Deep Learning in Ad-Hoc Wireless Networks

Description

Deep Learning in Ad-Hoc Wireless Networks is a reference for researchers and data engineers developing enhanced management, security, and privacy features. It proposes deep learning-based approaches using recent algorithms to present applications. It addresses the application of recent Deep Learning algorithms on Wireless Ad-Hoc Networks (WANET) Contents Recent Deep Learning Based Trust Solutions 1 Introduction 2 Characteristics of VANET 3 Trust Mechanisms in VANET 4 Deep Learning in VANETs 4.1 Deep Neural Networks (DNN) 4.2 Deep Belief Networks (DBN) 4.3 Recurrent Neural Networks (RNN) 4.4 Convolutional Neural Networks (CNN) 4.5 Deep Maxout Networks (DMN) 4.6 Deep Learning Based Trust Approaches A Literature Review References Smart Mobility Solutions: The Role of Deep Learning in Traffic Management 1 Introduction 1.1 Scope of the Document 1.2 Literature Survey 1.3 Objectives 2 Understanding Traffic and Mobility 2.1 Importance and Impact 2.2 Key Challenges 3 Traffic Management Strategies 3.1 Traffic Flow Optimization 3.2 Congestion Management 3.3 Traffic Signal Control 3.4 Road Infrastructure Development 4 Mobility Management Approaches 4.1 Public Transport System 4.2 Active Transportation Promotion 4.3 Sustainable Urban Planning 4.4 Smart Mobility Solutions 5 Integrated Traffic and Mobility Management 5.1 Coordination Between Traffic and Mobility Authorities 5.2 Data Integration and Analysis 5.3 Policy and Regulatory Frameworks in India 6 Real-Time Case Study of Ludhiana 7 Example Case Studies 7.1 Future Trends and Challenges 8 Conclusion References A Survey of Routing Protocols for Low Power and Lossy IoT Network 1 Introduction 2 Necessities and Design Concerns of IoT Routing Protocols 2.1 Traffic Pattern 2.2 Scalability and Reliability 2.3 Availability 2.4 Mobility 2.5 Routing Loops and Convergence Time 2.6 Routing Table 2.7 Security and Privacy 2.8 Life Span of LLN 2.9 Load Balancing 2.10 Heterogeneity 3 Classification of Routing Protocols for LLNs 3.1 Peer-to-Peer Routing Protocols 3.2 Mobility Based Routing Protocols 3.3 Diverse Traffic Pattern and Mode of Operations Based Routing Protocols 3.4 Energy Efficient Routing Protocols 3.5 Routing Protocols Based on Objective Function 3.6 Security Aware Deep Learning Based Routing Protocols 4 About RPL 5 Improvements over RPL Routing Protocols 5.1 Peer to Peer Communication Based Routing Protocols 5.2 Mobility Based Routing Protocols 5.3 Traffic Diversity and MOP Based Routing Protocols 5.4 Routing Protocol Based on Objective Function 5.5 Energy Efficient Based Routing Protocols 5.6 Security Aware Deep Learning Based RPL Routing Protocols 5.7 The Persisting Challenges 6 Conclusion References Generative Artificial Intelligence Using Deep Learning on Wireless Ad-Hoc Networks 1 Introduction 2 Related Works 3 Materials and Methods 3.1 Experimental Data 3.2 Setting Parameters 3.3 Parameter Classification 4 Experimentation 4.1 Experimental Setup 4.2 Dataset 4.3 Performance Evaluation 5 Discussion 6 Conclusion References Deep Learning for Intrusion Detection on Controller Area Networks 1 Introduction 2 Related Works 3 Materials and Methods 3.1 Software-Defined Networking (SDN) 3.2 VANET Communication Types 3.3 HCRL-VANET 3.4 Intrusion Detection on HCRL-VANET 3.5 Dataset 3.6 Classifiers 4 Experimental Results 5 Discussion and Conclusion References