QoI and Energy-Aware Mobile Sensing Scheme: A Tabu-Search Approach

Mobile phones equipped with a rich set of embedded sensors enhance participatory sensing to collect data for different applications. However, many challenges arise when selecting participants to perform sensing tasks. Among these challenges, we can cite energy consumption, users' mobility impact and the quality of retrieved data, recently defined as Quality of Information (QoI). In this work, we study the QoI and Energy-aware Mobile Sensing (QEMSS) problem. Hence, for a given set of users, a sensing area and data quality requirements, the objective of QEMSS is to find the subset of users that maximizes QoI in terms of spatial and temporal metrics while minimizing the overall energy consumption and reducing the redundancy during the sensing process. We propose a meta-heuristic algorithm based on Tabu-Search to provide a sub-optimal solution. Simulation results, for both deterministic and unknown participants' trajectories, are compared to other state-of-the-art methods. This allows showing that our approach outperforms both the greedy- based and the random selection strategies. Particularly, the achieved data quality by our scheme is significantly higher in challenging scenarios such as low dense areas or scarce users' energy resources.