关键词： Mobile edge computing   Path planning   Resource allocation   Task offloading   Unmanned aerial vehicles   User equipments  

摘要： As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption.

关键词： Internet of vehicles   mobile edge computing   blockchain   double deep Q-networks   task offloading  

摘要： The Internet of Vehicles (IoV) represents a paradigm shift in vehicular communication, aiming to enhance traffic efficiency, safety, and the driving experience by leveraging interconnected vehicles. Despite its promise, the IoV faces challenges such as efficient task offloading, energy management, and data security. Mobile Edge Computing (MEC) emerges as a solution to some of these challenges by bringing computational resources closer to the vehicular network's edge, yet it raises critical concerns regarding resource management, service continuity, and scalability in dynamic vehicular environments. Addressing both IoV and MEC challenges necessitates robust and dynamic optimization mechanisms. In response to these challenges, our study introduces a multi-objective approach using Double Deep Q-Networks (DDQN). This algorithm combines the strengths of Deep Neural Networks (DNNs) and Deep Learning (DL) techniques, enabling dynamic decision-making that can adapt to changing conditions. By considering multiple objectives, the DDQN algorithm allows for a sophisticated trade-off analysis, efficiently balancing between the different objectives to optimize overall system performance. Through the use of Blockchain technology, known for its secure, decentralized structure, our model enhances the integrity of data, providing a reliable and efficient solution for IoV-MEC systems. We conducted a comparative analysis of our model against the standard Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms, which are prevalent in this field. Our model demonstrated significant improvements over these traditional methods: energy consumption was reduced by 26.4%, latency decreased by 6.87%, and the cost was minimized by 7.41%.

关键词： Mobile edge computing  

摘要： The edge intelligence (EI) has been widely applied recently. Splitting the model between device, edge server, and cloud can significantly improve the performance of EI. The model segmentation without user mobility has been investigated in detail in previous studies. However, in most EI use cases, the end devices are mobile. Few studies have been conducted on this topic. These works still have many issues, such as ignoring the energy consumption of mobile device, inappropriate network assumption, and low effectiveness on adapting user mobility, etc. Therefore, to address the disadvantages of model segmentation and resource allocation in previous studies, we propose mobility and cost aware model segmentation and resource allocation algorithm for accelerating the inference at edge (MCSA). Specifically, in the scenario without user mobility, the loop iteration gradient descent (Li-GD) algorithm is provided. When the mobile user has a large model inference task that needs to be calculated, it will take the energy consumption of mobile user, the communication and computing resource renting cost, and the inference delay into account to find the optimal model segmentation and resource allocation strategy. In the scenario with user mobility, the mobility aware Li-GD (MLi-GD) algorithm is proposed to calculate the optimal strategy. Then, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation ratio. The experimental results demonstrate the effectiveness of the proposed algorithms. © 2002-2012 IEEE.

关键词： cloud computing   Intelligent surveillance system   mobile edge computing   task offloading  

摘要： In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computational capabilities of mobile devices for tasks such as real-time video analysis, object detection, and tracking. However, the limited processing power and energy constraints of these devices hinder their ability to perform these tasks efficiently and effectively. The dynamic nature of the surveillance environment also adds complexity to the task-offloading process. To address this issue, mobile edge computing (MEC) comes into play by offering edge servers with higher computational capabilities and proximity to mobile devices. It enables ISS by offloading computationally intensive tasks from resource-constrained mobile devices to nearby MEC servers. Therefore, in this paper, we propose and implement an energy-efficient and cost-effective task-offloading framework in the MEC environment. The amalgamation of binary and partial task-offloading strategies is used to achieve a cost-effective and energy-efficient system. We also compare the proposed framework in MEC with mobile cloud computing (MCC) environments. The proposed framework addresses the challenge of achieving energy-efficient and cost-effective solutions in the context of MEC for ISS. The iFogSim simulator is used for implementation and simulation purposes. The simulation results show that the proposed framework reduces latency, cost, execution time, network usage, and energy consumption. © 1975-2011 IEEE.

关键词： semiring   centralizer   discrete logarithm problem   factor problem   undeniable signature   mobile edge computing  

摘要： The Undeniable Signature Scheme ( USS ) was first introduced by chaum and van in 1989. The advantage of the ( USS ) scheme is the signer's co-operation during the verification process. In this paper, the Discrete Logarithm Problem with Factor Problem ( DLFP ) is introduced. It's security and complexity is analyzed. Then, based on ( DLFP ) , a key exchange protocol is given. An undeniable signature scheme based on DLFP over semiring is proposed along with illustration we have also given application of the proposed USS in Mobile Edge Computing ( MEC ) . Then the proposed scheme's security and complexity is examined.

关键词： Mobile edge computing  

摘要： With the rise of 5G technology, Mobile (or Multi-Access) Edge Computing (MEC) has become crucial in modern network architecture. One key research area is the effective placement of edge nodes, which has attracted significant attention. Service providers strive to minimize deployment costs for these nodes within a network. Although many studies have explored optimal strategies for reducing these costs, most overlook the allocation of computational resources and the users' tolerance for delays. These factors add complexity, making previous methods less adaptable. In this paper, we define the Cost Minimization in MEC Edge Node Placement problem. Our goal is to find the optimal strategy for deploying edge nodes that minimize costs while cater to users' delay tolerance limits. We prove the NP-hardness of this problem and provide a range of solutions, including Cluster-based Mixed Integer Programming, Coverage First Search, and Distance-Aware Coverage First Search, to address this challenge effectively and efficiently. Additionally, we propose a fine-grained optimization approach for allocating computational resources to edge nodes based on user service requests, significantly lowering deployment costs. Extensive experiments on a large-scale real-world dataset show that our solutions outperform the state-of-the-art in efficiency, effectiveness, and scalability. © 2008-2012 IEEE.

关键词： Mobile edge computing  

摘要： With the rapid development of the Mobile Edge Computing (MEC) and the Internet of Things (IoT), intelligent interconnectivity and data exchange between devices have become feasible, bringing about the need for efficient energy management and complex network communication strategies. This paper proposes an Energy-Aware Low-Latency Routing Data Driven Model in Mobile Edge Computing (EALLR). This model introduces edge nodes that can perform computation and data processing close to the data source, thereby reducing latency and bandwidth consumption, optimizing data transmission paths, and ensuring that messages reach the target nodes quickly and efficiently. It overcomes the challenges posed by the social attributes of nodes, the diversity of network topology, and energy constraints. EALLR utilizes constraints on social strength between nodes, energy status, packet size, and message Time-To-Live (TTL) to optimize routing decisions. By optimizing the selection of routes with minimal energy consumption and adjusting the transmission strategy based on the size of the data packet, the algorithm enhances network energy efficiency and overall transmission efficiency. Simulation tests have proven that EALLR significantly reduces transmission delay while improving message delivery rates, especially in resource-limited and topologically complex network environments, demonstrating superior performance. © 1975-2011 IEEE.

关键词： Mobile edge computing  

摘要： The proliferation of intelligent Internet of Things (IoT) applications has led to an increase in the complexity of tasks generated by IoT devices putting pressure on the timely execution of these tasks. Mobile Edge Computing (MEC) has emerged as a promising paradigm to deliver low-latency computing services, enabled by task offloading from users to MEC servers. Meanwhile, as the IoT applications become increasingly diversified, the demand for communication and computing resources significantly varies over different tasks, highlighting the importance of efficient task offloading and resource allocation strategies in supporting low-latency task processing. Considering the heterogeneity of tasks, this paper investigates the problem of task offloading and resource allocation strategies in MEC system with heterogeneous tasks and propose a Deep Reinforcement Learning (DRL) based solution. Specifically, we consider task offloading strategies across various combinations of different task types and focus on optimizing channel allocation to minimize task completion delay. The effectiveness of proposed approach in reducing task completion latency is demonstrated through simulation results. © 2014 IEEE.

关键词： Robots   Microservice architectures   Task analysis   Cloud computing   Millimeter wave communication   5G mobile communication   Image edge detection   Containerization   ground robot   millimeter wave   mobile edge computing   mobile edge learning   object detection   orchestration   smart city   software defined networking  

摘要： The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios.

关键词： Mobile edge computing  

摘要： Mobile Augmented Reality (MAR) has gained increased attention thanks to its potential to transform applications in different domains. One of the challenges to realizing MAR systems is the processing of video frames efficiently. MAR user devices are often resource-constrained and unsuitable for real-time object detection and recognition from video streams. Edge computing has tremendous potential to enable MAR systems, where processing instances (e.g., serverless functions, containers, or virtual machines) can implement and manage the execution of convolutional neural networks (CNN) for processing MAR offloaded video frames. One of the challenges is how to balance the video frames across edge servers and processing instances. In this paper, we proposed the LAOS orchestrator for resource management and load balancing of distributed edge servers for MAR systems. The LAOS orchestrator balances incoming video frames among processing instances at edge servers that process video frames. It also determines when to spawn new instances of the CNN functions aimed at ensuring a predefined latency threshold for the processing of video frames. Besides, we devised a novel queuing-based framework for modeling the resource management problem of distributed edge servers for MAR systems. The obtained numerical results show that the proposed LAOS orchestrator reduces the latency and efficiently manages the edge computing resources when dynamic workload peaks are considered. © 2014 IEEE.