As a promising architecture, Mobile Data Collector (MDC) enhanced Internet of Things (IoT) exhibits broad prospects in efficient data collection and data aggregation especially for sparse deployment scenarios. Combining the tools from queueing theory and stochastic geometry, we propose an analytical framework to study the network performance of an MDC enhanced IoT network, in terms of coverage probability, end-to-end delay and energy consumption. We derive the closed-form expressions for average contact and inter-contact time between a sensor and its associated MDC. By modeling the data collection system between a sensor and its associated MDCs as an M/G/1 queue system with vacations and general limited (G-limited) service, we first derive the queueing delay at the tagged sensor, and further obtain the end-to-end delay. The proposed analytical framework enables us to quantify the effect on network performance of key system parameters, such as MDC velocity, packet arrival rate, densities of sensors and MDCs, and contact radius. This study reveals that the MDC velocity has little impact on the coverage probability, and provides guidelines to minimize the end-to-end delay by optimizing the density and contact radius of sensors, and the velocity and density of MDCs.
Enhanced-BLE: A Hybrid BLE-ESB Framework for Dynamically Reconfigurable and Energy-Efficient 2.4 GHz IoT Communication
Nanyang Technological University（南洋理工大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）
Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework
Jilin University（吉林大学）；Nanyang Technological University（南洋理工大学）；Edith Cowan University（伊迪丝科文大学）；the University of Technology Sydney（悉尼科技大学）；The University of Sydney（悉尼大学）；Tohoku University（东北大学）
EDRP: Enhanced Dynamic Relay Point Protocol for Data Dissemination in Multi-hop Wireless IoT Networks
University of California, Merced（加州大学默塞德分校）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Information Technology R&D Center, Mitsubishi Electric Corporation（三菱电机株式会社信息技术研发中心）
Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving
State Key Laboratory of Automotive Simulation and Control, Jilin University（吉林大学汽车仿真与控制国家重点实验室）；Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya（马来西亚大学计算机科学与信息技术学院网络安全与网络研究中心）；Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；IMT, Department of Humanities and Technology, Roskilde University（罗斯基勒大学人文与技术系IMT）；Center for Security, Theory and Algorithmic Research, International Institute of Information Technology（国际信息技术研究所安全、理论与算法研究中心）；Department of Computer Science and Engineering, College of Informatics, Korea University（韩国大学信息学院计算机科学与工程系）
Enhanced distributed data aggregation method in the internet of things
\emph{"Internet of Things (IoT)"}作为一个新的概念在技术和通信领域已经出现。在这样的现代技术中，为每个生物体（例如，人、动物、物体等）提供了通过数据通信网络（诸如因特网或内联网）发送数据的能力。由于有限的硬件和通信操作能力以及小尺寸，物联网经历了相当多的挑战。这些固有的挑战不仅会导致节点之间的聚合、传输和通信效率的根本限制，而且还会降低路由性能。为了应对可用时间缩短和节点间通信不稳定的问题，网络中的数据聚合和传输方法被设计得更加智能化。本文提出了一种分布式的方法来设置节点间的子平衡。该方法通过限制度来增加网络图的高度，从而减少网络拥塞。此外，一个动态的数据聚合方法-命名为LA-RPL-提出了RPL网络。更具体地，每个节点配备有学习自动机，以便执行数据聚合和传输。仿真结果表明，所提出的方法优于以前建议的基础方法的能量消耗，网络控制开销，和丢包率。
Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting
School of Information Technology, York University（约克大学信息科技学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；National Research Council of Canada（加拿大国家研究委员会）
