Mobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to many participants considering data quality and the diversity of sensing devices. However, participant selection is based on the individual in many research. The efficiency of individual recruitment is low. Individuals need higher transportation costs to go to the task location alone, and the data quality perceived by individuals is difficult to guarantee. This paper proposes a team-based multitask data acquisition scheme under time constraints to address these challenges. The scheme optimised the number of participants, traffic cost, and data quality and designed four team-based multitask allocation algorithms under time constraints in the MCS: T-RandomTeam, T-MostTeam, T-RandomMITeam, and T-MostMITeam. The team size is associated with the number of participants required for the first task or the vehicle capacity to perform the task. We conducted extensive experiments based on a real large-scale dataset to evaluate the four algorithms' performances compared to two baseline algorithms (T-Random and T-most). The efficiency of the four algorithms has been significantly improved by team recruitment. The transportation cost can be multiplicatively reduced by carpooling. Data quality can be improved by at least 2% through reputation screening and team members' communication.

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2022.2043825?needAccess=true

A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing

Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we propose a Reputation-based Collaborative User Recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to deduce the user reputation equation by combining the user’s past reputation score with an instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate.

/pdf/a-reputation-based-collaborative-user-recruitment-algorithm-30hicc1j.pdf

A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing

Quantity sensitive task allocation based on improved whale optimization algorithm in crowdsensing system

Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

In the field of mobile crowd sensing (MCS), worker selection is a key research issue and has progressively gained considerable interests in the academic community in recent years. The goal of worker selection is to choose the superior workers for tasks that require high-performance characteristics. To solve the problems of long delay and poor perceived quality, we present a worker selection architecture for a recommendation system applied to the MCS system. A worker selection algorithm with high quality of service (QoS) is designed within the architecture, which considers the worker’s reputation and willingness attributes to address the challenge of efficiently selecting excellent workers. Based on these two attributes, we then compute worker QoS and develop a three-dimensional tensor to optimize the worker’s service. Finally, we get a continuously updated list of workers. Extensive experiments on real-world datasets show that the proposed algorithm performs better than the benchmarks, including random, greedy, and matrix-based algorithm. The results indicate that the proposed algorithm’s efficiency has risen by 31% compared to the matrix-based algorithm.

Worker Selection towards High Service Quality in Mobile Crowd Sensing

With the rapid popularization and application of smart sensing devices, mobile crowd sensing (MCS) has made rapid development. MCS mobilizes personnel with various sensing devices to collect data. Task distribution as the key point and difficulty in the field of MCS has attracted wide attention from scholars. However, the current research on participant selection methods whose main goal is data quality is not deep enough. Different from most of these previous studies, this paper studies the participant selection scheme on the multitask condition in MCS. According to the tasks completed by the participants in the past, the accumulated reputation and willingness of participants are used to construct a quality of service model (QoS). On the basis of maximizing QoS, two heuristic greedy algorithms are used to solve participation; two options are proposed: task-centric and user-centric. The distance constraint factor, integrity constraint factor, and reputation constraint factor are introduced into our algorithms. The purpose is to select the most suitable set of participants on the premise of ensuring the QoS, as far as possible to improve the platform’s final revenue and the benefits of participants. We used a real data set and generated a simulation data set to evaluate the feasibility and effectiveness of the two algorithms. Detailedly compared our algorithms with the existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, we established a step data pricing model to quantitatively compare the quality of data uploaded by participants. Experimental results show that two algorithms proposed in this paper have achieved better results in task quality than existing algorithms.

/pdf/participant-recruitment-method-aiming-at-service-quality-in-p4kcg3apqs.pdf

Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing

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Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing