities, and uncontrollable mobility, a multitask-oriented QoI (Quality of Information) optimization problem is discussed and converted to a nonlinear knapsack problem. Zhang [18,19] proposed an event-driven QoI-aware participatory sensing framework with energy and budget constraints, where the main method is boundary detection. He [20] devised an efficient local ratio based algorithm and designed a motivating mechanism that decides the fair prices of sensing tasks.…”
Section: Related Workmentioning
confidence: 99%