IoT Networks
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks
Announce Type: new Abstract: With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification, dataset integration, and temporal feature learning to enhance detection performance in IoT networks. Using network traffic data, the proposed approach is evaluated on...
Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models
Announce Type: new Abstract: AI-based intrusion detection systems (IDS) have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network configurations, and provide insights into the performance of foundation models for attack identification.
Deterministic versus Stochastic Optimization for Joint Path Planning and Dynamic Time Splitting in Multiple-UAV-Cached IoT Networks
Announce Type: new Abstract: This paper examines wireless-powered Internet of Things (IoT) networks involving multiple unmanned aerial vehicles (UAVs) equipped with backscatter and caching technologies to relay and transmit signals. For data communication and energy harvesting (EH), the source transmits information and power to UAVs using the dynamic time splitting (DTS) method. UAVs use harvested energy for passive communication (backscatter) and for active communication (transmitting...
MeshGuard: MUD-Based Network Access Control for Large-Scale Thread-Powered IoT Networks
Announce Type: new Abstract: The IETF standard Manufacturer Usage Description (MUD) enables manufacturers to equip IoT devices with certified URLs that provide traffic profiles for those devices, helping administrators enforce network access control. However, MUD assumes devices operate on full IP stacks and therefore does not account for constrained IoT devices running Thread--the dominant low-power mesh networking standard--which lacks complete TCP/IP functionality. While prior work...
Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
Announce Type: new Abstract: We consider a federated learning (FL) system in which Industrial Internet-of-Things (IIoT) devices collaboratively train a global model over wireless channels without sharing local data. In such systems, communication time is a primary bottleneck that constrains overall training efficiency. Unlike conventional networks that prioritize individual quality-of-service requirements, FL systems collectively aim to converge to an optimal global model as efficiently as...
Quantum-Inspired Reinforcement Learning for Low-Latency Intrusion Detection in V2X and Internet-of-Vehicles Networks
Announce Type: new Abstract: Smart cities increasingly depend on dense edge, IoT, and vehicular networks to deliver critical urban services, including traffic control, connected mobility, infrastructure monitoring, and energy management. In this ecosystem, the Internet of Vehicles (IoV) is central to intelligent transportation, enabling continuous communication among vehicles, roadside infrastructure, and cloud-edge platforms. This connectivity, however, also enlarges the attack surface and...
Rethinking IoT Intrusion Detection: Augmenting Routing Metrics with Radio Features
Announce Type: new Abstract: Machine learning-based intrusion detection systems (IDS) for RPL-based IoT networks often rely solely on routing layer features, which provide only a partial view of network behaviour. In this work, we investigate whether incorporating Transmit (TX) and Receive (RX) radio features alongside the standard RPL feature set can improve detection performance in an LSTM-based IDS. We evaluate the proposed approach across three different attack types, namely...
Magnetic Indoor Localization through CNN Regression and Rotation Invariance
Announce Type: replace Abstract: Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT systems. Combining convolutional neural networks (CNNs) and magnetic field-based features offers a low-cost, infrastructure-free solution for precise positioning. While magnetic fingerprints are a promising approach for indoor positioning, models trained on raw 3D magnetometer data are highly sensitive to device...
Q-FE: A Quantum-Native 6G Far-Edge Architecture Securing Industrial IoT Digital Twins via CSIDH-PQC and Asynchronous Federated Learning
arXiv:2606.03611v1 Announce Type: new Abstract: Sixth-generation (6G) wireless networks will underpin ultra-dense Industrial IoT (IIoT) ecosystems in which resource-constrained Far-Edge devices -- autonomous mobile robots, industrial actuators, connected vehicles -- must simultaneously satisfy sub-millisecond latency, $10^{-7}$-class reliability, and decades-long cryptographic security. Current architectures delegate Digital Twin (DT) computation to centralised cloud or Mobile Edge Computing...
A distributed routing protocol for sending data from things to the cloud leveraging fog technology in the large-scale IoT ecosystem
Announce Type: replace Abstract: Fog computing integrates cloud and edge resources. According to an intelligent and decentralized method, this technology processes data generated by IoT sensors to seamlessly integrate physical and cyber environments. Internet of Things uses wireless and smart objects.