bioMCS

Abstract

Mobile crowdsensing (MCS) leverages the participation of active citizens and establishes a cost-effective sensing infrastructure using their devices. The MCS platform allocates sensing tasks, for which individual user reports are collected to enable decision making. Task sensing and communication not only consume user's device energy, but also spawn redundant data leading to network congestion and issues in data management at the platform's end. MCS, being a building block of sustainable smart city applications, must ensure judicious utilization of device energy and network resources. To address these challenges, this paper proposes a bio-inspired data transfer framework, bioMCS, deployed over a fog computing platform and capable of enforcing collaborative sensing among proximate users. bioMCS achieves energy efficiency and robustness through the topological properties of a biological network called transcriptional regulatory network. It employs collaborative sensing to further restrict device energy overhead by taking advantage of energy efficient device-to-device communications like Wi-Fi direct data transfer via group owner. We evaluate our framework through extensive simulation-based experiments and demonstrate that the bioMCS framework achieves better energy and network efficiency compared to individual user-centric data transfer mechanism.