Search
5 articles for “data traffic reduction”
-
Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing
Abstract: The exponential growth of internet of things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 46–62 Read article
-
A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article
-
Dynamic Skip-Layer Trees: A Novel Data Structure for Efficient Multi-level IoT Data Processing in Smart Cities
Abstract: This paper introduces dynamic skip-layer trees (DSLTs), a novel hierarchical data structure specifically designed for processing and managing multi-layered Internet of Things (IoT) sensor data in smart city environments. DSLTs extend the traditional skip list concept by incorporating dynamic layer adjustment and spatial awareness, enabling efficient querying and updates across various geographical and temporal dimensions. Our experimental results demonstrate that DSLTs achieve up to 40% faster query processing and a …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 11–21 Read article
-
Assessing the Robustness of Machine Learning Models for Wireless Intrusion Detection Under Adversarial Traffic Perturbations
Abstract: As the Internet of Things (IoT) devices and wireless communication networks continue to grow rapidly, protecting systems from cyber threats has become increasingly important. Machine learning–based intrusion detection systems (IDS) have shown strong potential in detecting abnormal and malicious network activities, yet their effectiveness and resilience when facing adversarial attacks are still not sufficiently explored. This research evaluates Machine Learning (ML) models–XGBoost, random forest, and multi-layer perceptron (MLP)—in detecting attacks …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 29–34 Read article
-
Energy Conservation Using Street Light Monitoring and Vehicle Vibrations for EV Charging
Abstract: In the contemporary era, the escalating demand for sustainable energy solutions has prompted the exploration of innovative technologies to address energy conservation challenges. This research introduces a novel approach to energy conservation by integrating street light monitoring and utilizing vehicle vibrations for Electric Vehicle (EV) charging. The proposed system leverages smart sensors and advanced communication technologies to monitor street lights and harness vehicle vibrations, thereby contributing to the optimization of …
Published in International Journal of Energy and Thermal Applications · Vol. 3, Issue 1, 2025 · pp. 1–11 Read article