Method for Predicting failures in Equipment using Sensor data. Sensors mounted on devices like IoT devices, Automated manufacturing like Robot arms, Process monitoring and Control equipment etc., collect and transmit data on a continuous basis which is Time stamped.

Human locomotion affects our daily living activities. Losing limbs or having neurological disorders with motor deficits could affect the quality of life. Gait analysis is a systematic study of human locomotion, which is defined as body movements through aerial, aquatic, or terrestrial space. This analysis has been used to study people ambulation, registration, and reconstruction of physical location and orientation of individual limbs used to quantify and characterize human locomotion using different gait parameters including gait activities such as walking, stairs ascending/descending, … etc., phases, and spatiotemporal parameters of human gait. Additionally, gait analysis parameters can be used to evaluate the functionality of patients and wearable system users. The evaluation is based on patient's stability, energy consumption, gait symmetry, ability to recover from perturbations, and ability to perform activities of daily living. Many companies develop assistive, wearable, and rehabilitation devices for patients with lower limb neurological disorders. These devices are tested and evaluated inside controlled lab environments. However, they don’t have enough data on the patient's performance in real world and harsh environments. Collecting large datasets of device users and their gait performance data in real environment are notoriously difficult. Additionally, collecting data on less prevalent or on gait activities other than level walking, stair ascending/descending, sitting, standing, …etc. on hard surfaces is rarely attempted. However, the scope for collecting gait data from alternative sources other than traditional gait labs could be attained with the help of IoT data collection embedded on the wearable and assistive devices and well-established cloud platforms equipped with big-data analytics and data visualization capabilities. This project aims to develop a cloud platform capable of collect data from wearable and assistive devices such as prostheses, exoskeleton, gait analysis wearable sensors, …etc. using IoT technologies. This platform is capable of automatically use data mining and visualization tools. Additionally, it uses statistical and machine learning techniques to estimate gait events, gait symmetry, gait speed, gait activities, stability, energy consumption, …etc. Also, it is capable of predicting patient's progress over time. The project will be composed of two major components, hardware component and software component. In hardware component, the students will design and implement the IoT that collects the different readings for gait analysis and send them to the cloud. Meanwhile, in the software component, the students will design and implement a set of algorithms to visualize the collected data, then design and implement data analytics to automatically analyze the collected data, so that we can estimate gait events, gait symmetry, gait speed, classify gait activities, stability, energy consumption, …etc. and predicting patient's progress over time. By analyzing the collected data, the patient's progress can be predicted over time. Additionally, these data can be used through manufacturers of prostheses legs to improve their products, as well as through health-care centers to assess the patient's performance. The following figures describe the main modules of our graduation project.

Advanced analytics can be applied in various areas of IoT like "Change Point detection using IoT Sensor data" and "Predictive Analytics using IoT Sensor data". Once a Descriptive or Prescriptive model outcome is arrived, it needs to be translated into action. This journey demonstrates how such a communication to the Edge layer can be implemented.

Objective : To help Doctors (or) Supervisors remotely monitor the Condition of the ambiance, Health condition, and the proximity from the set location of a Patient (or) Person under observation. This project imbibes the concept of the Internet of Things (IoT) and so the data is accessible seamlessly even if the supervisor is remote. Using range detection techniques and health parameters, Manipulations are done in the backend such that the alerts are notified based on set conditions to the respective person in case of emergency, we can conclusively predict the condition of a Patient (or) Person under observation remotely and accurately. Further, the data received from a patient is integrated into the database for analytics in Machine Learning (ML) to predict the reaction of another patient who suffers from the same disease or condition in the future. In addition, the product is feasible to be designed as a Handy and User-Friendly prototype, Cost-Efficient model, Less power-consuming mechanism, and Alterable Design.

Self-Adaptive Real-Time Data Orchestrator for YunaHive, a Scalable Distributed IoT-Engine with Predictive Analytics capabilities.

An IoT-powered system for real-time air quality monitoring and analysis. This project integrates environmental sensors with a machine learning model to predict and assess air quality indices. Features include data visualization, predictive analytics, and automated alerts for actionable insights.

TrackSecure is an IoT-powered parcel tracking platform built on a secure, scalable microservices architecture. It integrates MQTT, Kafka, Spring Boot, and Keycloak to deliver real-time logistics insights, predictive analytics, and proactive alerting with cloud integration.

VR Property Pro is a cutting-edge real estate solution that leverages virtual reality technology, predictive analytics, and IoT integration to redefine the real estate industry

By instrumenting trap’s with IoT sensors, cataloging the specific positions, and capturing trap & clear events in the cloud, we can apply machine learning algorithms to understand patterns and provide predictive analytics to optimally schedule trap visits and ideal location placement for trap & field technician efficiency.

The proposed system is an IoT based SSA platform that makes use of sensors and predictive analytics for sustainable crop management.

An AI & IoT Powered Real-Time Air Pollution Monitoring and Predictive Analytics.

Applying time series to predict Smart Home energy consumption and demonstrating how data can enable households to save energy (IoT Analytics)

Predictive Analytics and Green House Automation using Internet of Things for Remote Monitoring and Alert Generation

🏆 WINNER @ HardHack Forge 2026 | A proprietary biomedical IoT system for post-operative monitoring. Features real-time edge computing, secure cloud telemetry, and AI-driven predictive analytics.

Monitor gas levels, pressure, and temperature in real-time. Detect leaks automatically with instant alerts. IoT integration enables remote monitoring. Use data analytics for predictive maintenance and optimization.

A medical decision making diagnosis system, which is an Android Application that predicts its user’s future possibility of having Cardio-vascular diseases using Machine Learning, Data Analytics and IOT.

Supply Chain Verification system leveraging blockchain (Hyperledger Fabric & Ethereum), IoT, and AI. Features real-time tracking, zero-knowledge proofs, predictive analytics, and ERP integration. Ensures product authenticity and ethical sourcing with tokenized incentives. Scalable architecture for transparent, secure global supply chains.

AirShield is a micro-level air pollution monitoring and prediction system built using IoT sensors and machine learning. It collects real-time gas and temperature data, classifies air quality into Safe/Moderate/Dangerous levels, triggers alerts, and supports backend integration for predictive analytics and heatmap visualization.

MedGuard is a smart healthcare solution that combines wearable sensors, IoT devices, and AI analytics to monitor vital signs in real time. It detects anomalies, predicts risks, and alerts caregivers instantly. With secure apps and dashboards, MedGuard empowers patients, supports doctors, and ensures safer, faster medical care.

A IOT + ML project where the environment is sensed and data is collected ,ml models are used for predictive analytics using real time data .A whatsapp chatbot gives you real timedata.

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This project is a smart health monitoring solution that integrates multiple biomedical sensors with an IoT-enabled processing unit (like Raspberry Pi or ESP32). It continuously collects vital signs such as ECG, heart rate, blood oxygen (SpO₂), body temperature, hydration levels, and respiratory gases.

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Industrial IoT Analytics: Predictive maintenance and anomaly detection on CNC sensor data using Python and Power BI.

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Real-Time IoT Sensor Data Dashboard with Predictive Analytics | A Node.js application leveraging PostgreSQL logical replication and real-time data visualization to monitor and predict IoT sensor metrics.

EnergicView: IoT-based energy monitoring and predictive analytics for efficient energy management and remote control via a mobile app.

A Java-based smart home dashboard that simulates electricity, gas, and water usage, with predictive cost analytics using random IoT-like data.

A machine learning powered digital farming system that combines IoT sensors and predictive analytics to optimize crop health monitoring and irrigation management.