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检索条件"任意字段=6th IEEE International Conference on Distributed Computing in Sensor Systems"

共 6276 条记录，以下是11-20 订阅

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Developing Scalable and Fault Tolerant distributed Architecture for Big Unstructured Data systems 6

Developing Scalable and Fault Tolerant Distributed Architect...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Saranya, G. Kumaran, K. Pratheep, V. Dhanush, S.R. School of Computer Science and Engineering Vellore Institue of Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the era of big data, efficiently processing and retrieving insights from unstructured data presents a critical challenge. this paper introduces a scalable leader-worker distributed data pipeline designed to handle large sets of unstructured data blobs. Leveraging data parallelism, the system distributes workload across worker nodes, ensuring high performance for embarrassingly parallel tasks with minimal inter-process communication. Fault tolerance is achieved through an optimized next-in-line leader re-election strategy, addressing cascading failures and mitigating the thundering herd effect. the use of Apache Zookeeper for service discovery ensures seamless coordination within the cluster while maintaining a loosely coupled architecture for enhanced scalability. Google Protocol Buffers are employed for secure data transmission, offering tamper resistance against eavesdropping and man-in-the-middle attacks. the system also integrates the TF-IDF algorithm for efficient information retrieval, demonstrating the ability to achieve data parallelism while preserving task uniformity across nodes. Performance evaluation highlights significant reductions in response times with increasing cluster size, validating the system’s scalability, efficiency, and robustness. the proposed architecture is well-suited for real-world applications requiring secure, fault-tolerant, and scalable solutions for unstructured data analysis. © 2025 ieee.

关键词： Big data

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GreenCloud AI: Framework for Sustainable distributed Data Management in Cloud Ecosystems 6

GreenCloud AI: Framework for Sustainable Distributed Data Ma...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kamath, Shashank K.M. Parkar, Trupti Vinayak Gogri, Dhaval Jani, Yash Jani, Arth Patil, Shalmali Amazon SeattleWA United States Microsoft RedmondWA United States Walmart FremontCA United States Docusign FremontCA United States Amazon VancouverBC Canada Amazon DallasTX United States

ISBN: (纸本)9798331522667

the rapid expansion of cloud computing and data-intensive applications has led to significant environmental concerns due to increasing energy consumption in data centers. this paper introduces the concept of "GreenCloud AI," a theoretical framework for sustainable and energy-efficient distributed data management in cloud ecosystems. the proposed conceptual model envisions the integration of artificial intelligence techniques to address the challenge of dynamic data placement optimization in heterogeneous cloud storage environments. GreenCloud AI, as a theoretical construct, proposes leveraging machine learning algorithms to predict workload patterns, analyze energy consumption profiles, and inform decision-making processes for data allocation across diverse storage tiers and geographically distributed data centers. By considering factors such as varying energy costs, carbon intensities, renewable energy availability, and regulatory compliance, the framework aims to conceptually achieve a balance between energy efficiency, carbon emission reduction, and system performance. this research contributes to the field of sustainable informatics by outlining potential approaches for energy-efficient data management in cloud ecosystems, providing a foundation for future research and development in creating more environmentally responsible cloud infrastructure. © 2025 ieee.

关键词： distributed cloud

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AI based Decision Tree Classifier for Detecting Anomalies in IoT Wireless sensor Networks 6

AI based Decision Tree Classifier for Detecting Anomalies in...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Akhther, Parveen A. Sreelekha, Gopavaram Prasanth, V.S. Dept. of CSE Sathyabama Institute of Science & Technology Chennai600 119 India Dept. of ECE Madanapalle Institute of Technology & Science Andhra Pradesh 517 325 India Dept. of ECE Sathyabama Institute of Science & Technology Chennai600 119 India

ISBN: (纸本)9798331522667

this research study applies a Decision Tree Classifier to detect anomalies in IoT networks using the NSL-KDD collection of data, which is a used widely as a benchmark for intruder identification. Following categorical feature preprocessing with label encoding, a class-weight-adjusted Decision Tree was applied to handle class imbalance. the model achieved 92% accuracy and an F1 score of 0.89, showing very strong performance in distinguishing between normal and anomalous network behaviours. the results were further validated using performance indicators such as the ROC curve, classification matrix, and F1-score. this approach has great promise for real-time anomaly detection in IoT systems, enhancing network security with high accuracy and reliable threat identification. the novelty is in the integration of feature selection and hyperparameter optimization to improve the performance of the decision tree model, addressing the challenges of high-dimensional data and dynamic network conditions that have not been explored much in the existing literature. © 2025 ieee.

关键词： Feature Selection

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Event-based sensor Fusion and Application on Odometry: A Survey 6

Event-based Sensor Fusion and Application on Odometry: A Sur...

引用

6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Zhang, Jiaqiang Yu, Xianjia Sier, Ha Zhang, Haizhou Westerlund, Tomi University of Turku TIERS Lab Turku Finland

ISBN: (纸本)9798331506520

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness. they offer notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. these distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this survey presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly investigating fusion strategies that incorporate frame-based cameras, inertial measurement units, and LiDAR. the survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications. © 2025 ieee.

关键词： Computer vision

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Design and Development of Touch sensor-based Headlight and Mirror Controller for Automobiles 6

Design and Development of Touch Sensor-based Headlight and M...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Vasuki, A. tharmaraj, R. Saruvardhan, K.S. Aakashpriyadharshan, M. Praveen, D. Department of ECE SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Modern cars frequently have manual regulators and switches for headlight and rearview mirror control, which can be difficult and distracting for drivers. this paper presents a more efficient alternative through the implementation of a touch- sensitive control system utilizing the ESP32 microcontroller. the device substitutes conventional controls with touch sensors to streamline the management of headlamp intensity and rearview mirrors. the technology rotates a stepper motor 20 degrees with a single touch to enhance headlight brightness, 40 degrees with a double touch for additional adjustment, and 60 degrees with a triple touch, facilitating precise control over the illumination. Furthermore, an additional touch sensor is employed to regulate servo motors that facilitate the automation of rearview mirror adjustment. A single touch activates the mirrors, while a double touch deactivates them, providing a more fluid experience. this touch-based technology offers drivers a safer and more intuitive method for controlling essential vehicle functions while simultaneously upgrading the car’s interface. this design employs readily available components and straightforward programming, making it both economical and scalable for future smart car applications. © 2025 ieee.

关键词： Stepping motors

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Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM 6

Design of a Smart Home Door Security System with Face Detect...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Salsabila, Nurhusna Siswanto, Apri Bayuaji, Luhur Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (纸本)9798331522667

this research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. the system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. the architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. the results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. this innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security. © 2025 ieee.

关键词： Ultrasonic sensors

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Proceedings - 14th Annual international conference on distributed computing in sensor systems, DCOSS 2018

Proceedings - 14th Annual International Conference on Distri...

引用

14th Annual international conference on distributed computing in sensor systems, DCOSS 2018

ISBN: (纸本)9781538654705

the proceedings contain 25 papers. the topics discussed include: distributed mining of popular paths in road networks;white space prediction for low-power wireless networks: a data-driven approach;projection-based constrained fusion performance with link loss and measurement bias;recursive truth estimation of time-varying sensing data from online open sources;on designing provably correct DODAG formation criteria for the IPv6 routing protocol for low-power and lossy networks (RPL);duty-cycle-aware real-time scheduling of wireless links in low power WANs;knowledge transfer between embedded controllers;resilient distributed diffusion for multi-task estimation;leveraging knowledge for path exposure;frequency scaling in time synchronization for wireless sensor networks;and on cost-sensitive task allocation in social sensing: an online learning approach.

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Energy Efficient LEDCA Analysis for Improved throughput in Wireless sensor Networks 6

Energy Efficient LEDCA Analysis for Improved Throughput in W...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Padmageetha, B.G. Naik, Pramod Kumar Patil, Mallanagouda Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (纸本)9798331522667

Wireless sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. this paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. the algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA’s interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications. © 2025 ieee.

关键词： Resource allocation

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Proceedings - 12th Annual international conference on distributed computing in sensor systems, DCOSS 2016

Proceedings - 12th Annual International Conference on Distri...

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12th Annual international conference on distributed computing in sensor systems, DCOSS 2016

ISBN: (纸本)9781509014590

the proceedings contain 39 papers. the topics discussed include: SenseWit: pervasive floorplan generation based on only inertial sensing;undervolting in real world WSN applications: a long-term study;designing a cloud-based infrastructure for spectrum sensing: a case study for indoor spaces;energy efficient data collection via supervised in-network classification of sensor data;tree-structured linear approximation for data compression over WSNs;TransTrack: tracking multiple targets by sensing their zone transitions;unsupervised interesting places discovery in location-based social sensing;WiFi-ID: human identification using WiFi signal;error bounds for localization with noise diversity;LLSF: low latency scheduling function for 6TiSCH Networks;COIN: system architecture for programmable connected devices;model transition planning in participatory sensing cold start;optimal deployment of dense WSN for error bounded air pollution mapping;using human social sensors for robust event location detection;and round trip time based adaptive congestion control with CoAP for sensor network.

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Design and Development of a Rescue Robot to Identify Human Presence in Disaster Scenarios by using Artificial Intelligence Assisted sensor Support 6

Design and Development of a Rescue Robot to Identify Human P...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Umamaheswari, U. Suneel, Sajja Ravikumar, K. Baburao, D. Upadhyay, Shrikant Swapna, B. Saveetha School of Law Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India Institute of Aeronautical Engineering Dundigal Telangana Hyderabad500043 India Dept Of IT Dhanalakshmi Srinivasan College of Engineering and Technology Chennai India Telangana Hyderabad500 075 India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of ECE Dr. MGR Educational and Research Institute Chennai India

ISBN: (纸本)9798331522667

In disaster scenarios, rapid and accurate identification of human presence is critical for effective rescue operations. this study presents the design and development of an AI-assisted rescue robot equipped with advanced sensor support for detecting human presence in various disaster environments. the proposed system integrates multiple sensors, including thermal imaging, LiDAR, ultrasonic sensors, and cameras, to gather comprehensive environmental data. A YOLO-based AI model processes this data to identify human presence with high accuracy. the model was trained and validated on diverse datasets simulating real-world disaster conditions. Results show that the proposed model outperforms nine existing models, achieving an average accuracy of 97.3% across four different disaster scenarios. this accuracy is significantly higher compared to the closest competing models, highlighting the robustness and reliability of the proposed system. Additionally, the model demonstrated a precision of 96.7%, a recall of 96.5%, and a response time of 49.5 ms, all of which contribute to its superior performance in time-sensitive and complex environments. the findings suggest that this AI-assisted rescue robot is a promising solution for enhancing the efficiency and effectiveness of search and rescue operations in disaster scenarios. © 2025 ieee.

关键词： thermography (imaging)

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