A Location-Aware Duty Cycle Approach toward Energy-Efficient Mobile Crowdsensing

This paper aims to explore the problem of energy economy of mobile devices in the Mobile Crowdsensing (MCS) scenario. The neighbor scanning mechanism of mobile devices usually consumes most of the energy in multi-hop message transmission. Traditional mechanisms such as chaotic neighbor detection and continuous neighbor discovery, can easily exhaust the limited energy. Since they are actually unnecessarily considering the strong correlation between the transmission opportunity and the social characteristics. Therefore, diminishing energy consumption is of key importance toward energy-efficient MCS. Sleeping strategy stands out as a promising approach to improve energy efficiency and hold the the network metrics in MCS applications, while challenges still lie in achieving effective scheduling given the changing environment. In this study, we proposed a novel self-adapt sleeping scheduling approach based on the correlation between the pedestrian's own historical trajectory and geographic grid information for energy saving (ESGeo). As the grid-based method can well record the encounter characteristics of nodes, ESGeo is able to pick flexible duty-cycling strategies for mobile devices in each distinguishable grid. This enables, mobile devices to avoid excessive scanning in low-probability encounter areas. Extensive simulation results further demonstrated that the proposed approach can significantly outperform the typical routing approaches in terms of energy-efficiency, without largely affecting the overall networking performance.