Wireless Edge Computing in Internet of Everything - Proceedings of ICWCIE 2024

This book will showcase a wide array of applications where wireless edge computing plays a pivotal role, including smart cities, industrial IoT, and healthcare. It will also cover the integration of edge devices with cloud services, addressing scalability and interoperability challenges. This book will introduce readers to the fundamental concepts, technologies, and applications of wireless edge computing in the context of the evolving IoE landscape and will be a comprehensive guide for researchers who are willing to explore the intersection of edge computing and the Internet of Everything (IoE), focusing on wireless communication technologies.

Sommario

Energy Efficient Sleep Awake Aware (EESAA) Routing Protocols and Classifiers for Clone Node Detection (CND) in WSN.- AIoT Based Virtual FENSOR for Farm Field.- Deployment of Mobile Sensor Nodes for Enhancement of Coverage-Area through ANN approach in Wireless Sensor Networks.- QOS Aware Secure Cluster Based Routing for Wireless Sensor Networks Using a Multi Objective-Trust Centric Artificial Algae Algorithm.- Windows Sensor Driver Communication: An Analysis of Sensor Data Transmission to Sensor Interface Applications.- Enhancing Trolley Shopping with IoT and Computer Vision: A Secure Solution.- Plant Pest Detection and Identification Using Deep Learning Techniques.- Precision Agriculture: Soil Nutrient Analysis and Crop-Recommendation using IoT.- Leveraging Internet of Things for Accurate Load Forecasting in Power Systems: Opportunities, Challenges and Applications.- Superpixel based Regularized Convolutional Neural Network for Road Extraction in Satellite Internet of Things.- Internet of Things based Wheat Disease Detection and Prediction using Wasserstein Generative Adversarial Network with Gradient Penalty.- Temporal Attention Layer based Long Short-Term Memory for Diabetes Prediction in Internet of Things.- Big Data Collaboration with Block-chain Network in Internet of Things Edge Devices.- Blockchain based Internet of Things for Healthcare Systems using Optimized Blowfish Algorithm.- Hierarchical Multi-Class Classification with Binomial Logistic Regression for Abnormal Attacks Detection in Software-Defined Networking.- Distributed Denial of Service Anomaly Detection in Software Defined Networks using an Ensemble of Deep Learning Methods.- ACO-EERA: Ant Colony Optimization Integrated Energy Efficient Resource Allocation based on Software-Defined Networking.- Intrusion Detection System in Mobile Internet of Things using Deep Learning with Temporal Patterns.- Network Anomaly classification using Long Short Term Memory Networks in Cyber Physical Systems.- Feature Selection Using Modified Bald Eagle Search Algorithm for Anomaly Detection in Hydraulic Systems.- Battle Royale Optimization based Energy Efficient Routing in Internet of Things based Wireless Sensor Network.- ECRAR: Mean Differential Variation based Dung Beetle Optimization for Internet of Things Networks.- Task and Resource Allocation in IoT based on Improved Particle Swarm Optimization with Simulated Annealing.- Energy Efficiency Resource Allocation for Internet of Things Networks using Joint Optimization Algorithm.- Enhanced Pelican Optimization Algorithm Based Pilot Insertion for Channel Estimation in MIMO-OFDM.- PAPR Reduction Using Enhanced Fireworks Algorithm in MIMO-OFDM Systems.- Energy Efficient Cell-Free Massive MIMO Networks with Mixed-Integer Second-Order Cone Program and Load Balancing Optimization.- PAPR Reduction in OFDM Systems using Hybrid Iterative Clipping and Filtering with Alternating -Law Companding.- Optimized Pilot Insertion and Long Short-Term Memory Model based Channel State Estimation in MIMO-OFDM Systems.- Polar Coding Integrated Deep Learning for Estimating Channel in MIMO-OFDM System.- MIMO Channel Estimation Using Deep Learning with Hybrid Optimization Algorithm.- Complex Deep Learning approach for Detecting Signals in OFDM-IM Systems.- Automatic Modulation Classification using Deep Learning for Cognitive Radio Networks.- Bidirectional Long Short Term Memory with Recurrent Neural Network based Spectrum Sensing Technique in Cognitive Radio System.- Hybrid Jarratt and Butterfly Optimization-based Approach for Maximizing Throughput in Energy-Harvesting Cognitive Radio Networks.- Energy Efficient Resource Allocation in Cognitive Radio Networks using Self-Adaptive Harris Hawks Optimization Algorithm.- Predicting Network Traffic in Wireless Mesh Networks using Multi Head Attention with Long Short-Term Memory.- Anomaly Detection in Wireless Sensor Networks using Self Adaptive- Weighted Kernel Ridge Regression Approach.- Energy

Info autore

Durbadal Mandal received his B.E. degree in Electronics and Communication Engineering from Regional Engineering College, Durgapur, West Bengal, India, in 1996. He obtained his M.Tech. and Ph.D. degrees from the National Institute of Technology (NIT) Durgapur, West Bengal, India, in 2008 and 2011, respectively. He is currently serving as an Associate Professor in the Department of Electronics and Communication Engineering at NIT Durgapur. His research interests include array antenna design and digital filter optimization using evolutionary computing techniques. He has published more than 350 research papers in international journals and conferences. He has supervised fifteen Ph.D. scholars, and six more are currently pursuing their Ph.D. under his guidance. He regularly serves as a reviewer for several international journals.
M. N. Eshwarappa graduated with a degree in Electrical and Electronics Engineering from Malnad College of Engineering, Hassan (Mysore University), in 1991. He earned his M.Tech. in Industrial Electronics from NITK Surathkal (Mangalore University) in 1998 and completed his Ph.D. in Signal Processing from VTU-Belagavi in April 2013. He began his academic career as a Guest Lecturer at MCE-Hassan (1991–1992), followed by roles as Lecturer and later Assistant Professor at KIT-Tiptur (1992–2005). From 2005 to 2013, he served as Assistant Professor at SSIT-Tumkur and was promoted to Professor in August 2013. Between 2014 and 2016, he held the positions of Professor and Head of the ECE Department, as well as Principal at SIET-Tumkur. Since February 2016, he has been a Professor in the ECE Department at SSIT-Tumkur and has been serving as Head of the Department since February 2023.
Jaroslav Frnda received his M.Sc. and Ph.D. degrees from the Department of Telecommunications at VSB—Technical University of Ostrava, Czech Republic, in 2013 and 2018, respectively. He is currently an Associate Professor at the University of Žilina in Slovakia. He has authored or co-authored over 85 journal articles. His research interests include the quality of multimedia services in IP networks, data analysis, and machine learning algorithms. He has participated in three national research projects co-funded by the European Union. In 2022, he was a finalist in the “Outstanding Scientist Under 35” category of the ESET Science Award.

Riassunto

This book will showcase a wide array of applications where wireless edge computing plays a pivotal role, including smart cities, industrial IoT, and healthcare. It will also cover the integration of edge devices with cloud services, addressing scalability and interoperability challenges. This book will introduce readers to the fundamental concepts, technologies, and applications of wireless edge computing in the context of the evolving IoE landscape and will be a comprehensive guide for researchers who are willing to explore the intersection of edge computing and the Internet of Everything (IoE), focusing on wireless communication technologies.

Dettagli sul prodotto

• Con la collaborazione di: M. N. Eshwarappa (Editore), Jaroslav Frnda (Editore), Durbadal Mandal (Editore), M N Eshwarappa (Editore)
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 19.10.2025
• EAN: 9789819507870
• ISBN: 978-981-9507-87-0
• Pagine: 549
• Illustrazioni: VIII, 549 p. 139 illus., 119 illus. in color.
• Serie: Lecture Notes in Electrical Engineering
• Categorie: Scienze naturali, medicina, informatica, tecnica > Tecnica > Elettronica, elettrotecnica, telecomunicazioni; Nachrichtententechnik, Telekommunikation, Internet of things, Computernetzwerke und maschinelle Kommunikation, Communications Engineering, Networks, Wireless and Mobile Communication, privacy and security, Internet of Everything (IoE), quality-of-service (QoS), Wireless edge computing (WEC)