DATA ANALYTICS FOR IoT (KOT077)
B.Tech SEM VII – Complete Solved Paper (Exam-Oriented Answers)
SECTION A
(Attempt all questions – 2 × 10 = 20 marks)
(a) Needs of IoT for Futuristic Growth
IoT is required for futuristic growth because it enables automation, real-time monitoring, smart decision-making, and efficient resource utilization. It improves productivity in industries, healthcare, agriculture, transportation, and smart cities.
(b) Working of Actuator in IoT
An actuator is a device that converts electrical signals into physical action. In IoT systems, actuators receive commands from controllers or cloud platforms and perform actions such as opening valves, rotating motors, or switching devices ON/OFF.
(c) Define Protocols with Example
Protocols are a set of rules that define how data is transmitted and received between devices.
Example: MQTT is a lightweight protocol used in IoT for communication between sensors and servers.
(d) Use of Wireless Devices in IoT
Wireless devices enable communication without physical cables. They allow flexible deployment, mobility, and remote monitoring using technologies like Wi-Fi, Bluetooth, Zigbee, and LoRa.
(e) Concept of Cloud
Cloud computing provides on-demand access to computing resources such as storage, servers, and applications over the internet. In IoT, the cloud is used for data storage, analytics, and visualization.
(f) Short Note on Data Processing
Data processing involves collecting raw data, cleaning it, transforming it, and analyzing it to extract meaningful information. In IoT, data processing helps convert sensor data into actionable insights.
(g) Need to Explore Data in IoT
Exploring IoT data helps identify patterns, detect anomalies, optimize system performance, and improve decision-making through predictive analytics.
(h) Types of Data Used in IoT Network
IoT networks use structured data, semi-structured data, and unstructured data such as sensor readings, logs, images, videos, and time-series data.
(i) Define Machine Learning
Machine Learning is a branch of artificial intelligence that allows systems to learn from data and improve performance without being explicitly programmed.
(j) Use of Data Science to Manipulate Data
Data Science uses statistical methods, machine learning, and visualization techniques to clean, analyze, and transform data into valuable insights.
SECTION B
(Attempt any THREE – 10 × 3 = 30 marks)
(a) Architecture of Internet of Things and Applications
The IoT architecture consists of four main layers:
Perception Layer – Sensors and actuators collect real-world data.
Transport Layer – Transfers data using protocols like Wi-Fi, LoRa, 5G.
Processing Layer – Cloud or edge computing processes and analyzes data.
Application Layer – Provides services such as smart homes, healthcare, agriculture, and industrial automation.
Applications include smart cities, smart grids, healthcare monitoring, industrial IoT, and environmental monitoring.
(b) Networking Connecting Protocols in IoT
IoT networking protocols enable communication between devices.
Common protocols include MQTT, CoAP, HTTP, AMQP, and DDS.
MQTT is lightweight and suitable for low-bandwidth networks, while HTTP is used for web-based IoT applications.
(c) Data Management in IoT with Big Data Technologies
IoT generates massive data volumes that require Big Data technologies such as Hadoop, Spark, and NoSQL databases.
Data management involves data ingestion, storage, processing, analytics, and visualization to handle velocity, variety, and volume of IoT data.
(d) Steps in Data Visualization Process
The data visualization process includes data collection, data cleaning, data analysis, selecting visualization techniques, creating visual dashboards, and interpreting results for decision-making.
(e) Feature Engineering in IoT
Feature engineering involves selecting, extracting, and transforming relevant features from raw IoT data. It improves model accuracy by reducing noise and highlighting important patterns.
SECTION C
Q3 (a) Define Analytics and Use in IoT Network
Analytics refers to systematic computational analysis of data.
In IoT networks, data analytics helps in predictive maintenance, anomaly detection, energy optimization, and real-time monitoring of systems.
Q3 (b) Challenges in IoT Analytics
Challenges include data volume, data variety, real-time processing, data security, privacy concerns, scalability, and data quality issues.
Q4 (a) Analyzing Data to Infer Protocols and Device Characteristics
This process involves traffic analysis, packet inspection, data pattern recognition, and behavior analysis to identify communication protocols and device properties.
Q4 (b) IoT Networking Data Messaging Protocols
Messaging protocols like MQTT, CoAP, AMQP, and XMPP enable efficient data transfer in IoT networks. MQTT is widely used due to its low power consumption.
Q5 (a) Elastic Analytics in IoT
Elastic analytics refers to scalable data analytics that dynamically adjusts computing resources based on IoT data load. It supports real-time analytics using cloud infrastructure.
Q5 (b) Designing Steps in Data Processing
The steps include data acquisition, data preprocessing, data transformation, data analysis, and result interpretation.
Q6 (a) Technologies to Understand Data Quality
Technologies include data profiling tools, statistical analysis, data validation frameworks, and machine learning models to detect inconsistencies and errors.
Q6 (b) Statistical Analysis Process
Statistical analysis involves data collection, descriptive statistics, hypothesis testing, correlation analysis, and inference to derive conclusions from data.
Q7 (a) Deep Learning with IoT Data
Deep learning models such as CNNs and RNNs are used for image processing, speech recognition, and predictive maintenance in IoT systems.
Q7 (b) Validation Process in IoT with Machine Learning
Validation ensures model accuracy and reliability by splitting data into training, testing, and validation sets, ensuring unbiased performance evaluation.
