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Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Hongjing Ji, Osama Alfarraj, Amr Tolba
2020 IEEE Access  
To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing.  ...  Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory  ...  Reference [29] proposed a collaborative offloading scheme for vehicle-to-vehicle networks that is based on mobile edge cloud computing and cloud computing, and it developed a distributed computing offloading  ... 
doi:10.1109/access.2020.2983609 fatcat:b45abdrxbracnbfpvtvtu5uxui

Efficient resourceful mobile cloud architecture (mRARSA) for resource demanding applications

Asharul Islam, Anoop Kumar, Khalid Mohiuddin, Sadaf Yasmin, Mohammed Abdul Khaleel, Mohammad Rashid Hussain
2020 Journal of Cloud Computing: Advances, Systems and Applications  
For mobile clients, sufficient resources with the assurance of efficient performance and energy efficiency are the core concerns.  ...  This algorithm considers both device context (network parameters) and application content (task size) at run time when offloading an executable code to allocate the cloud resources.  ...  Acknowledgements The authors would like to express their gratitude to King Khalid University, Abha, Saudi Arabia for providing administrative and technical support such as computer lab equipment.  ... 
doi:10.1186/s13677-020-0155-6 fatcat:zha764vo4bh4nmxpig66z24s3y

Towards Computation Offloading in Edge Computing: A Survey

Congfeng Jiang, Xiaolan Cheng, Honghao Gao, Xin Zhou, Jian Wan
2019 IEEE Access  
Moreover, resource scheduling approaches, gaming and tradeoffing among system performance and overheads for computation offloading decision making are also reviewed.  ...  Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing pressure, as well as enhanced service responsiveness and data privacy protection.  ...  RESOURCES MANAGEMENT AND ALLOCATION In decentralized edge computing environment, resource must be allocated, such as processor, disk, and network bandwidth for distributed data processing.  ... 
doi:10.1109/access.2019.2938660 fatcat:qcpqojzxsnbsnmuez3x2ew4sqa

Towards distributed, fair, deadline-driven resource allocation for cloudlets

Stratos Dimopoulos, Chandra Krintz, Rich Wolski
2019 Proceedings of the 4th Workshop on Middleware for Edge Clouds & Cloudlets - MECC '19  
In this paper we present our vision for a two-level, distributed resource allocator that preserves fairness and satisfies deadlines of low latency workloads in a multi-cloudlet environment with offloading  ...  We analyze the opportunities and challenges that offloading and the multi-cloud environment impose and we suggest the changes required to a fair-preserving and deadline-driven resource allocator originally  ...  However, interactive mobile applications like real-time multimedia, gaming, augmentedreality, and location-aware and guidance services, generate data streams that not only demand significant computational  ... 
doi:10.1145/3366614.3368102 fatcat:r7oxdgwhjrht5hqwr4iepy52jm

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems

2021 KSII Transactions on Internet and Information Systems  
Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency  ...  To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle  ...  results of context-aware, make corresponding resource allocation strategy, offloading strategy and scheduling strategy, and send them to RSUs and vehicles for execution.  ... 
doi:10.3837/tiis.2021.02.001 fatcat:qw4y3a2t5ndwnmrfxs36dmwzjy

Analytical Study of Task Offloading Techniques using Deep Learning

Mr Almelu, Dr. S. Veenadhari, Kamini Maheshwar
and service quality.  ...  The consistency of this information is an essential problem for ensuring the quality of IoT services.  ...  Task Proactive Caching Based Computation Offloading and Resource Allocation in Mobile-Edge Computing Systems Compute offloading, resource allocation, and proactive process caching are all optimized together  ... 
doi:10.24113/ijoscience.v7i7.393 fatcat:4ijltsc42fei5lh6cnqjc4iokq

Resource Scheduling in Edge Computing: A Survey [article]

Quyuan Luo, Shihong Hu, Changle Li, Guanghui Li, Weisong Shi
2021 arXiv   pre-print
Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared.  ...  Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning  ...  For example, Meng et al. [230] proposed a game-theoretic based resource allocation mechanism to optimally allocate resources for each component task of a mobile application.  ... 
arXiv:2108.08059v1 fatcat:oo4lepcn3rhdfafefw5lkq2lia

Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

Shuchen Zhou, Waqas Jadoon, Junaid Shuja, Zhihan Lv
2021 Complexity  
This paper presents an in-depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach.  ...  Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-integer nonlinear optimization model for energy consumption minimization  ...  servers of both the roadside unit and the vehicles, and this distributed computing mode will reduce the number of tasks distributed for each edge node and also reduce the demand for computational resources  ... 
doi:10.1155/2021/6455617 fatcat:gs3fdutosjfrvj63k7hlpkajzm

A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

Kai Peng, Victor C. M. Leung, Xiaolong Xu, Lixin Zheng, Jiabin Wang, Qingjia Huang
2018 Wireless Communications and Mobile Computing  
Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation.  ...  MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements.  ...  mobile user allocation [87] A location-aware services deployment algorithm [88] AP ranking in the cloudlet placement Resource scheduling ES placement [51] Offloading an application to the most  ... 
doi:10.1155/2018/8267838 fatcat:tgbxnprsnrdefhbwqz5e65uvlu

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence

Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, Ying-Chang Liang
2022 IEEE Access  
More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they  ...  Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.  ...  By considering different speeds and varying service requirements of mobile users, speed-aware task offloading and resource allocation were further developed by the advantage actor-critic-based RL algorithm  ... 
doi:10.1109/access.2022.3183647 fatcat:pd5z6q4innd5jl25g4r7b4nq3i

Collaborative Task Offloading and Service Caching Strategy for Mobile Edge Computing

Xiang Liu, Xu Zhao, Guojin Liu, Fei Huang, Tiancong Huang, Yucheng Wu
2022 Sensors  
The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading  ...  Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC.  ...  Task offloading and resource allocation in a multi-users and multi-severs scenario is optimized without considering MEC collaboration, using the caching strategy in this paper for service caching; (3)  ... 
doi:10.3390/s22186760 pmid:36146113 pmcid:PMC9502834 fatcat:rja7oxypljf23cuzpxgx35fyzy

Guest Editorial Emerging Computing Offloading for IoTs: Architectures, Technologies, and Applications

Jiannong Cao, Deyu Zhang, Haibo Zhou, Peng-Jun Wan
2019 IEEE Internet of Things Journal  
We hope that the special issue can serve as a good reference for scientists, engineers, and academicians in the area of computation offloading in IoTs. JIANNONG CAO  ...  We also would like to thank all the reviewers who dedicated their efforts in reviewing the papers, and for their valuable comments and constructive suggestions to significantly improve the quality of the  ...  In "Joint Resource Allocation for Latency-Sensitive Services Over Mobile Edge Computing Networks With Caching," the authors jointly consider computation offloading, content caching, and resource allocation  ... 
doi:10.1109/jiot.2019.2921217 fatcat:yxc2v2whm5gtzhpefivtgt5uxy

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

Xiaolong Xu, Haoyuan Li, Weijie Xu, Zhongjian Liu, Liang Yao, Fei Dai
2022 Tsinghua Science and Technology  
Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading.  ...  Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically.  ...  To solve this problem, a distributed CO and resource allocation algorithm was designed.  ... 
doi:10.26599/tst.2020.9010025 fatcat:zgakvppxojawjb7cto4tu6237u

Special Issue on Artificial-Intelligence-Powered Edge Computing for Internet of Things

Lei Yang, Xu Chen, Samir M. Perlaza, Junshan Zhang
2020 IEEE Internet of Things Journal  
task latency for all the IoT users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing system. 2327-4662 c 2020 IEEE.  ...  In the article "Joint DNN partition deployment and resource allocation for delay-sensitive deep learning inference in IoT," He et al. studied joint optimization of partition deployment and resource allocation  ... 
doi:10.1109/jiot.2020.3019948 fatcat:mogalqnhnnaqpbxb7zivzdhvry

Mobile Cloud Computing: Taxonomy and Challenges

Ahmed Aliyu, Abdul Hanan Abdullah, Omprakash Kaiwartya, Syed Hamid Hussain Madni, Usman Mohammed Joda, Abubakar Ado, Muhammad Tayyab
2020 Journal of Computer Networks and Communications  
MCC has a qualitative, flexible, and cost-effective delivery platform for providing services to mobile cloud users with the aid of the Internet.  ...  Therefore, taxonomy for MCC is presented considering major themes of research including energy-aware, security, applications, and QoS-aware developments.  ...  Acknowledgments e research, whose summary has been provided in the abstract, was supported by the Ministry of Education Malaysia (MOE) and conducted in collaboration with Research Management Center (RMC  ... 
doi:10.1155/2020/2547921 fatcat:b3gjy7v6cvbfxlvdmghaqksciq
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