Results 61 to 70 of about 13,256,052 (164)

Graph-Attention Constrained DRL for Joint Task Offloading and Resource Allocation in UAV-Assisted Internet of Vehicles

Drones
Unmanned aerial vehicles (UAVs) acting as mobile aerial edge platforms can deliver on-demand communication and computing for the Internet of Vehicles (IoV) via flexible deployment and line-of-sight (LoS) links, improving reliability and reducing latency.
Peiying Zhang, Xiangguo Zheng, Konstantin Igorevich Kostromitin, Wei Zhang, Huiling Shi, Lizhuang Tan   +5 more
doaj   +1 more source

Adaptive power management for multiaccess edge computing‐based 6G‐inspired massive Internet of Things

IET Wireless Sensor Systems, Volume 15, Issue 1, January/December 2025.
The paper proposes a containerised edge computing model for power optimisation in 6G‐inspired massive Internet‐of‐Things applications. The problem is formulated as a central processing unit energy consumption cost function based on quasi‐finite system observations. Results show that the model performs better compared to a competitive baseline algorithm.
Babatunde S. Awoyemi, Bodhaswar T. Maharaj   +1 more
wiley   +1 more source

Autonomous Police Vehicles for Predictive Policing: A Sociotechnical Framework for Accountable Deployment

IEEE Access
Predictive policing is evolving from analytic dashboards to operational decision support; however, integrating these predictions into Autonomous Police Vehicles (APVs) introduces significant reliability, security, and governance challenges.
Swarnamouli Majumdar, Anjali Awasthi, Lorant Andras Szolga   +2 more
doaj   +1 more source

Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture

CAAI Transactions on Intelligence Technology, Volume 9, Issue 6, Page 1467-1484, December 2024.
Abstract To gain access to networks, various intrusion attack types have been developed and enhanced. The increasing importance of computer networks in daily life is a result of our growing dependence on them. Given this, it is glaringly obvious that algorithmic tools with strong detection performance and dependability are required for a variety of ...
Mohammad Hossein Modirrousta, Parisa Forghani Arani, Reza Kazemi, Mahdi Aliyari‐Shoorehdeli   +3 more
wiley   +1 more source

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

IET Cyber-Physical Systems: Theory &Applications, Volume 9, Issue 4, Page 392-406, December 2024.
The security and privacy of cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing ...
Nicholas Jacobs, Shamina Hossain‐McKenzie, Shining Sun, Emily Payne, Adam Summers, Leen Al‐Homoud, Astrid Layton, Kate Davis, Chris Goes   +8 more
wiley   +1 more source

Traffic Demands Dynamics-Aware Multi-Beam Satellite Framework for 6G Networks

IEEE Access
To meet the growing demand for dynamic and efficient satellite communication, multi-beam satellite (MBS) systems have emerged as a promising solution by subdividing wide coverage areas into smaller cells and directing multiple beams to illuminate them ...
Yumi Kim, Haneul Ko, Jaewook Lee
doaj   +1 more source

Recursive Markov Decision Processes and Recursive Stochastic Games [PDF]

, 2005
We introduce Recursive Markov Decision Processes (RMDPs) and Recursive Simple Stochastic Games (RSSGs), and study the decidability and complexity of algorithms for their analysis and verification.
Mihalis Yannakakis, Yannakakis, Mihalis, Etessami, Kousha; id_orcid, Kousha Etessami   +3 more
core   +1 more source

A safe exploration approach to constrained Markov decision processes [PDF]

We consider discounted infinite horizon constrained Markov decision processes (CMDPs) where the goal is to find an optimal policy that maximizes the expected cumulative reward subject to expected cumulative constraints.
Kamgarpour, Maryam, Ni, Tingting
core   +2 more sources

Dual-decoupling of deep reinforcement learning reward function for integrated energy system scheduling: A collaborative mechanism of constraint separation and hierarchical decision-making

International Journal of Electrical Power & Energy Systems
Deep reinforcement learning (DRL) applied to the integrated energy system (IES) scheduling tasks often leads to reward functions composed of multiple coupled components due to the complex structure of IES.
Xiangyan Yang, Shaobing Yang, Liang Hu, Peng Peng, Mingli Wu   +4 more
doaj   +1 more source

Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Alexandria Engineering Journal
Achieving stable and reliable autonomous driving in complex traffic environments while ensuring safety under unpredictable conditions is a critical challenge in autonomous driving technology.
Haitao Li, Tao Peng, Bangan Wang, Ronghui Zhang, Bolin Gao, Ningguo Qiao, Zhiwei Guan, Jiayin Li, Tianyu shi   +8 more
doaj   +1 more source