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.

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) can significantly improve the performance of mobile applications by leveraging nearby servers as the edge cloud to provide task offloading execution service for a smart mobile device (SMD) through wireless access points (APs). However, the edge cloud and AP will not provide free services. Their radio frequency resources and computing resource are limited but the service requests from various mobile devices could be massive. The goal of this article is to provide a pricing mechanism to efficiently allocate limited resources in the MEC system according to the budget of SMDs. To this end, we first present a market model of MEC resources that can give a real insight into the incentives for resource sharing at network edges. In the model, computation, and radio resources can be traded between resource suppliers (AP and edge cloud) and buyers (SMDs). Furthermore, we employ the microeconomic theory to get an optimal budget allocation strategy for the SMD to maximize its utility within a limited budget. Moreover, we propose an Equilibrium Price Finding (EPF) algorithm to find the equilibrium price of the MEC system, maximizing the whole system utility and leading to optimal resource allocation. Finally, simulation results show that, compared with state-of-the-art resource allocation methods, our optimal budget allocation algorithm can find budget allocation strategy more effectively and our equilibrium price finding algorithm can achieve market equilibrium to optimally allocate computation and radio resources in the MEC system.

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.

Edge cache service (ECS), as a prospective edge network service paradigm, can significantly reduce the data transmission latency and improve the Quality of Service (QoS) of digital content providers by offloading content data to edge devices in the network. Compared to centralized content service, ECS can provide digital content from nearby edge devices via a high-speed wireless network with fewer hops. However, how to motivate edge devices to share their cache resource and ensure the reliability of content data under the diversity of device behavior remains a challenging issue. In this article, we aim to design an ECS framework for cache resource trading and digital content sharing in the edge network. By using blockchain-based credentials, we first provide the cache resource trading mechanism for the trading between the content provider and edge devices. Then, we give a double auction mechanism for digital content trading between edge devices. The experimental results show the proposed framework can greatly improve the matching efficiency of cache resources and reduce the data transmission overhead in edge networks.

Smart city as a new paradigm for future city development leads to a large amount of computing workload and high network latency especially with artificial intelligence algorithms. Fog computing, as one of the mobile edge computing paradigms, deploys some servers at the edge of mobile networks to solve these problems. However, it still remains a challenging issue how to obtain the energy-effective cooperation policy among fog nodes (FNs) to enhance the users' quality of experience (QoE) under fairness, where the fairness ensures that FNs are willing to take part in cooperations. Therefore, we first build up a cooperative fog computing system to process offloading workload on the entire fog layer by data forwarding. Then, we formulate a joint optimization problem of QoE and energy in integrated fog computing process with fairness. After that, we prove the convexity of the optimization problem and design a fairness cooperation algorithm (FCA) to obtain the optimal fairness cooperation policy of all FNs. Finally, numerical results show that our FCA can quickly converge to its solution compared with three traditional convex optimization approaches, and FCA can effectively reduce the time overhead and the energy consumption compared to baseline algorithm and distributed optimization algorithm.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.

Mobile cloud computing (MCC), as a prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich center cloud. Compared to a center cloud, an edge cloud can provide services to nearby SMDs with lower latency. However, the edge cloud may be mobile and its resources are limited to multiple nearby users. In this paper, we aim to minimize the total execution cost of multiple devices by offloading the computation from SMDs onto edge clouds in an edge cloud computing (ECC) system. By considering the mobility of SMDs and edge clouds, we first formulate the total cost minimization problem under the constraints of application completion deadline and connection time between SMDs and edge clouds as well as the limited computing resource of both edge clouds and SMDs. Then, by solving the minimization problem, we propose an optimal offloading selection strategy based on a game model, and an edge cloud payoff competition algorithm to optimally allocate edge cloud resource to SMDs to achieve the minimum total execution cost. Experimental results show that our offloading strategy can effectively reduce energy consumption and application completion time compared with the state-of-the-art methods.