Most of the existing user recruitment methods are designed for the two-tier structure. However, the computational consumption of the cloud platform rises rapidly as data volumes increase. In recent years, edge computing has been introduced into mobile crowdsensing. In this paper, we study dynamic user recruitment with edge-aided mobile crowdsensing. Specifically, we consider the scenarios for different sensing tasks. One is the long-duration task, and the other is the short-duration task. For the former, it is time-insensitive, while the latter is time-sensitive. In the scenario with the long-duration task, we consider a simple offline scenario and propose an Edge-Node User Recruitment (ENUR) algorithm to recruit users statically. Then, we consider the online scenario and propose a Budget Re-Distribution algorithm for Edge Nodes User Recruitment (BRD-ENUR). It dynamically redistributes the budget according to the task completion ratio of different edge nodes. In the scenario with the short-duration task, the whole sensing process consists of multiple rounds. In each round, edge nodes need to recruit users between selected users (exploitation) and unselected users (exploration) under the budget. We model such a user recruitment process as a novel combinatorial multi-armed bandit problem. Then, we propose an Edge Nodes User Recruitment algorithm based on combinatorial Multi-armed Bandit (EN-URMB). Extensive experiments show the validity and reliability of our user recruitment algorithms based on the real data set.

Black-burst based multi-hop broadcast protocols are effective means to disseminate Emergency messages (EMs) in Vehicular ad hoc networks (VANETs). However, Clear to broadcast (CTB) collision will happen and reduce propagation speed. Aiming at this problem, we propose a Black-burst and multi-channel based multihop broadcast (BMMB) protocol. Vehicles with multiple antennas can transmit and sense black-burst at different channels simultaneously based on multi-channel technology. Compared with existing black-burst based methods, BMMB shortens the iterative procedure to find out the optimal segment. Only one relay vehicle can be rapidly selected within the optimal segment without CTB collision. BMMB enables alternative broadcast methods, i.e., unidirectional broadcast and multi-directional broadcast for straight roads and intersections respectively. Theoretical analysis is done for performance evaluation of BMMB, and simulation results demonstrate that BMMB performs better in terms of average one-hop delay and propagation speed.

Mobile crowdsensing (MCS) leverages large-scale mobile users to execute tasks and contribute sensing data. Developing an effective incentive mechanism is critical to ensure both the quality and quantity of sensing data. However, existing incentive mechanisms often overlook key factors, such as the social networks of users, the presence of malicious participants, and the dynamic interplay among multiple stakeholders. In this article, we propose a Trilateral Social-aware Incentive Mechanism (TSIM) to address these limitations. TSIM is built upon a three-stage Stackelberg game framework that incorporates social relationships to enhance recruitment and improve data quality. First, we analyze the data quality and the historical reputation of the users, and based on this, we construct utility functions for the requester, service provider, and mobile users, with the latter integrating data quality, personal, social, and historical reputation utilities. Second, we formulate the payment problem as a three-stage game among the three parties, employing backward induction to derive optimal strategies that maximize their respective utilities. Next, we theoretically prove the unique existence of the Stackelberg equilibrium, ensuring a multiwin outcome. Numerical experiments demonstrate that incorporating social networks significantly boosts task participation and rewards for users, increases profit for the requester and revenue for the service provider, and effectively mitigates malicious data uploads.

Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks.

Mobile edge computing (MEC) has emerged as a powerful solution for handling computation-intensive applications at the network edge. Task offloading has become a widely adopted strategy to optimize network resource utilization and deliver enhanced services to user equipment (UE). To meet stringent end-to-end delay and energy consumption requirements, users can offload tasks to edge servers where relevant services are pre-cached. However, the limited storage capacity and the heterogeneity of edge nodes make collaborative cache decision-making and computing resource allocation at the edge critical challenges. We address the optimization of task offloading, resource allocation, service caching, and push decisions within the MEC system through the coordinated efforts of edge servers. The objective is to minimize long-term offloading latency while maximizing the cache hit ratio, presenting a mixed-integer nonlinear programming (MINLP) problem. To tackle this challenge, we propose a novel soft actor-critic approach, transforming the problem into a Markov decision process (MDP). Numerical results demonstrate that our algorithm significantly outperforms existing methods in terms of system cost, average delay, and cache hit ratio.