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An AI-Assisted Autonomous IoRT Agent for Smart Spaces

An AI-Assisted Autonomous IoRT Agent for Smart Spaces

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International conference on Computer Science and Engineering (UBMK)

作者： Yakup Kayataş Sanem Kabadayı Department of Computer Engineering Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

The Internet of Robotic Things (IoRT) integrates autonomous robots into smart environments and enables advanced applications in various domains such as smart cities, warehouses, and factories. In this study, we implement a modular IoRT system that can be adapted to many application domains. Our system uses a differential drive autonomous robot with ROS (Robot Operating System) for localization and navigation along with an orchestrator for interoperability with IoT devices and processes environmental sensor data to dynamically assist the robot's actions. To enhance the robot's autonomy and decision- making, we use artificial intelligence (AI) in two areas: (i) real-time human detection using AI computer vision to improve spatial awareness, and (ii) determining and following the shortest path for navigation. When a human is detected at the smart station (designated area equipped with an IoT ultrasonic sensor and an IoT camera), the robot autonomously navigates there. This implementation illustrates an exemplary use of IoRT in the transportation domain. The novel contributions of this work are the integration of IoT devices, an autonomous robot, and AI-based methods in an IoRT system, modularity that supports adaptation to different applications, and real-time system monitorina throuah WebSockets.

关键词： Computer vision Navigation Smart cities Robot sensing systems Real-time systems Internet of Things Robots Monitoring Smart spaces Autonomous robots

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An Energy Efficient UAV Path Planning for Data Collection in Multi-Node IoT System

An Energy Efficient UAV Path Planning for Data Collection in...

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International Symposium on advanced Networks and Telecommunication systems (ANTS)

作者： Vihar Shah Manish Kumar Abhishek Jindal Dhirubhai Ambani Institute of Information and Communication Technology (DAIICT) Gandhinagar India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

Unmanned aerial vehicles (UAVs) have become indispensable instruments in various applications, including surveillance, environmental monitoring, and data collection in Internet-of-Things (IoT) applications. The effectiveness and efficiency of UAV missions are inextricably linked to the path-planning strategies employed. With the modern approach to minimization, the requirement to minimize UAV energy consumption and boost battery time available is growing rapidly. With this objective, this paper presents Energy Constrained-Adaptive Path Construction (EC-APC), as an improved optimization technique for UAV path planning keeping in consideration the battery limits of the UAV. The proposed algorithm, named MNES, uses EC-APC to generate several paths for the UAV to satisfy its energy constraints. Simulation results and complexity analyses corroborate the time complexity advantage and energy-efficient path planning of the proposals.

关键词： Wireless sensor networks Surveillance Data collection Autonomous aerial vehicles Path planning Energy efficiency Batteries Telecommunications Internet of Things Time complexity

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Security Considerations and Network Parameters in WSN 2nd

Security Considerations and Network Parameters in WSN

引用

2nd International conference on Intelligent Computing Techniques for Smart Energy systems, ICTSES 2021

作者： Sharma, Vivek Bhatt, Devershi Pallavi Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811902512

Wireless sensor networks (WSN) are attracting researchers. Recent advancements in electronics and wireless communications prompted the world for crating cost efficient, multifunctional, small in size, inexpensive, dynamic, and smart sensors. For such networks, deploying specially in scarce areas provides extensive benefits. With advanced applications and deployment of such network, one major thing to be concerned is security issues within WSN. Currently, there are a large number of opportunities within the research in WSN Security, and many advancements have already been proposed in this certain area. This paper firstly presents the explored complications in security of WSNs;after that, the required security constraints in WSN;then, we have listed type of the attacks and persistent threats within WSNs. We have also reviewed security mechanisms proposed by current research works, and at the end, we have discussed parameters which are necessary to address while considering the threats and attacks related to WSN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

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Spectro-Temporal Recurrent Neural Network for Robotic Slip Detection with Piezoelectric Tactile sensor

Spectro-Temporal Recurrent Neural Network for Robotic Slip D...

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IEEE/ASME (AIM) International conference on advanced Intelligent Mechatronics

作者： Théo Ayral Saifeddine Aloui Mathieu Grossard CEA Leti Grenoble France CEA List Université Paris-Saclay Palaiseau France

In this paper, we present a novel tactile-based method for detecting slippage in robotic manipulation, using a single piezoelectric sensor. The method combines spectral analysis (FFT) and deep learning (GRU) for improved efficiency and adaptability. We implement an automated data-collection process with accurate and unbiased labels of slip events. The proposed method is evaluated through an ablation study characterizing the influence of model hyperparameters and interaction settings. The results show a high classification accuracy of 98.70% at 100Hz and detection delays of 8.5 ± 23.7ms, demonstrating the relevance of our spectro-temporal pipeline. The proposed method has the potential to enhance the performance of robotic systems and increase their reliability in robotic grasping applications.

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Low-Loss 3D-Coplanar Line Structure for Millimeter Wave applications Using Laser Direct Structuring Technology 22

Low-Loss 3D-Coplanar Line Structure for Millimeter Wave Appl...

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22nd International conference on Electromagnetics in advanced applications, ICEAA 2021

作者： Seewald, S. Manteuffel, D. Wolf, M. Barth, M. Eberhardt, W. Zimmermann, A. Institute of Microwave and Wireless Systems Leibniz University Hannover Hannover30167 Germany Hahn-Schickard Stuttgart70569 Germany University of Stuttgart Institute for Microfabrication Ifm Stuttgart70569 Germany

ISBN: (纸本)9781665413862

In this paper, we introduce an improved, three-dimensional coplanar line structure manufactured using Laser Direct Structuring (LDS) technology. The low-loss and robust against tolerance transmission line structure can be used for building conformal millimeter wave (mmWave) sensor applications as e.g. automotive radar. © 2021 IEEE.

关键词： Millimeter waves

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Real-time Monitoring and Control systems Using IoT-Integrated Wireless sensor Networks - Leveraging Machine Learning Algorithms for Enhanced Performance and Efficiency

Real-time Monitoring and Control Systems Using IoT-Integrate...

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Cybernation and Computation (CYBERCOM), International conference on

作者： Subbiah Swaminathan N. Poongavanam M. Arun S. Pushparani A. Muthukrishnan Prashant Johri Department of Information Technology Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India School of Computer Application and Technology Galgotias University

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

This paper introduces a developed real-time monitoring and control system using Internet of Things (IoT) incorporated Wireless sensor Networks (WSNs) supported by machines learning algorithms. The system focuses at meeting core issues and questions in terms of productivity, reliability and timeliness within evolving settings. The current paper contributes to the ongoing research and development in the area of IoT and WSN to enable, always-on real-time data gathering from a variety of discrete sensors which is invaluable for various applications including industrial, environmental and smart city applications. The heart of the system is based on the idea of utilising machine learning methods to acquire and elaborate sensor signals. More specifically, the deep learning models that are used include Long Short-Term Memory (LSTM) networks as well as Reinforcement Learning (RL) algorithms for predictive analytics, as well as anomaly detection. The work also entails a comparison of the system performance based on energy consumption and other factors such as latency, packet delivery ratio and even the accuracy of the predictions made by the system. The findings suggest improved utilisation within substantial ranges; decreased energy use by 30 per cent or more. 7%, latency decrease by 33. Although achieving this was accompanied by an increased data packet loss rate to 3%, the overall packet delivery ratio was boosted to 98%. Additionally, the system established overall accuracy rate of 95% with negligible error percentage. In conclusion, this research proves the applicability of incorporating the IoT and the advanced machine learning methods in the real-time monitoring and control systems development to overcome cases of low efficiency and reduced performance in numerous real-world applications..

关键词： Wireless sensor networks Energy consumption Machine learning algorithms Accuracy Smart cities Predictive models Control systems Real-time systems Reliability Long short term memory

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Improving Human Activity Recognition Through Multisensor Data Fusion Techniques in Wireless sensor Networks

Improving Human Activity Recognition Through Multisensor Dat...

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Computing and Machine Intelligence (ICMI), International conference on

作者： Rasha S. Gargees Department of Computer Science Central Michigan University Michigan USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Wireless sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity Recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector Machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Support vector machines Wireless communication Wireless sensor networks Accuracy Machine learning algorithms Refining Data integration

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Hospital Bed Vacancy Detection using Depthwise Separable CNN

Hospital Bed Vacancy Detection using Depthwise Separable CNN

引用

advanced Computing Technologies (ICoACT), International conference on

作者： R Balaji V Deepan S Faiz J Mythili Satyanarayana Murthy Polisetty Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Bank of America United States

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The rising demand for real-time hospital bed man-agement in healthcare institutions requires creative solutions to improve patient care and resource allocation. This research introduces an IoT-enabled system for detecting hospital bed vacancies utilising a Depthwise Separable Convolutional Neural Network (DSCNN). The proposed system utilises IoT sensors to perpetually monitor bed occupancy, guaranteeing precise and prompt updates to medical personnel. The DSCNN is utilised to diminish computing complexity while preserving high detection accuracy, attaining a notable accuracy of 92.4%. The model's performance is assessed in comparison to traditional CNNs, revealing notable enhancements in both speed and accuracy, with accelerated convergence times enabled by the Adam optimiser. The system guarantees minimal latency in hospital bed detection, rendering it suitable for real-time applications on resource-limited devices. This system provides a scalable and effective method for optimising hospital resource management, especially during high demand or emergencies.

关键词： Accuracy Hospitals Rendering (computer graphics) Real-time systems sensor systems sensors Internet of Things Convolutional neural networks Resource management Personnel

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Multi-threshold Hysteresis-Based Congestion Control for UAV-Based Detection sensor Network 22nd

Multi-threshold Hysteresis-Based Congestion Control for UAV...

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22nd International conference on Next Generation Teletraffic and Wired/Wireless advanced Networks and systems, NEW2AN 2022

作者： Aliyu, Bashir Mokrov, Evgeny Samouylov, Konstantin 6 Miklukho-Maklaya st. Moscow117198 Russia Institute of Informatics Problems Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow119333 Russia

ISBN: (纸本)9783031302572

Nowadays, unmanned aerial vehicles (UAV) are considered for a variety of different applications. One of the important ones is automatic collection of sensory data. In this work, we study the use of UAV-based for secure remote monitoring network enabled via LoRaWAN lower-power wide area (LPWA) network. In the considered use-case the UAVs carry sensors collecting data and LoRa modules to transmit collected data to the stationary gateway. The work focuses on congestion control on the gateway. Specifically, we propose a multi-threshold hysteresis-based control mechanism to alleviate system overload by dropping some of the data send by the sensors. The considered system is modeled using the queuing system with multiple thresholds. The work studies time-dependent characteristics of the system: average time the system spends in overload and reduced load states. Our results demonstrate that using hysteresis control allows for the system to accept priority data traffic even under relatively high system load. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wide area networks

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FishTankv2 Insights: Proactive Environmental Management with Predictive Analytics

FishTankv2 Insights: Proactive Environmental Management with...

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International conference on Computational Intelligence and applications (ICCIA)

作者： William P. Rey School of Information Technology Mapua University Makati City Philippines

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

This study presents FishTankv2, an advanced Internet of Things (IoT) based smart aquarium management system designed for freshwater fish enthusiasts. FishTankv2 integrates a range of sensors to monitor critical water parameters such as ammonia, nitrite, nitrate, dissolved oxygen, pH levels, and temperature. These enhancements provide users with comprehensive insights into their aquarium's health, facilitating proactive maintenance and care. The system features a mobile application that allows users to remotely monitor and control their aquariums, offering functionalities such as real-time parameter tracking, automated feeding, and maintenance reminders. A key innovation in FishTankv2 is the incorporation of predictive analytics, which utilizes machine learning algorithms to forecast potential issues based on historical data. This enables timely interventions to prevent adverse conditions, ensuring a stable and healthy environment for aquatic life. User feedback collected through surveys and usability testing indicates high satisfaction with the system's intuitive interface, comprehensive monitoring capabilities, and predictive alert features. Comparative analysis shows that FishTankv2 outperforms many existing smart aquarium systems in terms of sensor integration, predictive analytics, and overall user experience. The study concludes that FishTankv2 effectively meets its objectives of enhancing aquarium management through advanced technology, making it a valuable tool for both novice and experienced aquarium enthusiasts. Future developments will focus on further refining predictive models, expanding device compatibility, and adding more automation features to maintain its competitive edge.

关键词： Temperature sensors Temperature measurement Water quality Predictive models Fish sensor systems Maintenance Predictive analytics Intelligent sensors Testing

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