arXiv subjects

Muhammad Atif Ur Rehman

Publications and source records attributed to Muhammad Atif Ur Rehman.

3 recordsLinked to original sources

arXiv2026

LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems

Uncrewed Aerial Vehicle (UAV) swarms have significant potential for applications such as Search and Rescue (SAR) and environmental monitoring, but their real-world deployment is limited by a lack of situational awareness, intermittent connectivity, and significant cybersecurity risks. Agentic Artificial Intelligence (AI) represents a shift from standalone Large Language Model (LLM) toward closed-loop cognitive architectures that integrate perception, memory, reasoning/planning, and action to enable adaptive, goal-directed swarm behavior. Within this framework, Agentic AI provides a unifying structure for autonomous and adaptive swarm operations while expanding the system attack surface compared to conventional AI systems. This paper proposes LLM-Centric Agentic AI for UAV Swarms (LAUS) and reviews key enabling technologies such as onboard and edge computing, 5G/6G connectivity, multimodal intelligence, and cybersecurity mechanisms, and analyzes threats such as Priority Manipulation Attacks (PMA) that can distort decision-making and degrade network performance. Finally, it identifies open research challenges, including hallucination-resistant reasoning, onboard LLM deployment under SWaP constraints, and standardized security benchmarks for perception-reasoning attacks in agentic UAV systems.

cs.RO↗

arXiv2023

FoggyEdge: An Information Centric Computation Offloading and Management Framework for Edge-based Vehicular Fog Computing

The recent advances aiming to enable in-network service provisioning are empowering a plethora of smart infrastructure developments, including smart cities, and intelligent transportation systems. Although edge computing in conjunction with roadside units appears as a promising technology for proximate service computations, the rising demands for ubiquitous computing and ultra-low latency requirements from consumer vehicles are challenging the adoption of intelligent transportation systems. Vehicular fog computing which extends the fog computing paradigm in vehicular networks by utilizing either parked or moving vehicles for computations has the potential to further reduce the computation offloading transmission costs. Therefore, with a precise objective of reducing latency and delivering proximate service computations, we integrated vehicular fog computing with roadside edge computing and proposed a four-layer framework named FoggyEdge. The FoggyEdge framework is built at the top of named data networking and employs microservices to perform in-network computations and offloading. A real-world SUMO-based preliminary performance comparison validates FoggyEdge effectiveness. Finally, a few future research directions on incentive mechanisms, security and privacy, optimal vehicular fog location, and load-balancing are summarized.

cs.NI↗

arXiv2020

CCIC-WSN: An Architecture for Single Channel Cluster-based Information-Centric Wireless Sensor Networks

The promising vision of Information-Centric Networking (ICN) and of its realization, Named Data Networking (NDN), has attracted extensive attention in recent years in the context of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). However, a comprehensive NDN/ICN-based architectural design for WSNs, including specially tailored naming schemes and forwarding mechanisms, has yet to be explored. In this paper, we present single-Channel Cluster-based Information-Centric WSN (CCIC-WSN), an NDN/ICN-based framework to fulfill the requirements of cluster-based WSNs, such as communication between child nodes and cluster heads, association of new child nodes with cluster heads, discovery of the namespace of newly associated nodes, and child node mobility. Through an extensive simulation study, we demonstrate that CCIC-WSN achieves 71-90% lower energy consumption and 74-96% lower data retrieval delays than recently proposed frameworks for NDN/ICN-based WSNs under various evaluation settings.

cs.NI↗