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SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2024年第1期79卷

作者： Tabassum, Saleha Babu, Attuluri R. Vijay Dheer, Dharmendra Kumar Vignans Fdn Sci Technol & Res Dept Elect & Elect Engn Guntur 522213 Andhra Pradesh India KSRM Coll Engn Dept Elect & Elect Engn Kadapa 516003 AP India Natl Inst Technol Dept Elect Engn Patna 800005 Bihar India

To tackle the challenge of improving Power quality (PQ) in modern power grids, we introduce an innovative internet of Things (IoT)-based Smart Grid (SG) energy surveillance system. Our research is driven by the necessity to enhance power quality and optimize energy management in increasingly complex grids that incorporate renewable energy sources like Solar PV and Wind Generating systems. Traditional methods for managing power quality often fall short, resulting in inefficiencies and potential disruptions. Our solution features an advanced IoT-based system that utilizes the Adaptive Neuro-Fuzzy Inference System (ANFIS), combining Artificial Neural networks (ANN) and Fuzzy Logic systems to enhance power distribution and control. This system uses a Wireless Sensor network for real-time data collection and analysis, allowing for precise monitoring of electricity usage and improved energy management and cost reduction. Our findings indicate that this innovative approach not only boosts power quality but also significantly enhances the efficiency of renewable energy sources, showing a 20.50% performance increase during the startup phase of Solar PV-Wind Generating systems. This highlights the system's potential to advance power quality management and provide substantial benefits in energy regulation and cost efficiency.

关键词： Adaptive neuro-fuzzy inference system internet of things Solar PV module Smart grid Wind generating system Wireless sensor networks

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PandORA: Automated Design and Comprehensive Evaluation of Deep Reinforcement Learning Agents for Open RAN

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3223-3240页

作者： Tsampazi, Maria D'Oro, Salvatore Polese, Michele Bonati, Leonardo Poitau, Gwenael Healy, Michael Alavirad, Mohammad Melodia, Tommaso Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA Dell Technol P&O OCTO Adv Wireless Technol Round Rock TX 78682 USA

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 ORAN 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 intelligent control loops. Recent work has showed how Deep Reinforcement Learning (DRL) is effective in dynamically controlling O-RAN systems. However, how to design these solutions in a way that manages heterogeneous optimization goals and prevents unfair resource allocation is still an open challenge, with the logic within DRL agents often considered as a opaque system. In this paper, we introduce PandORA, a framework to automatically design and train DRL agents for Open RAN applications, package them as xApps and evaluate them in the Colosseum wireless network emulator. We benchmark 23 xApps that embed DRL agents trained using different architectures, reward design, action spaces, and decision-making timescales, and with the ability to hierarchically control different network parameters. We test these agents on the Colosseum testbed under diverse traffic and channel conditions, in static and mobile setups. Our experimental results indicate how suitable fine-tuning of the RAN control timers, as well as proper selection of reward designs and DRL architectures can boost network performance according to the network conditions and demand. Notably, finer decision-making granularities can improve Massive Machine-Type Communications (mMTC)'s performance by si

关键词： Deep reinforcement learning network intelli- gence O-RAN open RAN open RAN resource allocation resource allocation resource allocation

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From Earth-to-Moon networking: A Software-Defined Temporal Perspective

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IEEE TRANSACTIONS ON network SCIENCE AND ENGINEERING 2025年第1期12卷 369-380页

作者： Chiti, Francesco Picchi, Roberto Pierucci, Laura Univ Florence Dept Informat Engn I-50139 Florence Italy

Considering the scientific and economic opportunities, several public and private organizations are going to establish colonies on the Moon. In particular, lunar colonization can be a first step for deep space missions, and the initial phase is accomplished with the deployment of many internet of Things (IoT) devices and systems. Therefore, a dedicated Earth-Moon backbone, which results from the combination of terrestrial and lunar satellite segments, must be designed. Considering that its elements are inherently mobile, to ensure the connection, the constituent devices are supposed to be programmed to properly operate during specific time intervals. The features of the Software-Defined networking (SDN) paradigm allows achieving this aim. Moreover, the Temporal networks (TNs) theoretical framework makes it possible to optimize the forwarding rules. In light of these principles, this paper proposes an SDN-based architecture and analyzes the overall communications scenario proposing a specific strategy to optimize the data rate. The performance was evaluated considering the End-to-End (E2E) best path duration, the number of hops, the control packets latency, the power budget and capacity. The results point out that it is feasible to establish a networking strategy on-demand to support the transmission of continuous IoT data flows with limited overhead.

关键词： performance evaluation Space missions Moon quality of service Organizations Throughput internet of Things Uplink Software defined networking Monitoring Earth-moon routing interplanetary internet satellite systems software-defined networking temporal networks

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Research on the Design and performance Optimization of Intelligent Traffic Signal control System Based on internet of Things 4

Research on the Design and Performance Optimization of Intel...

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4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Cao, Yang Institute of Smart City and Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu610000 China

ISBN: (纸本)9798350359985

With the rapid development of internet of Things technology, intelligent traffic signal control systems have become an excellent way to improve traffic efficiency and safety. In this paper, we model an intelligent traffic signal control system based on the basic theory of the internet of Things. It introduces a graph neural network and principal component analysis for performance optimization. Numerical examples include the relationship between the traffic flow prediction or signal control results in the sample library and the traffic flows, the relationship with devices in the internet of Things, the relationship with graph neural networks, and the relationship with principal component analysis error. It turns out that an intelligent traffic signal control system based on the internet of Things can more accurately predict traffic flow and optimize signal control using graph neural networks and principal component analysis. It works with the internet of Things to realize intelligent traffic management. In this paper, we use theoretical analysis to elucidate the system's internal mechanisms and provide scientific support for further improving the quality of transportation systems and meeting users' expectations. © 2024 IEEE.

关键词： Graph neural networks

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Voice over internet Protocol: Custom SIP Server with QoS Optimization for Real-Time Communication 3

Voice over Internet Protocol: Custom SIP Server with QoS Opt...

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3rd International conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Dinesh Krishna, B.V. Yogendrasai, P. Rajeev, S. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

VoIP is a revolutionary technology for transmission of voice over internet protocol than from plain telephony systems. But current VoIP solutions have some important issues which are packet loss, latency, and jitter especially when the network is in motion. This work outlines the design, implementation and performance of a locally developed VoIP system which includes a SIP Server Node, Sender Node and a Receiver Node. The SIP Server Node was designed nearly from the ground up to handle session initiation, packet forwarding, and adaptive QoS control. Both Sender and Receiver Nodes had facilities to record, compress and playback audio. The system’s performance was evaluated in a LAN environment, achieving significant improvements in quality of Service (QoS) metrics: achieving low latency under 150 ms, 95% packet recovery and achieving a good audio quality with the MOS of 4.2. Evaluating the obtained results, it can be concluded that the proposed system is efficient in handling the issues of the packet-switched network and providing high-quality real time voice communication. © 2025 IEEE.

关键词： Packet loss

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AS-MPCA: An adaptive scheduling algorithm for industrial internet of Things based on multi-population co-evolution

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internet OF THINGS 2025年 31卷

作者： Chai, Anying Wang, Lei Guo, Chenyang Li, Mingshi Yin, Wanda Fang, Zhaobo Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China China Ind Control Syst Cyber Emergency Response Te Beijing 100040 Peoples R China Shenyang Key Lab Adv Comp & Applicat Innovat Shenyang 110167 Peoples R China

The Industrial internet of Things (IIoT) enables real-time data collection, analysis, and decision making by tightly connecting physical devices, sensors, control systems, and information systems. Meanwhile, in the communication environment of industrial parks, a multi-dimensional heterogeneous information sensing network is constructed by sensor networks, shop Ethernet, field buses, etc. In this kind of network, multi-source heterogeneous data types complex and diverse, the data scale is huge, and all kinds of data flows have different quirements for transmission volume and real-time performance. Especially in a communication environment with limited network resources, the transmission delay of real-time service makes it difficult to meet the actual production requirements. These lead to problems as low real-time and insufficient reliability of sensory data transmission. To address problems, we propose an Adaptive Scheduling Algorithm for the Industrial internet of Things Based on Multi-swarm Co-evolution(AS-MPCA). The algorithm combines a two-stage multiple swarm genetic algorithm with an adaptive routing mechanism. Firstly, the two-stage multiple swarm genetic algorithm expands the search space and enhances the diversity of the scheduling scheme through the combination of global search and multiple swarm strategies, which provides diversified path selection strategies for the adaptive routing mechanism. Then, the adaptive routing mechanism dynamically adjusts the optimal path according to the scheduling results the above genetic algorithm. Simulation and experimental results demonstrate that the proposed method significantly enhances system schedulability. Compared with traditional algorithms, proposed algorithm improves the task acceptance rate by an average of 10% across various conditions, effectively reduces the transmission delay of time-sensitive data, and ensures quality of service for industrial IoT communication systems.

关键词： Industrial internet of Things Real-time scheduling Adaptive routing Global optimization QoS

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Efficient Resource Allocation for Multimedia Streaming in Software-Defined internet of Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第12期24卷 14718-14731页

作者： Montazerolghaem, Ahmadreza Univ Isfahan Fac Comp Engn Esfahan *** Iran

Due to the rapid growth of the internet of Vehicles (IoV) and the rise of multimedia services, IoV networks' servers and switches are facing resource crises. Multimedia vehicles connected to the internet of Things are increasing;there are millions of vehicles and heavy multimedia traffic in the IoV network. The network's scarcity of resources results in overload, which, in turn, leads to a degradation of both quality of Service (QoS) and quality of Experience (QoE). Conversely, when resources are abundant, it leads to unnecessary energy wastage. Managing IoV network resources optimally while considering constraints such as Energy, Load, QoS, and QoE is a complex challenge. To address this, the study proposes a solution by decomposing the problem and designing a modular architecture named ELQ(2). This architecture enables simultaneous control of the mentioned constraints, effectively reducing overall complexity. To achieve this objective, network Softwarization and Virtualization concepts are employed. This modern architecture allows dynamically adjusting of the scale of the resources on demand, effectively reducing energy usage. Additionally, this architecture provides some other potentials, such as "the distribution of multimedia traffic among servers", "determining the route with high QoS for traffic", and "selecting a media with high QoE". A real test field is provided by Floodlight controller, Open vSwitch, and Kamailio Server tools to evaluate the performance of ELQ(2). The findings suggest that the utilization of ELQ(2) holds promise in reducing the count of active servers and switches via effective resource management. Additionally, it demonstrates enhancements in various QoS and QoE parameters, encompassing throughput, multimedia delay, R Factor, and MOS, accomplished through load balancing strategies. As an illustration, the deployment of flows has achieved a commendable success rate of 95% owing to the utilization of SDN-based and comprehensive management

关键词： Streaming media Servers Resource management quality of service quality of experience Energy consumption Vehicle dynamics Software-defined internet of vehicles (SD-IoV) network functions virtualization (NFV) software-defined networking (SDN) efficient network resource allocation internet of multimedia vehicles

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Strategies for Optimizing Uplink Spectrum Efficiency in Cell-Free Massive MIMO Satellite-UAV network

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IEEE internet OF THINGS JOURNAL 2024年第22期11卷 37154-37169页

作者： Tran, Thong-Nhat Yu, Heejung Kim, Taejoon Chungbuk Natl Univ Sch Informat & Commun Engn Chungju 28644 South Korea Korea Univ Dept Elect & Informat Engn Sejong 30019 South Korea

This article introduces an innovative cell-free massive MIMO (CF-mMIMO) architecture integrating a low-Earth orbit satellite with unmanned aerial vehicles (UAVs) as flying access points (FAPs), referred to as a CF-mMIMO satellite-UAV network, aimed at enhancing uplink spectrum efficiency (SE) for ground users (GUs). Expressions are derived to estimate both direct and cascaded channels involving satellites, UAVs, and GUs, while proposing local combining strategies for the network. A straightforward dynamic clustering framework is developed to mitigate interference among GUs effectively, leveraging the inherent characteristics of UAV-based networks to optimize user service quality. An optimization model for FAP trajectory and energy consumption is proposed to effectively manage energy in limited-resource scenarios and enhance network performance. Additionally, max-min fairness power control issue is explored, with a closed-form solution being presented that ensures the equitable SE distribution among GUs. We also tackle a nonconvex optimization challenge to maximize the network's sum-rate by refining GU power levels, incorporating large-scale fading decoding (LSFD) to further elevate SE. Extensive simulations validate the effectiveness of our proposed methods, illustrating significant improvements over traditional configurations. These approaches not only advance CF-mMIMO satellite-UAV network performance but also lay foundational strategies for future aerial communication systems.

关键词： Cell-free massive MIMO (CF-mMIMO) dynamic clustering large-scale fading decoding low-Earth orbit (LEO) satellite local combining maximum sum-rate (MSR) max-min fairness (MMF) power control unmanned aerial vehicle (UAV) large-scale fading decoding low-Earth orbit (LEO) satellite local combining maximum sum-rate (MSR) max-min fairness (MMF) power control unmanned aerial vehicle (UAV)

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D-PSO: An Optimization Algorithm for Wireless Sensor network Layout in Power systems 9

D-PSO: An Optimization Algorithm for Wireless Sensor Network...

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9th International conference on Automation, control and Robotics Engineering (CACRE)

作者： Ma, Pengwei Zhu, Yuanjiao Sun, Zheng Wei, Jingyi Luo, Yunchen Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China

ISBN: (纸本)9798350350319;9798350350302

The effective deployment of wireless sensor networks (WSNs) is a crucial foundation for the intelligent development of power systems. To address the optimization of wireless sensor distribution in power systems, this paper proposes a novel method named the Distributed Particle Swarm Optimization algorithm (D-PSO). This method mitigates the premature convergence issue of heuristic algorithms by introducing a regional operator. Additionally, considering the high-interference environment in power systems, relay node strategy (RNS) is incorporated to ensure communication quality. Simulation results validate the effectiveness and superiority of the D-PSO method and demonstrate the necessity of the RNS. Compared to some advanced particle swarm algorithms, this method better balances energy consumption, coverage, and communication quality, thereby significantly enhancing the overall performance of the wireless sensor network.

关键词： wireless sensor network power systems Particle Swarm Optimization algorithm

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Design of network Data Monitoring and control System Based on internet of Things Technology 2

Design of Network Data Monitoring and Control System Based o...

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2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Zhang, Xiaolu Shen, Wuqiang Cui, Lei Guangdong Power Grid Guangdong China

ISBN: (纸本)9798350317558

This article is supported by wireless sensing technology, wireless communication technology, and software development technology of the internet of Things. A power quality monitoring system based on the internet of Things is proposed, and comprehensive evaluation of power quality is studied. A power quality data collection platform is constructed, and a new type of internet of Things transmission technology is used to achieve multi-point collection of power quality data, The reliable transmission and efficient processing of power quality information have been achieved using TCP/IP wireless communication technology and middleware technology. The results indicate that an indicator measurement evaluation matrix is established to determine the comprehensive weight of power quality evaluation indicators, overcome the one-sidedness of a single weighting method, and make the evaluation results more scientific and accurate by comparing the advantages and disadvantages of different comprehensive evaluation methods in order to improve the accuracy of evaluation methods. © 2024 IEEE.

关键词： control system evaluation algorithm Forewarning model internet of Things network data

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