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30th International conference on Mobile Computing and networking, ACM MobiCom 2024

作者： Wan, Haoran Cao, Xuyang Marder, Alexander Jamieson, Kyle Department of Computer Science Princeton University PrincetonNJ United States Department of Computer Science Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9798400704895

5G New Radio cellular networks are designed to provide high quality of Service for application on wirelessly connected devices. However, changing conditions of the wireless last hop can degrade application performance, and the applications have no visibility into the 5G Radio Access network (RAN). Most 5G network operators run closed networks, limiting the potential for co-design with the wider-area internet and user applications. This paper demonstrates NR-Scope, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that can passively measure fine-grained RAN capacity, latency, and retransmission information. Application servers can take advantage of the measurements to achieve better millisecond scale, application-level decisions on offered load and bit rate adaptation than end-to-end latency measurements or end-to-end packet losses currently permit. We demonstrate the performance of NR-Scope by decoding the downlink control information (DCI) for downlink and uplink traffic of a 5G Standalone base station in real-time. © 2024 Copyright held by the owner/author(s).

关键词： 5G mobile communication systems

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Implementation and Analysis of Energy Efficient LEACH-C Algorithm for network of Unmanned Aerial Vehicles 14

Implementation and Analysis of Energy Efficient LEACH-C Algo...

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14th IEEE International conference on Communication systems and network Technologies, CSNT 2025

作者： Sharma, Urvashi Kulkarni, Sudarshan Patil Thyagarajamurthy, A. Dept of ECE JSS STU Mysuru India

ISBN: (纸本)9798331531935

The proliferation of bandwidth-hungry apps and the growing number of connected devices have resulted in an unprecedented demand for wireless communication networks. The introduction of fifth generation (5G) networks has created new opportunities to improve connectivity, especially in difficult situations such as distant locations or places affected by disasters. In this regard, unmanned aerial vehicles (UAV) have been shown to be a flexible tool. As the 5G network did, the 6G network is anticipated to offer millions of interconnected devices massive connectivity with a wide range of quality of service (QoS) requirements, ubiquitous coverage, a high level of embedded artificial intelligence (AI), energy efficiency, and adaptive network security. UAVs are expected to play an important role in 6G networks. In this paper, LEACH-C algorithm is implemented and analyzed for improving the network throughput and energy efficiency. The experiment is performed in MATLAB. The proposed algorithm results are compared with optimal UAV placement algorithm, resulting in better performance. © 2025 IEEE.

关键词： internet

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A Software-Defined networks Approach for Cyber Physical systems 37

A Software-Defined Networks Approach for Cyber Physical Syst...

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37th International conference on Information networking (ICOIN)

作者： Khrais, Jumana Al-Issa, Mariam Al-Omari, Reham Al-Hammouri, Ahmad T. Jordan Univ Sci & Technol Dept Network Engn & Secur Irbid 22110 Jordan

ISBN: (纸本)9781665462686

Cyber Physical systems (CPSs), especially Industrial control systems (ICSs), require real-time communication to ensure, first, stability and, second, efficiency, of the operation of the controlled physical processes. Therefore, intelligent control and management of the underlying communication network is needed to achieve lower and predictable network delays. In this paper, we leverage the Software-Defined networking (SDN) to address the delay intolerance issue of ICSs. In particular, we propose, implement, and evaluate a SDN-based application running above the SDN controller that dynamically re-configures the network to improve the performance of the ICS when the network becomes congested.

关键词： Software Defined networking Cyber Physical systems Industrial control systems OpenFlow Mininet MiniCPS quality of Service

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Emotional internet of Behaviors: A QoE-QoS Adjustment Mechanism 4th

Emotional Internet of Behaviors: A QoE-QoS Adjustment Mechan...

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4th International conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 25th International conference on Human-Computer Interaction (HCII)

作者： Alipour, Mina Moghaddam, Mahyar T. Vaidhyanathan, Karthik Kristensen, Tobias Asmussen, Nicolai Krogager Univ Southern Denmark DK-5230 Odense Denmark IIIT Hyderabad Hyderabad 500032 India

ISBN: (纸本)9783031358906;9783031358913

The internet of Behaviors (IoB) approach supports developing socio-technical systems based on humans' goals, characteristics, behaviors, and emotions. This paper shows how emotions and behaviors could impact the quality of software systems. We propose interactive control loops that supervise application and architecture adaptations toward enhancing the system quality of service (QoS) and human quality of experience (QoE). Under the IoB conceptual model, we first show how historical and real emotions could be the source of the design and adaptation of socio-technical systems. We further use a Reinforcement Learning (RL)-based approach as a self-adaptation supervisor of user interfaces (UIs) to users' emotions. The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users' QoE. If the control system detects a drop in QoS in emotion-based adaptations or other functions, another level of adaptation reconfigures the architecture towards better quality. We used the emotional IoB approach to develop a mobile application as a recommender system in emergency evacuation training. The app takes users' facial emotions and positions as input and adapts its UI to impact users' target emotions and task completion. In addition to UI adaptation, the system supports architecture adaptations to decrease response time if required. The evaluation process confirms the efficiency of the RL in iterations, as well as compared to other possible UI adaptation techniques. The results also show that architecture adaptations positively impact the system performance and users' emotions and performance.

关键词： internet of Behaviors Emotions quality of Experience quality of Service Software Architecture User Interface Adaptation

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SDN-Enabled IoT Security Frameworks-A Review of Existing Challenges

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TECHNOLOGIES 2025年第3期13卷 121-121页

作者： Mishra, Sandipan Rakeshkumar Shanmugam, Bharanidharan Yeo, Kheng Cher Thennadil, Suresh Charles Darwin Univ Energy & Resources Inst Fac Sci & Technol Darwin NT 0810 Australia Charles Darwin Univ Fac Sci & Technol Darwin NT 0810 Australia

This comprehensive systematic review examines the integration of software-defined networking (SDN) with IoT security frameworks, analyzing recent advancements in encryption, authentication, access control techniques, and intrusion detection systems. Our analysis reveals that while SDN demonstrates promising capabilities in enhancing IoT security through centralized control and dynamic policy enforcement, several critical limitations persist, particularly in scalability and real-world validation. As intrusion detection represents an integral security requirement for robust IoT frameworks, we conduct an in-depth evaluation of Machine Learning (ML) and Deep Learning (DL) techniques that have emerged as predominant approaches for threat detection in SDN-enabled IoT environments. The review categorizes and analyzes these ML/DL implementations across various architectural paradigms, identifying patterns in their effectiveness for different security contexts. Furthermore, recognizing that the performance of these ML/DL models critically depends on training data quality, we evaluate existing IoT security datasets, identifying significant gaps in representing contemporary attack vectors and realistic IoT environments. A key finding indicates that hybrid architectures integrating cloud-edge-fog computing demonstrate superior performance in distributing security workloads compared to single-tier implementations. Based on this systematic analysis, we propose key future research directions, including adaptive zero-trust architectures, federated machine learning for distributed security, and comprehensive dataset creation methodologies, that address current limitations in IoT security research.

关键词： internet of things software-defined networking cybersecurity machine learning authentication access control network security cloud computing edge computing intrusion detection

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Towards the New Generation of Smart Home-Care with Cloud-Based internet of Humans and Robotic Things

Towards the New Generation of Smart Home-Care with Cloud-Bas...

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2024 International conference on Intelligent Robots and systems

作者： Zhang, Dandan Zheng, Jin Imperial Coll London Dept Bioengn Imperial X Initiat London England Univ Bristol Dept Engn Math Bristol Avon England

ISBN: (纸本)9798350377712;9798350377705

The burgeoning demand for home-care services, driven by a rapidly aging global population, necessitates innovative solutions to alleviate the burden on caregivers and enhance care quality. This paper introduces the development of an internet of Human and Robotic Things (IoHRT) framework, which synergizes cloud computing and the internet of Robotic Things (IoRT) with human-robot collaborative control mechanisms for home-care applications. The IoHRT framework is designed to enable the seamless integration of customizable robotic platforms with modular, scalable, and compatible features, thereby creating a dynamic and adaptable home-care ecosystem. By leveraging the scalability and computational power of cloud computing, the framework facilitates real-time data analysis and remote monitoring, thus enhancing the efficiency and effectiveness of home-care. We present an in-depth analysis of the key characteristics of IoHRT, supported by evidence embedded in our design, and conduct user studies to evaluate the framework from users' perspectives. We demonstrate the performance and utility of our proposed framework for the future of home-care applications.

关键词： Cloud platforms

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A Deep Learning Approach for Anomaly Detection for Industrial control systems 50

A Deep Learning Approach for Anomaly Detection for Industria...

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50th Latin American Computing conference (CLEI)

作者： Giracca, Damian Martinez Pires, Fabio Lopez Baran, Benjamin Jara, Eustaquio Alcides Martinez Univ Nacl Este Ciudad Del Este Paraguay Univ Tres Fonteras Ciudad Del Este Paraguay Univ Comunera Asuncion Paraguay

ISBN: (纸本)9798331540982;9798331540975

Anomaly Detection in Cyber Physical systems (CPS) like Industrial control systems (ICS) presents research opportunities in different industries considering intelligent systems, mainly for Intrusion Detection systems (IDS). This work surveys the specialized literature focusing on main published testbeds, datasets and methodologies for Anomaly Detection based on industrial process measurement data to develop and evaluate the performance of IDS applied for ICS. In this context, this work proposes a novel Deep Learning approach for an IDS based on a Long Short-Term Memory (LSTM) Neural network. An experimental evaluation of obtained results for the HIL-based Augmented ICS (HAI) testbed demonstrate that the proposed LSTM-based IDS outperforms state-of-the-art alternative IDS based on other algorithms such as K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC) and Random Forest (RF), considering performance metrics such as Accuracy (0.9996), Precision (0.9978), F1-Score (0.9978) and Recall (0.9978).

关键词： Anomaly Detection Intrusion Detection systems Deep Learning Anomaly Detection LSTM

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A Comparative Analysis of Deep Reinforcement Learning-based xApps in O-RAN

A Comparative Analysis of Deep Reinforcement Learning-based ...

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IEEE conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Tsampazi, Maria D'Oro, Salvatore Polese, Michele Bonati, Leonardo Poitau, Gwenael Healy, Michael Melodia, Tommaso Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA Dell Technol P&O OCTO Adv Wireless Technol Round Rock TX USA

ISBN: (纸本)9798350310900

The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse quality of Service (QoS) demands. Open Radio Access network (RAN) technologies, and specifically those being standardized by the O-RAN Alliance, make it possible to integrate network intelligence into the once monolithic RAN via intelligent applications, namely, xApps and rApps. These applications enable flexible control of the network resources and functionalities, network management, and orchestration through data-driven control loops. Despite recent work demonstrating the effectiveness of Deep Reinforcement Learning (DRL) in controlling O-RAN systems, how to design these solutions in a way that does not create conflicts and unfair resource allocation policies is still an open challenge. In this paper, we perform a comparative analysis where we dissect the impact of different DRL-based xApp designs on network performance. Specifically, we benchmark 12 different xApps that embed DRL agents trained using different reward functions, with different action spaces and with the ability to hierarchically control different network parameters. We prototype and evaluate these xApps on Colosseum, the world's largest O-RAN-compliant wireless network emulator with hardware-in-the-loop. We share the lessons learned and discuss our experimental results, which demonstrate how certain design choices deliver the highest performance while others might result in a competitive behavior between different classes of traffic with similar objectives.

关键词： Open RAN O-RAN Resource Allocation network Intelligence Deep Reinforcement Learning

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Optimizing Environment-aware VANET Clustering using Machine Learning

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INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION systems RESEARCH 2023年第3期21卷 394-408页

作者： Fahmy, Yasmine Alsuhli, Ghada Khattab, Ahmed Cairo Univ EECE Dept Giza Egypt Khalifa Univ Syst On Chip Ctr SoCC Abu Dhabi U Arab Emirates

Clustering is important to improve the quality of service in many VANET protocols and applications, such as data dissemination, media access control, internet of vehicles and intrusion detection systems. Since the performance of a clustering algorithm is highly influenced by the surrounding environment, an environment-aware clustering algorithm that can adapt itself to follow the changes in the environment features is required. In this paper, we propose a machine learning-based framework to include this awareness to an arbitrary clustering algorithm. This is done by optimizing the clustering algorithm's parameters taking into consideration the road structure and the traffic features that affect the clustering performance. Our framework aims to model how the optimum values of different configuration parameters change with the considered features. Then, these models are used to allow the clustering algorithm to adjust its configurations in real-time according to the measured environment features. After applying this framework on a state-of-the-art clustering algorithm, the performance of the resulting algorithm is compared to other clustering schemes as well as the original algorithm. The obtained results prove the efficiency of the proposed approach reflected by the significant improvements in the different quality metrics and maintaining these metrics in the highest possible levels despite the changes in the considered environment features.

关键词： Vehicular Ad hoc network (VANET) Clustering algorithm Machine learning

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User Behavior Forensics on Encrypted Traffic in the Industrial internet of Things 20th

User Behavior Forensics on Encrypted Traffic in the Industri...

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20th IFIP WG 11.9 International conference on Digital Forensics

作者： Zhu, Zhishen Gou, Gaopeng Wang, Chonghua Zhou, Hao Lin, Chen Xiong, Gang Chinese Acad Sci Beijing Peoples R China China Ind Control Syst Cyber Emergency Response T Beijing Peoples R China

ISBN: (纸本)9783031710278;9783031710254;9783031710247

The Industrial internet of Things is an emerging technology that has rapidly penetrated diverse applications. MindSphere from Siemens stands out as a leader among Industrial internet of Things solutions. Unlike most industrial control systems, MindSphere utilizes robust encryption protocols such as SSL, TLS and even QUIC for data transmission, presenting significant challenges to traditional traffic forensic approaches. Conducting effective user behavior forensics in this context requires the identification of relevant traffic in extensive IP network flows along with the execution of fine-grained classification tasks. The challenges significantly reduce the effectiveness of mainstream encrypted traffic classification methods on MindSphere. This chapter describes a novel flow-correlation framework that is designed to automatically extract user behavior patterns from MindSphere network traffic. The approach engages a hybrid feature set encompassing statistical and sequential data to create feature vectors for flow nodes. By leveraging traffic correlation, the approach incorporates a burst time-domain mechanism to construct a communications diagram. The constituent traffic graphs are processed using a graph neural network model to enable effective classification. Comprehensive experiments demonstrate that the approach exhibits outstanding performance that surpasses state-of-the-art methods.

关键词： Industrial internet of Things Encrypted Traffic Classification User Behavior Forensics Graph Neural network Model

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