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Research and Thesis Topics for Masters and PhD

Hot Research and Thesis Topics in Internet of Things (IoT) for Masters and PhD

With the rapid advancements in the information society, numerous applications generate a large volume of data at high speed, including click-streams, network traffic data, stock data, Internet of Things (IoT) data stream, and so on. The research directions in handling the vast amount of IoT data streams generated by the variety of devices have opened great ways for innovative applications across different fields.

• IOT Enabling Technologies
• Service-oriented IoT Architecture
• Middleware Technologies for IoT
• Routing Protocols for IoT
• Mobility-aware RPL for Mobile IoT
• Securing RPL Routing Protocol in IoT
• Congestion Control Mechanisms in COAP Protocol
• DTLS Security for COAP Protocol
• Security Mechanisms for COAP Protocol
• Design and Analysis of MQTT Protocol
• Security Mechanisms for MQTT Protocol
• Data Access Control Framework for IoT
• DDoS Attack Detection in the IoT
• Identity-based Encryption in the IoT
• Lightweight Authentication for the IoT
• Ultra-Low-Power Sensing Framework for IoT
• Industrial IoT
• Edge Computing for Industrial IoT
• 6TiSCH Communication Architecture in Industrial IoT
• Big Data Management for IoT
• Internet of Vehicles
• Internet of Everything
• Federated learning for IoT
• Internet of Electric Vehicles
• Internet of Medical Things
• Satellite IoT
• IoT Cybersecurity
• IoT Future Internet Design
• IoT Enabled Business Models
• Context-Aware Computing for IoT
• IoT with Next Generation Wireless Systems
• IoT with Edge Computing
• IoT with Fog Computing
• IoT with Blockchain
• Internet of Underwater Things
• IoT Smart Applications
• Privacy Preserving Data Collection in the IoT
• Deep Reinforcement Learning for IoT
• Predictive Maintenance for Effective Resource Management in Industrial IoT
• Internet of Multimedia Things

Latest Research and Thesis Topics in Machine Learning for Masters and PhD

Machine learning has become a key component in providing potential benefits in the research area of Artificial Intelligence. The algorithmic decision-making ensures the penetration of automated decisions for the dynamically changing, massive, and variety of data modalities in every aspect of human life. The advancements in machine learning algorithms and the combination of the algorithms help to improve the classification, regression, and clustering outcomes.

• Natural Language Processing Algorithms and Applications
• Federated Learning for Natural Language Processing
• Federated Learning for Healthcare Data Analytics
• Machine Learning for Cyber Security
• Federated Learning for Cyber Security
• Machine Learning in Evolutionary Computation
• Machine Learning Methods for Pattern Recognition
• Recent Advances in Deep Recurrent Neural Networks
• Deep Autoencoder Architecture and Applications
• Advanced Deep Learning Methods for Medical Imaging
• Generative Deep Neural Networks
• Deep Neural Networks for Speech Recognition
• Federated Learning for Robotics and Automation
• Deep Neural Networks for Computer Vision
• Deep Learning for Data Stream Processing
• Deep Learning for Time Series Analysis
• Deep Ensemble Learning
• Deep Reinforcement Learning
• Convolutional Neural Networks
• Deep Learning for Malware Detection System
• Federated Learning for Computer Vision
• Federated Learning for Edge Computing
• Deep Learning for Recommendation Systems
• Deep Learning for Opinion Mining
• Federated Learning for Smart City Application
• Medical Machine Learning Algorithms for Healthcare
• Deep Learning for Sentiment Analysis
• Machine Learning for Disease Prediction
• Deep Learning for Intrusion Detection System
• Deep Learning for Intelligent Wireless Networks
• Deep Learning for Big Data Analytics
• Extreme Learning Machines
• Deep Learning for Intelligent Vehicular Networks
• Federated Learning for Vehicular Networks
• Deep Learning for Traffic Congestion Prediction
• Dynamic Neural Networks
• Optimizing and Fine-Tuning the Deep Neural Networks
• Deep Learning for Stock Market Prediction
• Deep Learning for Autonomous Vehicles
• Radial Basis Function Networks
• Long Short-Term Memory Networks
• Restricted Boltzmann Machines
• Self-Organizing Maps
• Personality-aware Recommendation Systems
• Transfer Reinforcement Learning
• Multi-Goal Reinforcement Learning
• Extreme Multi-Label Classification
• Generalized Few-Shot Classification
• Multimodal Deep Learning
• Hierarchical Reinforcement Learning
• Multiple Instance Learning
• Interpretable Machine Learning
• Imitation Learning
• Federated Transfer Learning
• Contextualized Word Representations
• Neural Architecture Search
• Meta-Learning
• Data, Image, and Text Augmentation
• Domain Adaptation for Machine Learning Models
• Representation Learning
• Object Detection With Deep Learning
• Attention Mechanism for Natural Language Processing
• Graph Neural Networks
• Multi-Objective Evolutionary Federated Learning
• Explainable Deep Neural Networks
• Evidential Deep Learning
• Graph Representation Learning
• Research Topics in Graph Convolutional Networks
• Hopfield Neural Networks
• Quaternion Factorization Machines
• Reservoir Computing
• Recurrent Neural Networks for Edge Intelligence
• Federated Learning for Smart Intrusion Detection Systems
• Deep Extreme Classification
• Neural Machine Translation
• Deep Reinforcement Learning for IoT
• Federated Learning for the IoT
• Hyperbolic Deep Neural Networks
• Few-Shot Class-Incremental Learning
• Non-Local Graph Neural Networks
• Deep Learning-based Semantic Similarity
• Deep Contextual Word Embedding Models for Semantic Similarity
• Distributed Active Learning
• Triple Generative Adversarial Network
• Shallow Broad Neural Network
• Pre-training of Deep Bidirectional Transformers for Language Understanding
• Federated Learning for Internet of Vehicles
• Spiking Neural Networks
• Bayesian Neural Networks

Latest Research and Thesis Topics in Fog Computing for Masters and PhD

Fog computing is one of the recent digital innovations in the real world with the potential advantage of providing an ultra-fast response for the end-users with the system privacy by offering the benefits of executing the high computation tasks such as the multimedia streaming and game rendering near the device itself without transferring the data into the cloud servers. The wide variety of research topics in the domain of fog computing assists fog computing researchers in developing energy-efficient fog systems for resource-constrained devices.

• Computational Offloading in Fog Computing
• Scheduling in Fog Computing
• Fog Device Virtualization
• Cloud-fog Collaborations
• Adaptive Fog Computing
• Green Fog Computing
• IoT Data Processing in Fog Computing
• Reliability-aware Fog Computing
• Delay-aware Fog Computing
• Quality of Experience-based Fog Computing
• Context-aware Fog Computing
• Container-based Virtualization in Fog Computing
• Mission-Critical Application Execution in Fog Computing
• Proactive Service Discovery using Fog Computing
• Resource Management and Provisioning in Fog Computing
• Resource Discovery and Selection in Fog Computing
• Resource Monitoring and Allocation in Fog Computing
• Resource Estimation and Sharing in Fog Computing
• Profit-aware Resource Allocation in Fog Computing
• Load Balancing and Migration in Fog Computing
• Dynamic Load Balancing in Fog Computing
• VM Migration for Load Balancing in Fog Computing
• VM Selection and Placement in Fog Computing
• Energy-aware Task Scheduling in Fog Computing
• Energy Efficient Resource Provisioning in Fog Computing
• Energy-aware Load Balancing in Cloud Computing
• Energy-Efficient VM Selection and Placement in Fog Computing
• Application and Service placement in Fog Computing
• Optimization of Task Scheduling in Fog Computing
• Optimization of Resource Allocation in Fog Computing
• Multi-Objective Optimization in Fog Computing
• QoS-aware Control and Monitoring in Fog Computing
• Security and Privacy in Fog Computing

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