Results 281 to 290 of about 365,445 (329)

DeepMECagent: multi-agent computing resource allocation for UAV-assisted mobile edge computing in distributed IoT system

Applied intelligence (Boston), 2022
Xiangxiang Zhang, Yichao Wang
semanticscholar   +1 more source

A trustworthy agent-based encrypted access control method for mobile cloud computing environment

Pervasive and Mobile Computing, 2019
To strengthen the security of access control protocols for mobile cloud environment, dynamic attributes of mobile devices are used. The weak or disconnection issue of the mobile network is a critical task to deal with.
Neha Agrawal, S. Tapaswi
semanticscholar   +1 more source

Mobile code, distributed computing, and agents

IEEE Intelligent Systems, 2001
The author briefly describes some of the Jini networking system's features and attempts to show how this system's assumptions differ from those used to construct other distributed system frameworks. The author then characterizes mobile agent systems, showing such system's intrinsic differences from Jini and some of their basic problems.
openaire   +1 more source

Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach

IEEE Transactions on Mobile Computing
Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services.
Zhiwei Wei, Bing Li, Rongqing Zhang, Xiang Cheng, Liuqing Yang   +4 more
semanticscholar   +1 more source

Joint Data Offloading and Resource Allocation for Multi-Cloud Heterogeneous Mobile Edge Computing Using Multi-Agent Reinforcement Learning

Global Communications Conference, 2019
In this work, we consider a heterogeneous multi-cloud mobile edge computing (Het-MEC) network, where multiple independent cloud centers (CCs) publish tasks to edge devices (EDs) and MEC servers, and compete for their computing and transmission resources.
Yutong Zhang, Boya Di, Zijie Zheng, Jinlong Lin, Lingyang Song   +4 more
semanticscholar   +1 more source

Agents as Catalysts for Mobile Computing

2005
Agent-Oriented Programming (AOP) offers an alternative and radical approach to the development of information systems in various domains. However, one domain that AOP has only minimally affected, at least up until now, is that of mobile computing. Until recently, the use of strong intentional agents in such a domain has been considered impractical and,
G. M. P. O’Hare, M. J. O’Grady, R. W. Collier, S. Keegan   +3 more
openaire   +1 more source

A Mobile Multi-agent System for Distributed Computing

2007
The paper describes a peer-to-peer distributed-computing platform, called yaca, based on mobile agents. Next to the client agents, which seek computational resources in a cluster, yaca consists of four agents, who manage the computers within a cluster. (1) the directory agent keeps track of the computers belonging to the cluster.
Kleijkers, Stefan, Wiesman, Floris, Roos, Nico   +2 more
openaire   +2 more sources

MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices

arXiv.org
The attainment of autonomous operations in mobile computing devices has consistently been a goal of human pursuit. With the development of Large Language Models (LLMs) and Visual Language Models (VLMs), this aspiration is progressively turning into ...
Jiayi Zhang, Chuang Zhao, Yihan Zhao, Zhaoyang Yu, Ming He, Jianpin Fan   +5 more
semanticscholar   +1 more source

Building distributed mobile computing applications with Oracle mobile agents

2022
This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.
openaire   +1 more source

Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing

Adaptive Agents and Multi-Agent Systems
Deep reinforcement learning (DRL) is gaining popularity in task-offloading problems because it can adapt to dynamic changes and minimize online computational complexity.
Zemuy Tesfay Gebrekidan, Sebastian Stein, Timothy J. Norman   +2 more
semanticscholar   +1 more source