Search

Multiaccess edge computing (MEC) enables autonomous vehicles to handle time-critical and data-intensive computational tasks for emerging Internet-of-Vehicles (IoV) applications via computation offloading. However, a massive amount of data generated by colocated vehicles is typically redundant, introducing a critical issue due to limited network bandwidth. Moreover, on the edge server side, these computation-intensive tasks further impose severe pressure on the resource-finite MEC server, resulting in low-performance efficiency of applications. To solve these challenges, we model the data redundancy and collaborative task computing scheme to efficiently reduce the redundant data and utilize the idle resources in nearby MEC servers. First, the data redundancy problem is formulated as a set-covering problem according to the spatiotemporal coverage of captured images. Next, we exploit the submodular optimization technique to design an efficient algorithm to minimize the number of images transferred to the MEC servers without degrading the quality of IoV applications. To facilitate the task execution in the MEC server, we then propose a collaborative task computing scheme, where an MEC server intentionally encourages nearby resource-rich MEC servers to participate in a collaborative computing group. Accordingly, a cost model is formulated as an optimization problem, the objective of which is to prompt the MEC server to judiciously allocate computing tasks to nearby MEC servers with the goal of achieving the minimal cost while the latency of tasks is guaranteed. Experimental results show that the proposed scheme can efficiently mitigate data redundancy, conserve network bandwidth consumption, and achieve the lowest cost for processing tasks.

Multi-access edge computing (MEC) can improve the users’ computational capacity and battery life by moving computing services to the network edge. In addition, data-content caching on a MEC server improves the user quality of experience and decreases the backhaul network congestion. Moreover, non-orthogonal multiple access (NOMA) has recently been implemented to increase network throughput and capacity. Combining these techniques can improve the user performance and benefit the network. This paper investigates a combined computational offloading and data-content caching problem for NOMA-MEC systems. The aim was to achieve the minimum total completion latency of all users by jointly optimizing the offloading decision, caching strategy, computational resource, and power allocation. This satisfies the constraints within the scope of the potential violation for energy consumption, offloading decision, and the computation and storage capacity of the MEC server. The formulated problem is a mixed-integer non-linear programming and a non-convex problem. To solve this challenging problem, a block successive upper-bound minimization method was implemented to obtain efficient solutions. Numerous simulation results were presented to demonstrate the convergence and efficacy of the proposed algorithm. Compared with other schemes of all-offloading, local-only, and equal resources, our proposed algorithm can approximately reduce the total completion latency by 17.68%, 26.02%, and 70.98%.

The goal of sixth-generation (6G) wireless communication network is towards space-atmosphere-land-submarine connectivity. 6G communications are mainly characterized by massive connectivity, ultra-reliability, low latency, and extreme high bandwidth to meet the requirements of practical applications such as robotics and autonomous vehicles, transportation management systems, healthcare as well as telepresence, virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications. Multi-Access edge computing (MEC) is a key component of future 6G wireless networks, enabling latency-sensitive, bandwidth-intensive, and compute-intensive devices. This article presents an overview of the technologies assisting, challenges, and opportunities for envisioning MEC in future 6G wireless networks.

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications, therefore, it increases the user&rsquo, s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

Mobile Edge Computing (MEC) enhances mobile cloud computing capabilities by fetching services close to the edge of the network, which adds 4C (Computing, Communication, Control and Caching) to the edges. MEC-enabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS). The research work regarding MEC-enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS-MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency-sensitive tasks and resources-hungry mobile applications in small-cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS-MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS-MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS-MEC with cloud scheme will provide lower task execution latency than others in small-cell networks.

In recent years, computation offloading in multi-access edge computing (MEC) has received a lot of attention. For example, the joint optimization of computing resource allocation and offloading decision was studied to reduce the overall latency and energy consumption of MEC offloading. Such problem of optimizing the computing resource allocation and offloading decision is often formulated as a mixed-integer nonlinear programming (MINLP) problem, which is also NP-hard in general. Various solution approaches have been proposed such as heuristics, dynamic programming, branch-and-bound, and machine learning. Motivated by applications to a variety of complex optimization problems, in this paper, we provide an alternative meta-heuristic method using whale optimization algorithm (WOA). We have proposed a joint computing resource allocation and offloading decision system using the WOA to minimize the total computing overhead of mobile users (MUs), including completion time and energy consumption. Through numerical simulations, our proposed algorithm is more efficient than several baseline schemes for reducing the total computing overhead of MUs.

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.

Sport event participation has changed much recently with effective support of technology. Advanced developments in recommender systems and World Wide Web bring chances such as efficiently distantly booking to alone travelers which allows them to enjoy sport events without being dependent on expensive tourist agencies as in the past. However, currently, popular information sources for such a recommender system are isolated and mainly relied on Web 2.0 formats which are difficult to stored and processed, especially among different platforms and communities. To utilize huge resources of Web 2.0 as well as apply cutting-edge features of Web 3.0 and under-developing Web 4.0, the authors propose an implementation of a hybrid system which collects data from different sources in the Internet (Mashup), apply machine learning to process raw information (Natural Language Processing and Unsupervised Clustering), add semantics to the processed data and make it compatible to latest web generation (Ontology), and provide recommendations based on smart content-based filtering and social-network-based user profiles for sport events. Empirical results show promising applications of such a framework to the market portion of alone travelers and also set an example as a demonstration for the authors’ expectation toward Web 4.0 applications in the future.

Recently, Multi-Access Edge Computing (MEC), which has been emerged as a key technology in 5G networks, enhances computation capabilities and power limitations of mobile devices (MDs) by offloading computation task to the nearby MEC servers. However, offloading the computation tasks can increase network traffics and incur extra delays. Most existing approaches focus on the computation offloading with multi-user single-MEC scenarios to decrease energy consumption and latency of the MDs. Towards this goal, we investigate a computation offloading strategy for two-tier 5G heterogeneous networks integrated with multi-MEC. In addition, we propose a random offloading search algorithm, called ROSA, that rapidly achieve the minimized energy consumption of the system considering the computation offloading decision strategies. Simulation results show that our proposed algorithm based on offloading scheme outperforms other two schemes in terms of energy consumption.