In the era of rapid artificial intelligence development, human–agent collaboration holds the potential to significantly enhance work efficiency. While existing studies have explored various collaboration strategies and resource methods, there remains a notable lack of in-depth research on how to economically allocate a limited budget to acquire both human and agent computing capacities. To address this gap, we first construct a developer model based on theories from psychology and economics, providing a quantitative description of human working time and efficiency. Building upon this, we further investigate the impact of dynamic budget allocation strategies on consumer decision-making. Specifically, a novel budget recycling mechanism is introduced to redistribute unused resources, thereby enhancing system responsiveness. Experimental results demonstrate a 56% improvement in resource utilization and a 32% increase in task completion. This confirms the effectiveness of our proposed method in optimizing collaboration and supporting sustainable project execution.

The rapid advancement of Internet of Things (IoT) and multi-agent systems has transformed how complex IoT tasks are managed across domains. While individual edge agents demonstrate proficiency in specialized tasks such as data collection and edge learning, they encounter substantial challenges when confronting complex IoT scenarios that demand diverse skill sets. This paper introduces a novel group formation framework facilitating effective IoT agent collaboration in complex task environments, including smart manufacturing, intelligent transportation, and smart cities. We propose a hybrid competition mechanism that optimizes initial multi-agent cooperation strategies by integrating task requirements, agent capabilities, and system-wide performance metrics. Our approach combines intra-task and inter-task competition to achieve optimal agent-task matching and resource allocation in large-scale IoT networks. Through comprehensive simulations across various IoT scenarios, we demonstrate that our framework substantially enhances task completion efficiency and system performance compared to existing methods. The results confirm our approach’s effectiveness in resource-constrained environments, achieving minimal agent grouping time costs while increasing total task revenue by 30%-42% and resource utilization by 38% compared to baseline heuristic methods.

Deduplication (redundancy elimination) and cooperative video delivery are two effective ways to save the bandwidth and energy consumption and ensure video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video routing. To solve such problems, this paper propose a cooperative upload method for sensing videos, which performs the local video deduplication without excessive comparison and feature exchange. Also, we combine the content-aware deduplication with the dynamic relay selection to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and load balance. We formulate the deduplication-supported cooperative video upload as a multi-stage decision problem. To solve the uncertainty of destinations in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVU) to schedule video chunks and remove duplicates. Extensive experiments are conducted to compare MAVU with the existing algorithms. The numerical results validate that our MAVU has advantages over the other algorithms in collected video size and upload latency

With the development of edge computing and sharing economy, more services and contents are decentralized to the edge of the network. At present, most existing studies combine the content caching with service offloading mechanisms. However, few studies focus on what strategies the content providers (CPs) can implement to maximize their utilities. In this paper, we first characterize the content supply and demand model, the CPs' cost and utilities in edge content market by considering both the time sensitivity of edge content and the heterogeneity of CAs. Furthermore, according to the edge content market environment characteristics, we divide the edge content market into a monopoly environment, where the content is only provided by a certain CP, and an open environment, where content services are provided by multiple CPs. In the monopoly environment, we establish a two-stage Stackelberg game to design the incentive mechanism. In the open environment, also we formulate the competitive behavior among CPs as a stochastic game. Since the CPs are not aware of each other's strategies and environmental uncertainty, the reinforcement learning-based algorithm (RLIMO) is used to derive the pricing strategy of CP. Finally, numerical results show that the proposed incentive mechanisms are reliable and effective.

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether to offload or where to offload the task but ignore the motivations of edge clouds to offer service. To stimulate service provisioning by edge clouds, it is essential to design an incentive mechanism that charges mobile devices and rewards edge clouds. In this paper, we first propose an incentive mechanism in a non-competitive environment. We utilize market-based profit maximization pricing model to establish the relationship between the resources provided by edge clouds and the price charged to mobile devices. By solving the optimization problem, we provide a reasonable pricing strategy to not only ensure the profit of resource providers but guarantee the quality of experience (QoE) of mobile devices. Furthermore, we design an online profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by buyers to use the resources provided by sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profit of edge clouds.

Mobile-edge computing (MEC) is considered as a promising technology in 5G, as it can solve the contradiction between the explosive growth of computation-intensive tasks and the limited computation power and battery life of local devices. However, in the 5G environment, most of the existing studies on task offloading in MEC have either failed to study the compatible multiple access technologies or have not considered the hierarchical relationship between small cell base station (SBS) and macro base station (MBS). Therefore, to explore the MEC offloading problem under the unique 5G architecture is of great significance at present. In light of this, we study the task offloading strategy in the nonorthogonal multiple access (NOMA)-enabled small cell MEC network. Specifically, we first describe a noval small cell MEC architecture in which MBS and SBS are both deployed with edge servers and there is a hierarchical relationship between the two. Based on this architecture, we have established the communication model and computation model, respectively. Then, we formulate the energy and delay weighted sum minimization problem, which aims at minimizing the total cost of task offloading under different requirements and takes into account the constraints of computation capabilities. To solve the problem, we develop a hybrid genetic hill climbing (HGHC) algorithm that can quickly find the optimal solution. Moreover, we perform a lot of simulation experiments to evaluate the performance of our algorithm under different parameters. The experimental results show that our algorithm can converge within about 20 iterations, which is superior to traditional heuristic algorithms.

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether or where a task is offloaded but ignore the motivation of the edge cloud to offer service. To stimulate service provisioning by edge clouds, it is essential to design an incentive mechanism that charges mobile devices and rewards edge clouds. In this paper, we utilize market-based pricing model to establish a relationship between the resources provided by edge clouds and the price paid by the mobile devices in a non-competitive environment. Furthermore, we design a profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by the buyers to use the resources provided by the sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profits of resource providers.

Mobile edge computing is an emerging computing paradigm to augment computational capabilities of mobile devices by offloading computation-intensive tasks from resource- constrained smart mobile device onto edge clouds nearby with potential computation capability. However, in general, edge clouds have limited computation resource and energy. Thus it is critical to achieve high energy efficiency while ensuring satisfactory user experience. In this paper, we first formulate the computation offloading problem for mobile edge computing into the system cost minimization problem by taking into account the completion time and energy. We then transform the optimization problem into a convex problem and propose a distributed algorithm consisting of offloading strategy selection, clock frequency configuration, transmission power allocation and channel rate scheduling. Finally, the experimental results show that our algorithm can achieve energy-efficient offloading performance compared to other existing algorithms.