Novel constrained QoS routing approach of energy-aware optimization based on learning automata for mobile edge computing (MEC)

Internet of Things (IoT) has been regarded as one of the most significant network paradigms in the future. For IoT, it is crucial to ensure the correctness of detection which includes the factors of accuracy and precision. On the other hand, Mobile Edge Computing (MEC) has emerged as a promising way to process big IoT data at the network edge so as to reduce the computation and transmission energy in the networks. In this paper, we explore the energy minimization problem in MEC networks by considering both the accuracy and precision requirements of IoT. Specifically, given 1) a set of IoT devices, 2) a set of observed targets, 3) an MEC network, 4) the energy consumption model, and 5) the accuracy and precision requirements, we formulate a new optimization problem, named Accuracy and Precision-Aware IoT Device Selection (APAIDS), to minimize the overall energy consumption in MEC networks. We prove the NP-hardness of APAIDS and then propose a new algorithm, named Energy Efficient Device and MEC Server Selection (EDMS), to minimize energy consumption by jointly selecting IoT devices, configuring MEC association, and selecting processing servers for dealing with the data of each target. Finally, we evaluate EDMS on two real networks. In comparison with the baseline schemes, the results manifest that the overall energy consumption can be reduced by more than 60%.

Because of the rapid evolution of communications technologies, such as the Internet of Things (IoT) and fifth generation (5G) systems and beyond, the latest developments have seen a fundamental change in mobile computing. Mobile computing is moved from central mobile cloud computing to mobile edge computing (MEC). Therefore, MEC is considered an essential technology for 5G technology and beyond. The MEC technology permits user equipment (UEs) to execute numerous high-computational operations by creating computing capabilities at the edge networks and inside access networks. Consequently, in this paper, we extensively address the role of MEC in 5G networks and beyond. Accordingly, we first investigate the MEC architecture, the characteristics of edge computing, and the MEC challenges. Then, the paper discusses the MEC use cases and service scenarios. Further, computations offloading is explored. Lastly, we propose upcoming research difficulties in incorporating MEC with the 5G system and beyond.

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. Mobile Edge Cloud Computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decision-maker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learning-based approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.

The visions of Internet of Things and 5G communications have driven the evolution of mobile computing paradigm, shifting from the centralized mobile cloud computing toward mobile edge computing (MEC). By pushing mobile computing, network control, and storage to the network edges (e.g. base stations and wireless access points), MEC is expected to enable computation‐intensive and latency‐critical applications at the resource‐limited mobile devices. MEC promises dramatic reduction in mobile energy consumption and latency, tackling the key challenges for realizing the 5G visions. The promised gains of MEC have motivated extensive research efforts in both academia and industry. A main thrust of MEC research is to seamlessly integrate the two disciplines of wireless communications and mobile computing, leading to a wide range of new designs ranging from techniques for energy‐efficient computation offloading to network architectures. This article first introduces the basic principles of computation offloading in MEC, and then provides a comprehensive survey of the state‐of‐the‐art MEC research with emphasis on energy‐efficient computation offloading and joint radio‐and‐computational resource management. Last, we introduce recent 5G standardization efforts on MEC as well as typical use scenarios.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

Nowadays, for different purposes and contexts, we interact with a lot of different smart devices in our daily lives. Most of these devices are connected to the Internet and are therefore commonly referred to as the Internet of Things (IoT). Mobile edge computing (MEC) has recently evolved as an emerging technique by moving the computing and storage resources from the cloud to the edge of the network. The MEC supports IoT devices to improve their efficiency and scalability; helps to reduce latency delay for real-time applications, bandwidth bottlenecks, and energy consumption; and delivers contextual information processing. MEC offers many features and capabilities, such as access to a multitude of network interface (from 4G and 5G to Wi-Fi), support for device mobility, device context, geo-location awareness, and geographical distribution. Such attributes can support the real-time processing requirements of the Internet of Everything application, such as patient care, disaster management and detection (e.g., earthquakes), and flood monitoring. However, to fully exploit the potential of MEC in the IoT applications, many challenges need to be addressed, such as issues related to IoT Big Data, effective management of data storage and computing, privacy and security concerns, and innovative and emerging communication paradigm (e.g., 5G), require new architectures, applications, and methods.

Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions

Data traffic unloading method of internet of things based on mobile edge computing

In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.

Adaptive delay-constrained resource allocation in mobile edge computing for Internet of Things communications networks

In the era of rapid technological advancement, Mobile Edge Computing (MEC) has become essential for supporting latency-sensitive applications such as internet of things, autonomous driving, and smart cities. However, efficient resource allocation remains a challenge due to the dynamic nature of MEC environments. The primary difficulties stem from fluctuating workloads, varying network conditions, and heterogeneous computational capabilities, which make real-time task offloading and resource management complex. Traditional centralized approaches suffer from high computational overhead and poor scalability, while conventional machine learning-based methods often require extensive labeled data and fail to adapt quickly in dynamic settings. To address these issues, this study proposes an advanced Multi-Agent Reinforcement Learning (MARL) framework combined with a lightweight neural network, LtNet, to optimize task offloading and resource management in MEC. MARL enables decentralized decision-making, allowing each device to learn optimal offloading strategies and adapt dynamically. Compared to prior single-agent or heuristic methods, our approach improves scalability and efficiency while reducing computational complexity. LtNet further enhances performance using H-Swish activation and selective Squeeze-and-Excitation modules, ensuring lower computational overhead. Experimental results demonstrate that the proposed methods achieve a 12–22% reduction in task completion time, a 5–8% decrease in energy consumption, and consistently high resource utilization, making them highly effective in managing dynamic MEC environments.

Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers.