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Virtual Reality-Enabled Data Processing Framework for iot sensor networks with Augmented and Mixed Reality Integration, along with E-commerce, CRM, and Management Information System 3

Virtual Reality-Enabled Data Processing Framework for IoT Se...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Venkatasubramanian, S. Kharat, Avinash Manohar Abilasha, N. Raha, Shrinwantu Manerkar, Ganesh Vasudeo Manoharmayum, Dolpriya Devi Saranathan Coll Engn Dept Comp Sci & Business Syst Tiruchirappalli India Venkateshwara Inst Management Peth Maharashtra India Univ Mumbai Mulund Coll Commerce Autonomous Mulund West Mumbai Maharashtra India BhairabGanguly Coll Belgharia Kolkata State Aided Coll Teacher Category 1 Dept Geog Kolkata W Bengal India Goa Univ Goa Coll Engn Informat Technol Govt Goa Taleigao Goa Goa India KoneruLakshmaiah Educ Fdn KLEF KL Coll Agr Guntur Andhra Pradesh India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

This exploratory research aims to offer a slice-edge frame that merges Virtual Reality (VR) technology with Internet of Things (iot) detector networks, augmented reality (AR), and mixed reality (MR) to achieve greater data processing capabilities. The purpose of this study is to accomplish this goal. The framework incorporates the fundamentals of e-commerce, customer relationship management (CRM), and management information systems (MIS) to produce an output that is comprehensive in nature. The frame seeks to give immersive and engaging gestures while simultaneously handling and analyzing huge amounts of data efficiently. In addition to leveraging virtual reality, augmented reality, and mixed reality, the frame will also utilize mixed reality. Through the implementation of this integration, it is possible to enhance decision-making processes, increase customer involvement, and speed up operational operations across a wide range of industries. Throughout the course of this investigation, the suggested framework's design, implementation, and evaluation are thoroughly examined. The study also highlights the potential for the framework to be used to adjust data recycling methods and to reshape the geographical landscape of digital ecosystems. Both of these possibilities are highlighted in the research

关键词： Virtual Reality iot sensor networks Augmented Reality Integration Mixed Reality Integration E-commerce Integration CRM Integration Management Information System Integration

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XGate: Explainable Reinforcement Learning for Transparent and Trustworthy API Traffic Management in iot sensor networks

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sensorS 2025年第7期25卷 2183-2183页

作者： Jin, Jianian Xing, Suchuan Ji, Enkai Liu, Wenhe Columbia Univ Fu Fdn Sch Engn & Appl Sci New York NY 10027 USA Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

The rapid proliferation of Internet of Things (iot) devices and their associated application programming interfaces (APIs) has significantly increased the complexity of sensor network traffic management, necessitating more sophisticated and transparent control mechanisms. In this paper, we introduce XGate, a novel explainable reinforcement learning framework designed specifically for API traffic management in sensor networks. XGate addresses the critical challenge of balancing optimal routing decisions with the interpretability demands of network administrators operating large-scale iot deployments. Our approach integrates transformer-based attention mechanisms with counterfactual reasoning to provide human-comprehensible explanations for each traffic management decision across distributed sensor data streams. Through extensive experimentation on three large-scale sensor API traffic datasets, we demonstrate that XGate achieves 23.7% lower latency and 18.5% higher throughput compared to state-of-the-art black-box reinforcement learning approaches. More importantly, our user studies with sensor network administrators (n=42) reveal that XGate's explanation capabilities improve operator trust by 67% and reduce intervention time by 41% during anomalous sensor traffic events. The theoretical analysis further establishes probabilistic guarantees on explanation fidelity while maintaining computational efficiency suitable for real-time sensor data management. XGate represents a significant advancement toward trustworthy AI systems for critical iot infrastructure, providing transparent decision making without sacrificing performance in dynamic sensor network environments.

关键词： explainable reinforcement learning API traffic management iot sensor networks counterfactual reasoning hierarchical explanations dual-objective optimization

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Iterative NDA Positioning Using Angle-of-Arrival Measurements for iot sensor networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第10期69卷 11369-11382页

作者： Monfared, Shaghayegh Nguyen, Trung-Hien Van der Vorst, Thomas De Doncker, Philippe Horlin, Francois Univ Libre Bruxelles ULB B-1050 Brussels Belgium

Various positioning techniques have been developed to localize Internet-of-Things (iot) devices accurately. Because iot communications are often narrowband, efficient localization can be achieved by deducing the device position from the estimated signal Angle-of-Arrival (AOA) at multiple arrays of antennas. It has recently been shown that significant accuracy gains can further be obtained by iterating between the AOA estimation and multi-lateration steps. However, the existing method relies on the knowledge of the transmitted signal (Data-Aided (DA) estimation) which makes it impractical for narrowband communications where the preamble is short. Non-Data-Aided (NDA) estimation is recommended to improve the positioning accuracy for low capacity iot sensors. This paper proposes an NDA iterative (NDA-It) algorithm using AOA measurements to determine the position of an iot sensor. Simulation results show that the proposed algorithm significantly outperforms the DA-It in a Bluetooth Low Energy (BLE) context because it can use a much higher number of samples (snapshots);however, it needs more iterations to converge. The computational complexity analysis proves the competitiveness of the proposed NDA-It. The performance of the algorithms is further investigated in multipath and Non-Line-of-Sight (NLOS) propagation environments. Finally, an experimental setup is built to validate the performance of the proposed algorithms.

关键词： Iterative AOA-based positioning non-data-aided estimation iot sensor networks performance and complexity analysis multipath and NLOS propagation experimental setup

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A Conceptual Model of Measurement Uncertainty in iot sensor networks

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sensorS 2021年第5期21卷 1827-1827页

作者： Cofta, Piotr Karatzas, Kostas Orlowski, Cezary UTP Univ Sci & Technol Fac Telecommun Comp Sci & Technol PL-85796 Bydgoszcz Poland Aristotle Univ Thessaloniki Environm Informat Res Grp Sch Mech Engn Thessaloniki 54124 Greece WSB Univ Gdansk Inst Management & Finance PL-80266 Gdansk Poland

The growing popularity of inexpensive iot (Internet of Things) sensor networks makes their uncertainty an important aspect of their adoption. The uncertainty determines their fitness for purpose, their perceived quality and the usefulness of information they provide. Nevertheless, neither the theory nor the industrial practice of uncertainty offer a coherent answer on how to address uncertainty of networks of this type and their components. The primary objective of this paper is to facilitate the discussion of what progress should be made regarding the theory and the practice of uncertainty of iot sensor networks to satisfy current needs. This paper provides a structured overview of uncertainty, specifically focusing on iot sensor networks. It positions iot sensor networks as contrasted with professional measurement and control networks and presents their conceptual sociotechnical reference model. The reference model advises on the taxonomy of uncertainty proposed in this paper that demonstrates semantic differences between various views on uncertainty. This model also allows for identifying key challenges that should be addressed to improve the theory and practice of uncertainty in iot sensor networks.

关键词： measurement uncertainty iot sensor networks conceptual model

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Eventual Convergence of the Reputation-Based Algorithm in iot sensor networks

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sensorS 2021年第18期21卷 6211-6211页

作者： Lebiedz, Jacek Cofta, Piotr Orlowski, Cezary Gdansk Univ Technol Fac ETI PL-80233 Gdansk Poland WSB Univ Gdansk Inst Management & Finance PL-80266 Gdansk Poland

Uncertainty in dense heterogeneous iot sensor networks can be decreased by applying reputation-inspired algorithms, such as the EWMA (Exponentially Weighted Moving Average) algorithm, which is widely used in social networks. Despite its popularity, the eventual convergence of this algorithm for the purpose of iot networks has not been widely studied, and results of simulations are often taken in lieu of the more rigorous proof. Therefore the question remains, whether under stable conditions, in realistic situations found in iot networks, this algorithm indeed converges. This paper demonstrates proof of the eventual convergence of the EWMA algorithm. The proof consists of two steps: it models the sensor network as the UOG (Uniform Opinion Graph) that enables the analytical approach to the problem, and then offers the mathematical proof of eventual convergence, using formalizations identified in the previous step. The paper demonstrates that the EWMA algorithm converges under all realistic conditions.

关键词： iot sensor networks EWMA algorithms eventual convergence proof of convergence

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Optimization of Cognitive Radio Secondary Base Station Positioning and Operating Channel Selection for iot sensor networks 8

Optimization of Cognitive Radio Secondary Base Station Posit...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Jinyi, Wen Qin, Yang Shrestha, Anish Prasad Yoo, Sang-Jo Inha Univ Dept Informat Telecommun Engn Incheon South Korea Inha Univ Sch Informat Telecommun Engn Incheon South Korea

ISBN: (纸本)9781509040322

In our paper, a novel method is proposed to obtain the optimal positions of secondary base stations (SBSs) for cognitive radio Internet of Things (iot) sensor networks and to select the optimal operating channel in order to maximize the secondary capacity, while also protecting the primary systems. We proposed an appearance probability matrix for secondary iot sensor devices in order to maximize the supportable number of sensor devices based on the optimal deployment case and probability. We derived fitness functions based on the above objectives and also considered the constraint. The particle swarm optimization (PSO) technique is used to find the best position and operating channel of SBSs.

关键词： iot sensor networks secondary base station optimal positioning operating channel selection secondary appearance probability

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Streaming Processing for ADL Monitoring in Smart Home Environments

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IEEE ACCESS 2024年 12卷 100700-100724页

作者： Cardinale, Yudith Ramirez, Jose Manuel Negrete Bragado, Irache Garamendi de Fez, Ismael Valencian Int Univ Ctr Estudios Ciencia Datos Inteligencia Artificial Valencia 46002 Spain Univ Paul Sabatier ICS IRIT F-31062 Toulouse France

Monitoring and detection of Activities of Daily Living (ADL) is a frequent practice to determine the independence of elderly/disabled people in their homes instrumented with a set of sensors, which conform smart home environments. In these scenarios there are two main problems that need to be addressed: the representation of the ADL and the real time processing of the data gathered by the sensor network. Regarding the first aspect, a reliable representation and modeling of ADL to support their automatic representation must consider factors such as human location, presence of physical objects, and time. Concerning the second aspect, the huge volume of data produced by the sensor network at different velocities and with varied formats must be processed as they are generated in order to be able to provide timely responses. In this sense, we extend a previously proposed framework aimed at assessing the level of independence of an elder person living in a smart home by integrating: (i) the HAMSTERS-XL notation to represent ADL and to instantiate independence evaluation models;and (ii) capabilities for batch and streaming processing, based on Big Data engines. We illustrate the suitability and functionality of the extended framework with a use case consisting of a virtual smart home environment and an inhabitant performing five ADL represented with HAMSTERS-XL and the AGGIR grid model. With this experience, we highlight the benefits of task models to represent ADL combined with the use of Big Data tools to process data. We also identify the limitations of the current version of the framework, in terms of the number of people that can be monitored in the same smart home and the deployment in real scenarios, which will be approached in future research.

关键词： sensors Monitoring Smart homes Intelligent sensors Task analysis Magnetic sensors Temperature sensors Internet of Things Big Data iot sensor networks activity of daily living (ADL) pervasive health systems and services AGGIR grid big data smart homes

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Neural Network-based Positioning System for Localisation and 3D Shape Detection in Crowded iot networks

Neural Network-based Positioning System for Localisation and...

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25th IEEE Applied Sensing Conference (IEEE APSCON)

作者： Singh, Anand Raj, Rishu Kilper, Dan Vellore Inst Technol Vellore India Trinity Coll Dublin CONNECT Ctr Dublin Ireland

ISBN: (纸本)9798350317275

This paper proposes a visible light-based positioning (VLP) model for estimating an object's three-dimensional (3-D) parameters such as height and radius, in addition to location in an indoor environment. The model is built using neural networks (NN), trained by simulating numerous multiple-object scenarios in an indoor environment. It takes into account the shadowing effects so it can be implemented in a crowded environment with multiple obstacles. The proposed algorithm has numerous applications, such as positioning-assisted communication, suspicious object monitoring, and surveillance in iot sensor networks. The error in location prediction by the proposed model is approximately 2.4 cm. The errors in estimating the height and radius of the objects using the proposed framework are observed to be 4.39 cm and 1.37 cm, respectively. These errors can be alleviated at the expense of extra hardware. We also analyse the effect of system parameters like grid size and number of obstacles on the VLP system performance.

关键词： Indoor Visible Light Communication 3D shape estimation Localization Sensing iot sensor networks

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Advanced iot-Based Wireless sensors for Remote Geotechnical Monitoring and Structural Diagnostics 18

Advanced IoT-Based Wireless Sensors for Remote Geotechnical ...

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18th IFAC Conference on Programmable Devices and Embedded Systems (PDES)

作者： Pies, Martin Velicka, Jan Hajovsky, Radovan VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Cybernet & Biomed Engn 17 Listopadu 15 Ostrava Czech Republic

This paper presents an innovative design and implementation of a wireless sensor module using low-power Internet of Things (iot) technologies for applications in geotechnical engineering and structural diagnostics, with a main focus on monitoring rock formations below the water surface in areas without access to electrical power. Designed for continuous monitoring of linear displacements and other critical parameters in structural engineering, geotechnical engineering and engineering diagnostics, this sensor module is particularly useful in remote locations. Utilizing wireless data transmission and advanced iot technologies such as LoRa, IQRF and NB-iot, this system allows for flexible coverage options and efficient remote management. The system allows users to remotely change measurement parameters, the period of messaging and set thresholds for alarm activation. The study highlights the importance of innovative iot solutions to improve accuracy and efficiency in monitoring geotechnical and civil engineering structures, especially in challenging sub-surface environments, opening new opportunities for predictive maintenance and risk management in these specific applications. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Multi sensor systems networks of sensors and actuators Measurement and instrumentation iot sensor networks Geotechnical Monitoring Wireless Data Transmission Underwater Rock Formations Remote Energy-independent Systems

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Advanced iot-Based Wireless sensors for Remote Geotechnical Monitoring and Structural Diagnostics

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IFAC-PapersOnLine 2024年第9期58卷 193-198页

作者： Martin Pies Jan Velicka Radovan Hajovsky Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17.listopadu 15 Ostrava Czech Republic

关键词： Multi sensor systems networks of sensors actuators Measurement instrumentation iot sensor networks Geotechnical Monitoring Wireless Data Transmission Underwater Rock Formations Remote Energy-independent Systems

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