For the cloud market, we proposed a Dynamic Combinatorial Double Auction (DCDA) model to improve the social welfare and resource utilization. In the model, cloud-agents represent cloud service providers, and user-agents represent cloud users. They bid for various combinations of resources in a dynamic environment. To overcome the computational complexity of combinatorial auctions, we employed a greedy approximation method to solve the winner determination problem together with a truthful payment scheme. The proposed model is proven to be approximately efficient, incentive compatible, individually rational, and budget-balanced. Considering both parties' interests and the relative scarcity of cloud resources, this model also ensures fairness and balances resource allocation.

The fifth-generation (5G) and beyond networks are designed to efficiently utilize the spectrum resources to meet various quality of service (QoS) requirements. The unlicensed frequency bands used by WiFi are mainly deployed for indoor applications and are not always fully occupied. The cellular industry has been working to enable cellular and WiFi coexistence. In particular, 5G New Radio in unlicensed channel spectrum (NR-U) supports the uplink and downlink transmission on the maximum channel occupation time (MCOT) duration. In this paper, we consider maximizing the total throughput of both downlink and uplink in NR-U by jointly optimizing the time and power allocation during MCOT while ensuring fair coexistence with WiFi. Fairness is guaranteed in two steps: 1) tuning the access related parameters of NR-U to achieve proportional fairness, and 2) including 3GPP fairness from the throughput perspective as a constraint in NR-U throughput maximization. Numerical analysis and simulation have demonstrated the superior performance of the proposed resource allocation algorithm compared to conventional deployment strategies.

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.

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.