Multi-access Mobile Edge Computing Research Articles

Joint optimization for computation offloading and 3C resource allocations over wireless-powered and NOMA-enabled multi-access MEC

Multi-access mobile edge computing (MEC) enables mobile users (MUs) to offload tasks to proximal multiple MEC servers for fast task processing. Since MUs generally have stringent delay requirements and limited energy and MEC servers have finite communication, computation, and caching (3C) resources, the joint co-design and optimization over computation offloading and 3C resource allocation for wireless-powered multi-access MEC systems have been highly demanded, while having not been well studied yet. We consider a wireless-powered multi-access MEC, where multiple energy harvesting (EH) based MUs and multiple base stations (BSs) each equipped with an MEC server coexist. Each MU first harvests energy from nearby MEC servers, and then offloads its sub-tasks to multiple MEC servers concurrently based on non-orthogonal multiple access (NOMA), so as to reduce data offloading delay. We first formulate a delay minimization problem, by jointly optimizing MUs’ computation offloading, MEC servers’ computation and caching resources allocation, and system communication resources allocation. Then we propose an alternating direction multiplier method (ADMM) based distributed scheme to decompose the formulated optimization problem into several sub-problems, and use the block coordinate descending (BCD) method and the successive convex approximation (SCA) method to transform all sub-problems each corresponding to one MEC server to convex subproblems. Finally, we validate and evaluate our proposed scheme through numerical analyses, which show that our proposed distributed scheme can greatly reduce the min–max delay of all MUs by allowing each MU’s concurrent data offloading to multiple MEC servers using NOMA and by jointly optimizing computation offloading and 3C resource allocations. Also, the delay imposed by our proposed scheme is much smaller than that imposed by the Interior Point method, which shows the effectiveness of our proposed scheme.

Suitability of SDN and MEC to facilitate digital twin communication over LTE-A

Haptic is the modality that complements traditional multimedia, i.e., audiovisual, to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications. This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet. While the 5th generation of mobile networks is not yet widely deployed, Long-Term Evolution (LTE-A) latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin. This work investigates an interesting solution based on the incorporation of Software-defined networking (SDN) and Multi-access Mobile Edge Computing (MEC) technologies in an LTE-A network, to deliver future multimedia applications over the Tactile Internet while overcoming the QoS challenges. Several network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators (KPIs) such as throughput, End-2-End (E2E) delay, and jitter. The best scenario possible is clearly the one integrating MEC and SDN approaches, where the overall delay, jitter, and throughput for haptics- attained 2 ms, 0.01 ms, and 1000 packets per second. The results obtained give clear evidence that the integration of, both SDN and MEC, in LTE-A indicates performance improvement, and fulfills the standard requirements in terms of the above KPIs, for realizing a Digital Twin/Tactile Internet-based system.

DCEC: D2D-Enabled Cost-Aware Cooperative Caching in MEC Networks

Various kinds of powerful intelligent mobile devices (MDs) need to access multimedia content anytime and anywhere, which places enormous pressure on mobile wireless networks. Fetching content from remote sources may introduce overly long accessing delays, which will result in a poor quality of experience (QoE). In this article, we considered the advantages of combining mobile/multi-access edge computing (MEC) with device-to-device (D2D) technologies. We propose a D2D-enabled cooperative edge caching (DCEC) architecture to reduce the delay of accessing content. We designed the DCEC caching management scheme through the maximization of a monotone submodular function under matroid constraints. The DCEC scheme includes a proactive cache placement algorithm and a reactive cache replacement algorithm. Thus, we obtained an optimal content caching and content update, which minimized the average delay cost of fetching content files. Finally, simulations compared the DCEC network architecture with the MEC and D2D networks and the DCEC caching management scheme with the least-frequently used and least-recently used scheme. The numerical results verified that the proposed DCEC scheme was effective at improving the cache hit ratio and the average delay cost. Therefore, the users’ QoE was improved.

Deep Reinforcement Learning for Computation and Communication Resource Allocation in Multiaccess MEC Assisted Railway IoT Networks

Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to support compute-intensive and delay-sensitive applications in railway Internet of Things (RIoT) networks. However, the time-varying channel variations in RIoT scenarios make it challenging to achieve efficient resource allocation. The emerging deep reinforcement learning (DRL) is able to respond to the above-mentioned challenge. In this paper, with the aim of reducing the total computational cost (weighted sum of consumed energy and delay), we investigate the dynamic resource management issue of joint subcarrier assignment, offloading ratio, power allocation and computation resource allocation in multi-access MEC assisted RIoT networks. To address this intractable mixed integer nonlinear programming issue, we put forward a hybrid DRL (HDRL) scheme, which is an integration of deep double Q-learning (DDQN) and deep deterministic policy gradient (DDPG). The HDRL algorithm is capable of learning the advisable strategies for actions including discrete-continuous hybrid variables. In HDRL algorithm, DDQN plays the role of making subcarrier assignment decision, and DDPG plays the role of making offloading ratio, power allocation as well as computation resource allocation decisions. Numerical results demonstrate that HDRL scheme can yield much less computational cost than the existing baselines for multi-access MEC assisted RIoT networks. In addition, the HDRL scheme is close to the near-optimal performance with comparatively low execution time.

Multi-Access Edge Computing assisted ultra-low energy scheduling and harvesting in multi-hop Wireless Sensor and Actuator Network for energy neutral self-sustainable Next-gen Cyber-Physical System

Energy Neutrality is the need of the hour for future Cyber-physical infrastructure. Many past research works suggest low duty cycle networking for energy-efficient wireless sensor network; however, research problem still persist as these proposals have some practical deployment bottlenecks like sleep-latency and may trade-off with data-fidelity and Quality-of-Service (QoS) parameters. The research objective of this work is to propose a novel design for wireless sensor–actuator mote system to minimize energy consumption and compensate with harvesting to achieve energy neutral operation in multi-hop Wireless Sensor and Actuator Network (WSAN); A network scenario that is much needed for Internet of Thing (IoT) applications in future ready Next-gen Cyber Physical System (NG-CPS). In this paper, we have proposed a novel state-of-the-art Multi-Access/Mobile Edge Computing (MEC) assisted Low-Duty-Cycle Scheduling of Trans-Receiver for Store-Process-Then-Forward (LDCS-TR-SPTF) scheme in multi-hop WSAN for reducing the overall energy consumption. In addition to energy conservation, we have also proposed a hybrid harvesting to compensate energy to achieve energy neutral self-sustainable NG-CPS. The proposed novel LDCS-TR-SPTF has been implemented on multiple custom-made sensor–actuator motes equipped with ARM-based System-on-Chip (SoC) and IEEE 802.11 network interface. These motes are working both as end-nodes and relay nodes in a multi-hop WSAN testbed scenario for testing and validation of our proposed scheme. The results are promising with an overall energy conservation of 49.1% using our novel LDCS-TR-SPTF in comparison to stock IEEE 802.11. We have implemented solar energy harvester on wireless sensor–actuator motes to compensate the reduced energy consumption and achieved complete energy-neutrality. Motes are completely energy-autonomous in multi-hop WSAN scenario by eliminating the need of access to external power-grid for future cyber–physical system.

Adaptive VR Video Data Transmission Method Using Mobile Edge Computing Based on AIoT Cloud VR

Aiming at the high requirements of cloud service-based virtual reality in AIoT for data transmission rate and delay sensitivity, a cloud VR system scheme based on MEC (Mobile Edge Computing) is proposed, which mainly incorporates viewpoint-based VR video data processing and hybrid digital-to-analog (HDA) transmission optimization and can be served for AIoT transmission filed. Firstly, a learning-driven multiaccess MEC offloading strategy is designed, in which the VR terminal automatically selects the optimal MEC server for task offloading, thereby effectively improving network efficiency and reducing service delay. Secondly, the progressive transmission of the VR data is realized through viewpoint-aware dynamic streaming based on RoI (region of interest) and the priorities of different objects. The transmission priority of each object in the scene is determined through the ROI layering, which effectively solves the contradiction between the large data volume in the VR scenes and the network bandwidth limitation when applied in AIoT domain, and further improves the real-time performance of the system. Then, the HDA (hybrid digital-analog) technique is introduced to optimize the transmission. Finally, the base station protocol stack is modified on the basis of the LTE (Long-Term Evolution) system, and the MEC technology is integrated to realize a complete cloud VR system in AIoT. The experimental results show that compared with other advanced schemes, the proposed scheme can achieve more robust and efficient data transmission performance and provide better VR user experience.

User-Aware and Flexible Proactive Caching Using LSTM and Ensemble Learning in IoT-MEC Networks

To meet the stringent demands of emerging Internet-of-Things (IoT) applications, such as smart home, smart city, and virtual reality in 5G/6G IoT networks, edge content caching for mobile/multiaccess edge computing (MEC) has been identified as a promising approach to improve the quality of services in terms of latency and energy consumption. However, the limitations of cache capacity make it difficult to develop an effective common caching framework that satisfies diverse user preferences. In this article, we propose a new content caching strategy that maximizes the cache hit ratio through flexible prediction in dynamically changing network and user environments. It is based on a hierarchical deep learning architecture: long short-term memory (LSTM)-based local learning and ensemble-based meta-learning. First, as a local learning model, we employ an LSTM method with seasonal-trend decomposition using loess (STL)-based preprocessing. It identifies the attributes for demand prediction on the contents in various demographic user groups. Second, as a metalearning model, we employ a regression-based ensemble learning method, which uses an online convex optimization framework and exhibits sublinear “regret” performance. It orchestrates the obtained multiple demographic user preferences into a unified caching strategy in real time. Extensive experiments were conducted on the popular MovieLens data sets. It was shown that the proposed control provides up to a 30% higher cache hit ratio than conventional representative algorithms and a near-optimal cache hit ratio within approximately 9% of the optimal caching scheme with perfect prior knowledge of content popularity. The proposed learning and caching control can be implemented as a core function of the 5G/6G standard’s network data analytic function (NWDAF) module.

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

The next generation (6G) wireless systems aim to cater to the Internet of Everything (IoE) and revolutionize customer services and applications to a fully intelligent and autonomous system. To achieve this, the digital twin edge network (DITEN) is proposed to combine mobile/multi-access edge computing (MEC) and digital twin (DT), thereby improving the network performance such as throughput and security, and reducing the cost of communication, computation, and caching. In DITENs, the network status can be continuously monitored, and based on the obtained network states, the networking schemes, such as routing and resource management, can be studied in the established DITENs from a centralized perspective. In this survey, we present a comprehensive overview of DITEN for 6G. First, we present the fundamental aspects of DITEN, including concept, framework, and potential. Second, a comprehensive design of DITEN is devised, including the DT modeling/updating, DT deployment, key issues, and enabling technologies. Then, the typical applications of DITEN towards 6G are provided, including the Internet of Things (IoT), vehicular network, space-air-groud integrated network (SAGIN), healthcare, wireless systems, and other applications, along with the design of DITEN in each application, such as DT modeling, DT association, incentive mechanisms, and so on. Finally, challenges and open issues are discussed.

Secrecy Offloading Rate Maximization for Multi-Access Mobile Edge Computing Networks

This letter considers a multi-access mobile edge computing (MEC) network consisting of multiple users, multiple base stations, and a malicious eavesdropper. Specifically, the users adopt the partial offloading strategy by partitioning the computation task into several parts. One is executed locally and the others are securely offloaded to multiple MEC servers integrated into the base stations by leveraging the physical layer security to combat the eavesdropping. We jointly optimize power allocation, task partition, subcarrier allocation, and computation resource to maximize the secrecy offloading rate of the users, subject to communication and computation resource constraints. Numerical results demonstrate that our proposed schemes outperform the benchmark schemes substantially and emphasize the necessity of conducting computation offloading over multiple MEC servers.

Enabling Machine Learning with Service Function Chaining for Security Enhancement at 5G Edges

With massive sorts of terminals, devices, and machines connecting to 5G, a tremendous surge of data makes cyber-security a pressing issue, and conventional countermeasures are facing unprecedented challenges. Recently, with the rise of ML (Machine Learning) and SDN/NFV-based (Software-Defined Networks/Network Functions Virtualization) SFC (Service Function Chaining) techniques, how to leverage them for security enhancement in MEC (Multi-access/Mobile Edge Computing) has received much attention. Hence, in this article, we first propose an elastic framework to integrate ML with virtualized SFC, aiming at smart and efficient provision of different services at MEC. Then, we propose an ML-based anomaly detection algorithm used as a kind of service policy for SFC classifiers, which guides the latter for quick traffic classification and subsequent redirections of attack flows. Finally, we build a corresponding prototype system and evaluate the performance of the proposed algorithm through extensive experiments. Related results have confirmed the feasibility and advantages of the proposed framework and algorithm.

Cooperative Offloading in D2D-Enabled Three-Tier MEC Networks for IoT

Mobile/multi-access edge computing (MEC) takes advantage of its proximity to end-users, which greatly reduces the transmission delay of task offloading compared to mobile cloud computing (MCC). Offloading computing tasks to edge servers with a certain amount of computing ability can also reduce the computing delay. Meanwhile, device-to-device (D2D) cooperation can help to process small-scale delay-sensitive tasks to further decrease the delay of tasks. But where to offload the computing tasks is a critical issue. In this article, we integrate MEC and D2D cooperation techniques to optimize the offloading decisions and resource allocation problem in D2D-enabled three-tier MEC networks for Internet of Things (IoT). Mobile devices (MDs), edge clouds, and central cloud data center (DC) make up these three-tier MEC networks. They cooperate with each other to finish the offloading tasks. Each task can be processed by MD itself or its neighboring MDs at device tier, by edge servers at edge tier, or by remote cloud servers at cloud tier. Under the maximum energy cost constraints, we formulate the cooperative offloading problem into a mixed-integer nonlinear problem aiming to minimize the total delay of tasks. We utilize the alternating direction method of multipliers (ADMM) to speed up the computing process. The proposed scheme decomposes the complicated problem into 3 smaller subproblems, which are solved in a parallel fashion. Finally, we compare our proposal with D2D and MEC networks in simulations. Numerical results validate that the proposed D2D-enabled MEC networks for IoT can significantly enhance the computing abilities and reduce the total delay of tasks.

Joint Resource Allocation and Trajectory Optimization for Multi-UAV-Assisted Multi-Access Mobile Edge Computing

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has been considered as a promising approach to offering extensive coverage and massive computing capacities for Internet of Things (IoT). In this letter, we propose a novel multi-UAV-assisted multi-access MEC model by allowing each IoT user to offload task bits to multiple MEC servers deployed at UAVs simultaneously for parallel computing, which can effectively reduce the energy consumption of users and UAVs. The weighted sum energy consumption of UAVs and users is minimized by jointly optimizing the bit allocation, transmit power, CPU frequency, bandwidth allocation and UAVs' trajectories. Due to the non-convexity of the formulated problem, it is decomposed into two subproblems and a joint resource allocation and trajectory design algorithm is proposed by alternative optimization. Simulation results show that our proposed algorithm with multiple radio access outperforms the fixed trajectory, fixed bandwidth allocation and the single access schemes.

Joint computation offloading and resource allocation for NOMA-based multi-access mobile edge computing systems

Due to explosive growth of computation-intensive applications, multi-access mobile edge computing (MEC) has been proposed as an efficient technology to relieve the burden on mobile users by offloading computation tasks to MEC servers. Meanwhile, nonorthogonal multiple access (NOMA) has been proposed to improve spectrum efficiency by allowing multiple users transmitting on the same time and spectrum resource. Thus, NOMA can be naturally adopted by the MEC systems to improve the offloading performance. In this paper, we consider a NOMA-based multi-access MEC system with multiple MEC servers and multiple users. It is assumed that each user has multiple computation tasks, where each task shall be offloaded to one MEC server for computing. NOMA is applied for uploading multiple tasks to MEC servers as well as downloading multiple task results. The problem of jointly optimizing the MEC server allocation, the transmit power allocation of the users, the transmit power allocation of the MEC servers, the computation resource allocation of the MEC servers, the time allocation and the channel allocation, for minimizing the overall delay of all tasks is investigated. We then propose a heuristic algorithm to solve the problem, where the computation resource allocation of the MEC servers and the time allocation are optimized by the bisection search method, the transmit power allocations are obtained in closed-form, and the MEC server allocation and the channel allocation are optimized by the swap matching algorithm. Extensive simulations demonstrate that the proposed algorithm significantly outperforms various benchmark algorithms.

Federated-Learning-Empowered Collaborative Data Sharing for Vehicular Edge Networks

The Internet of Vehicles connects all vehicles and shares dynamic vehicular data via wireless communications to effectively control vehicles and improve traffic efficiency. However, due to vehicular movement, vehicular data sharing based on conventional cloud computing can hardly realize real-time and dynamic updates. To address these challenges, artificial intelligence (AI)-empowered mobile/multi-access edge computing (MEC) has been regarded as a promising technique for intelligently supporting various vehicular services and applications in proximity of vehicles. In this article, we investigate the issue of collaborative data sharing in vehicular edge networks (VENs) with the deployment of AI-empowered MEC servers. Furthermore, we present a specific mode for collaborative data sharing. Then we propose a novel collaborative data sharing scheme with deep Q-network and federated learning to ensure efficient and secure data sharing in the VEN. Evaluation results demonstrate the effectiveness of the proposed scheme on reducing latency of vehicular data sharing. Finally, we discuss several open issues and future challenges of the AI-empowered VEN.

Energy-Efficient Multi-Access Mobile Edge Computing With Secrecy Provisioning

Thanks to the wide deployment of heterogeneous radio access networks (RANs) in the past decades, the emerging paradigm of multi-access mobile edge computing, which allows mobile terminals to simultaneously offload the computation-workloads to several different edge-computing servers via multi-RANs, has provided a promising scheme for enabling the computation-intensive mobile Internet services in future wireless systems. The broadcasting nature of radio transmission, however, may lead to a potential secrecy-outage during the offloading transmission. In this paper, we thus investigate the energy-efficient multi-access mobile edge computing with secrecy provisioning. Specifically, we first investigate the scenario of one wireless device's (WD's) multi-access offloading subject to a malicious node's eavesdropping. By characterizing the WD's secrecy based throughput in its offloading transmission, we formulate a joint optimization of the WD's multi-access computation offloading, secrecy provisioning, and offloading-transmission duration, with the objective of minimizing the WD's total energy consumption, while providing a guaranteed secrecy-outage during offloading and a guaranteed overall-latency in completing the WD's workload. Despite the non-convexity of this joint optimization problem, we exploit its layered structure and propose an efficient algorithm for solving it. Based on the study on the single-WD scenario, we further investigate the scenario of multiple WDs, in which a group of WDs sequentially execute the multi-access computation offloading, while subject to a malicious node's eavesdropping. Taking the coupling effect among different WDs into account, we propose a swapping-heuristic based algorithm (that uses our proposed single-WD algorithm as a subroutine) for finding the ordering of the WDs to execute the multi-access computation offloading, with the objective of minimizing all WDs’ total energy consumption. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms. The results demonstrate that our algorithms can outperform some conventional fixed offloading scheduling scheme and randomized offloading ordering scheme.

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

Multi-Access Mobile Edge Computing (MEC) is proclaimed as a key technology for reducing service processing delays in 5G networks. One of the use cases in MEC is content caching as a way of bringing resources closer to the end-users. Consequently, both latency and QoE are reduced. However, MEC has a limited storage space compared to the cloud. Therefore, there is a need to effectively manage the cache storage. This article proposes and evaluates a novel scheme (PCR) that combines proactive prediction, collaboration among MECs and replacement algorithm to manage content caching in MEC. Results show that the proposed replacement scheme outperforms conventional baseline content caching algorithms LFU, LRU, MQ, FBR, LFRU. This has been validated with experimental results using a real dataset (MovieLens20M dataset) and comparison with contemporary Long Short Term Memory (LSTM) based caching algorithm.

Joint Task Offloading and Resource Allocation for NOMA-Enabled Multi-Access Mobile Edge Computing

Multi-access mobile edge computing (MEC) allows each user to offload partial task bits to multiple MEC servers via multi-access radio transmission, enabling the collaboration of edge computing. By leveraging the advantage of nonorthogonal multiple access (NOMA) in improving the transmission efficiency, it is expected to effectively reduce the energy consumption of users in multi-access MEC with the aid of NOMA. However, considering the co-channel interference of NOMA and computation collaboration among MEC servers, the joint task offloading and resource allocation is a challenging problem. In this article, with the objective to minimize the weighted sum energy of users, we first propose optimal task offloading and resource allocation algorithms for the special single-user (OTORA-SU) and general multi-user (OTORA-MU) cases, by exploiting the convex and layered structure of the formulated problem. Interestingly, it is found that the single user always preferentially offloads task bits to the MEC servers with better channel gains, regardless of the computation capacity of each MEC server. Then, a low-complexity algorithm (LTORA-MU) is proposed for the general multi-user case, which converges fast and achieves near-optimal performances. Considering the channel estimation error, the impacts of imperfect channel state information (CSI) and successive interference cancellation (SIC) are also investigated. Simulation results demonstrate that for both perfect and imperfect CSI and SIC, 1) the proposed OTORA-SU and LTORA-MU outperform the local computing, full offloading, FDMA-based offloading and non-collaborative MEC schemes; 2) as the number of MEC servers grows, the ratio of offloading task bits increases while the energy consumption is decreased.

Two-Layer Stackelberg Game-Based Offloading Strategy for Mobile Edge Computing Enhanced FiWi Access Networks

Carbon footprints of both computation and communications infrastructures attract growing public attention, raising concerns about global warming and motivating research efforts in developing energy-efficient solutions. Since Fiber-Wireless (FiWi) access networks have succeeded in interconnecting our digitalized world and mobile/multi-access edge computing (MEC) further removes unnecessary data transmission from optical backbone networks, MEC enhanced FiWi emerges as the win-win architecture for resource-hungry and delay-sensitive mobile apps. However, the assumption of unlimited and free resources offered by FiWi infrastructure is impractical, and energy-efficient offloading mechanisms for MEC enhanced FiWi still remain underexplored, conflicting with globally advocated green communications. This motivates us to design the Two-layer Stackelberg Game based Offloading (TSGO) strategy, including the FiWi layer and mobile edge computation offloading (MeCo) layer subgames capturing bandwidth allocation and offloading decision respectively. Then the backward induction and an iterative algorithm are exploited to acquire the Nash equilibrium of the two-layer Stackelberg game. Furthermore, we propose three energy efficiency benchmarks from both user-side and system-wide perspectives to evaluate mechanisms proposed for, but not limited to, FiWi, MEC, or a combination thereof. Simulation results validate the proposed TSGO strategy, which echoes the profound notion of green communications.

The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection

The Multi-access Edge Computing (MEC) paradigm increases the computational capabilities of distributed sensing architectures, such as Mobile CrowdSensing platforms, which are designed to collect heterogeneous data from the crowd by exploiting mobile devices. In this context, our work focusses on the impact of three community detection algorithms to our edge selection strategy. In particular, we study TILES, Infomap, and iLCD which are specifically designed to identify evolving communities of users in dynamic networks. Our analysis is based on the ParticipAct data set that offers real human mobility data. We first measure the quality of the data set during an observation period of 1 year, during which the data set provides the 75% of the expected traces collected by approximately 170 users. We then compare some structural properties of the communities detected, namely Similarity, Forward Stability, Cohesion and Coverage. We conclude our study with a performance analysis of the selected Mobile MECs by varying the community detection algorithms adopted. In particular, we measure the latency and the number of satisfied requests and we show that the average latency obtained with Infomap is slightly lower than that of the other algorithms, while the average number of satisfied requests is higher when we adopt the TILES algorithm.

NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things

Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key approaches for enabling computation-intensive yet delay-sensitive services in future industrial Internet of Things (IoT). In this article, we exploit nonorthogonal multiple access (NOMA) for computation offloading in MA-MEC and propose a joint optimization of the multiaccess multitask computation offloading, NOMA transmission, and computation-resource allocation, with the objective of minimizing the total energy consumption of IoT device to complete its tasks subject to the required latency limit. We first focus on a static channel scenario and propose a distributed algorithm to solve the joint optimization problem by identifying the layered structure of the formulated nonconvex problem. Furthermore, we consider a dynamic channel scenario in which the channel power gains from the IoT device to the edge-computing servers are time varying. To tackle with the difficulty due to the huge number of different channel realizations in the dynamic scenario, we propose an online algorithm, which is based on deep reinforcement learning (DRL), to efficiently learn the near-optimal offloading solutions for the time-varying channel realizations. Numerical results are provided to validate our distributed algorithm for the static channel scenario and the DRL-based online algorithm for the dynamic channel scenario. We also demonstrate the advantage of the NOMA assisted multitask MA-MEC against conventional orthogonal multiple access scheme under both static and dynamic channels.