Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks

Mobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of decentralized wireless sensor nodes. Typically, quality information obtained from largely affected by various factors, such diverse requirements tasks, varying across different crowd workers, dynamic changes channels conditions environment. In this paper, considering dynamics’ we focus on spatial-temporal model aim maximize value at point interest, optimizing recruiting range time duration for workers. particular, system includes mobile access (MAP) set As requester, MAP can broadcast its task then estimate collecting responses Each node in will receive payment MAP. We utility requester waiting firstly define metrics characterize MAP’s information. The optimal be closed-form expressions. Furthermore, aging effect, propose gradient-based method Specifically, first determine choose within each slot. Via simulation, compare sum, max, min values verify effectiveness optimize