Peicong He, Yang Xin^*, Yixian Yang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777

Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >

Yu Li, Mingxiao Li, Dongyang Ou^*, Junjie Guo, Fangyuan Pan

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 893-909, 2024, DOI:10.32604/cmes.2023.031350

Abstract With the rapid development of mobile Internet, spatial crowdsourcing has become more and more popular. Spatial crowdsourcing consists of many different types of applications, such as spatial crowd-sensing services. In terms of spatial crowd-sensing, it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models. Besides collecting sensing data, spatial crowdsourcing also includes spatial delivery services like DiDi and Uber. Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications. Previous research conducted task assignments via traditional matching approaches or using simple… More > Graphic Abstract