JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech III INTERNET OF THINGS Previous Question Paper (August/September - 2021 - R18)

IoT enabling technologies are the fundamental building blocks that allow physical objects to connect, communicate, and exchange data. The primary enabling technologies include:

1. Wireless Sensor Networks (WSN): WSNs consist of distributed autonomous sensors that monitor physical or environmental conditions. These nodes communicate wirelessly to collect data and pass it to a central gateway.
2. Cloud Computing: This provides the necessary infrastructure for data storage, processing, and analytics. Since IoT devices often have limited computational power, cloud platforms offer scalable resources to manage massive volumes of sensor data.
3. Big Data Analytics: IoT generates vast amounts of unstructured and structured data. Big Data tools are essential to extract meaningful insights, identify patterns, and support automated decision-making processes.
4. Communication Protocols: These protocols define how devices exchange information. Common examples include MQTT (Message Queuing Telemetry Transport) for lightweight messaging, CoAP (Constrained Application Protocol) for low-power devices, and HTTP/REST for web-based integration.
5. Embedded Systems: These are the hardware components, including microcontrollers, sensors, and actuators, integrated into physical objects to enable sensing, processing, and actuation capabilities.
6. Security and Privacy Technologies: Given the sensitivity of IoT data, technologies such as encryption (AES, RSA), authentication mechanisms, and secure boot processes are critical to protect devices and networks from cyber threats.
7. Artificial Intelligence (AI) and Machine Learning (ML): AI/ML algorithms are used to process data at the edge or in the cloud to enable predictive maintenance, anomaly detection, and intelligent automation based on historical data trends.

A forest fire detection system generates diverse data types including environmental metrics (temperature, humidity), gas concentrations (CO, CO2, smoke particles), and visual data (images or video feeds). Data collection is primarily achieved through a distributed Wireless Sensor Network (WSN) where sensor nodes are deployed across the forest floor or canopy. These nodes utilize low-power communication protocols like LoRaWAN, ZigBee, or NB-IoT to transmit readings to a central gateway, which then forwards the aggregated data to a cloud-based server for processing.

The alternative approach involves Satellite-based Remote Sensing or UAV (Unmanned Aerial Vehicle) surveillance. Unlike ground-based sensors, satellites use thermal infrared imaging to monitor large geographical areas from orbit, providing a macro-level view. UAVs equipped with multispectral cameras can be deployed on-demand to perform localized, high-resolution inspections of areas identified as high-risk by satellite alerts, offering a dynamic and flexible data acquisition method that does not require permanent infrastructure installation in remote terrain.

The analysis required for effective forest fire detection involves three main stages:

• Descriptive Analysis: Real-time monitoring of sensor thresholds to identify immediate anomalies, such as sudden spikes in temperature or smoke density, which trigger initial alerts.
• Predictive Analysis: Utilizing machine learning models, such as Random Forests or Support Vector Machines, to analyze historical weather patterns, fuel moisture content, and wind direction to forecast the probability of fire ignition and potential spread paths.
• Prescriptive Analysis: Applying computer vision algorithms, such as Convolutional Neural Networks (CNNs), on image/video data to verify fire presence, classify fire intensity, and provide actionable insights to emergency responders regarding the optimal containment strategy.