A Task-Oriented User Selection Incentive Mechanism in Edge-Aided Mobile Crowdsensing

The designs of existing incentive mechanisms in mobile crowdsensing (MCS) are primarily platform-centered or user-centered, while overlooking the multidimensional consideration of sensing task requirements. Therefore, the user selection fails to effectively address the task requirements or the relevant maximization and diversification problems. To tackle these issues, in this paper, with the aid of edge computing, we propose a task-oriented user selection incentive mechanism (TRIM), in an effort toward a task-centered design framework in MCS. Initially, an edge node is deployed to publish the sensing task according to its requirements, and constructs a task vector from multiple dimensions to maximize the satisfaction of the task requirements. Meanwhile, a sensing user constructs a user vector to formalize the personalized preferences for participating in the task response. Furthermore, by introducing a privacy-preserving cosine similarity computing protocol, the similarity level between the task vector and the user vector can be calculated, and subsequently a target user candidate set can be obtained according to the similarity level. In addition, considering the constraint of the task budget, the edge node performs a secondary sensing user selection based on the ratio of the similarity level and the expected reward of the sensing user. By designing a secure multi-party sorting protocol, enhanced by fuzzy closeness and the fuzzy comprehensive evaluation method, the target user set is determined aiming at maximizing the similarity of the task requirements and the user's preferences, while minimizing the payment of the edge node, and ensuring the fairness of the sensing user being selected. The simulation results show that TRIM achieves feasible and efficient user selection while ensuring the privacy and security of the sensing user in MCS. Among the dynamic changes of task requirements, TRIM excels with user selections reaching nearly 90% on the data quality level compliance rate and 70% on the task budget consumption ratio, superior to the other incentive mechanisms.

I. Introduction

MOBILE crowdsensing (MCS) [1], [2] utilizes billions of Internet-based devices (smartphones, tablets, smart cameras, GPS locators, wearables, environmental sensors, etc.) possessed by ordinary users as basic sensing units to accomplish the mission of sensing tasks and the collection of sensing data through the Internet of Things (IoT) [3], [4] and 5G wireless networks [5]–[7]. The collected sensing data is then extracted and analyzed in the cloud for providing personalized services to the majority of users [8]. With its ubiquitous and countless applications and services, such as environmental monitoring [9], intelligent transportation [10], [11], smart vehicular networks [12], [13], social analysis [14], intelligent electrical service [15], health monitoring [16], among many others, MCS and its services have flourished rapidly in the recent years.