Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 707-712· 0 citations· 9 references
Abstract
The fast changing nature of connected vehicles requires smart systems that can monitor health in real-time and predict failures. This paper introduces an IoT-based Smart Vehicle Monitoring and Predictive Maintenance System that involves onboard sensors, edge computing, and cloud analytics to ensure high reliability and reduce unscheduled downtimes. Multi-modal transportation data (temperature, vibration, fuel consumption, and brake parameters) are continuously measured with the help of ESP32/Arduino devices connected to the CAN bus. An Extended Kalman Filter (EKF) is an algorithm that does nonlinear sensor fusion at the edge to enhance data reliability and noise minimization. The degradation prediction and Remaining Useful Life estimation of the fused time-series data are done with a Long Short-Term Memory (LSTM) network. A new adaptive thresholding system is used to dynamically regulate anomaly sensitivity according to driving context and historical trends. It was evaluated experimentally on 100,000 real-time sample divided into 70% training, 15% validation and 15% testing sample. The proposed framework was found to have 97.4% prediction accuracy with Precision (94.8%), Recall (95.6%), and F1-score (95.2%). EKF preprocessing minimized RMSE by 0.84, and it is a 56-percent improvement in estimation accuracy. The adaptive detection module reduced false positive rate to 4.1% which was 63% lower than the approaches to static threshold. The system forecasted failures almost 22 minutes earlier than it was actually detected which enhanced early detection by almost 30 percent. The edge deployment decreased latency by a factor of 4 to 160 ms, and thus, allowed the creation of nearly real-time alerts. The implementation of visualization and alert management was realized based on the use of Node-RED and Grafana dashboards, along with MQTT-based secure communication. All in all, the framework is statistically proven to be robust, scaled and applicable to next-generation intelligent vehicular IoT ecosystems.
The Sudden equipment failures are common in industrial systems, which means more downtime and higher maintenance costs. A Smart AI-Integrated Predictive Maintenance and Condition Monitoring System is proposed to solve this problem. It will allow for real-time monitoring and early fault detection in industrial machines. The system uses an Arduino Uno (ATmega328P) microcontroller that is connected to several sensors, such as voltage, current, temperature (LM35), vibration, proximity, and MPU6050 sensors, to collect important operational data. The ESP8266 NodeMCU Wi-Fi module sends the processed data to an IoT cloud platform so that it can be monitored and analyzed from afar. There is also an I2C display developed in for real-time viewing on position. The proposed system uses AI-based analysis to find problems and assume when equipment might break down, which enables maintenance be performed on time. By using a hardware prototype to evaluate shows that the system reliably monitor in real time and make accurate predictions, which cuts down on downtime and makes the system work better. The Integration of AI and IoT technologies makes predictive maintenance in modern industrial settings cheaper, more flexible, and smarter.
V. R. Kumar, P. Poornima, M. Abinesh et al.· International Conference on...· 0 citations
A new hybrid BMS for SOC estimation based on a transaction-oriented adaptive Kalman Filter that merges traditional voltage-based SOC estimation has been developed in this study and demonstrates that it serves as an efficient and effective alternative for achieving long-term energy optimization in IoT-enabled smart agriculture applications.
Mohd Kamir Yusof, Nur Yasmin Salleh, Senny Luckyardi et al.· International Journal of Adv...· 0 citations
A Smart HVAC system that integrates Artificial Intelligence (AI), Internet of Things (IoT) sensors, cloud-based analytics, and machine learning to enhance energy efficiency, thermal comfort, and reliability is presented.
Suresh Babu Reddy· International Journal of Mod...· 0 citations
Due to an increase in the demand for water and the limited availability of real time monitoring technologies, efficient management of residential water resources poses a unique problem. This study describes the development of an inexpensive Internet of Things (IoT)-based electronic system capable of providing real time monitoring and artificial intelligence (AI)-driven analysis of residential water consumption. The electronic system consists of the use of a YF-DN50 flow sensor connected to an ESP32 microcontroller allowing for continuous data acquisition. The data collected is stored in a cloud-based spreadsheet on Google Colab for processing and analysis, where an unsupervised Long Short-Term Memory (LSTM) Autoencoder model was developed for anomaly detection. The developed model produced a final training loss value of 0.038 and a validation loss value of 0.045, with the threshold for anomaly detection determined to be mean + 3 standard deviations above the reconstruction error. During the observation period, the system detected a total of 7 anomalous events as well as detecting an overall 85.7% of the detected known events and having a 33% reduction in false negative detections compared to a simple fixed threshold baseline. The results of this study demonstrate that the combination of IoT sensors with LSTM Autoencoder analysis of water consumption allows for the monitoring of water consumption patterns and early detection of anomalies, thus providing a means to achieve a reduction in average monthly household water consumption from approximately 21 m3 to 18 m3.
C. Quinto, José Insuasti, M. Simbaña et al.· Electronics· 0 citations
Intelligent monitoring and control of modern microgrids have become essential to increase the efficiency, reliability and fault tolerance of the microgrid. This research work proposes an autonomous microgrid management system with AI-powered load and fault prediction that combines renewable energy sources, IoT and cloud computing in one embedded system. The proposed system encompasses an ESP32 microcontroller that collects the values of voltage and current using sensors, calculates electrical parameters, and transfers data to the cloud database (Firebase) via Wi-Fi connection. A web dashboard is used to provide real-time monitoring of the system, while a machine learning algorithm is used to predict future load and detect abnormal states for proactive fault management. An automatic relay-based load control scheme is applied to protect the system from abnormal load faults. Experimental results have shown that the system operates stably with supply voltage in the range of 12.18-12.35 V, near-real-time cloud synchronization, and average prediction time of 145 ms. The proposed AI prediction model has achieved 95.3% accuracy, proving the feasibility of the proposed system design.
Chellan P., D. M., Sivasubramani. G. et al.· Journal of Electrical Engine...· 0 citations
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
