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： caching   decentralization   mobile edge computing   blockchain   smart contract  

摘要： In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

关键词： Mobile edge computing   Non-orthogonal multiple access   Privacy outage probability   Resource allocation   Partial offload  

摘要： Mobile edge computing can use the wireless access network to provide the services required by telecom users and cloud computing functions nearby, thereby creating a service environment with high performance, low latency and high bandwidth. Computational task offloading is a core issue in mobile edge computing. In order to improve the security when computing tasks are partially offloaded in non-orthogonal multiple access-based mobile edge computing systems, this paper studies the physical layer security of MEC networks considering the presence of eavesdroppers. We employ the secrecy outage probability to measure the secrecy performance of computation offloading. Under the premise of comprehensively considering the joint constraints of transmit power, local task calculation and confidentiality outage probability, we introduce the energy consumption weight factor to balance the transmission energy consumption and calculation energy consumption, and finally achieve the minimum weighted sum of system energy consumption. On the premise of satisfying two user priorities, in order to reduce system overhead, we design a joint task offloading and resource allocation mechanism. The mechanism uses binary search-based iterative optimization algorithm to seek the optimal solution after problem transformation, and obtain optimal task offloading and power allocation. A large number of simulation results show that the algorithm proposed in this paper can effectively reduce system energy consumption.

关键词： Internet of vehicles   Mobile edge computing   Offloading strategy   Resource allocation   Deep reinforcement learning  

摘要： To meet the stringent requirements for real-time performance of computing tasks on the Internet of Vehicles (IoV) scenario, the Mobile Edge Computing (MEC) technique is introduced to combine edge servers with vehicles that have storage and computing resources, thereby reducing latency. However, successfully accessing the channel and completing the offloading computation within the specified time remains a big challenge in scenarios with multitasking vehicles and multibase stations equipped with multiple channels. To address this problem, we propose the Multi-Agent Deep Deterministic Policy Gradient-based Offloading and Resource Allocation (MADDPG-RA) algorithm. First, a sub-optimal offloading strategy is determined using the MADDPG algorithm to minimize the sum of system latency and energy consumption. Specifically, this strategy determines whether each vehicle should perform local or offload computation, which MEC server to choose, and which channel to access if offloaded. Based on the above, a closed form expression for the optimal computational resource allocation for MEC is derived using Lagrange multipliers. The simulation results demonstrate that the proposed MADDPG-RA algorithm can effectively reduce the total system latency and energy consumption compared to the existing algorithms.

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： Mobile edge computing   Service migration   Hybrid learning   Deep reinforcement learning   Multi-user decisions  

摘要： Mobile edge computing, a new computing model, aims to reduce network latency and improve user experience by placing computing resources closer to the users. However, when users move away from the current service areas, the quality of services will decline. To guarantee the quality of services, service migration technique is proposed and studied. Due to the wireless resource contention among users, there is service migration decisionmaking coupling among multiple users. Meanwhile, making real-time service migration decisions for multiple users to minimize service cost with application deadlines remains an open challenge because of the huge state space. In this paper, we formalize the service migration problem with deadlines as a Markov decision process (MDP) and propose a hybrid learning based service migration strategy. The proposed approach integrates Monte Carlo method and DQN learning, which adopts online training without Q-values and neural network approximation to generate service migration decisions for multiple users. Experimental results demonstrate that the proposed hybrid learning based service migration strategy outperforms the existing migration strategies in terms of service success ratio and service cost.

关键词： Virtual reality   video code quality   mobile edge computing   rendering offloading   resource allocation  

摘要： Fully immersive virtual reality (VR) applications require ultra-high data rates and time-varying viewport rendering to ensure smooth operation. Therefore, this letter proposes a mobile edge computing-assisted wireless VR streaming transmission scheme to pursue a better quality of experience (QoE) and alleviate the computing burden of VR users. Our goal is to minimize the weighted energy consumption and distortion while ensuring the required QoE. The formulated problem is NP-hard and involves the optimization of quantization parameters, rendering offloading decisions, transmit precoding, and computing resource allocation. Simulation results validate the efficiency of the proposed algorithm in terms of the QoE performance.

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： 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   augmented reality   reward maximization   online learning   multi-armed bandits  

摘要： In the coming era of Metaverse, Augmented Reality (AR) has become a key enabler of diverse applications including healthcare, education, smart cities, and entertainments. To provide users with interactive and immersive experience, most AR applications require extremely high responsiveness and ultra-low processing latency. Mobile edge computing (MEC) has demonstrated great potentials in meeting such stringent latency requirements and resource demands of AR applications, by implementing AR requests in edge servers within the proximity of users. In this paper, we investigate the reward maximization problem for AR applications with uncertain resource demands in an MEC network, such that the accumulative reward of services provided for AR applications is maximized, while ensuring that the responsiveness of AR applications is enhanced, subject to network resource capacity. To this end, we formulate an exact solution when the problem size is small, otherwise we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We also develop an online learning algorithm with a bounded regret for the dynamic reward maximization problem without the knowledge of future arrivals of AR requests, by adopting the Multi-Armed Bandits (MAB) technique. Considering maximizing the reward may defer the implementations of some urgent yet low-award requests, we propose a fairness-aware online learning algorithm for the dynamic reward maximization problem, through a data rate prediction mechanism that adopts a multi-task and multi-timescale Long Short-Term Memory (MT2 -LSTM) method. Finally, we evaluate the performance of the proposed algorithms for AR applications by building a real test bed. Experimental results show that the proposed algorithms outperform existing studies by improving the award by 13%.