001 Energy-Efficient Joint Task Offloading and Resource Allocation in OFDMA-Based Collaborative Edge Computing​

This article is a study on OFDMA-based collaborative mobile edge computing (C-MEC). The article first introduces the background and advantages of C-MEC, and then presents a joint optimization problem for task offloading, collaborative decision making, and resource allocation. The article models a mixed integer nonlinear programming (MINLP) problem with the objective of minimizing the total energy consumption of all mobile users while satisfying task delay constraints. Since this problem is NP-hard, the article proposes a two-layer framework of alternating methods to solve it. In the first layer, the article utilizes an ant colony system (ACS)-based heuristic algorithm to optimize task offloading decisions; in the second layer, the article utilizes a deep reinforcement learning algorithm based on deep Q-networks (DQN) to optimize resource allocation. The article verifies the excellent performance of the proposed algorithm in terms of energy efficiency and task completion rate through simulation experiments. The experimental results show that the proposed algorithm can effectively reduce the energy consumption of mobile users and ensure the task completion within the specified time. In addition, the convergence and robustness of the algorithm are analyzed in the paper.

002 The Case for FPGA-Based Edge Computing​

This article focuses on an FPGA-based edge computing model that takes advantage of the customizability of FPGAs and the low latency of edge computing to accelerate the response time and save energy of mobile interactive applications. The article selects three typical computer vision applications as case studies, namely, handwritten digit recognition, object recognition, and face detection. The article experimentally compares the performance of four schemes: FPGA edge offload, CPU edge offload, CPU cloud offload, and mobile local processing, and the results show that FPGA edge offload outperforms the other schemes in terms of response time, execution time, and energy consumption. The article also explores data parallel processing methods between mobile and edge nodes to further reduce the response time of batch requests. The article concludes with a discussion of the advantages, limitations, and future research directions of the FPGA edge computing model.

第二周

003 Joint Task Offloading and Cache Placement for Energy-Efficient Mobile Edge Computing Systems​

This article is about joint task cache placement and offloading design for cache-enabled multi-user Mobile Edge Computing (MEC) systems. The goal of the article is to minimize the total system-weighted energy consumption in the task caching and task arrival/execution phases, taking into account the constraints of cache capacity, task causality, and task completion deadline. The article first solves the optimal offline solution of the problem using the branch-and-bound (BnB) method, and then proposes two low-complexity schemes based on task popularity and convex relaxation. The article demonstrates the advantages of the proposed schemes over existing benchmark schemes through numerical results.

004 Energy-Efficient Computation Offloading in Mobile Edge Computing Systems With Uncertainties​

This article is about the problem of energy-efficient computational offloading in mobile edge computing systems. The article proposes a new approach to this problem that relaxes the strong assumptions on radio channel and network queue sizes made in existing research and takes into account the uncertainty inherent in the network. The article uses extreme value theory to limit the probability of occurrence of uncertain events and develops a column generation-based ε-bounded approximation algorithm to solve the posed problem. The algorithm is effective in finding a feasible solution that is less than (1 + ε) times the optimal solution. The article also implements the proposed scheme on an Android smartphone and conducts extensive experiments using real-world applications. The experimental results confirm that the energy consumption of the client device can be reduced by taking into account the inherent uncertainty in the computational offloading process. The proposed computational offloading scheme also significantly outperforms other schemes in terms of energy savings.

第三周

005 Joint power control and computation offloading for mobile edge networks​

This article investigates how to minimize the energy consumption of mobile devices by offloading computationally intensive tasks to MEC servers, taking into account co-channel interference and task latency requirements. The article presents an analytical model to decouple power control and computational resource allocation and shows that the joint optimization problem is invex and can be solved by a CCP-based algorithm. The article also proves that the joint power and CPU cycle allocation problem is a type I invex problem, which guarantees that each KKT stabilization point of the problem is a global minimum. The article also provides an offloading decision criterion for optimal energy efficiency computation based on the partial derivatives of the total energy consumption of the mobile device.The article models the communication channel as block fading and the computational task as a tuple of input data size, required CPU cycles, and maximum latency. The article defines the transmission power, rate, latency, and energy consumption of each offloaded mobile device, as well as the local execution power, latency, and energy consumption. The article also introduces offloading decision variables to indicate whether a mobile device chooses to offload its task or not.The article proves that the total transmission energy consumption function is a concave function monotonically increasing with respect to each power configuration component. The article also derives a set of linear equations to represent the relationship between transmission power and computational resources, where the coefficient matrix is an inverse positive M matrix that depends on the CPU cycle allocation.

006 Joint Offloading and Resource Allocation Using Deep Reinforcement Learning in Mobile Edge Computing​

This article investigates the problem of partial task offloading and resource allocation in Mobile Edge Computing (MEC). The article proposes a Deep Reinforcement Learning (DRL)-based Energy Efficiency Algorithm (EEDRL) that decomposes the original non-convex optimization problem into two sub-problems, i.e., offloading ratio selection and resource allocation.The EEDRL employs an actor-critic network architecture, where the actor network learns the optimal mapping from the time-varying wireless channel to offloading ratios, and the critic network utilizes an advanced convex optimization algorithm to solve the the resource allocation subproblem.EEDRL devises an annealed Gaussian noise addition method for exploring more satisfactory offloading ratios in actor networks and explores different exploration strategies and verifies the generalization of the method. Numerical experiments are conducted to compare the method with various existing offloading schemes, and the results show that EEDRL is able to save up to 57.6% of energy consumption relative to binary offloading and achieves significant computation time speedups relative to the SQP algorithm. It is also shown that jointly optimizing the energy consumption of SMDs and MEC servers by choosing appropriate weighting factors for the MEC servers can reduce up to half of the total energy consumption, relative to a greedy strategy that only considers the energy reduction of SMDs.

第四周

007 Energy-Efficient Resource Management in UAV-Assisted Mobile Edge Computing​

This paper investigates the energy efficiency optimization problem in UAV-assisted mobile edge computing systems with the goal of minimizing the energy consumption of mobile devices and UAVs. The paper considers factors such as UAV trajectory optimization, communication and computational resource allocation, and task offloading, and presents a non-convex optimization problem. To solve this problem, this paper introduces an algorithm based on block-by-block upper bound minimization (BSUM), which successively minimizes a tight upper bound of the objective function and updates the variables step by step. In this paper, we demonstrate the effectiveness of the proposed algorithm through numerical simulation results, which can significantly reduce the total energy consumption of the network compared to other benchmark algorithms.

008 Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks​

This paper investigates the use of deep reinforcement learning in mobile edge networks for energy efficient task offloading and resource allocation optimization for augmented reality applications. The study builds a more specific and detailed model of an augmented reality application by dividing an application into five subtasks and considering the dependencies and latency requirements between the subtasks. In order to solve the hybrid problem of multi-user competition and cooperation and simultaneously satisfy the energy minimization and quality of service guarantee for each user, a multi-intelligent deep deterministic policy gradient (MADDPG) algorithm is proposed. The effectiveness and superiority of the proposed algorithm in single-edge server and multi-edge server systems are verified through simulation experiments.

001 Cooperative Dynamic Voltage Scaling and Radio Resource Allocation for Energy-Efficient Multiuser Mobile Edge Computing​

This article investigates cooperative dynamic voltage regulation and wireless resource allocation in multi-user mobile edge computing for energy efficient computation offloading. The article proposes a suboptimal algorithm based on Lagrangian pairwise decomposition to minimize the weighted sum of mobile energy consumption by jointly optimizing the computational speed of smart mobile devices, subcarrier allocation, transmit power of each subcarrier, data size sent per subcarrier, and offloading ratio. Simulation results show that the algorithm converges quickly and can significantly reduce energy consumption. In addition, the paper finds that the total mobile energy consumption remains stable or increases with the variance of the delay requirement for a given delay mean, which can guide the access control in practice.

002 Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading​

The article investigates the resource allocation of multi-user mobile edge computing offload (MECO) systems based on time division multiple access (TDMA) and orthogonal frequency division multiple access (OFDMA). The article first investigates TDMA MECO systems with infinite or finite cloud computing capabilities and formulates the optimal resource allocation problem as a convex optimization problem. Then, the authors consider OFDMA MECO systems and formulate the optimal resource allocation problem as a mixed integer problem. By converting the OFDMA problem into its TDMA counterpart, the authors propose a low-complexity suboptimal algorithm and show near-optimal performance in simulations. In summary, this paper investigates the energy efficient resource allocation problem for mobile edge computing offload and proposes corresponding optimal and suboptimal algorithms.

第二周

003 Game and Contract Theory-Based Energy Transaction Management for Internet of Electric Vehicle​

This is a research paper on game and contract theory based power transaction management in smart grid systems. The article proposes a three-tier bi-directional electric energy trading management strategy, including an energy grid as an energy supplier, an energy aggregator as an energy distributor, and an electric vehicle as an energy provider. The article uses a Stackelberg game to solve the optimal pricing and electric vehicle discharging problems, and proposes an incentive mechanism based on contract theory to motivate electric vehicles to participate in energy trading and optimize the utility of energy aggregators. Simulation results show that the proposed scheme performs significantly better than other existing schemes in various scenarios.

004 Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems​

In this paper, the study the problem of joint offloading and computational optimization in wireless mobile edge computing systems. The paper considers a wirelessly powered multi-user MEC system consisting of multiple antenna access points (APs) and multiple users, where the APs (integrated MEC servers) transmit energy via radio waves to charge multiple users, and each user node relies on the collected energy to perform latency-sensitive computational tasks. Through the MEC, these users can perform their respective tasks locally by themselves or offload all or part of them to the AP according to the time division multiple access (TDMA) protocol.In this case, this study optimizes the MEC-WPT system by jointly optimizing the transmitted energy beamformer of the AP, the central processing unit (CPU) frequency and offload bits of each user, and the time allocation between different users design to pursue energy efficiency. Specifically, this study minimizes the energy consumption of the access point in a given time block under the computational delay and energy harvesting constraints for each user. By transforming this problem into a convex framework and using the Lagrangian dual method, an optimal solution in semi-closed form is obtained in this paper. Numerical results show that the proposed joint design outperforms other benchmark solutions in terms of achieving energy efficiency.

第三周

005 Learning Based Energy Efficient Task Offloading for Vehicular Collaborative Edge Computing​

This paper is a paper on Vehicular Collaborative Edge Computing (VCEC). The article proposes an energy efficient task offloading approach based on learning, which aims to reduce energy consumption within the VCEC system by maximizing the use of idle and redundant resources of vehicles. The authors apply Lyapunov optimization to decompose the original problem into three subproblems and solve them one by one by addressing the challenges of short-term decision and long-term queueing delay constraints, information uncertainty, and task offloading conflicts. These three subproblems are 1) short-term task unloading decision, 2) long-term queueing delay constraint, and 3) information uncertainty and task unloading conflict. The results of extensive numerical simulations show that the method outperforms the benchmark method in terms of energy consumption, learning regret, task backlog and end-to-end delay.

006 Mobile Edge Computing-Enabled Internet of Vehicles: Toward Energy-Efficient Scheduling​

This article discusses the construction of green cities in modern transportation systems. The article points out that although modern transportation systems facilitate the daily life of citizens, increasing energy consumption and air pollution pose challenges to the construction of green cities. Currently, research on green IoVs has focused on battery-backed RSUs or energy management of electric vehicles. However, the computational tasks and load balancing among RSUs have not been fully studied. To meet the heterogeneous requirements of communication, computation and storage in IoV, this paper constructs an energy-efficient scheduling framework for minimizing the energy consumption of RSUs in MEC-supported IoV. Specifically, the paper proposes a heuristic algorithm to achieve this by jointly considering task scheduling among MEC servers and the downlink energy consumption of RSUs. To the best of our knowledge, this is the first work to focus on the problem of energy consumption control of MEC-enabled RSUs. The performance evaluation shows that the framework is effective in terms of energy consumption, latency and task blocking possibilities. Finally, the paper details some of the main challenges and open issues and identifies future research directions including renewable energy recharge, sustainable and reliable MEC, incentive and trusted offloading, and deep learning-based scheduling.

第四周

007 Multi-User Multi-Task Computation Offloading in Green Mobile Edge Cloud Computing​

This article discusses multi-user multi-task computation offloading in green mobile edge cloud computing. The article proposes a multi-user multi-task computation offloading framework that takes into account the dynamics of energy in the mobile edge cloud and the dynamics of tasks in different mobile devices. The article also proposes a centralized and distributed greedy maximum scheduling algorithm and discusses the performance bounds of the proposed scheme. Simulation results show that the proposed scheduling algorithm provides an average system utility improvement of 18.8% to 31.9% over the random scheduling scheme.

008 Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System​

This article investigates a UAV-assisted multi-fleet mobile edge computing system that aims to maximize the weighted global energy efficiency of the system. The article designs a fleet controller based on a 2-D path-tracking model and the Frenet framework, and simulates the coupling characteristics of air-to-ground communication and on-board computing. Due to the non-convexity of the objective function and constraints of the optimization problem, the article proposes an optimization algorithm based on sequential quadratic programming (SQP) method. Simulation results show that the proposed method significantly outperforms the conventional scheme. This paper provides new ideas and methods to improve the energy efficiency of the system by studying the UAV-assisted multi-fleet mobile edge computing system.

第五周

009 Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning​

This article presents a fast adaptive task offloading method based on meta-reinforcement learning for solving the task offloading problem in multi-access edge computing (MEC). The article first introduces the background of MEC and the challenges of the task offloading problem. Then, the article proposes a meta-reinforcement learning-based task offloading method that can quickly adapt to new environments with a small number of gradient updates and samples. The article models the mobile application as a directed acyclic graph (DAG) and uses a custom sequence-to-sequence (seq2seq) neural network to model the offloading strategy. To effectively train the seq2seq network, the article proposes a method that combines first-order approximations and truncated alternative targets. Experimental results show that the method is able to reduce the latency by up to 25% compared to three benchmark algorithms, while being able to quickly adapt to new environments. In conclusion, this article proposes a fast adaptive task offloading method based on meta-reinforcement learning that can effectively solve the task offloading problem in MEC.

010 Meta Reinforcement Learning for Multi-task Offloading in Vehicular Edge Computing​

This paper investigates the problem of multitask offloading in vehicular edge computing. Due to the highly dynamic nature of the vehicle environment and the heterogeneous characteristics of vehicle services, traditional expert-based or learning-based strategies require updating manual parameters or retraining learning models, which leads to intolerable overhead. Therefore, in this paper, a Seq2seq-based meta-reinforcement learning algorithm is proposed for solving the multitask offloading problem. Specifically, a bidirectional gated cyclic unit integrated attention mechanism is designed to determine unloading actions by encoding sequential unloading actions and displaying different preferences for input sequences. In particular, a meta-reinforcement learning framework is designed based on a model agnostic meta-learning framework that trains meta-strategies offline and quickly adapts to new multitask offloading scenarios within a few training steps. Finally, this paper evaluates the performance based on the task generator DAGGEN and real vehicle trajectories, and the results show that SMRL-MTO reduces the task execution time by 11.36% on average compared to the greedy algorithm.

001 A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered​

https://doi.org/10.1016/j.apenergy.2020.114983

This article is about a study that proposes a coordinated control approach for optimizing the performance of building complexes with energy storage, electric vehicles and energy sharing.This study proposes a coordinated control approach to optimize the performance of a building complex with energy storage, electric vehicles, and energy sharing. The study first develops an electric vehicle charging and discharging model, then based on predicted electricity demand and renewable energy generation data for the next 24 hours, the coordinated control first considers the entire complex as an "integrated" building and uses a genetic algorithm to optimize its operation and the charging and discharging of electric vehicles. Next, non-linear planning is used to coordinate the operation of each building over the next 24 hours. For validation, the developed control has been tested on a real building complex in Ludvika, Sweden. The results of the study show that the developed control can increase the daily renewable self-use rate at the cluster level by 19% compared to the conventional control, while reducing the daily electricity bill by 36%.

002 An Improved SOC Control Strategy for Electric Vehicle Hybrid Energy Storage Systems​

https://doi.org/10.3390/en13205297

This is an article on hybrid energy storage system for electric vehicles. The article proposes an optimized power distribution method using two isolated soft-switched symmetrical half-bridge bidirectional converters connecting a battery and a supercapacitor as a composite structure for the protection structure. The article mentions that hybrid energy storage system (HESS) is an effective method to improve the performance of electric vehicles and extend the battery life. In such systems, batteries and supercapacitors are connected in parallel to provide higher peak power and better energy management. The article proposes a novel HESS structure in which two isolated soft-switched symmetrical half-bridge bidirectional converters are used to connect the battery and the supercapacitor. This structure provides better protection and can be optimized for energy management through an improved energy distribution strategy based on SOC control. This strategy allows the supercapacitor to be charged and discharged at a peak current of about 4ibat and can be adapted to different types of load profiles. Experimental results show that the use of this HESS structure and energy allocation strategy can improve the acceleration performance of electric vehicles by about 50% and reduce energy losses by about 69% compared to the battery-only mode. This approach not only improves energy utilization, but also reduces battery aging effects.

第二周

003 Adaptive DE Algorithm for Novel Energy Control Framework Based on Edge Computing in IIoT Applications​

DOI 10.1109/TII.2020.3007644

This is a paper on a novel energy control framework based on edge computing for industrial IoT applications. The paper proposes an efficient energy control framework to reduce energy waste and increase benefits for industrial users through edge computing. For this purpose, battery storage systems are used to store energy to ensure supply stability and power quality. With this framework, the optimal load pattern and the corresponding storage capacity of the battery storage system can be obtained based on historical load data from energy markets and industrial users. However, calculating these requires consideration of trade-offs between equipment costs, time-of-use tariffs, operating costs, and other relevant factors, which would be an NP-hard problem. To address this challenge, the authors also propose an adaptive hybrid differential evolutionary algorithm with a novel variational strategy. The experimental results show that the proposed algorithm and framework have good results.

004 Secure and Efficient Vehicle-to-Grid Energy Trading in Cyber Physical Systems: Integration of Blockchain and Edge Computing​

This is an academic paper on vehicle-to-grid (V2G) energy trading. The article proposes a secure and efficient framework for V2G energy transactions by integrating blockchain, contract theory, and edge computing. First, the article develops a secure energy trading mechanism based on a federated blockchain. Then, an efficient incentive mechanism based on contract theory is proposed considering the information asymmetry. Next, edge computing is introduced to improve the success probability of block creation. Finally, the performance of the proposed framework is verified by numerical results and theoretical analysis. In summary, this paper investigates how to integrate blockchain, contract theory, and edge computing to achieve security and efficiency in V2G energy transactions.

第三周

005 Energy Efficient Task Caching and Offloading for Mobile Edge Computing​

This paper investigates task caching and offloading in mobile edge computing. The authors first present task caching on edge clouds, which is the first study of task caching in mobile edge computing. The article further investigates task caching and offloading policies that determine which tasks should be cached and how many tasks should be offloaded. The goal is to minimize the total energy consumption of mobile devices while satisfying user latency requirements. The authors formulate this problem as a mixed-integer nonlinear programming problem and propose an efficient algorithm to solve it. Simulation results show that the proposed scheme in this paper has a lower energy cost compared to other schemes. Future work will consider multiple edge cloud task caching and offloading strategies. The experimental results show that the energy cost of mobile devices can be effectively reduced by rational deployment of cache location and task offloading.