The rapid development of Internet of Things applications, along with the limitations of cloud computing due mainly to the far distance between Internet of Thing devices and cloud-based platform, has promoted a newly distributed computing platform based on collaboration between cloud computing and fog computing. Fog computing helps to reduce transmission latency and monetary cost for cloud resources, while cloud computing helps to fulfill the increasing demands of large-scale compute-intensive offloading applications. In this article, we study the tradeoff issue between the makespan and cloud cost when scheduling large-scale applications in such a platform. We propose a scheduling algorithm called Cost-Makespan aware Scheduling heuristic whose major objective is to achieve the balance between the performance of application execution and the mandatory cost for the use of cloud resources. Additionally, an efficient task reassignment strategy based on the critical path of the directed acyclic graph modeling the applications is also proposed to refine the output schedules of the Cost-Makespan aware Scheduling algorithm to satisfy the user-defined deadline constraints or quality of service of the system. We also verify our proposal by extensive simulations, and the experimental results show that our scheduling approach is more cost-effective and achieves better performance compared to others.

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In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable humanlike intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.

The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

In recent years, with the advent of the Internet of Things (IoT), fog computing is introduced as a powerful complement to the cloud to handle the IoT's data and communications needs. The interplay and cooperation between the edge (fog) and the core (cloud) has recently received considerable attention. In this paper, we consider task scheduling in a cloud-fog computing system, where a fog provider can exploit the collaboration between its own fog nodes and the rented cloud nodes for efficiently executing users' large-scale offloading applications. We first formulate the task scheduling problem in such cloud-fog environment and then propose a heuristic-based algorithm, whose major objective is achieving the balance between the makespan and the monetary cost of cloud resources. The numerical results show that our proposed algorithm achieves better tradeoff value than other existing algorithms.

By bringing the computation and storage resources close proximity to the mobile network edge, mobile edge computing (MEC) is a key enabling technology for satisfying the Internet of Vehicles (IoV) infotainment applications’ requirements, e.g., video streaming service (VSA). However, the explosive growth of mobile video traffic brings challenges for video streaming providers (VSPs). One known issue is that a huge traffic burden on the vehicular network leads to increasing VSP costs for providing VSA to mobile users (i.e., autonomous vehicles). To address this issue, an efficient resource sharing scheme between underutilized vehicular resources is a promising solution to reduce the cost of serving VSA in the vehicular network. Therefore, we propose a new VSA model based on the lower cost of obtaining data from vehicles and then minimize the VSP’s cost. By using existing data resources from nearby vehicles, our proposal can reduce the cost of providing video service to mobile users. Specifically, we formulate our problem as mixed integer nonlinear programming (MINP) in order to calculate the total payment of the VSP. In addition, we introduce an incentive mechanism to encourage users to rent its resources. Our solution represents a strategy to optimize the VSP serving cost under the quality of service (QoS) requirements. Simulation results demonstrate that our proposed mechanism is possible to achieve up to 21% and 11% cost-savings in terms of the request arrival rate and vehicle speed, in comparison with other existing schemes, respectively.

Increasing demands for location-based services require accurate wireless indoor location information. Location-based services include indoor navigation for people or robots, personnel, asset tracking, guiding blind people, factory automation, workplace safety, locating patients in a hospital, and location-based advertising [1]. Additionally, such services are becoming essential in various other fields such as mobile commerce, parcel or vehicle tracking, discovering the nearest shops or restaurants, and social