Long-Term Incentives for Contributor-Initiated Proactive Sensing in Mobile Crowdsensing
Chongyu Zhou, Chen‐Khong Tham, Mehul Motani · IEEE Transactions on Systems Man and Cybernetics Systems · 2020
Mobile crowdsensing (MCS) is an emerging human-powered service for large-scale sensing and data collection. Most existing frameworks for controlling MCS services focus on a system-initiated setting where the crowdsourcer selects a subset of contributors and incentivizes them to collect the sensing data after the queries from the consumers arrive at the system. Such a system-initiated setting may cause large delays for the system to answer the consumers’ queries, which is not suitable for many real-time sensing applications. In this article, we propose contributor-initiated proactive sensing (CIPS) frameworks for MCS where the sensing data are collected in a proactive manner before the consumers’ queries arrive. In CIPS, the consumers can get answers about their queries with virtually no delay, which opens the door for MCS to many real-time applications. We first propose a centralized algorithm called C-CIPS as a benchmark for sensing scheduling by assuming the contributors are truthful and the consumer queries are knowna priori. Next, we propose a distributed algorithm called D-CIPS to deal with strategic contributors and unknown consumer queries. Through rigorous theoretical analysis, we prove that both C-CIPS and D-CIPS can achieve near-optimal solutions. Furthermore, D-CIPS is proved to be truthful. Through comprehensive simulations with both synthetic and real-world data sets, we demonstrate the effectiveness of the proposed algorithms.