Wireless Ad-hoc and Sensor Networks

Wireless Ad-hoc and Sensor Networks

Architecture, Protocols, and Applications

Rai, Abhay Kumar; Kumar, Sudesh; Singh, Pawan; Gupta, Sachin Kumar; Saif, Abdu

Taylor & Francis Ltd

11/2024

390

Dura

9781032718347

Pré-lançamento - envio 15 a 20 dias após a sua edição

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1. Orthogonal Time Frequency Space Modulation and its Application to VANETs. 2. Semantic Representation and Communication in Ad Hoc Networks Enabling Intelligent Information Exchange and Collaboration. 3. The Diverse Technology of Flying Ad-hoc Networks (FANETs): An Extensive Survey. 4. Radio Frequency Based Drone Detection and Identification: Challenges and Opportunities. 5. Learning Processes for Internet of Autonomous Vehicles, Intelligent Transportation Systems. 6. Future Envision of UAVs Elevating Human Life: ITS in Smart Cities Lensing Wireless Applications. 7. An Analysis of Clustering Algorithms for Wireless Sensor Network Applications. 8. Hybrid Metaheuristic Optimization for Energy-Efficient Routing, Clustering, and Sleep Scheduling in WSNs. 9. Selection of Relay Node using Multi-Criteria Decision Making in Wireless Body Area Network. 10. Artificial Intelligence in Wireless Communication. 11. Leveraging AI-Enabled WSNs for Environmental Monitoring: An In-depth Analysis with RF-Pose Technology. 12. Cybersecurity Challenges for Social, Ad-Hoc, and Sensor Networks in IoT. 13. Detection of DDoS Attacks Using Machine Learning in Network Environments. 14. Predictive Malware Detection in SDN-enabled Wireless Networks: A Machine Learning Approach. 15. Enhancing Healthcare Wireless Communication Security with Deep Learning-Driven IDS. 16. Blockchain-Powered Defense: Securing WSN Against DDoS Attacks with Decentralized Authentication. 17. An Analytical Study of Classification Techniques on Car Evaluation Data using Machine Learning Algorithms in IoT. 18. Deep Dive into Underwater Sensor Networks (UWSNs): Architecture, Protocols, Challenges, Applications, and Security.
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Wireless Communication;Mobile Ad hoc Networks;Flying ad hoc Networks;Underwater Sensor Networks;Machine Learning;Deep Learning