In the domain of Internet of Things (IoT), there are numerous thesis topics and ideas that are continuously emerging. Internet of Things is an interesting domain to workout but it is always hard for scholars to get interesting topics and thesis done only experts can handle your work like a pro. Get our superior thesis help on any areas of IOT. The following is a collection of thesis topics and plans that are relevant to IoT, along with problem statement, suggested approach, and significant tools:

  1. Privacy-Preserving Data Aggregation in IoT Networks
  • Problem Description:
  • Huge amounts of complicated data are produced by IoT networks that must be gathered and examined. Mostly, this data is susceptible to confidentiality violations.
  • Major Solution:
  • Through the utilization of differential privacy or homomorphic encryption approaches, aim to create a confidentiality-preserving data collection model.
  • Appropriate for resource-limited IoT devices, deploy lightweight aggregation protocols.
  • Tools:
  • Python Libraries: numpy, scipy, PyCryptodome
  • IoT Framework: Raspberry Pi, Node-RED
  • Homomorphic Encryption Libraries: TenSEAL, PySEAL
  1. Blockchain-Based IoT Device Authentication System
  • Problem Description:
  • Possible device imitation and illicit access are resulting, since IoT devices have scarcity of a standardized, safe authentication technology.
  • Major Solution:
  • By utilizing smart contracts, focus on constructing a blockchain-related decentralized authentication framework.
  • It is approachable to deploy device identity verification through zero-knowledge proofs.
  • Tools:
  • Blockchain Framework: Hyperledger Fabric, Ethereum
  • IoT Protocol: CoAP, MQTT
  • Smart Contract Languages: Chaincode, Solidity
  • Zero-Knowledge Proof Libraries: Bulltetproofs, Zokrates.
  1. Federated Learning for Privacy-Preserving IoT Analytics
  • Problem Description:
  • Network traffic and confidentiality issues are caused due to the centralized learning in IoT networks.
  • Major Solution:
  • A federated learning model has to be created in such a manner that contains the capability to facilitate distributed machine learning among numerous IoT devices when maintaining data in a regional way.
  • Specifically, for additional protection aim to deploy differential privacy approaches.
  • Tools:
  • Federated Learning Libraries: PySyft, Flower
  • Machine Learning: PyTorch, TensorFlow
  • IoT Frameworks: Eclipse Kura, EdgeX Foundry
  1. Machine Learning-Based Intrusion Detection System for IoT Networks
  • Problem Description:
  • Because of the heterogeneous essence of protocols and devices, IoT networks are vulnerable to different cyber assaults.
  • Major Solution:
  • To examine IoT network congestion for abnormalities, it is appreciable to model a machine learning-related intrusion detection system (IDS).
  • For IoT network congestion data, deploy feature extraction approaches.
  • Tools:
  • Network Simulators: Cooja, NS-3
  • Intrusion Detection: Suricata, Snort
  • Machine Learning: TensorFlow, Scikit-Learn
  1. Adaptive Duty-Cycling Protocol for Energy-Efficient LPWAN Networks
  • Problem Description:
  • Minimal energy utilization is caused when Low-Power Wide-Area Networks (LPWAN) mostly employ static duty cycles.
  • Major Solution:
  • It is appreciable to model an adaptive duty-cycling protocol that has the ability to dynamically adapt on the basis of network situations and congestion trends.
  • To preserve energy and reduce retransmissions, aim to utilize collision avoidance technologies.
  • Tools:
  • Network Simulators: OMNeT++, NS-3
  • Protocols: Sigfox, NB-IoT, LoRaWAN
  • Programming Languages: Python, C++
  1. QoS-Aware Routing Protocol for Real-Time IoT Applications
  • Problem Description:
  • The significant necessities of actual-time IoT applications are certain Quality of Service (QoS) metrics such as high packet delivery ratio and low latency.
  • Major Solution:
  • A QoS-aware routing protocol has to be constructed which prefers latency-vulnerable data in actual-time IoT applications.
  • To assure QoS in differing network situations, focus on utilizing congestion control technologies.
  • Tools:
  • Network Simulators: NS-3, OMNeT++
  • Protocols: 6LoWPAN, CoAP, RPL
  • Programming Languages: Python, C++
  1. IoT-Based Smart Healthcare System
  • Problem Description:
  • Effective data collection, anomaly identification, and safe data sharing are the major requirements of remote patient tracking models.
  • Major Solution:
  • It is significant to create an IoT-related smart healthcare model along with safe data transmission and wearable sensors.
  • To detect possible health problems, aim to utilize a machine learning-related anomaly identification model.
  • Tools:
  • Wearable Sensors: ECG, Heart rate, SpO2
  • Programming Languages: C++, Python
  • Machine Learning: TensorFlow, Scikit-Learn
  • IoT Protocols: CoAP, MQTT
  1. Digital Twin Framework for Real-Time Monitoring in Industrial IoT Systems
  • Problem Description:
  • Because of data heterogeneity and delay, developing precise digital twin systems for industrial IoT models is determined as complicated.
  • Major Solution:
  • A digital twin model has to be constructed in such a manner that coincides the physical and virtual frameworks in actual-time.
  • Typically, for forecasting maintenance and anomaly identification, focus on combining predictive analytics frameworks.
  • Tools:
  • Digital Twin Platforms: MATLAB/Simulink, Eclipse Ditto
  • Machine Learning: PyTorch, Scikit-Learn
  • Programming Languages: C++, Python
  1. Interoperability Framework for Multi-Protocol Communication in Heterogeneous IoT Networks
  • Problem Description:
  • Interoperability problems are caused because IoT networks encompass heterogeneous devices employing various protocols and data structures.
  • Major Solution:
  • It is approachable to create an interoperability model in a way that transforms among various smart home protocols such as Z-Wave, Wi-Fi, Zigbee.
  • For combined data exchange, it is better to utilize semantic data systems.
  • Tools:
  • Middleware Platforms: FIWARE, Node-RED
  • Programming Languages: JavaScript, Python
  • Semantic Tools: RDFLib, Protégé
  1. IoT Network Resilience Against Distributed Denial of Service (DDoS) Attacks
  • Problem Description:
  • Because of the decentralized essence, IoT networks are susceptible to Distributed Denial of Service (DDoS) assaults.
  • Major Solution:
  • To identify and reduce DDoS assaults, it is appreciable to construct an IoT network resilience system that utilizes machine learning.
  • In order to decrease the influence of DDoS assaults, aim to deploy adaptive traffic filtering technologies.
  • Tools:
  • Network Simulators: OMNeT++, NS-3
  • Intrusion Detection Systems: Suricata, Snort
  • Machine Learning: PyTorch, TensorFlow

What are some interesting and good thesis topics for a B.Tech student related to Internet of Things (IoT) or data science?

Relevant to Internet of Things (IoT) and Data Science, there are several thesis topics that are progressing in recent years. But some are determined to be intriguing and efficient for B.Tech students. We offer few fascinating thesis topics for B.Tech student on the basis of IoT and Data Science:

IoT Thesis Topics and Ideas

  1. Smart Home Automation System Using IoT and Voice Commands
  • Problem Description: Typically, consistent control and personalization are insufficient in conventional home automation models.
  • Major Solution:
  • An IoT-related smart home model has to be constructed that is regulated through voice commands by employing NLP.
  • On the basis of utilization trends, aim to deploy energy-conserving suggestions.
  • Tools/Technologies:
  • Google Assistant or Alexa SDK
  • Raspberry Pi, Arduino
  • MQTT, Zigbee, Node-RED
  1. IoT-Based Smart Traffic Management System
  • Problem Description: In city regions, traffic congestion is examined as a significant issue.
  • Major Solution:
  • It is appreciable to develop an IoT-related traffic management model that employs actual-time traffic data from sensors and cameras.
  • Specifically, for traffic forecasting and enhancement, focus on utilizing machine learning systems.
  • Tools/Technologies:
  • Node-RED, Grafana
  • OpenCV, TensorFlow
  • LoRaWAN, Zigbee
  1. Precision Agriculture Using IoT and Machine Learning
  • Problem Description: Overutilization of resources and ecological destruction are resulted due to the traditional agriculture actions.
  • Major Solution:
  • Aim to create an IoT-related accurate agriculture framework that employs soil sensors, drones, and machine learning systems.
  • It is beneficial to deploy actual-time tracking of crop welfare, soil dampness, and temperature.
  • Tools/Technologies:
  • Python, TensorFlow
  • DHT11, Soil Moisture Sensors
  • LoRaWAN, Zigbee, UAV
  1. IoT-Based Wearable Health Monitoring System
  • Problem Description: Mostly, precise actual-time tracking and analytics are insufficient in wearable health devices.
  • Major Solution:
  • To offer actual-time health data and analytics, develop an IoT-related wearable health tracking framework.
  • For earlier identification, aim to create predictive analytics systems.
  • Tools/Technologies:
  • Python, TensorFlow, Grafana
  • ECG, Heart Rate, SpO2 Sensors
  • NodeMCU, Raspberry Pi
  1. IoT-Based Smart Waste Management System
  • Problem Description: In city regions, ineffective waste gathering and management is the main issue.
  • Major Solution:
  • To track waste levels, create an IoT-based smart waste management model which employs smart bins.
  • Specifically, for waste generation trends, focus on deploying predictive analytics. Aim to enhance the collection paths.
  • Tools/Technologies:
  • Python, Grafana, Machine Learning
  • Ultrasonic Sensors, Raspberry Pi

Data Science Thesis Topics and Ideas

  1. Real-Time Anomaly Detection in IoT Networks Using Machine Learning
  • Problem Description: Because of the variety of data and devices, anomaly identification in IoT networks is considered as complicated.
  • Major Solution:
  • Through the utilization of machine learning methods, aim to construct an actual-time anomaly identification model.
  • To examine network congestion data, it is appreciable to utilize feature extraction approaches.
  • Tools/Technologies:
  • NS-3, OMNeT++
  • Scikit-Learn, TensorFlow
  • Wireshark, Suricata
  1. Time Series Analysis of IoT Sensor Data
  • Problem Description: As the result of the high volume and diversity, time series exploration of IoT sensor data is difficult.
  • Major Solution:
  • In order to identify trends and patterns in IoT sensor data, develop a time series analysis system.
  • Mainly, for predictive analytics, it is appreciable to utilize LSTM, ARIMA, or Prophet frameworks.
  • Tools/Technologies:
  • Grafana, InfluxDB
  • Python Libraries: pandas, scikit-learn, statsmodels, prophet, tensorflow
  • IoT Models: Node-RED, MQTT
  1. Big Data Analytics for IoT Applications
  • Problem Description: Particular processing and analysis approaches are the key requirements when managing big data that are produced by IoT.
  • Major Solution:
  • Specifically, for processing IoT data streams, focus on creating a big data analytics environment.
  • Aim to deploy Apache Spark for analytics and Apache Kafka for actual-time streaming.
  • Tools/Technologies:
  • MQTT, CoAP
  • Apache Kafka, Apache Spark, Hadoop
  • Python Libraries: pandas, numpy, scikit-learn
  1. Machine Learning-Based Predictive Maintenance System
  • Problem Description: Productivity loss in production is caused because of unexpected tool interruption.
  • Major Solution:
  • By employing machine learning systems such as LSTM and Random Forests, develop a predictive maintenance framework.
  • To forecast equipment faults, investigate vibration and temperature data.
  • Tools/Technologies:
  • Grafana, Node-RED
  • Python Libraries: pandas, scikit-learn, tensorflow
  • Vibration Sensors, Temperature Sensors
  1. Sentiment Analysis on Social Media Data for IoT Insights
  • Problem Description: Typically, product advancement and marketing can be enhanced when obtaining valuable perceptions from social media relevant to IoT.
  • Major Solution:
  • To explore social media posts relevant to IoT devices, aim to construct a sentiment analysis model.
  • In order to categorize sentiment as positive, negative, or neutral, it is appreciable to utilize NLP frameworks.
  • Tools/Technologies:
  • Jupyter Notebook
  • Python Libraries: nltk, scikit-learn, textblob, vaderSentiment
  • Social Media APIs: Facebook API, Twitter API
Internet of Things Thesis Ideas

Internet Of Things Thesis Topics & Ideas

We offer 100% tailored Internet of Things Thesis Topics & Ideas that exclusively to fit your prerequisites. Latest ideas are updated by us as per your interest we provided original topics that has proper word alignment in it. All your work will be maintained highly confidential so don’t hesitate to reach us. All your work will be submitted on time we do our best to fulfil your needs.

  1. AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques
  2. An efficient IoT-based perspective view of food traceability supply chain using optimized classifier algorithm
  3. Modeling and controlling IoT-based devices’ behavior with high-level Petri nets
  4. An Investigation of Energy Consumption in Fused Deposition Modelling using ESP32 IoT Monitoring System
  5. Backtracking search algorithm with dynamic population for energy consumption problem of a UAV-assisted IoT data collection system
  6. An intelligent fuzzy-based system for handover decision in 5G-IoT networks considering network slicing and SDN technologies
  7. A fully distributed robust optimal control approach for air-conditioning systems considering uncertainties of communication link in IoT-enabled building automation systems
  8. Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms
  9. Detecting vulnerabilities in IoT software: New hybrid model and comprehensive data analysis
  10. BMS: Bandwidth-aware Multi-interface Scheduling for energy-efficient and delay-constrained gateway-to-device communications in IoT
  11. An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks
  12. A taxonomy of IoT firmware security and principal firmware analysis techniques
  13. AutoCert: Automated TOCTOU-secure digital certification for IoT with combined authentication and assurance
  14. The challenges of IoT-based applications in high-risk environments, health and safety industries in the Industry 4.0 era using decision-making approach
  15. An IoT-based energy management system for AC microgrids with grid and security constraints
  16. AIDA—A holistic AI-driven networking and processing framework for industrial IoT applications
  17. Low-complexity systolic array structure for field multiplication in resource-constrained IoT nodes
  18. Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges
  19. Enablers to achieve zero hunger through IoT and blockchain technology and transform the green food supply chain systems
  20. A survey on solutions to support developers in privacy-preserving IoT development

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