QoI-aware energy-efficient participant selection

In increasingly popular participatory sensing systems, new challenges are arising to select the most appropriate participants when considering their hand-held smart device´s different energy conditions, uncontrollable mobility pattern, and associated sensing capabilities to best satisfy the quality-of-information (QoI) requirements of sensing tasks. This paper proposes a QoI-aware energy-efficient participant selection strategy, where four key design elements are proposed. First is QoI satisfaction metric of a sensing task that uses the data granularity and quantity collected by participants to measure to what extend the task´s QoI requirements are satisfied. Second is an “energy consumption index”, which estimates the impact of energy cost during the data collection on different participant´s smart devices with different remaining energy levels. Third is the estimation of the collected amount of data by participants, where a probability-based movement model is proposed. Fourth is the proposal of a multi-objective constrained optimization problem for participant selection, where task QoI requirements and energy consumption index of all participants are taken as optimization objectives, and solved by our proposed suboptimal, easy-to-implement solution. Real and extensive trace-based experiments show that, the proposed participant selection scheme can well balance the trade-off between the task QoI and energy consumptions by selecting most efficient participants, compared with existing schemes.