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Dynamic Real-Time Anchor Selection for Accurate UWB Indoor Positioning-Based Deep Neural networks

IEEE ACCESS 2025年 13卷 80283-80307页

作者： Majeed, Ammar Fahem Arsat, Rashidah Baharudin, Muhammad Ariff Latiff, Nurul Mu'Azzah Abdul Albaidhani, Abbas Univ Teknol Malaysia UTM Fac Elect Engn Skudai 81310 Johor Malaysia Air Nav Acad Gen Co Air Nav Serv Baghdad 10023 Iraq

In wireless localization systems, enhancing location estimation performance is critical, particularly in challenging environments, such as military urban operations and emergency response scenarios. Ultra-wideband (UWB) positioning systems using two-way-ranging (TWR) schemes avoid synchronization issues, but face challenges related to scalability, anchor selection, and poor channel characteristics. Existing methods often rely on exhaustive geometric calculations, leading to inefficiencies in dynamic and time-critical scenarios. This paper proposes a lightweight sequential branching deep network for dynamic indoor positioning (LSB-DIP Net) is proposed to address these challenges. By integrating multi-scale feature extraction, sequential learning, and advanced activation functions with the conventional linearized least squares method, the LSB-DIP Net enables robust, accurate, and dynamic UWB positioning in real-time. The model effectively mitigates non-line-of-sight (NLOS) ranging errors, evaluates anchor channel quality online, and selects optimal anchor combinations, ensuring scalability and adaptability for diverse deployment scenarios. The proposed approach demonstrates exceptional performance in dynamic setups, achieving low mean squared error (MSE) of 0.0051m2, high accuracy in identifying anchor channels of 99.44%, with a maximum positional error of less than 0.17 m in harsh environments. Validated across public datasets, the system ensures generalizability and outperforms state-of-the-art counterparts in the market, making it a reliable tool for real-time applications in communication and navigation systems.

关键词： Accuracy Location awareness Real-time systems Heuristic algorithms Prevention and mitigation Nonlinear optics Scalability Indoor positioning systems Wireless communication Navigation Deep neural network (DNN) dynamic indoor positioning systems (IPs) anchor selection (AS) long short-term memory (LSTM) robotics

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Feasibility Study of Time-Sensitive network for 5G Fronthaul Transmission 4

Feasibility Study of Time-Sensitive Network for 5G Fronthaul...

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4th International conference on Electronic Materials and Information Engineering (EMIE)

作者： Zhu, Jinyu Li, He Xu, Qichen Duan, Shihui China Acad Informat & Commun Technol Key Lab Internet & Ind Integrat & Innovat MIIT Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350390230;9798350390223

Vertical industries such as industrial control networks and internet of vehicles have extremely high performance requirements for the reliability and certainty of 5G networks. The application of time-sensitive networks characterized by bounded deterministic transmission to the fronthaul networks of 5G is of great value in improving the end-to-end deterministic capability of 5G, and is gradually becoming a research hotspot. This paper first introduces the fronthaul network and time-sensitive network and the integration deployment of the two, then analyzes the factors affecting the quality of fronthaul network service, and describes the comparative test scheme of TSN and optical fiber for 5G fronthaul network, which provides a basis for the feasibility of TSN carrying 5G fronthaul network and lays a foundation for the evolution and deployment of TSN carrying 5G fronthaul network.

关键词： time-sensitive network 5G fronthaul network service quality

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Precision Pistachios: Enhancing Agricultural quality control with Deep Learning 2

Precision Pistachios: Enhancing Agricultural Quality Control...

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2nd International conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kaur, Arpanpreet Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

Originating from the pistachio tree (Pistacia vera) and prized for their nutritional content and adaptability in the kitchen, pistachios have great financial worth in the agricultural field. Ensuring the quality and safety of pistachio nuts is absolutely vital and calls for effective industrial post-harvest methods. Modern technology, especially image processing and computer vision algorithms, are augmenting conventional approaches of pistachio categorization and separation. This work intends to build an enhanced classification model for pistachio identification using deep learning, most especially the VGG16 Convolutional Neural network (CNN). After gathering a dataset of 2,148 high-resolution photos of pistachios, the approach uses resizing and data augmentation methods including rotation, flipping, and zooming to improve the generalization and prevent overfitting of the model. Pretrained on ImageNet, the VGG16 model is fine-tuned to fit the particular job of pistachio recognition by means of feature extraction capacity. For feature extraction, the model architecture comprises convolutional layers;for classification, it features fully connected layers. With performance criteria including accuracy, precision, recall, and F1-score watched to guarantee efficient learning, optimization methods including gradient descent and backpropagation are utilized during training. On a test set, the model shows an extraordinary accuracy of 0.98, so highlighting the potential of deep learning models-especially VGG16-in automating and improving quality control systems in agricultural uses. With possible uses in related agricultural research, this highperformance classification model not only meets the demand for effective separation of pistachio species but also considerably increases their economic value.

关键词： Pistachio detection Convolutional Neural networks (CNNs) VGG16 Image processing Computer vision Deep learning Classification model Data augmentation quality control Agricultural Feature extraction post-harvest processes Economic species

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Integrating Fuzzy Logic, Solar Power and IoT for Real-Time Water quality Monitoring in Cage Fish Culture

Integrating Fuzzy Logic, Solar Power and IoT for Real-Time W...

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Africa conference (IST)

作者： Shitsukane, Aggrey Egessa, Mathew Orina, Paul Omieno, Kelvin Amuti, Mathews Tech Univ Mombasa Box 90420 Mombasa 80100 Kenya Kenya Marine & Fisheries Res Inst POB 1881 Kisumu 40100 Kenya Kaimosi Friends Univ POB 385 Kaimosi 50309 Kenya Kenya Ports Author POB 95009 Mombasa 80104 Kenya

ISBN: (纸本)9781905824731;9798350356595

An innovative solution to enhance cage fish culture through the integration of Fuzzy Logic, Solar Power, and internet of Things for Real-Time Water quality Monitoring is presented. The system employs a wireless sensor network featuring embedded microcontroller and ZigBee wireless modules to manage water quality parameters in fish pond. Fuzzy controllers autonomously adjust water quality conditions to optimize fish growth. Experimental results through simulation demonstrate successful use of fuzzy logic for real-time water quality monitoring system, showcasing its effectiveness. The proposed approach exhibits robust performance in real-time monitoring of fish ponds, offering potential for heightened efficiency in aquaculture practices. This advancement not only contributes to sustainable aquaculture but also bolsters global food security initiatives by ensuring a consistent and high-quality nutrient supply for human consumption.

关键词： Aquaculture Fuzzy Logic control Real-time monitoring and control Photovoltaic system internet of things

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Design of a microservices-based architecture for residential energy efficiency monitoring

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INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS 2024年第4期70卷 1089-1098页

作者： Nunez, Ivonne Rovetto, Carlos Cruz, Edmanuel Smolarz, Andrzej Concepcion, Dimas Cano, Elia Esther Univ Tecnol Panama Sch Comp Syst Engn Panama City Panama Lublin Univ Technol Lublin Poland

the significant advancement of electrical infrastructure in the context of smart buildings and smart homes, the need arises to overcome the limitations of the traditional energy efficiency control system based on service- oriented architecture (SOA). To address these challenges, this study proposes a distributed architecture based on microservices, with the main objective of improving the performance and stability of these systems. This proposal seeks to enable end users to effectively monitor and control their electrical devices while effectively integrating them into a wide network of power systems. The proposed architecture relies on a series of cloud services that enable better performance and control in energy efficiency management, highlighting key features of microservices such as fault tolerance, performance, and scalability. Using a structural methodology centered on preexisting components and an iterative approach, a versatile and scalable architecture was designed that addresses current challenges in energy efficiency management. The results show a significant impact on key performance indicators such as demand response, energy savings, and power quality, highlighting the resilience and scalability of the proposed architecture. The conclusions highlight the importance of energy efficiency in reducing the environmental impact and costs associated with electric power, suggesting future improvements in data access and the implementation of advanced machine learning algorithms.

关键词： - Microservices software architecture energy efficiency internet of things (IoT) smart homes

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Accountable and Distributed Industrial control systems with Autonomous Contracts 26

Accountable and Distributed Industrial Control Systems with ...

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26th conference on Innovation in Clouds, internet and networks and Workshops (ICIN) - Distributed Intelligence across network and Edge-to-Cloud Continuum

作者： Makhijani, Kiran Faisal, Tooba Futurewei Technol Santa Clara CA 95050 USA Kings Coll London London England

ISBN: (纸本)9798350398045

Industrial control systems (ICS) have specific data requirements in terms of quality of Service (QoS). Lost or delayed critical data, such as control signals, can damage widespread production, such as machine performance. The problem is compounded in the next-generation Industry 4.0, where the operations will be even less human-dependent, involving multiple organizations. Moreover, the communication aspects across multistakeholders require a disaggregated approach contrary to the current closed ICS architecture. This paper presents a framework for decentralization of ICS called Operations and control networks (OCN) in which 'contracts' are the basic units of abstraction to exchange information with trust and precision between different actors. In the lower layers, High-Precision Communication (HPC) contracts execute control functions with requested service level guarantees in the network. A high-level trusted delegation of tasks is executed as smart contracts using Distributed Ledger Technology (DLT) to enable multi-stakeholder auditability and accountability.

关键词： Smart Contracts New IP DLT High-Precision Communication HPC-Contract Operation & control networks (OCN) and control systems

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SDN-controlled Resource-Tailored Analytics for Healthcare IoT System

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IEEE systems JOURNAL 2023年第2期17卷 1777-1784页

作者： Misra, Sudip Pal, Saswati Ahmed, Nurzaman Mukherjee, Anandarup Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Sch Nanosci & Technol Kharagpur 721302 W Bengal India

In this work, we propose phSDN, a programmable healthcare network architecture for resource-tailored management and on-demand allocation of analytic modules in internet of Things (IoT) healthcare system. The identification, dynamic management of services, and fast analysis of healthcare data are a few challenges in existing remote healthcare monitoring devices. phSDN programs and reconfigures the resources and services, while updating the associated flow-rules in the data-plane of software-defined network (SDN) architecture. It is especially useful in scenarios with intermittent or constrained network connectivity and/or device availability. The newly identified flows associated with different resources are explicitly considered, while forwarding services over the SDN network. The on-the-fly programmable services enable healthcare devices to provide resource-specific analytics. The programmability over services also allows unified control over the end-devices by reconfiguring on-the-fly vendor-specific heterogeneity of resource types. Our prototype of phSDN supports three different types of resources: 1) body temperature, 2) pulse rate, and 3) blood pressure. Experiments using our implementation show that the proposed architecture minimizes the network response time by at least 43.87% and 78.4%, compared to the fog- and cloud-based approaches, respectively. The implemented system's scalability is further evaluated using Mininet by considering latency, packet delivery ratio, and throughput, as the performance metrics.

关键词： Switches Medical services Protocols control systems Computer architecture quality of service Dynamic scheduling Healthcare internet of Things (IoT) system IoT programmability software-defined network (SDN)

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TIM: A Novel quality of Service Metric for Tactile internet 23

TIM: A Novel Quality of Service Metric for Tactile Internet

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14th ACM/IEEE International conference on Cyber-Physical systems (ICCPS) part of CPS-IoT Week

作者： Kroep, H. J. C. Gokhale, V. Simha, A. Prasad, R. R. Venkatesha Rao, V. S. Delft Univ Technol Delft Netherlands Cognizant Technol Solut Amsterdam Netherlands

ISBN: (纸本)9798400700361

Tactile internet (TI) envisions communicating haptic sensory information and kinesthetic feedback over the network and is expected to transfer human skills remotely. For mission-critical TI applications, the network latency is commonly mandated to be between 1-10 ms, due to the sensitivity of human touch, and the packet delivery ratio to be 99.99999%, failing which can lead to catastrophic outcomes. However, with humans-in-the-loop, their dexterity and adaptability to varying responses to stimuli under different network conditions, measuring the performance of a TI session only with latency and packet losses are insufficient and presents an incorrect representation of the experience of the TI application. To develop an objective measure of the quality of TI sessions, we propose a framework that models TI applications as networked control systems, including humans-in-the-loop. We derive a closed-form expression for measuring the difference between the application performance in ideal and non-ideal network conditions. Based on Weber's law of Just Noticeable Difference, we provide a metric called TIM to estimate the impact of the network on haptic feedback. We implemented TIM on multiple applications on a TI testbed to show that our approach is feasible and TIM strongly follows real subjective measurements. Further, we propose a channel compensation spring based on TIM, to alleviate the network conditions' negative effects. We demonstrate the efficacy of the channel compensation spring in improving the user experience. We also present implementation notes for TI application developers.

关键词： Tactile internet user experience QoS teleoperation

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TransFiLM: An Efficient and Lightweight Audio Enhancement network for Low-Cost Wearable Sensors 21

TransFiLM: An Efficient and Lightweight Audio Enhancement Ne...

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21st IEEE International conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Chen, Shilin Weng, Jianhao Hong, Shicong He, Yanbo Zou, Yongpan Wu, Kaishun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Hong Kong Univ Sci & Technol Guangzhou Informat Hub Guangzhou Peoples R China

ISBN: (纸本)9798350363999;9798350364002

Wearable devices such as headphones are increasingly popular in people's lives, and there is an increasing focus on how to achieve continuous and reliable information input using these devices. However, due to constraints in computing power, low power consumption, and low operating frequencies, such devices often record and transmit signals at lower sampling rates, the resultant lower-quality signals often have catastrophic implications for system performance. Efficient real-time conversion of low-resolution speech signals to full-resolution high-quality signals using low-cost wearable sensors on edge devices is a challenging research endeavor. To address this, this paper designs TransFiLM, a mobile deep learning network. It allows users to obtain full-resolution high-quality audio signals using low-cost wearable sensors on edge devices. TransFiLM integrates residual learning and super-resolution networks and employs effective signal processing strategies to achieve audio upscaling and noise reduction, significantly improving audio quality. We implement a prototype on commercial devices and conduct a series of experiments to evaluate its performance. Using signal-to-noise ratio (SNR) and log-spectral distance (LSD) as evaluation metrics, TransFiLM exhibits superior performance compared to other time-domain methods in cross-user, cross-corpus, and cross-noise environment testing. Additionally, our TransFiLM network handles 8192 samples with a response time of 181 ms, which meets the requirement to run in real-time on edge devices.

关键词： Wearable Sensors Audio Super-Resolution Mobile Deep-Learning

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Exploring NetGPT-Enabled Dynamic Resource Management for Mobile D2D in Industrial IoT

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IEEE network 2025年第3期39卷 37-46页

作者： Chen, Baotong Liang, Haiquan Wan, Jiafu Wang, Chuangjian Yan, Xuguo Xia, Xuhui Alqahtani, Salman A. Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China King Saud Univ Coll Comp & Informat Sci Comp Engn Dept Riyadh Saudi Arabia

AI large model-centered intelligent applications holds promises to revolutionize network quality of service (QoS). therefore, the deployment and utilization of such models in the Industrial internet of Things (Industrial IoT) are considered as an increasingly pressing priority. In particular, the integration of Generative AI (GenAI) into Industrial IoT, with a focus on establishing a robust NetGPT network architecture that leverages Mobile Edge Computing (MEC) and Device-to-Device (D2D) collaboration has been subjected to significant academic attention. Accordingly, this paper explores the optimization of dynamic network resource management in mobile D2D networks through the innovative application of NetGPT. First, a NetGPT-based system architecture for network resource management is proposed. Thereafter, mobile D2D network model is established with a detailed account of NetGPT training and NetGPT fusion. Finally, the workload scheduling strategy in industrial IoT based on Stackelberg game model is formulated though multi-agent GenAI and present the NetGPT-based orchestration of task co-offloading for mobile network nodes. Deep reinforcement learning is implemented at the edge side to compete and cooperate in addressing complex network problems, which enabling GenAI nodes in a co-offloading and load balancing manner. The implementation of NetGPT-enhanced D2D networks has been conducted in a Mininet simulation environment. Compared to traditional network solutions, our findings show significant improvements in several key performance indicators, including network response latency, task processing capacity, and transmission power.

关键词： Cloud computing Device-to-device communication Industrial internet of Things Training Servers Computational modeling Biological system modeling Collaboration Dynamic scheduling systems architecture Mobile computing Edge computing quality of service Resource management Telecommunication network management Generative AI Multi-access edge computing GenAI Industrial IoT NetGPT D2D Co-offloading

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