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

作者： Jin, Tiankai Ma, Yehan Ji, Zhiduo Chen, Cailian Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9798350310900

Edge sensing supported by wireless transmission is one of the core enabling technologies for flexibly implementing the Industrial internet of Things (IIoT). Balancing network resource consumption and sensing accuracy under dynamic network conditions is a critical challenge. In this work, we bridge the gap between edge sensing performance and transmission design through observability analysis and learning-based methods. Particularly, utilizing observability probability as the key metric, we design the network resource reservation for specific sensing performance demands including stability based on our derived upper and lower probability bounds. Then, to further reduce the overall cost of edge sensing and transmission, an intelligent transmission scheduling method (ITSM) based on deep reinforcement learning is provided, which dynamically schedules the number of transmissions for each sensor. In ITSM, the action space is determined according to the amount of our reserved resources, and both the states of sensing error and fading channel are taken into account. Finally, the superiority of our proposed methods is fully demonstrated through numerical simulations in a typical IIoT system of industrial hot rolling.

关键词： Industrial internet of Things (IIoT) edge sensing observability transmission scheduling deep reinforcement learning

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Future-Proofed Intrusion Detection for internet of Things with Machine Learning 4

Future-Proofed Intrusion Detection for Internet of Things wi...

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4th IEEE International conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Adeyemi, Taiwo Ngobigha, Felix Ez-Zizi, Adnane University of Suffolk School of Technology Business and Arts Ipswich United Kingdom

ISBN: (纸本)9798331518882

The rapid increase in the deployment of internet-of-Things (IoT) devices necessitates robust intrusion detection systems. This study evaluates the effectiveness of machine learning models, including Decision Tree, Random Forest and XGBoost in classifying both known and emerging IoT malware threats. Using the CICIoT2023 dataset, we conducted analyses across binary, 8 -class and 34 -class classifications. Our findings demonstrate that Random Forest consistently outperforms other models, achieving near-perfect accuracy in binary classification and excellent results in multi-class scenarios. Notably, we observed almost no gains in performance beyond a dataset size of 1.5 million records, challenging the notion that larger datasets always equate to better models in malware detection. Furthermore, we investigated machine learning models' ability to detect zero-day attacks, showing that machine learning techniques offer robust and adaptive solutions for IoT intrusion detection, even in the face of emerging malware attacks. © 2025 IEEE.

关键词： network intrusion

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Extending internet of Things systems with Fog and Edge Computing 30

Extending Internet of Things Systems with Fog and Edge Compu...

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30th IEEE International conference on Engineering, Technology, and Innovation

作者： Dumitru, Marian Cosmin Moisescu, Mihnea Alexandra Pietraru, Radu Nicolae Pop, Eugen Natl Univ Sci & Technol Fac Automat Control & Comp Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9798350362442;9798350362435

In recent years, whether we are talking about industrial implementations or whether we are talking about products offered to the masses, there is an accelerated trend of the appearance of IoT devices, devices that collect information and, depending on their specifics, can make certain decisions in the process. This increase automatically leads to the generation of more generated data that must be sent for processing, analyzed, stored, a certain decision being made based on the result of the analysis. This process can be long, the duration strictly determined by the volume of data and the performance of the Cloud infrastructure. Fog and Edge computing has come to our aid with an innovative solution, acting as an additional layer between IoT and Cloud devices. This layer aims to reduce the response time by analyzing the data at the edge of the network, in this way it is no longer necessary to send all the information directly to the Cloud. Whether we are talking about smart cities, the health field or the industrial fields, the presence of IoT devices shows the usefulness of the need to implement Fog and Edge systems. Starting from this growth in the IoT field, the authors wish through this article to analyze the existing implementations that use Fog and Edge computing, the existing architectural levels, the analysis of the areas that present vulnerabilities, as well as the possible improvements that can be added to make the processes more efficient.

关键词： Fog Computing Edge computing internet of Things (IoT) Cloud

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Queueing Theory Approach for an IoST-Based Space Debris Tracking network System and its performance Evaluation 5

Queueing Theory Approach for an IoST-Based Space Debris Trac...

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5th International conference on IoT Based control networks and Intelligent systems, ICICNIS 2024

作者： Christ, Richard J.T. Lavanya, G. College of Engineering and Technology Department of Mathematics SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798331518097

As humanity evolves, space exploration is inevitable. There are several limitations for space exploration, notably space debris has also caused the growth of a new challenge for space exploration efforts. Functional satellites and space stations are endangered by collisions with space debris, it shortens their service life and is a threat to future missions. In this paper, a novel satellite network model is presented. The model works via a fleet of low Earth orbit femto-satellites that can detect space debris and they transmit that information back to a larger nanosatellite as the central unit for processing, in both time and data. The nanosatellite would then prepare and transmit the data to ground stations for evaluation and further action, or the nano satellite transmit the information to the nearest satellite, so that the satellite would transfer the data to the ground station forming an IoST (internet of Space Things). This network is constructed and evaluated using queueing principles presumably open Jackson queueing system, through which the network's response time, utilization of nodes etc. are obtained. This article shows how IoST -based satellite networks can help cope with the space debris problem. This capability can help space operators better track and monitor an increasingly crowded Earth's orbit debris, leading to sustained space exploration. © 2024 IEEE.

关键词： Space debris

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AQuA-CEP: Adaptive quality-Aware Complex Event Processing in the internet of Things 23

AQuA-CEP: Adaptive Quality-Aware Complex Event Processing in...

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17th ACM International conference on Distributed and Event-based systems, DEBS 2023

作者： Lotfian Delouee, Majid Koldehofe, Boris Degeler, Viktoriya University of Groningen Bernoulli Institute Groningen Netherlands Technische Universität Ilmenau Institute of Applied Computer Science Ilmenau Germany University of Amsterdam Informatics Institute Amsterdam Netherlands

ISBN: (纸本)9798400701221

Sensory data profoundly influences the quality of detected events in a distributed complex event processing system (DCEP). Since each sensor's status is unstable at runtime, a single sensing assignment is often insufficient to fulfill the consumer's quality requirements. In this paper, we study in the context of AQuA-CEP the problem of dynamic quality monitoring and adaptation of complex event processing by active integration of suitable data sources. To support this, in AQuA-CEP, queries to detect complex events are supplemented with consumer-definable quality policies that are evaluated and used to autonomously select (or even configure) suitable data sources of the sensing infrastructure. In addition, we studied different forms of expressing quality policies and analyzed how it affects the quality monitoring process. Various modes of evaluating and applying quality-related adaptations and their impacts on correlation efficiency are addressed, too. We assessed the performance of AQuA-CEP in IoT scenarios by utilizing the notion of the quality policy alongside the query processing adaptation using knowledge derived from quality monitoring. The results show that AQuA-CEP can improve the performance of DCEP systems in terms of the quality of results while fulfilling the consumer's quality requirements. quality-based adaptation can also increase the network's lifetime by optimizing the sensor's energy consumption due to efficient data source selection. © 2023 Owner/Author(s).

关键词： internet of things

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Dynamic Pilot Allocation and Pricing Strategies for Priority-Based Access in LEO Satellites IoT networks 15

Dynamic Pilot Allocation and Pricing Strategies for Priority...

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15th International conference on Information and Communication Technology Convergence, ICTC 2024

作者： Zhang, Huan Xie, Huiyang Sun, Yu Xing, Zhi Jin, Hu Hanyang University Dept. Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350364637

The rapid growth of internet of Things (IoT) devices has increased the demand for reliable communication networks, particularly within low earth orbit (LEO) satellite mega-constellations. These networks face significant challenges in resource allocation and access control due to high device density and the inherent delayed feedback in satellite communications. This paper proposes a novel priority-based access control frame-work for the LEO constellation IoT network, incorporating a priority-based pilot allocation protocol (PPAP) that effectively manages delayed feedback scenarios. The PPAP dynamically adjusts pilot resources and transmission probabilities to optimize access performance across different priority levels. Furthermore, a dynamic pricing model is introduced to balance service quality and fairness between the two priority levels while maximizing the revenue of the operator. © 2024 IEEE.

关键词： internet of things

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A Novel Hybrid control Strategy for Grid-Connected PV-Battery systems under Adverse Environmental Conditions 13

A Novel Hybrid Control Strategy for Grid-Connected PV-Batter...

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13th IEEE International conference on Communication systems and network Technologies, CSNT 2024

作者： Pandey, Pawan Kumar Gyan Ganga Institute of Technology and Science Dept. of Electrical Engineering Jabalpur India

ISBN: (纸本)9798350305463

This work presents a new controller for gridconnected PV/Battery systems that combines a bidirectional battery controller with a voltage source converter (VSC) to solve problems caused by power fluctuations on the DC-link. The hybrid VSC controller is developed to maintain a continuous power supply on the DC-link. It does this by responding to changes in environmental factors and load fluctuations in the PV-generated output, which in turn triggers dynamic adjustments to the inverter pulse signals. To increase system stability, the bidirectional battery controller orchestrates the charge and discharge operations in collaboration with the hybrid VSC, optimizing power delivery to the grid. Through a detailed performance study of the proposed controller, faults on both the DC and AC sides of the system are analyzed and efficiently addressed. The controller proved important in enhancing power quality and decreasing harmonic distribution. Furthermore, the hybrid controller's performance is assessed under several one-day environmental situations. Ultimately, the results of simulation done in MATLAB/Simulink corroborate the usefulness of the suggested controller in providing optimal performance in the described grid-connected PV/Battery system. © 2024 IEEE.

关键词： controllers

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Multi-Agent DRL-Based Two-Timescale Resource Allocation for network Slicing in V2X Communications

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2024年第6期21卷 6744-6758页

作者： Lu, Binbin Wu, Yuan Qian, Liping Zhou, Sheng Zhang, Haixia Lu, Rongxing Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Univ Macau Zhuhai UM Sci & Technol Res Inst Zhuhai 519072 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China Shandong Univ Shandong Key Lab Wireless Commun Technol Jinan 250061 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

network slicing has been envisioned to play a crucial role in supporting various vehicular applications with diverse performance requirements in dynamic Vehicle-to-Everything (V2X) communications systems. However, time-varying Service Level Agreements (SLAs) of slices and fast-changing network topologies in V2X scenarios may introduce new challenges for enabling efficient inter-slice resource provisioning to guarantee the quality of Service (QoS) while avoiding both resource over-provisioning and under-provisioning. Moreover, the conventional centralized resource allocation schemes requiring global slice information may degrade the data privacy provided by dedicated resource provisioning. To address these challenges, in this paper, we propose a two-timescale resource management mechanism for providing diverse V2X slices with customized resources. In the long timescale, we propose a Proximal Policy Optimization-based multi-agent deep reinforcement learning algorithm for dynamically allocating bandwidth resources to different slices for guaranteeing their SLAs. Under the coordination of agents, each agent only observes its partial state space rather than the global information to adjust the resource requests, which can enhance the privacy protection. Moreover, an expert demonstration mechanism is proposed to guide the action policy for reducing the invalid action exploration and accelerating the convergence of agents. In the short-term time slot, with our proposed Cross Entropy and Successive Convex Approximation algorithm, each slice allocates its available physical resource blocks and optimizes its transmit power to meet the QoS. Simulation results show our proposed two-timescale resource allocation scheme for network slicing can achieve maximum 8.4% performance gains in terms of spectral efficiency while guaranteeing the QoS requirements of users compared to the baseline approaches.

关键词： network slicing V2X communications multi-agent deep reinforcement learning resource allocations

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Deep Reinforcement Learning-based Edge Caching for Industrial control Applications 35

Deep Reinforcement Learning-based Edge Caching for Industria...

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35th Chinese control and Decision conference (CCDC)

作者： Zhang, Lei Xu, Hao Wang Guilin Yan, Wang Wang, Xiaojun Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing Peoples R China PipeChina West East Gas Pipeline Co Shanghai Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing Peoples R China

ISBN: (纸本)9798350334722

To enhance the quality of service for field users in industrial control applications, a suitable caching strategy at edge servers is essential. This paper proposes a cache replacement strategy based on deep reinforcement learning. Status space, action space and reward function are defined considering the varying real-time requirements of the application files. The performance of the proposed algorithm is compared with baseline algorithms using user requests with dynamic popularities. The experimental results demonstrate that the proposed algorithm can effectively enhance the hit rate of control files while maintaining the overall cache hit rate, without sacrificing performance.

关键词： industrial applications edge cache Deep Q-network

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Enhancing Intrusion Detection systems in IoT networks: A Hybrid Approach using CNN, ANN, LSTM, GRU for Improved Security 8

Enhancing Intrusion Detection Systems in IoT Networks: A Hyb...

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8th International conference on Inventive systems and control, ICISC 202

作者： Pradeep, M. Gopalakrishnan, S. Hindustan Institute of Technology and Science Department of Information Technology TamilNadu Kelambakkam603103 India

ISBN: (纸本)9798350386578

The automation of daily tasks via the internet of Things (IoT) has drastically changed people's lives in the last few years. By connecting different physical devices with various functionalities, this is accomplished. As a result, as IoT networks grow, so does the rate of cyber threats, risking the stability and integrity of data. Numerous intrusion detection systems (IDS s) are currently developed to identify harmful actions based on predefined attacks patterns, thereby safeguarding data against misuse and abnormal attempts. The current IDS have to be improved due to the sharp rise in these types of attacks. The key to enhancing intrusion detection systems is now machining learning. A proposed intrusion detection methodology in this study is hybrid approach. After evaluating the model's performance using a number of popular, advanced methods currently in use, the suggested model uses CNN, ANN, LSTM and GRU model. The model gives better trade -off compared to other approaches. © 2024 IEEE.

关键词： network intrusion

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