Deep learning-based computation offloading with energy and performance optimization

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, the MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using graph coloring. The outcomes of the offloading decision and PRB allocation are then used to allocate the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

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.

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 development of mobile edge computing and neural network Deep Learning (DL), more and more scholars are studying the combination of the two. This paper mainly studies the application of mobile edge computing and neural network DL in IoT computing collaboration and data-aware routing algorithms. Therefore, this paper proposes the deployment options of MEC technology and ETSIMEC in mobile edge computing, combining mobile edge computing with DL, designs an optimization algorithm based on Markov decision process and feature expression learning, and then analyzes and optimizes IoT computing and VANET routing algorithm. In order to have a clearer direction for the optimization algorithm, this paper also designs the edge computing model training and experiment comparison, the DL algorithm comparison experiment, and the routing algorithm simulation experiment and performance analysis. Combined with the experimental results, it is optimized and compared with traditional IoT computing and routing algorithms. Finally, it is concluded that the computing efficiency of IoT computing based on edge computing and DL designed in this paper is 21.33% higher than that of traditional IoT computing. The efficiency of the routing algorithm based on edge computing and DL designed in this paper is 9.29% higher than that of the traditional routing algorithm.

This paper considers the computation offloading and resource allocation for system sum cost minimization in a mobile edge computing (MEC)network. The computation offloading decision of the user equipments (UEs)can be made through wireless link to access the MEC server. Our intent is to minimize the weighted sum cost of the system, which takes into account the energy consumption and time delay, for both task processing and data transmissions, subject to the force delay requirement of the devices. We first formulate the problem as a mixed-integer and non-convex optimization scheme. Enlightened by the superior performance of reinforcement learning (RL)on solving resource control problems, we put forward a novel RL-based scheme for computation offloading and resource allocation in MEC network. Specifically, the MEC node is considered as an “agent”, which first makes decision on whether to offloading the computing task to the MEC server, and then utilizes convex optimization algorithms to settle the radio spectrum and computation resource scheduling problem in each decision phase. The simulation results present that the RL-based method can achieve better computation offloading and resource allocation performance compared to those of local computing and remote computing modes. It also achieves very similar or slightly better performance compared to the genetic algorithm based method.

Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing has been proposed as an efficient task-offloading solution for user equipments (UEs). Nevertheless, the presence of heterogeneous UAVs makes centralized navigation policies impractical. Decentralized navigation policies also face significant challenges in knowledge sharing among heterogeneous UAVs. To address this, we present the soft hierarchical deep reinforcement learning network (SHDRLN) and dual-end federated reinforcement learning (DFRL) as a decentralized navigation policy solution. It enhances overall task-offloading energy efficiency for UAVs while facilitating knowledge sharing. Specifically, SHDRLN, a hierarchical DRL network based on maximum entropy learning, reduces policy differences among UAVs by abstracting atomic actions into generic skills. Simultaneously, it maximizes the average efficiency of all UAVs, optimizing coverage for UEs and minimizing task-offloading waiting time. DFRL, a federated learning (FL) algorithm, aggregates policy knowledge at the cloud server and filters it at the UAV end, enabling adaptive learning of navigation policy knowledge suitable for the UAV's performance parameters. Extensive simulations demonstrate that the proposed solution not only outperforms other baseline algorithms in overall energy efficiency but also achieves more stable navigation policy learning under different levels of heterogeneity of different UAV performance parameters.

In future 6G networks, mobile edge computing (MEC) is envisioned to offer integrated computing, communication, and storage services at the network edge, enhancing both computational efficiency and communication quality. However, most existing MEC designs neglect the impact of jamming attacks, especially those from intelligent and adaptive jammers. To fill this important research gap, this paper investigates a jamming-resilient MEC framework that aims to improve communication reliability and reduce system delay under adversarial interference. Leveraging the emerging movable antenna (MA) technology, which allows dynamic adjustment of antenna positions and orientations, we propose a novel MA-assisted anti-jamming MEC architecture. Unlike existing works, our model explicitly considers the movement delay caused by MA, which is critical for practical deployment. We jointly optimize the MA positions at both the user equipment (UE) and the base station (BS), BS transmit beamforming, and task offloading ratios to minimize the total system delay. The resulting optimization problem is non-convex and highly coupled. Thus, we develop an efficient algorithm based on penalty dual decomposition (PDD) and successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed-position antenna (FPA) baselines in terms of jamming resilience and delay minimization, offering new insights into robust MEC system design for 6G networks.

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

Offloading computationally intensive tasks from user equipments (UEs) to mobile edge computing (MEC) servers is a promising technique to boost up the computational capacity of UEs. However, MEC will incur extra energy consumption and time delays, which motivates the deployment of energy harvesting (EH) small cell networks with MEC in mobile networks. Due to the complexity of such networks, it is challenging to effectively allocate resources for UEs. In this paper, we investigate the offloading decision, wireless and computational resources allocation problem in energy harvesting (EH) small cell networks with MEC. Different from existing literatures, our research focuses on improving mobile operators' revenue by maximizing the amount of the offloaded tasks while decreasing the energy expenditure and time-delays. Besides, queues are created at the MEC server side to store the un-executed tasks in a time slot, which is used as a punishment in our utility function to avoid serious delay. Considering the varying lengths of queues, the states of EH-batteries of small base stations (SBSs) and down-link channels, the above problem is modeled as a Markov decision process (MDP). Since the states and actions in the MDP are infinite, an online and on-policy actor-critic with eligibility traces algorithm is proposed to resolve the problem. Simulation results show the proposed algorithm has superior performances compared with the policy-gradient algorithm and Q- learning.

The rapid growth of mobile services and Internet of Things (IoT), have caused a severe demand of a management system for Mobile Edge Computing (MEC) where User Equipments (UEs) benefit from high computational power, capacity and communication as well as the offered services by MEC. However, a comprehensive management is required to orchestrate the services and resource in MEC to fairly distribute to UEs with the aim of ensuring the Quality of Experience (QoE). In this paper, we propose a new and trustable framework for MEC management/orchestration system with crucial security and authentication components by which it ensures the delivery of users’ quality of experience.

Smart computational offloading for mobile edge computing in next-generation Internet of Things networks

In current world, processing the data by the users, researchers, organizations, etc. through online source (ie. cloud) is increasing tremendously, where it offers various services like storing the data, allocating the resources, retrieval of data, security, etc. Since the data is stored in cloud, processing it leads to increase in latency, due to its long-distance communication. As distance increases, data transmission produces a delay between source and destination which is called as latency. To get over this problem, MEC (Mobile Edge Computing) system can be positioned between the cloud infrastructure and the user equipment(UE) in the IoT environment. Instead of sending data to the cloud directly which is collected by IoT sensors, edge computing processes those data within the network. The study of this review focuses on the various techniques such as deep learning algorithms, deep reinforcement learning approaches for offloading decisions and optimization techniques in 5G networks for minimizing the latency in the MEC systems, for producing higher processing capability to the clients. For increasing the MEC system performance, efficient offloading strategies can be used to minimize latency for various tasks.

Computationally intensive and latency sensitive applications put forward strict requirements on mobile user equipments (UEs) to provide the required computation capacity and delay. Mobile edge computing (MEC) in ultra-dense network (UDN) is considered as a promising solution that can reduce the computation limitations of UEs and extend their battery life by computation offloading. But the dense deployment of UEs, and the limited resources of small cell base stations (SBSs), which imposes more delays on computation offloading in MEC environment. In this paper, using the characteristics of UDN, we study the computation offloading management scheme based on the weighted sum of task completion time and energy consumption in MEC system. Then, we propose a computation offloading framework and formulate joint task offloading and resource allocation problem in UDNs to minimize overall cost consumption. Based on Genetic algorithm (GA) and Whale optimization algorithm (WOA), we adopt a heuristic algorithm named as GAWOA to solve this problem. Numerical results show that the algorithm has a fast convergence speed and better performance than other benchmark algorithms.

As the perpetually increasing demands of users for high speed, higher data rates over wide bandwidths, lower system latencies in mobile communications, LTE/LTE-Advanced as a 4th Generation of Wireless networks infrastructures widely deployed to provide better support for these requirements. Carrier Ag-gregation(CA) is an important technology for LTE-A providing enhancements to peak data rates, both uplink and downlink, allowing the User Equipment(UE) to receive data on more than one LTE carrier. This increased complexity is expected to affect the UE current consumption. As LTE is restricted with packet-switched services only, the 3G native circuit-switched (CS) voice mechanisms are used in LTE to support voice calls and SMS. For this purpose, the network operator may consider Circuit-switched fallback(CSFB) or IP-Multimedia Subsystems(IMS) as alternatives, for concerned voice quality, call continuity and reliable SMS delivery in deployed LTE networks. In this paper, the performance of UE under CA(10+10 MHz) is analyzed for maximizing data throughput and also describes how the CA affects UE current consumption measurements with different traffic profiles. As a part of Voice over LTE(VoLTE), this paper evaluates the performance of UE under CSFB with its effect on peak data rates and reliability on IMS for deploying voice and SMS services.

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.