关键词： Contracts   Backscatter   Wireless communication   Resource management   Autonomous aerial vehicles   Task analysis   Optimization   Mobile edge computing   UAV   contract incentive   energy harvesting   deep reinforcement learning  

摘要： Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this article, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs' energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users' random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. First, we use deep Q-network (DQN) algorithm to update the HSs' offloading decisions according to the changing network environment. Second, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.

关键词： Federated learning   intermediate result exchanging   mobile edge computing   P2P communication  

摘要： The emerging Federated Learning (FL) permits all workers (e.g., mobile devices) to cooperatively train a model using their local data at the network edge. In order to avoid the possible bottleneck of conventional parameter server architecture, the decentralized federated learning (DFL) is developed on the peer-to-peer (P2P) communication. In DFL, model exchanging among workers is usually regarded as an atomic operation, which largely affects the total bandwidth consumption during model training. Given the limited communication resource on workers, model exchanging will pose a great challenge when meeting with the large-scale models. Herein, we propose to let workers exchange the intermediate results, instead of the entire model, with each other. We provide theoretical analysis of DFL based on intermediate result exchanging, which reveals the relationship between the training performance and the exchanging interval (i.e., the number of local updating iterations) of intermediate results. According to the convergence bound, we propose an adaptive exchanging interval (or frequency) algorithm called Fed-IR, which optimizes the trade-off between communication cost and training performance. Extensive simulation results show that compared with the model exchanging methods, our proposed algorithms can save communication traffic of around 42%similar to 81% while still achieving the similar accuracy.

关键词： Costs   Delays   Quality of service   Optimization   Servers   Cloud computing   Trajectory   Cost-efficient   mobile edge computing   online service provisioning   quality -of-service (QoS)  

摘要： The vigorous development of IoT technology has spawned a series of applications that are delay-sensitive or resource-intensive. Mobile edge computing is an emerging paradigm that provides services between end devices and traditional cloud data centers to users. However, with the continuously increasing investment of demands, it is nontrivial to maintain a higher quality-of-service (QoS) under the erratic activities of mobile users. In this paper, we investigate the service provisioning and updating problem under the multiple-users scenario by improving the performance of services with long-term cost constraints. We first decouple the original long-term optimization problem into a per-slot deterministic one by using Lyapunov optimization. Then, we propose two service updating decision strategies by considering the trajectory prediction conditions of users. Based on that, we design an online strategy by utilizing the committed horizon control method looking forward to multiple slots predictions. We prove the performance bound of our online strategy theoretically in terms of the trade-off between delay and cost. Extensive experiments demonstrate the superior performance of the proposed algorithm.

关键词： DenseNet   detection   mobile edge computing   parking spots   smart cities  

摘要： Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT-based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO-based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities.

关键词： Mobile edge computing   service function chain   digital twin   reliable service provisioning   approximation algorithm   online algorithm   resource allocation and optimization  

摘要： Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We third provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the prop

关键词： Mobile applications   Optimization   Smart phones   Vehicles   Urban areas   Sensors   Real-time systems   Mobile edge computing   urban traffic optimization   vehicle-to-infrastructure   cyber-physical system  

摘要： The stop-and-go traffic pattern at urban intersections leads to excessive energy use due to frequent vehicle braking, idling, and acceleration. This pattern, amplified by the growing use of automobiles, adversely affects city sustainability, causing delays, pollution, and increased carbon emissions. Addressing this, we introduce a mobile edge computing framework utilizing Internet of Things (IoT) and edge computing. This framework incorporates real-time vehicle-to-infrastructure communication and intelligent speed control algorithms into a mobile app, targeting speed optimization at signalized intersections to alleviate the negative impacts of stop-and-go traffic at urban intersections. The framework comprises three components: a cyberinfrastructure-based messaging system for real-time traffic and signal data from IoT-connected signal controllers and traffic sensors;a speed optimization algorithm generating speed advisories using phone-based sensor data (like GPS and driving directions), signal phase and timing information, and roadside vehicle detector data;and an ad-hoc mobile computing environment turning smartphones into edge devices for hosting the algorithm. This enables intelligent speed advisory along signalized corridors. The paper presents the detailed design and implementation of the proposed framework, highlighting its practicality, utility, and energy-saving potential. Our studies, including traffic simulations, real-vehicle lab experiments, evaluation surveys, and field tests, demonstrate its robustness and effectiveness. Specifically, simulations indicate that using the mobile app universally could lead to a 24% energy reduction in urban transportation systems.

关键词： Fog Computing   Mobile Edge Computing   Mobile Cloud Computing  

摘要： Today, cloud computation offloading may not be an appropriate solution for delay-sensitive applications due to the long distance between end-devices and remote datacenters. In addition, offloading to a remote cloud can consume bandwidth and dramatically increase costs. However, end-devices such as sensors, cameras, and smartphones have limited computing and storage capacity. Processing tasks on such battery-powered and energy-constrained devices becomes even more complex. To address these challenges, a new paradigm called Edge Computing (EC) emerged nearly a decade ago to bring computing resources closer to end-devices. Here, edge servers located between the end-device and the remote cloud perform user tasks. Recently, several new computing paradigms such as Mobile Edge Computing (MEC) and Fog Computing (FC) have emerged to complement Cloud Computing (CC) and EC. Although these paradigms are heterogeneous, they can further reduce energy consumption and task response time, especially for delay-sensitive applications. Computation offloading is a multi-objective, NP-hard optimization problem. A significant part of previous research in this field is devoted to Machine Learning (ML) methods. One of the essential types of ML is Reinforcement Learning (RL), in which an agent learns how to make the best decision using the experiences gained from the environment. This article provides a systematic review of the widely used RL approaches in computation offloading. It covers research in complementary paradigms such as mobile cloud computing, edge computing, fog computing, and the Internet of Things. We explain the reasons for using various RL methods in computation offloading from a technical point of view. This analysis includes both binary offloading and partial offloading techniques. For each method, the essential elements of RL and the characteristics of the environment are discussed regarding the most important criteria. Research challenges and Future trends are also ment

关键词： High-speed mobile business   mobile edge computing   service component caching   computation task offloading  

摘要： With the rapid development of high-speed assistant driving and smart inspections, the edge network is required to provide quick and reliable service to avoid large service response delays and frequent re-transmissions caused by interruption. However, the reasonable service component caching, efficient edge collaboration and reliable cross-domain computation offloading are still key problems to be solved. Thus, we consider an AI and mobile edge computing (MEC) integrated service framework, which is highly reliable for high-speed mobile businesses, and we divide the service process into component caching phase and task offloading phase. In the first phase, we novelly define the edge collaborative service domain (ECSD) which allows multiple edge nodes to collaboratively share resources from a global perspective and design a user behavior aware service component pre-caching method to increase resource utilization. In the second phase, based on the formed ECSDs and cached service components, we present an AI-empowered cross-domain computation task offloading mechanism including task partition and backup to enhance the reliable service capability of edge networks. Simulation results verify that the proposed mechanism can jointly optimize the allocation of caching, computation, and communication resources, while improving the service response speed and resource utility of edge networks.

关键词： Mobile edge computing   Emergency tasks   SDN   Collaborative computation offloading  

摘要： Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. Collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)'s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by

关键词： Task analysis   Games   Resource management   Multitasking   Bandwidth   Wireless communication   Costs   Multi-task   mobile edge computing   computation offloading   potential games   Nash equilibrium  

摘要： Mobile edge computing emerges to serve mobile users with low-latency computation offloading in edge networks, which are resource-constrained with massive users and workloads. However, existing communication and computing resource allocation schemes for offloaded tasks aren't efficient enough, where finished tasks still occupy resources, wasting constrained resources. Besides, the multi-user offloading is usually for scenarios of one task per user, ignoring real-world multi-task offloading scenarios where each user has multiple tasks, lack generality and flexibility. Meanwhile, local computing resource allocation schemes in multi-task scenarios ignore resource readjustment, causing low resource utilization. To solve these problems, we propose ECO-GAME, an efficient multi-task offloading scheme, which dynamically allocates bandwidth and computing resources to unfinished tasks, resulting in high resource utilization. We initially formulate the multi-task offloading problem as the game minimizing each user's cost, which is NP-hard. Thus we re-formulate the game utilizing potential games to optimize user's objective either locally or globally, and prove the existence of its Nash equilibrium. We then design an efficient multi-task offloading algorithm to obtain an approximate solution in polynomial time, together with computational complexity analysis. We further conduct performance evaluation on ECO-GAME utilizing price of anarchy. Numerical results demonstrate the efficiency of ECO-GAME, and show ECO-GAME reduces 49.2% cost over the state-of-the-art work, and scales well with the increasing number of tasks and users.