关键词： Vehicle-to-everything(V2X) applications   mobile edge computing   task offloading   latency prediction   Long Short-Term Memory (LSTM)   end-to-end (E2E) delay  

摘要： Mobile edge computing (MEC) is a key enabler of time-critical vehicle-to-everything (V2X) applications. Under MEC, a vehicle has the option to offload computationally intensive tasks to a nearby edge server or to a remote cloud server. Determining where to execute a task necessitates accurate estimation of the end-to-end (E2E) offloading delay. In this paper, we first conduct extensive measurements of the round-trip time (RTT) between a vehicular user and edge/cloud servers. Using these measurements, we present a latency-estimation framework for optimal task offloading. The propagation delay, measured by the RTT, is divided into two components: one that follows a trackable trend (baseline) and the other (residual) that is quasi-random. For the baseline component, we first cluster measured RTTs into several groups, depending on signal strength indicators. For each group, we develop a Long Short-Term Memory (LSTM) regression model. A statistical approach is provided for predicting the residual component, which combines the Epanechnikov Kernel and moving average functions. Predicted propagation delays are incorporated into virtual simulations to estimate the transmission, queuing, and processing delays, hence accounting for the E2E delay. Based on the estimated E2E delay, we design a task offloading scheme that minimizes the offloading latency while maintaining a low packet loss rate. Simulation results show that the proposed offloading strategy can reduce the E2E delay by approximately 60% compared to a random offloading scheme while keeping the packet loss rate below 3%.

关键词： Mobile edge computing   Bayesian optimization   online learning   task offloading   resource allocation  

摘要： Recent years have witnessed the emergence of mobile edge computing (MEC), on the premise of a cost-effective enhancement in the computational ability of hardware-constrained wireless devices (WDs) comprising the Internet of Things (IoT). In a general multi-server multi-user MEC system, each WD has a computational task to execute and has to select binary (off)loading decisions, along with the analog-amplitude resource allocation variables in an online manner, with the goal of minimizing the overall energy-delay cost (EDC) with dynamic system states. While past works typically rely on the explicit expression of the EDC function, the present contribution considers a practical setting, where in lieu of system state information, the EDC function is not available in analytical form, and instead only the function values at queried points are revealed. Towards tackling such a challenging online combinatorial problem with only bandit information, novel Bayesian optimization (BO) based approaches are put forth by leveraging the multi-armed bandit (MAB) framework. Per time slot, the discrete offloading decisions are first obtained via the MAB method, and the analog resource allocation variables are subsequently optimized using the BO selection rule. By exploiting both temporal and contextual information, two novel BO approaches, termed time-varying BO and contextual time-varying BO, are developed. Numerical tests validate the merits of the proposed BO approaches compared with contemporary benchmarks under different MEC network sizes.

关键词： Mobile edge computing   5G   Edge server placement   Load balancing   Heterogeneous capacities   Knapsack problem  

摘要： The rapid proliferation of low-latency and high-bandwidth applications has brought edge computing to the forefront of mobile network architectures. However, the strategic placement of edge servers plays a vital role in balancing price-performance trade-offs significantly. Existing works addressing the Edge Server Placement Problem have assumed homogeneous computational capabilities across ESs, which is not a pragmatic assumption considering variations in user densities and workload fluctuations across typical cityscapes. This work proposes a solution to the Edge Server Placement Problem with heterogeneous ES capacities and introduces a novel scheme to evaluate the workload of ESs for 5G networks. Additionally, this paper also proposes a novel Knapsack-based Metaheuristic for allocating base stations to edge servers, turning the Edge Server Placement Problem into a 0-1 Knapsack problem. Experimental evaluation using popular 5G traffic demand datasets has found that the proposed approach improves workload balance by 40.79%, utilisation rates by 57.58%, and reduces energy consumption by 44.68% approximately vis-a-vis homogeneous counterparts.

关键词： Mobile edge computing   Deep reinforcement learning   Meta reinforcement learning   Virtual machine placement  

摘要： Mobile edge computing requires more and more high-performance servers, resulting in increased energy consumption. As an effective means to reduce energy consumption, virtual machine placement (VMP) has been widely studied. In the edge computing environment, as the number of terminal device requests continues to increase, the scale of VMP becomes larger and larger, and existing research algorithms may take a long time to converge. The reason is that as the number of VMs increases, the search space of the policy becomes larger and the agent needs to interact with the environment for a longer time to make the best decision. In addition, existing research methods only consider reducing energy consumption, rarely consider the response latency of virtual machines, and almost ignore the dynamic changes of the edge environment. To overcome these drawbacks, we propose a virtual machine placement algorithm based on meta-reinforcement learning, which consists of an inner and outer loop. The inner loop designs a deep reinforcement learning algorithm combined with the order exchange and migration mechanism to generate the best decision, and the outer loop provides meta-strategy parameters for the inner loop based on meta-learning to accelerate the convergence capability of the inner loop, thereby obtaining efficient virtual machine placement decisions quickly from a new environment. Through simulation experiments, we demonstrate that our approach effectively reduces the energy consumption of the edge server and the response latency of VMs at different problem sizes compared to the three baseline algorithms. At the same time, it quickly adapts to the new environment with only a small number of gradient updates.

关键词： Task analysis   Servers   Mobile handsets   Heuristic algorithms   Reinforcement learning   Costs   Deep learning   Mobile edge computing   task offloading   optimization algorithm   deep reinforcement learning  

摘要： The rapid advancement of mobile edge computing (MEC) networks has enabled the augmentation of the computational power of mobile devices (MDs) by offloading computationally intensive tasks to resource-rich edge nodes. This paper discusses the decision-making process for task offloading and resource allocation among multiple mobile devices connected to a base station. The primary objective is to minimize the time taken to complete tasks while simultaneously reducing energy consumption on the device under a time-varying wireless fading channel. This objective is formulated as an energy-efficiency cost (EEC) minimization problem, which cannot be solved by conventional methods. To address this challenge, we propose a dynamic offloading decision algorithm of dependent tasks (DODA-DT) that adjusts local task execution based on edge node status. The proposed algorithm facilitates fair competition among all devices for edge resources. Additionally, we use a deep reinforcement learning (DRL) algorithm based on an actor-critic learning structure to train the system to quickly identify near-optimal solutions. Numerical simulations demonstrate that the proposed algorithm effectively reduces the total cost of the task in comparison to previous algorithms.

关键词： federated learning   mobile edge computing   dataset management  

摘要： Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback-Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy.

关键词： mobile edge computing   resource allocation   resource pricing   risk assessment   Stackelberg game   Nash equilibrium   91-10  

摘要： The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users' tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract's autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments.

关键词： Mobile edge computing   Classification   Trust   Task offloading   Entropy weight method  

摘要： To address the problems of how to resist internal attacks caused by offloading traffic surveillance video processing tasks to the edge cloud and how to reasonably allocate edge computing resources to reduce service latency and system energy consumption, this paper proposes a trust -based offloading scheme for edge computing tasks. Firstly, a novel trust scheme is presented, which establishes a trust feature value matrix and constructs a difference function based on linear discriminant analysis. To achieve the best classification performance, the maximization of the difference function is transformed into an optimization problem of finding the optimal weights. This scheme effectively solves the subjectivity issue in threshold determination in previous trust mechanisms. Finally, a technique for order preference by similarity to an ideal solution similarity ranking technique for multi -criteria decision making based on the entropy weight method (EW-TOM) is proposed to jointly optimize system latency and energy consumption. Simulation results show that the proposed scheme achieves more than 95% accuracy in filtering normal nodes, which is more than 20% better than other schemes. In addition, the latency and energy cost are better than other schemes, with an improvement of more than 6%.

关键词： Mobile edge computing   blockchain   authenticated data transmission   key management   UC framework  

摘要： In mobile edge computing (MEC) systems, data is frequently transmitted between MEC servers and users holding mobile devices for supporting related services. However, critical threats towards data confidentiality and authenticity are raised: adversaries always attempt to extract data content from the transmission and impersonate others to spread malicious data for profits. Furthermore, users have to store the (secret and public) keys used for data transmission locally. Consequently, only devices maintaining the keys can be utilized to access the services provided by MEC servers, and "portability" cannot be achieved. In this paper, we propose a portable authenticated data transmission scheme (dubbed Biplane) via blockchain for MEC systems. Biplane is based on two techniques. One is a blockchain-based authenticated hybrid encryption mechanism, which guarantees data authenticity and confidentiality without requiring a third party (e.g., a Certificate Authority) to assist the MEC servers in certifying users' public keys. The other one is a blockchain-based portable key management mechanism, which enables the user to transmit data without maintaining any parameters in her/his local devices. We formally prove that Biplane achieves confidential and authenticated data transmission in the universally composable (UC) framework. We also conduct a comprehensive evaluation to demonstrate that Biplane is efficient.

关键词： Unmanned aerial vehicle   mobile edge computing   ultra-reliable and low-latency communications   computation latency  

摘要： In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where multiple ground devices offload portions of their latency-sensitive and mission-critical computational tasks to a UAV-carried MEC server for remote computing and compute the remaining portions locally. To meet the low-latency requirements of the MEC, ultra-reliable and low-latency communication (URLLC) is used to offload tasks from the devices to the UAV. We minimize the maximum computation latency among all devices by jointly optimizing the computing times and CPU frequencies of the devices and the UAV, the offloading bandwidths of the devices, and the three-dimensional location of the UAV. We propose an algorithm that decomposes the joint optimization problem into three subproblems, which optimize the UAV's horizontal location, the UAV's altitude, and the offloading bandwidths and computing CPU frequencies, respectively. In solving the subproblems, the data rate expression of the devices' finite-blocklength offloading is accurately approximated by a tractable logarithmic function, and the successive convex approximation technique is applied to tackle the non-convex structure. Furthermore, a semi-closed-form solution to the subproblem that optimizes the bandwidths and CPU frequencies is derived to reduce the complexity. Simulation results show that the proposed algorithm can significantly reduce the system's computation latency compared to the benchmark schemes.