Evaluating mobility models in participatory sensing
Proceedings of the first ACM international workshop on Mission-oriented wireless sensor networking
Utility-driven data acquisition in participatory sensing
Proceedings of the 16th International Conference on Extending Database Technology
Publish/subscribe middleware for energy-efficient mobile crowdsensing
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
PUCAA: 1st international workshop on pervasive urban crowdsensing architecture and applications
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Both sensor coverage maximization and energy cost minimization are the fundamental requirements in the design of real-life mobile sensing applications, e.g., (1) deploying environmental sensors (like CO2, fine particle measurement) on public transports to monitor air pollution, (2) analyzing smart phone embedded sensors (like GPS, accelerometer) to recognize people daily activities. However sensor coverage and energy cost contradict each other: the higher frequency mobile sensing takes, the more energy is used, and vise versa. In this paper, we design a novel two-step mobile sensing process ("OptiMoS") to achieve optimal mobile sensing that can effectively balance sensor coverage and energy cost. In the first step, OptiMoS divides the continuous mobile sensor readings into several segments, where the readings in one segment are highly-correlated rather than readings amongst different segments. In the second step, OptiMoS identifies optimal sampling for the sensor readings in each segment, where the selected readings can guarantee reasonably high sensor coverage with limited sampling rate. Various greedy & near-optimal segmentation and sampling methods are designed in OptiMoS, and are evaluated using real-life environmental data from mobile sensors.