Results 261 to 270 of about 365,445 (329)

Mobile agent evolution computing

Information Sciences, 2001
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openaire   +2 more sources

Collaborative Task Offloading Optimization for Satellite Mobile Edge Computing Using Multi-Agent Deep Reinforcement Learning

IEEE Transactions on Vehicular Technology
Satellite mobile edge computing (SMEC) achieves efficient processing for space missions by deploying computing servers on low Earth orbit (LEO) satellites, which supplements a strong computing service for future satellite-terrestrial integrated networks.
Hangyu Zhang, Hongbo Zhao, Rongke Liu, Aryan Kaushik, Xiangqiang Gao, Shenzhan Xu   +5 more
semanticscholar   +1 more source

Joint Secure Offloading and Resource Allocation for Vehicular Edge Computing Network: A Multi-Agent Deep Reinforcement Learning Approach

IEEE transactions on intelligent transportation systems (Print), 2023
The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks.
Ying Ju, Yuchao Chen, Zhiwei Cao, Lei Liu, Qingqi Pei, Ming Xiao, K. Ota, M. Dong, Victor C. M. Leung   +8 more
semanticscholar   +1 more source

Mobile computing deploying agents

Proceedings Twelfth International Conference on Information Networking (ICOIN-12), 2002
An agent based paradigm is proposed for mobile computing. The fixed host deploys an agent in the mobile host that is provided with a rule or event-condition-based power of attorney, to act on its behalf. The rule application policy enforces that the transactions are either time and attribute sensitive or time and attribute tolerant.
openaire   +1 more source

Mobile Software Agents — Insect-Inspired Computing

BT Technology Journal, 2000
Agents need resources and protection from hostile influences, in both natural and artificial environments. This paper specifically considers social insects and software mobile agent systems, in order to understand how the need for mobility, resource acquisition and security can influence the design and operation of agent systems.
P Marrow, R Ghanea-Hercock
openaire   +1 more source

Priority-Aware Resource Scheduling for UAV-Mounted Mobile Edge Computing Networks

IEEE Transactions on Vehicular Technology, 2023
In this paper, we investigate the joint impact of task priority and mobile computing service on the mobile edge computing (MEC) networks, in which one unmanned aerial vehicle (UAV) provides mobile computing service to help compute the tasks from users in
Wenqi Zhou, Lisheng Fan, Fasheng Zhou, Feng Li, Xianfu Lei, Wei Xu, A. Nallanathan   +6 more
semanticscholar   +1 more source

Parallel computing using Java mobile agents

Proceedings 25th EUROMICRO Conference. Informatics: Theory and Practice for the New Millennium, 1999
We propose a mobile-agent parallel processing framework that uses multiple Java mobile agents, each one of which utilizes multithreading. A mobile agent can travel at any Web site to perform its tasks. We also introduce the concept of task handlers, which are Java objects capable of implementing a particular task of the target application. TaskHandlers
Panayiotou, Christoforos, Samaras, George S., Pitoura, Evaggelia 1967-, Evripidou, Paraskevas, Panayiotou, Christoforos, Samaras, George S., Pitoura, Evaggelia 1967-, Evripidou, Paraskevas   +7 more
openaire   +2 more sources

Secure Transmission for Multi-UAV-Assisted Mobile Edge Computing Based on Reinforcement Learning

IEEE Transactions on Network Science and Engineering, 2023
UAV communication has received widespread attention in MEC systems due to its high flexibility and line-of-sight transmission. Users can reduce their local computing pressures and computation delay by offloading tasks to the UAV as an edge server ...
Weidang Lu, Yandan Mo, Yunqi Feng, Yuan Gao, Nan Zhao, Yuan Wu, A. Nallanathan   +6 more
semanticscholar   +1 more source

Mobile-agents for distributed market computing

Proceedings of the 1999 International Conference on Parallel Processing, 2003
This paper discusses the implementation using mobile agent and the performance of the market computing to allocate network quality of service most efficiently, based on users' preference. Though the protection of users' private preference and the efficient calculation are both important, these two requirements often contradict each other.
S. Tanaka, H. Yamaki, T. Ishida
openaire   +1 more source

Large-Scale Computation Offloading Using a Multi-Agent Reinforcement Learning in Heterogeneous Multi-Access Edge Computing

IEEE Transactions on Mobile Computing, 2023
Recently, existing computation offloading methods have provided extremely low service latency for mobile users (MUs) in multi-access edge computing (MEC).
Zhen Gao, Lei Yang, Yu Dai
semanticscholar   +1 more source