Group-based incentive and penalizing schemes for proactive participatory data sensing in IoT networks

摘要

Quality of Information in IoT network is based on the fair degree of cooperation between the crowd sensing nodes, crowdsourcing nodes, mobile social nodes and cloud application servers. The constraints such as service reliability, node interaction credibility and data confidentiality impact the performance of data sensing systems. Incentive mechanisms encourage the active IoT nodes to transmit reliable data and secure the network. In this regard, this article proposes the GRoup-based Incentive and Penalizing Schemes for Proactive Participatory Data Sensing (GRIPS-PPDS) in IoT Network. GRIPS-PPDS applies the minimum set cover theorem to select the nodes that further sense and aggregate the data with high accuracy rate, consistency and reliability. The proposed model implements the rigid and relaxed modes for proactive data sensing in IoT network. Based on attributes such as quality of information, data accuracy rate, consistency, reliability and node trustworthiness, the GRIPS-PPDS scheme defines the incentive and penalizing factors to optimize the coverage region, minimize the energy consumption and secure the network. Simulation results indicate that the proposed model optimizes the coverage levels with enhanced data sensing and incentive cost for the relaxed mode PPDS as compared to the rigid mode PPDS.