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Distributed Edge Intelligence Enabled Resource Control in IoV With Use Case in Emergency Healthcare Support

IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 4761-4772页

作者： Lyu, Xiaohong Taneja, Ashu Rani, Shalli Feng, Yanhong Jinzhou Med Univ Affiliated Hosp 1 Dept Imaging Jinzhou 121012 Peoples R China Chitkara Univ Inst Engn & Technol Rajpura Punjab India

Modern vehicles involve large number of sensors, cameras and communication systems for real-time traffic management, collision avoidance and vehicle health monitoring. As the Internet of Vehicles (IoV) ecosystem evolve with more number of connected vehicles, handling of the enormous data is a challenge. This is overcome with the promising distributed edge intelligence (DEI) approach in which the computational tasks are distributed among the intelligent road side units (RSUs) at the network edge. The edge servers cooperate among themselves so as not to overload the central cloud server. This article presents a cooperative vehicular communication network which exploits the existing 5G infrastructure in roadside building as the edge/relay nodes. To overcome the communication and energy overhead, network resource management is enabled through proposed edge node selection algorithm. Further, a joint edge node and antenna selection algorithm is proposed for enhanced energy efficiency (EE) and reduced outage. The closed-form expression for the outage probability of the proposed cooperative communication scheme is derived. Our analysis shows that the proposed selection approach achieves improved outage probability and energy-efficiency. In particular, the proposed edge node selection approach improves the EE by 10.46% at total transmit power to noise power ratio of 16 dB. Moreover, the overall system performance is further enhanced by proposing a joint selection scheme. Specifically, the analysis shows that the energy-efficiency improves by 27.87% with the joint selection scheme. In the end, a use case scenario of DEI empowered IoVs in emergency healthcare support is discussed along with the future research directions.

关键词： Relays Artificial intelligence Cloud computing Servers Energy efficiency Antennas 5G mobile communication Delays Signal to noise ratio Probability computational offloading edge intelligence energy efficiency (EE) Internet of Vehicles (IoVs) smart healthcare

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Acceleration offloading for differential privacy protection based on federated learning in edge intelligent controllers

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 163卷

作者： Shang, Wenli Ge, Jingbo Ding, Lei Jiang, Zhenbang Sui, He Guangzhou Univ Guangdong Higher Educ Inst Sch Elect & Commun Engn Key Lab On Chip Commun & Sensor Chip Guangzhou 510006 Peoples R China Civil Aviat Univ China Coll Aeronaut Engn Tianjin 300300 Peoples R China

When implementing Federated Learning (FL) on Edge Intelligence Controllers (EIC) in the Industrial Internet of Things (IIoT), it is important to consider the limitations of the EICs' computational capabilities and address potential privacy concerns. For the efficient and secure implementation of FL on EICs, three key issues require attention: (i) efficient deployment on EICs with limited computational capacity, (ii) avoiding privacy issues that arise from offloading strategies when using offloading to accelerate, and (iii) mitigating privacy leaks that may result from disclosed parameters. To address the aforementioned concerns, this paper proposes a task offloading model called Fedoffloading. . Employing Deep Reinforcement Learning (DRL) techniques, Fedoffloading accelerates EIC training by offloading the training tasks of the model to the Edge servers (ES). It utilizes the Laplace distribution to safeguard the privacy of the offloading strategies. Meanwhile, prevent privacy breaches caused by disclosed parameters, Fedoffloading allows EICs to inject different levels of artificial noise before transmitting training data. Experimental studies conducted on a test platform reveal that, compared to classical FL, Fedoffloading can reduce training time by 54.70%, and even up to 78.06% when training larger models. The Security Module effectively protects the offloading strategies, meeting privacy requirements while also minimizing training time. In addition, to prevent privacy leakage caused by EICs, we introduce noise in the parameters disclosed during training, and show that the intermediate activation data more susceptible to noise.

关键词： Industrial Internet of Things Edge intelligent controllers computational offloading Federated learning Differential privacy

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Optimizing Mobile Cloud Computing: A Comparative Analysis and Innovative Cost-Efficient Partitioning Model

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SN Computer Science 2025年第1期6卷 1-25页

作者： Ali, Mushtaq Muhammad, Dost Khalaf, Osamah Ibrahim Habib, Raja Faculty of Computing Riphah International University Islamabad Pakistan CRT-AI and ADAPT Research Centers School of Computer Science University of Galway Galway Ireland Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Iraq Department of Computing Shifa Tameer-e-Millat University Islamabad Pakistan

Mobile cloud computing (MCC) integrates mobile devices with cloud computing to enhance performance and provide scalable services. It enables the partitioning of computationally intensive mobile applications, utilizing the cloud resources for remote execution. This process is vital for optimizing resource utilization and energy efficiency in distributed computing environments. Initially, static application partitioning was developed, dividing system resources into fixed-sized partitions. Though it is simple and reduces operational overhead, it lacks adaptability to dynamic computing environments due to mobility. Modern frameworks use runtime partitioning, dynamically allocating resources based on runtime profiling, which increases computational overhead and energy consumption. Despite various studies on MCC, there is a notable oversight in numerical analysis of application partitioning. This paper presents a comparative numerical study of existing partitioning techniques and proposes a Cost-Efficient Partitioning (CEP) model. The CEP model combines the static nature to reduce computational overhead with the dynamic nature to address runtime challenges. This research addresses the shortcomings of existing approaches, potentially transforming computational offloading into energy-saving solutions. Our results show that the execution time (ET) of a task in the CEP model is 0.826 s, which is longer than the ET of static partitioning however shorter than the 0.962 s of dynamic partitioning models. Similarly, the CEP's energy consumption in a test scenario is 0.609 J, slightly higher than the 0.587 J of static partitioning but lower than the 0.711 J of dynamic partitioning. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： computational offloading Dynamic partitioning Energy consumption Execution cost Mobile computing Remote execution Static partitioning

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Service Cache-Based offloading and Resource Optimization Algorithm for UAV-Assisted Computing

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ELECTRONICS 2025年第8期14卷 1578-1578页

作者： Li, Zihao Zhu, Qi Nanjing Univ Posts & Telecommun Dept Commun & Informat Engn Nanjing 210009 Peoples R China

Edge computing minimizes data transmission delay and fulfills the requirements of real-time applications for the wireless network coverage problem in edge computing. This paper proposes a resource optimization algorithm based on service caching, and designs a hybrid computing model of UAV-assisted computation offloading and service programs, in which the UAV (Unmanned Aerial Vehicle) not only carries a server for computation offloading but also serves as a relay to assist in the downloading of service programs. The objective is to minimize the system's total energy consumption while adhering to constraints such as delay, cache capacity, and other relevant factors. Because of the non-convex nature of the objective problem, it is divided into three sub-problems for more effective handling. Through analysis, the relationship between the resource allocation ratio and total system energy consumption is established, and the simulated annealing algorithm, greedy algorithm, and genetic algorithm are used to solve these sub-problems, respectively. Finally, the global optimal solutions for location deployment, service caching, resource allocation, and offloading decisions are obtained through iteration. The simulation results show that the proposed algorithm successfully reduces the total energy consumption of the system while maintaining task completion delay constraints.

关键词： UAV service caching computational offloading simulated annealing genetic algorithm

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Task offloading and Data Compression Collaboration Optimization for UAV Swarm-Enabled Mobile Edge Computing

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DRONES 2025年第4期9卷 288-288页

作者： Hu, Zhijuan Liu, Shuangyu Zhou, Dongsheng Shen, Chao Wang, Tingting Xian Technol Univ Sch Comp Sci & Engn Xian 710021 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China

The combination of Unmanned Aerial Vehicles (UAVs) and Mobile Edge Computing (MEC) effectively meets the demands of user equipments (UEs) for high-quality computing services, low energy consumption, and low latency. However, in complex environments such as disaster rescue scenarios, a single UAV is still constrained by limited transmission power and computing resources, making it difficult to efficiently complete computational tasks. To address this issue, we propose a UAV swarm-enabled MEC system that integrates data compression technology, in which the only swarm head UAV (USH) offloads the compressed computing tasks compressed by the UEs and partially distributes them to the swarm member UAV (USM) for collaborative processing. To minimize the total energy and time cost of the system, we utilize Markov Decision Process (MDP) for modeling and construct a deep deterministic policy gradient offloading algorithm with a prioritized experience replay mechanism (PER-DDPG) to jointly optimize compression ratio, task offloading rate, resource allocation and swarm positioning. Simulation results show that compared with deep Q-network (DQN) and deep deterministic policy gradient (DDPG) baseline algorithms, the proposed scheme performs excellently in terms of convergence and robustness, reducing system latency and energy consumption by about 32.7%.

关键词： UAV swarm mobile edge computing computational offloading data compression deep reinforcement learning

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Deep Reinforcement Learning-Based Mobile Health Task offloading Strategy for Vehicle Fog Computing 24

Deep Reinforcement Learning-Based Mobile Health Task Offload...

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Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education

作者： Xiaolu Zheng Zhibin Gao Yanglong Sun Navigation College Jimei University Xiamen Fujian China

ISBN: (纸本)9798400712692

Mobile Health Services (MHS) is applied in "Intelligent Transportation + Healthcare", in which edge devices provide an efficient solution for data collection and processing of mobile devices. However, inefficiency of the computational offloading scheme may lead to high latency. This paper proposes an optimal task offloading scheme to assist the IoT-Ambulance (IA) edge computing environment by building a stable communication topology between vehicles based on fog computing, in which a car has limited arithmetic resources, and a Cloud Server (CS) is used as a centralized controller to complete the scheduling of computational resources and to build a temporary edge computing cluster to assist diagnostic and therapeutic application services. To jointly optimize user scheduling and resource allocation and minimize energy loss under delay constraints, we adopt a reinforcement learning-based PPO algorithm to balance the two objectives of "minimum system utility and maximum number of favorable service providers" and verify the optimality under different parameters. Simulation results show that the proposed algorithm outperforms the baseline in terms of convergence speed and computational complexity while ensuring the best system utility.

关键词： computational offloading

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SplitTracr: A Flexible Performance Evaluation Tool for Cooperative Inference and Split Computing 25

SplitTracr: A Flexible Performance Evaluation Tool for Coope...

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Companion of the 16th ACM/SPEC International Conference on Performance Engineering

作者： Nicholas Bovee Izhar Ali Suraj Bitla Gopi Krishna Patapanchala Shen-Shyang Ho Rowan University Glassboro New Jersey USA

ISBN: (纸本)9798400711305

We present SplitTracr, a performance evaluation tool used on deep neural networks (DNNs) to simplify the development overhead of split computation-based DNN models. Usage of such a tool allows the neural network to be dynamically split into component parts at any point. Our tool handles the management of configuration of hardware edge devices, export & injection of forward pass tensors during live tests, dataset handling, metrics & logging in the testing process. We examine the structure, methodology, and design choices we made to create SplitTracr, and review the results of our pre-provided base cases.

关键词： computational offloading

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Multi-User Task offloading to Heterogeneous Processors With Communication Delay and Budget Constraints

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2022年第3期10卷 1958-1974页

作者： Sundar, Sowndarya Champati, Jaya Prakash Liang, Ben Univ Toronto Elect & Comp Engn Toronto ON M5S Canada KTH Royal Inst Technol Informat Sci & Engn EECS S-11428 Stockholm Sweden

We study task scheduling and offloading in a cloud computing system with multiple users where tasks have different processing times, release times, communication times, and weights. Each user may schedule a task locally or offload it to a shared cloud with heterogeneous processors by paying a price for the resource usage. We consider four different models in this article: (i) zero task release and communication times;(ii) non-zero task release times and zero communication times;(iii) non-zero task release times and fixed communication times;and (iv) non-zero task release times and sequence-dependent communication times. Our article aims at identifying a task scheduling decision that minimizes the weighted sum completion time of all tasks, while satisfying the users' budget constraints. We propose an efficient solution framework for this NP-hard problem. As a first step, we use a relaxation and a rounding technique to obtain an integer solution that is a constant factor approximation to the minimum weighted sum completion time. This solution violates the budget constraints, but the average budget violation decreases as the number of users increases. Thus, we develop a scalable algorithm termed Single-Task Unload for Budget Resolution (STUBR), which resolves budget violations and orders the tasks to obtain robust solutions. We prove performance bounds for the rounded solution as well as for the budget-resolved solution, for all four models considered. Via extensive trace-driven simulation for both chess and compute-intensive applications, we observe that STUBR exhibits robust performance under practical scenarios and outperforms existing alternatives. We also use simulation to study the scalability of STUBR algorithm as the number of tasks and the number of users in the system increases.

关键词： computational offloading task scheduling communication delay weighted sum completion time budget constraints multi-user systems

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Security and privacy challenges in mobile cloud computing: Survey and way ahead

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2017年第Apr.期84卷 38-54页

作者： Mollah, Muhammad Baqer Azad, Md. Abul Kalam Vasilakos, Athanasios Jahangirnagar Univ Dept Comp Sci & Engn Dhaka Bangladesh Lulea Univ Technol Dept Comp Sci Elect & Space Engn Lulea Sweden

The rapid growth of mobile computing is seriously challenged by the resource constrained mobile devices. However, the growth of mobile computing can be enhanced by integrating mobile computing into cloud computing, and hence a new paradigm of computing called mobile cloud computing emerges. In here, the data is stored in cloud infrastructure and the actual execution is shifted to cloud environment so that a mobile user is set free from resource constrained issue of existing mobile devices. Moreover, to avail the cloud services, the communications between mobile devices and clouds are held through wireless medium. Thus, some new classes of security and privacy challenges are introduced. The purpose of this survey is to present the main security and privacy challenges in this field which have grown much interest among the academia and research community. Although, there are many challenges, corresponding security solutions have been proposed and identified in literature by many researchers to counter the challenges. We also present these recent works in short. Furthermore, we compare these works based on different security and privacy requirements, and finally present open issues.

关键词： Mobile computing Cloud computing computational offloading Virtualization Security and privacy

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Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

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IEEE ACCESS 2022年 10卷 32639-32688页

作者： Malazi, Hadi Tabatabaee Chaudhry, Saqib Rasool Kazmi, Aqeel Palade, Andrei Cabrera, Christian White, Gary Clarke, Siobhan Maynooth Univ Dept Comp Sci Maynooth Kildare Ireland Munster Technol Univ Dept Comp Sci Cork T12 P928 Ireland Trinity Coll Dublin Sch Comp Sci & Stat Dublin 2 Ireland Univ Cambridge Dept Comp Sci & Technol Cambridge CB2 1TN England

The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the th

关键词： Cloud computing Servers Vehicle dynamics Resource management Wireless fidelity Taxonomy Task analysis Mobile edge computing decentralised cloud MEC server service caching service offloading computational offloading service deployment resource management service orchestration

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