SDRS: A stable data-based recruitment system in IoT crowdsensing for localization tasks

Abstract

Mobile Crowdsensing (MCS), an important component of the Internet of Things (IoT), is a paradigm which utilizes people carrying smart devices, referred to as “workers”, to perform various sensing tasks. A type of such tasks is localization, where the location of a certain target or event is to be found. The recruitment of the right set of workers to perform a localization task plays a paramount role in the outcome quality in terms of localization time, energy, cost, and accuracy. The stability of workers in MCS, which is defined as their spatio-temporal availability, makes the problem of localization more complex, since such tasks are continuous. In this work, a novel Stable Data-based Recruitment System (SDRS) for localization tasks is proposed, which-a) integrates a new data-based recruitment parameter that dynamically exploits data readings to guide the recruitment system into selecting informative workers, while considering their mobility; b) presents a stable coverage assessment method that considers range-free sensors and the mobility of workers; and c) integrates a two-phase recruitment approach that is optimized using greedy and genetic methods. The testing and evaluation of the proposed approach is conducted using datasets of MCS workers and compared with existing benchmarks. The results demonstrate that the proposed approach efficiently and reliably leads to a speedy localization, with high outcome quality.