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
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 Deel 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
In the digital world, the advancements in Web technologies create an opportunity for digital crimes. The digital forensics field has emerged under the admissibility of a legal court of law to trace or investigate electronic evidence. Nowadays, the research topics need to be focused on critically extracting the digital evidence to avoid unjustified decisions with the integration of intelligent computing techniques.
• Forensic Investigation Process
• Digital Forensics Investigation Process Models
• Digital Forensic Readiness
• Forensic Standardization
• Quality and Legal Standards for Digital Forensics
• Criminal Analysis and Prediction using Machine Learning
• Criminal Network Analysis using Machine Learning
• Financial Crime Detection
• Dynamic Malware Analysis
• Network Forensics
• Mobile Device Forensics
• Mobile Forensic Readiness Model
• Smartphone Forensic Analysis
• Social Media Forensics for Android Device
• Evidence Triaging for Mobile Forensics
• Data Integrity-Assured Mobile Forensics
• Cloud Forensics Investigation Framework
• Evidence Acquisition in Cloud Forensics
• Cloud Storage Forensics
• Cloud Forensic Readiness Model
• Privacy-Preserving Cloud Forensics Model
• Virtual Machine Introspection for Cloud Forensics
• Logging and Log Synchronization for Cloud Forensics
• Mobile Cloud Forensics
• Machine Learning-assisted Evidence Identification in Mobile Cloud
• Cloud-based Mobile Application Forensics
• Mobile Cloud Forensic Process Models
• Big Data Forensic Analysis
• Proactive Big Data Analytics for Digital Forensics
• IoT Forensics
• IoT Forensic Readiness
• Digital Forensics in Multimedia
• Steganalysis for Multimedia Forensics
Over the decades, the cyber world or internet environment has become popular among people with social interactions. With the rapid adoption of web technology, individuals and organizations easily communicate with each other without great effort from a remote location. The World Wide Web (WWW) often confronts unauthorized access to sensitive data, personal safety risks, and different types of attacks. Hence, it is essential to model cyberspace with security measures to cope with the advancements in the vulnerabilities and threats over the Internet.
• Artificial Intelligence for Cyber Security Threats
• Block Chain Technology for Cyber Security Threats
• Cyber Security for IoT based Smart Systems
• Federated learning for Cyber Security Threats
• Zero Trust Access Architecture
• Fuzzing for Security Vulnerability Discovery
• Machine Learning for Cyber Security Threats
• Cyber-attacks and Countermeasure on Interconnected Critical Infrastructure
• Intrusion Detection Systems for IoT Applications
• Symbolic Execution for Detecting Vulnerabilities in Web Applications
• Intrusion Detection Systems for Wired and wireless network Applications
• Cybersecurity for Connected Autonomous Vehicles
Artificial Intelligence (AI) has become a popular technology in the form of cognitive, social, or emotional intelligence through its humanized, analytical, or human-inspired behaviors. The development of the systems with AI inestimably enhances people’s lives in healthcare, employment, safety, education, homes, transportation, and entertainment. The design of AI-assisted systems also provides unprecedented business opportunities for inventing new business models, intelligent products, and service offerings.
• Uncertainty Modelling
• Knowledge Representation and Reasoning
• Robotics
• Multi-Agent Systems
• Explainability in AI
• Machine Learning and Deep Learning
• Natural Language Processing
• Explainable Artificial Intelligence
• Pattern Matching
• Ethical issues of AI
• Computer Vision
• Planning and Scheduling
Blockchain technology allows the users to preserve, synchronize, and verify the data in a transaction ledger, increasingly adopted by the financial, healthcare, government, and manufacturing sectors. Blockchain technology emphasizes the revolutions and innovations of artificial intelligence and IoT (IoT) technologies, creating opportunities for all industries with enhanced business processes.
• Blockchain Standards, Software Tools, and Development Platforms
• Protocols and Algorithms for Blockchain
• Blockchain Schemes for Decentralization
• Smart Contracts in Blockchain
• Consensus Mechanisms in Blockchain
• Blockchain Interoperability
• Blockchain for Cryptocurrency
• Energy Efficiency Issues in Blockchain
• Attacks against Blockchain Integrity
• Blockchain Algorithms for Security
• Blockchain for Large-scale Applications
• Blockchain as a Service
• Blockchain Security for IoT
• Blockchain Security for Edge Computing
• Blockchain for Cybersecurity
• Artificial Intelligence for Blockchain-based Applications
• Autonomous Trust Management for Blockchain
• Privacy Leakage Issue in Blockchain
• Blockchain for Industrial IoT
• Blockchain for Internet of Vehicles
• Blockchain for Industry-4.0 Applications
• Emerging Blockchain Models for Digital Currencies
• Blockchain Models in Government and Public Services
• Blockchain with Big Data
• Permissioned and Permissionless Blockchain Models
• Blockchain for Decentralized Storage Systems
• Fraud Detection and Prevention of Financial Crime using Blockchain
• Distributed Consensus and Fault Tolerance Mechanisms in Blockchain
• Performance Analysis and Optimization of Blockchain
• Legal, Ethical and Societal Aspects of Blockchain
• Transaction Graph Analysis of Blockchain
• Advanced Cryptography Algorithms in Blockchain
• Regulation and Law Enforcement of Blockchain
• Blockchain in Crowdsourcing and Crowdsensing Applications
• Blockchain in Supply Chain Management
• Blockchain in Cyber-Physical Systems
• Blockchain in Social Networking
• Blockchain in Next Generation Communications and Networks
Metaheuristic computing has become a popular optimization method in the scientific community and proved the superior and viable solution to traditional mixed-integer optimization methods. Over the decades, most scientific tasks have increasingly adopted metaheuristic algorithms. The adoption of metaheuristic computing offers a trade-off between the quality of the solution and the computation time by searching for the optimal solutions for complex problems.
• Meta-Heuristics for Cloud Computing
• Meta-Heuristics for Energy Optimization in Cloud Computing
• Multi-Objective Metaheuristic Optimization in Cloud Computing
• Meta-heuristics-based Profit Maximization in Cloud Computing
• Meta-heuristics for Workflow Scheduling in Cloud Computing
• Meta-heuristics for Scheduling and Load Balancing in Fog Computing
• Multi-Objective Metaheuristic Optimization in Fog Computing
• Metaheuristic for Edge Computing
• Meta-heuristic Methods in Mobile Cloud Computing
• Meta-heuristics for Mobile Cloud Offloading
• Metaheuristic Methods for Routing in Mobile Ad Hoc Networks
• Metaheuristic Computing for Routing in Wireless Sensor Networks
• Metaheuristic Computing for Clustering in Wireless Sensor Networks
• Metaheuristic Computing for Energy Efficiency in Wireless Sensor Networks
• Metaheuristic Computing for Data Aggregation in Wireless Sensor Networks
• Optimization of Routing Protocols in VANET using Metaheuristics
• Metaheuristics for Feature Engineering in Machine Learning
VANET is a wireless multi-hop network consisting of self-organizing vehicles as mobile nodes. A main constraint of VANET is frequent topology changes due to the high node mobility. With increasing vehicles equipped with computing technologies and smart devices, inter-vehicle communication has become a promising field of research in wireless communication. VANETs provide various applications from entertainment to safety, such as dynamic route prediction with less traffic, blind crossing, lane changing assistance, parking payment, and real-time traffic condition monitoring. Another important application for VANETs is the provision of Internet connectivity to vehicular nodes.
• Federated Learning for Internet of Vehicles
• Mobility Management and Mobility Models in VANET
• Cognitive Radio-based VANET
• Artificial Intelligence Techniques for VANET
• Blockchain Models for VANET
• Enhancing emergency vehicle communication efficiency using VANET
• Congestion prediction-based emergency vehicle dynamic route discovery in VANET
• Localization System for VANET
• Reinforcement Learning based Routing Protocols for VANETs
• UAV assisted VANET architecture in smart cities
• VANET for Intelligent Transportation Systems
• Vehicle–to–Vehicle Communication in VANET
• Vehicle–to–RSU Communications in VANET
• Vehicle to Infrastructure Communications in VANET
• Cellular Networks for Vehicular Networking
• Cloud Computing for VANET
• Hybrid Networks for Next Generation VANET
• Smart City Environment for VANET
• Security in Service-oriented VANET
• Security Issues and Defense Mechanisms in VANET
• Emergency Communications in VANET
• Clustering in VANET
• Key Distribution in VANET
• Safety and Driver Assistance in VANET
• Authentication in VANET
• Trust Management in VANET
• Privacy Issues in VANET
• Location privacy in VANET
• Privacy and Trust Management in VANET
• Intrusion Detection System in VANET
• Sybil Attack Detection in VANET
• Video Streaming in VANET
• Routing Protocols for Smart City in VANET
• Intelligent Routing Protocols in VANET
• Opportunistic Routing in VANET
• Handover Schemes in VANET
• Bio-Inspired Routing in VANET
• Scalability Issues in VANET
• Congestion Control in VANET
• Congestion Avoidance in VANET
• Data Dissemination in VANET
• QoS Support in VANET
• Broadcast Communication in VANET
• Beaconing in VANET
• Street-Centric Routing in VANET
• Pseudonym Management in VANET
• Traffic Differentiation and Scheduling Schemes in Vehicular Sensor Networks
• Software-Defined Network in VANET
• Internet of Vehicles
• Applications of Game Theory in Vehicular Networks
• Deep Reinforcement Learning for Traffic Engineering
In recent years, Wireless Sensor Networks (WSN) have gained considerable attention in different applications involving the military, security, environment, and health. WSN-based solutions have been recognized as promising solutions for smart applications and their powerful capabilities. The research topics in the WSN penetrate a novel way for researchers to develop WSN-based applications and models.
• Clustering Techniques in WSN
• Bio-Inspired Clustering in WSN
• Data Aggregation in WSN
• Cluster-based Data Aggregation Techniques in WSN
• Secure Data Aggregation Techniques in WSN
• Energy-efficient MAC protocol for WSN
• Mobility Management in WSN
• Sink Mobility for WSN
• Energy Efficient Sink Placement for WSN
• Underwater Sensor Networks
• Routing Protocols for Underwater Sensor Networks
• Trust and reputation-based approaches in WSN
• Intermittently Connected Delay-Tolerant WSN
• Distributed Database Management Techniques for WSN
• Airborne Relaying in WSN
• Cooperative Relaying in WSN
• Deployment Strategies in WSN
• Replica Attacks in WSN
• Attack Detection and Prevention Schemes in WSN
• Efficient Flooding Techniques in WSN
• Intrusion Detection System for WSN
• Congestion control and Avoidance in WSN
• Cluster-based Routing Techniques in WSN
• Anycast Routing in WSN
• Multicast Routing Techniques in WSN
• Context-aware Routing in WSN
• Multipath Routing Protocols for WSN
• Opportunistic Routing in WSN
• Bio-Inspired Routing Techniques in WSN
• Energy-efficient Routing Protocols in WSN
• Cluster-based Intrusion Detection System in WSN
• Data Transmission Scheduling Techniques in MAC Layer
• Energy Efficient Sleep and Wake up Scheduling in WSN
• Security Attacks and Secure Routing in WSN
• Lightweight Cryptography algorithms for WSN
• Lightweight Authentication for WSN
• Secure Key Management for WSN
• Multichannel protocols for WSN
• Cross-layer protocols for WSN
• QoS in Wireless Multimedia Sensor Networks
• Neighbor Discovery Techniques in WSN
• Trust-Based Routing in WSN
• Connectivity Protocols for WSN
• Location Privacy in WSN
• Coverage Hole Healing Techniques in WSN
• Localization Algorithms in WSN
• Provenance Issues and Management in WSN
• Mobile Sink-based Data Gathering Techniques in WSN
• Secure Data Dissemination Methods in WSN
• Coverage and Connectivity Issues in Heterogeneous WSN
• Clustering Techniques in Heterogeneous WSN
• Congestion Avoidance in WSN
• Load Balancing in WSN
Software Defined Networking (SDN) has attracted significant attention from academia and industry. In recent years, service providers, vendors, and network operators have increasingly adopted the SDN paradigm and architecture with programmability characteristics on the control plane and decoupling control and data planes. The SDN research is growing to standardize the SDN for the different infrastructure modeling and implementation concepts.
The business and internet realms have been greatly revolutionized by the Cloud computing technology that impacts the e-commerce, e-learning, and healthcare fields with the advantage of low-cost and high-quality services. In recent years, various research topics, particularly cloud computing technology, have globally expanded with different technologies by integrating the characteristics of the different techniques to provide outstanding performance.
• Federated Cloud Computing
• Cloud Computing Infrastructure for IoT Data Processing
• Pricing Models for Cloud Computing Services
• Dynamic Security Provisioning in Cloud
• Cloud Usage Patterns
• Cloudlet Computing
• Cognitive Cloud Computing
• Container Computing
• Micro Cloud Computing
• Mist Computing
• Mobile Ad-Hoc Cloud Computing
• Serverless Computing
• Social Cloud Computing
• Software-Defined Computing
• Volunteer Computing
• Task Scheduling and Resource Allocation in Cloud Computing
• Load Balancing in Cloud Computing
• Heuristic-based Load Balancing in Cloud Computing
• VM Consolidation based Load Balancing in Cloud Computing
• VM migration for Load Balancing in Cloud Computing
• Virtual Machine Selection and Placement in Cloud Computing
• Energy Management in Cloud Computing
• Energy-aware Task Scheduling in Cloud Computing
• Energy-aware Resource Allocation in Cloud Computing
• Energy Efficient Load Balancing Techniques in Cloud Computing
• Energy Efficient VM Migration in Cloud Computing
• Energy Efficient Workflow Scheduling in Cloud Computing
• Meta-Heuristic-based Energy Optimization in Cloud Computing
• Energy-aware VM Selection and Placement in Cloud Computing
• Workload-aware Energy Management in Cloud Computing
• DVFS-aware Server Consolidation in Cloud Computing
• Energy-aware Resource Scaling in Cloud Computing
• Workflow Scheduling in Cloud Computing
• Hybrid workflow scheduling in Cloud Computing
• Soft Computing Techniques in Cloud Computing
• Task Scheduling Optimization in Cloud Computing
• Resource allocation Optimization in Cloud Computing
• Hybrid Metaheuristic Algorithm-based Task Scheduling in Cloud Computing
• Meta-heuristic Algorithm-based Optimization of Resource Allocation in Cloud Computing
• Multi-Objective Optimization in Cloud Computing
• Meta-heuristic-based Profit Maximization in Cloud Computing
• Meta-heuristic-based Workflow Scheduling in Cloud Computing
• Genetic Algorithm-based Workflow Scheduling in Cloud Computing
• Scaling of Cloud Resources
• Resource Demand-based Allocation in Cloud Computing
• Resource Pricing for Profit Maximization in Cloud Computing
• Resource Utilization-based Scheduling and Allocation
• QoS-aware Resource Scaling in Cloud Computing
• Game Theory-based Methods for Cloud Computing
• Game Theory-based VM Placement in Cloud computing
• Cost Optimization using Game Theory in Cloud Computing
• Machine Learning methods for Cloud Computing
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
Edge computing provides the desired services to the end-users by enabling the data processing on edge due to the increasing demand for low-cost and high-quality computing services. The significant reduction of the delay during data transmission and traffic or load of the network bandwidth, greatly accomplished by the edge computing technology, guarantees the secure and efficient computation of time-critical applications for intelligent devices
• Reliable Edge Data Analytics
• Privacy in Edge Computing
• Federated Learning for Privacy Preservation in Edge Computing
• Blockchain-based Privacy Preservation in Edge Computing
• Pattern Recognition for Privacy in Edge Computing
• Privacy-preserving Monitoring in Edge Computing
• Intelligent Edge Computing for Internet of Vehicles
• Computation Intelligence-based Workload Prediction in Edge Computing
• Artificial Intelligence-based Decision Making in Edge Computing
• Placement Methods in Edge Computing
• Recurrent Neural Networks for Edge Intelligence
• Computation Offloading in Edge computing
• Edge Computing Architectures and Frameworks
• Edge Computing Service Orchestration
• Resource Allocation in Edge Computing
• Workload Allocation in Edge Computing
• Virtualization in Edge Computing
• Load Balancing in Edge Computing
• Profit-aware Resource Management in Edge Computing
• Workload-aware Resource Management in Edge Computing
• Quality of Experience-based Edge Computing
• Resiliency based Edge Computing
• Service Continuity-aware Edge Computing
• Context-aware Mobility Management in Edge Computing
• Distributed Data Aggregation in Edge Computing
• Distributed Data Analytics in Edge Computing
• Context-aware Stream Data Management in Edge Computing
• Real-time Data Analytics in Edge Gateway
• Agricultural Monitoring and Control in Edge Computing
• Environmental and Climate Change Monitoring in Edge Computing
• Lightweight Security Architecture in Edge Computing
• Lightweight Authentication in Distributed Edge Computing
• Deep Learning-based Security in Edge Computing
With the rapid and massive adoption of cloud computing technology by individuals and business organizations, Cloud security has become a primary concern in the technological world. The remarkable growth of cloud services potentially impacts data losses, malware injections, insecure Application Programming Interfaces (APIs), and data breaches. The advancements in cloud security research directions are imperative to cope with the growth of cloud computing technology.
• Cloud Computing Standards and Compliance
• Security for Cloud Infrastructure and Services
• Blockchain Technology for Cloud Security
• Secure Outsourcing of Big Data in Cloud
• Cloud Reliability Analysis
• Reliable VM Management in Cloud
• Artificial Intelligence for Cloud Reliability
• Intrusion Detection and Prevention in Cloud
• Deep Learning Solutions for Cloud Security
• Security for Anonymous Data Sharing in Cloud
• Encryption and Key Management in Cloud Security
• Distributed Authentication and Authentication
• Real-time Analysis of Security Log Data for Alert generation on Cloud Environment
• Security monitoring for Virtual Machines in Cloud Computing
• Reliable Virtual-Machine Management System for the Cloud
• Anonymous Data Sharing in Cloud Computing
• Cryptography and Key Management Strategies for Cloud Security
• Data Confidentiality in Cloud Security
• Data Integrity and Availability in Cloud Security
• Virtualization Security
• Confidentiality and Integrity of Virtualization
• Security Management in Cloud Computing
• Log Security in Cloud
• Real-time Analysis of Security Log Data for Alert Generation in Cloud
• Intrusion Detection System with Event Logging in Cloud
• Security Monitoring for Virtual Machines in Cloud
• Secure Data Segregation and Isolation in Cloud
• Efficient Searchable Data Encryption in Cloud Storage
• Cryptography and Access Control based Secure Storage in Cloud
• Secure Data Forwarding in Cloud Storage
• Privacy Preservation in Public Auditing of Cloud Storage
• Key Exchange Privacy Preservation for Cloud
• Data Mining Techniques for Privacy Preservation
• Machine Learning-based Privacy Preservation in Cloud
• Risk Assessment and Risk Management in Cloud
• Multi-Cloud Security Provisioning
• Identity Management and Multi-factor Authentication in Cloud
• Access Control Mechanisms in Cloud
• Access Control Governance in Cloud
• Cryptographic Protocols against Internal Attacks
• Secure Cryptographic Cloud Communication
• Security Solutions for Cloud attacks
• Forensic Techniques in Cloud Computing
• Anti Forensic Techniques in Cloud Computing
• Distributed Authentication and Authorization in Cloud Computing
• Cryptography and Key Management Strategies in Cloud Computing
• Efficient Searchable Data Encryption in Mobile Cloud Storage
Mobile cloud computing has gained significant attention among mobile users due to the explosive growth of accessing mobile applications over resource-constrained mobile devices. To handle the obstacles in improving the Quality of Service (QoS) of the application, mobile cloud computing models need to be enhanced in the offloading, task scheduling, resource allocation, optimization, and resource management to enable the elastic utilization of the on-demand cloud resources by the mobile users.
• Offloading and Application Partitioning in MCC
• MCC Architectures
• Context-aware Computing in MCC
• Machine Learning-based Offloading in MCC
• Resource Allocation in MCC
• Task Scheduling in MCC
• Resource Provisioning in MCC
• Load balancing in MCC
• Task Migration in MCC
• Energy Efficiency in MCC
• SLA-aware Task Scheduling in MCC
• SLA-based Resource Allocation in MCC
• SLA-based Resource Provisioning in MCC
• Meta-heuristic Techniques in MCC
• Game-theoretic Model in MCC
• Data Management and Synchronization in MCC
• Resource Management and Optimization in MCC
• Automatic Resource Management using Machine Learning in MCC
With the dramatic increase of the information available on the World Wide Web, mining or extracting the potential information from the massive data is a prerequisite. Automated mining of structured, unstructured, and semi-structured data becomes essential in various real-time applications, such as question answering, natural language processing, recommender system, sentiment analysis, and so on.
• Classification and Clustering Algorithms
• Association Rule Mining
• Text Mining and Summarization
• Topic Modeling
• Natural Language Processing
• Information Retrieval
• Question Answering System
• Social Network Analysis
• Spatial Data Mining
• Semantic Analysis
• Fraud Detection
• Data Mining in Healthcare
• Financial Analysis in Data Mining
• Stock Market Analysis
• Network Alignment Techniques
• Sentiment Analysis in Data Mining
• Recommender Systems in Data Mining
• Graph Mining
• Pattern mining
• Stream Data Mining
• Time-Series Data Mining
• Multimedia Data Mining
With the rapid proliferation of data-driven decision-making worldwide, the notion of big data has emerged among technological people anywhere. The growing amount of voluminous and variety of digital data increases the difficulties in data analysis and analytics. The big data management and decision-making task demand potential solutions in the different real-time application fields.
• Big Data Analytics
• Big Data Models and Algorithms
• Big Data Visualization
• Big Data Semantics
• Big Data Analytics for Business Intelligence
• Big Data Analytics for Smart Healthcare
• Parallel Programming Techniques for Big Data Processing
• Software and Tools for Massive Big Data Processing
• Scalable Architectures for Massively Parallel Data Processing
• Scalable Storage Systems for Big Data
• Cloud Computing Platforms for Big Data Adaptation and Analytics
• Large Scale Data Analysis for Social Networks
• Database Management Systems for Big Data
• Hadoop Programming and Map Reduce Architecture
• Machine Learning Methods for Big Data
• Stream Data Processing in Big Data
• Security and Privacy Issues in Big Data
• Uncertain Data Management in Big Data
• Privacy Preserving Big Data Analytics
• Anomaly Detection in Very Large Scale Systems
Over the past decades, the incredible development of mobile devices such as Smartphones, tablets, and laptops with an internet connection has emerged due to its primary advantage of mobility. Mobile computing is the self-governing computing of the mobile user, often confronted with the limited resource capabilities in the mobile device during the execution of complex tasks or applications. The mobile computing research topics allow the researchers to enhance the computation process of mobile devices.
• Generations of Mobile Communication Technologies
• Applications of Mobile Computing
• Mobility Models and Management
• Protocols for Mobile Computing
• Mobile Network Architecture
• Handover Techniques for Mobile Networks
• Energy-efficient Mobile Computing
• Mobile Device Operating Systems
• Mobile Application Security
• Security-aware Mobile Commerce
• Android Malware Detection
• Mobile Internet
• User-context-based Authentication and Access Control in Mobile Computing
• Privacy-risk Assessment of Mobile Applications
The social network has become an emerging online communication medium among people over the Internet. The information generated or exchanged between the individuals or groups involves the text, image, audio, and video. Analyzing such social network data and the structure of the social network provides insights into the numerous real-time applications such as customer personalization, marketing, trend prediction, stock market prediction, and so on.
• Social Networks and Analysis
• Contextual Social Network Analysis
• Machine learning Techniques for Social Media Analytics
• Mining Social Networks
• Community Discovery in Large-scale and Complex Social Networks
• Social Networks and Social Influence
• Learning Propagation Models for Social Networks
• Information and Influence Propagation in Social Networks
• Social Influence Analysis
• Stochastic Diffusion Models
• Influence Maximization Approaches
• Mobile and Stream Data Analysis for Social Network Applications
• Social Tagging and Applications
• Security and Privacy in Social Networks
• Social Network Personalization and Recommendations for E-Commerce
With the increasing utilization of Web technology by most individuals and organizations, managing the data and processing over the web-based applications is essential. The developments in web technology focus on creating, delivering, or managing massive web content. To assist the seamless execution of the Web-based applications, handling the dynamically changing the Web data has become a hot research area over the rapid expansion of the data in the society.
• Web Service frameworks, architectures, infrastructures
• Web Services Modeling and Performance
• Business Process Integration using Web Services
• Composite Web Service Creation and Enabling infrastructures
• Web Service Coordination Orchestration and Choreography
• QoS in Web Service
• Multimedia Applications using Web Services
• Resource Management for Web Services
• Security in Web Services
• Semantic Web Services
• Semantic Web Technologies
• Ontologies and Ontology Languages
• Simple Ontologies in RDF and RDF Schema
• Simple Protocol and RDF Query Language-SPARQL
• RDF Formal Semantics
• Developing the Semantic Web
• Methodology for Semi-automatic Ontology Construction
• Using Knowledge Discovery for Ontology Learning
• Semantic Annotation
• Approaches to Reasoning with Inconsistency
• Approaches in Ontology Mediation
• Mapping and Querying Disparate Knowledge Bases
• Ontology for Knowledge Management
• Knowledge Access and the Semantic WEB
• Searching for Semantic Web Resources
• Natural Language Generation from Ontologies
• The Web Services Modeling Ontology-WSMO
• The Web Service Modeling Language-WSML
• OWLS Approach
• WSDLS Approach
Mobile Ad-hoc wireless NETworks (MANETs) provide a high probability of creating ad-hoc, independent, and temporary networks without supporting any centralized infrastructure. Due to the unpredictable node movement, the MANET nodes provide an unstable topology, and the connection between the nodes can be broken unexpectedly. Thus, the strategies for designing MANET protocols depend on node mobility and scalability. The MANET nodes are free to join and leave the network anytime. The node can join the network when any node is in the radio range of the node. MANET has no secure boundaries, so attacks can easily target it.
• Self-Organizing Network Architectures and Protocols
• MAC Issues in MANET
• Proactive and Reactive Routing Protocols in MANET
• Geographic Routing Protocols in MANET
• Opportunistic Routing in MANET
• Multicast Routing Protocols in MANET
• Multipath Routing in MANET
• Bio-Inspired Routing in MANET
• Load Balanced Routing in MANET
• Link Breakage Prediction Based Routing Protocols
• Energy Efficient Routing Protocols in MANET
• Reducing Routing Overhead in MANET
• Transport Control Protocol Issues in MANET
• Congestion Control Techniques in MANET
• Quality Of Service Support in MANET
• Security Attacks in MANET
• Trust And Reputation Based Approaches in MANET
• Intrusion Detection Mechanisms in MANET
• Selfish Node Detection in MANET
• Defense Mechanism Against Packet Dropping Attacks in MANET
• Leader Election for Intrusion Detection Systems in MANET
• Privacy Preserving Routing in MANET
• Clustering in MANET
• Data Access Management in MANET
• Cache Management in MANET
• Cooperative Transmissions in MANET
• Cyclic MANET
• Position Update Schemes in MANET
• Anonymous Routing in MANET
• Scalable Routing in MANET
• Evolutionary Algorithms for Routing in MANET
• Flying Ad Hoc Networks
• Topology based Routing for Flying Ad Hoc Networks
• Geographic Routing Protocols for Flying Ad Hoc Networks
• Mobility Models for Flying Ad Hoc Networks
• Performance Evaluation of Routing Protocols for Flying Ad Hoc Networks
• Mobility Models for MANET
• Delay Tolerant Networks
• Routing Protocols for Delay Tolerant Networks
• Mobility Models for Delay Tolerant Networks
• Location Update Schemes for Geographical Routing in MANET
• Unmanned Aerial Vehicles
