期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 19, 期 2, 页码 362-375出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2018.2878821
关键词
Task analysis; Sensors; Optimization; Location awareness; Resource management; Mobile computing; Gallium nitride; Crowdsensing; dynamic task assignment; truth inference; Lyapunov optimization
资金
- National Key R&D Program of China [2018YFB1004705]
- NSFC China [61532012, 61822206, 61672342, 61602303, 61829201, 61572319]
- Science and Technology Innovation Program of Shanghai [17511105103, 18510761200]
- Open Research fund of National Mobile Communications Research Laboratory, Southeast University [2018D06]
- Shanghai Key Laboratory of Scalable Computing and Systems
Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues of matching workers to their assigned tasks and processing the collected data. In this paper, we reexamine these issues by considering the spatio-temporal worker mobility and task arrivals, which more fit the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the non-linear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby, we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Finally, we propose an optimization framework to aggregate the sensing data which can estimate worker expertise and task truth simultaneously. Performance evaluations on real and synthetic data set validate the proposed algorithm, where 80 percent gain of fairness is achieved at the expense of 12 percent loss of sensing value on average.
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