Fair QoI and energy-aware task allocation in participatory sensing

Participatory Sensing is an emerging paradigm that enables users carrying sensors-equipped smart devices to gather and share data about a particular phenomenon. However, involving individuals in sensing tasks raises new challenges such as the quality of collected data, denoted as Quality of Information (QoI), and the dedicated resources for its acquisition. Besides, a fair task assignment is highly recommended to ensure participants' commitment. This trade-off is challenging to achieve due to the two conflicting objectives of maximizing data quality and minimizing the sensing time among all participants. In this work, we design a Fair QoI and Energy-aware Mobile Sensing Scheme (F-QEMSS) to ensure both requestors' and participants' satisfaction. We formally model the corresponding multi-objective optimization problem and generate its weighted-sum objective function. Next, we employ our Tabu-Search based algorithm to solve it. Simulation results show the effectiveness of our solution. Particularly, our approach achieves up to 96% of fairness, measured by the Jain's index, while preserving the same data quality as non-fair algorithms.