The 6th International Conference on Wireless, Intelligent and Distributed Environment for Communication: WIDECOM 2023 (Lecture Notes on Data Engineering and Communications Technologies, 185)

Welcome Message from WIDECOM 2023 General Chair
Welcome Message from the WIDECOM 2023 Program Chair
WIDECOM 2023 Organizing Committee
WIDECOM 2023 Keynote Talks
	Privacy, Security, and Safety Concerns for Consumer-Based Small-Scale Internet of Things
	Intent Based Networking and Intelligent Network Assurance in Next Generation Networks
Contents
An AI-Enabled Vehicle Surveillance System to Tracking Entrance, Exit, and Parking of Vehicles on the University of Technology –Jamaica, Papine Campus
	1 Introduction
	2 Related Work
	3 Methodology
	4 Results
	5 Conclusion
	References
Differential Evolution-Based Weighted Voting Stacking Ensemble Classifier for Highly Skewed Binary Data Distribution
	1 Introduction and Background
		1.1 Differential Evolution Algorithm
			1.1.1 Initial Population
			1.1.2 Mutation
			1.1.3 Recombination
			1.1.4 Selection
			1.1.5 Termination
	2 Research Questions and Objectives
		2.1 Research Questions
		2.2 Research Objectives
	3 Research Design
		3.1 Stacking Ensemble Method
		3.2 Differential Evolution Optimization Method
	4 Performance Evaluation
	5 Conclusion
	References
Toward a Lightweight Cryptographic Key Management System in IoT Sensor Networks
	1 Introduction
	2 Related Works
	3 A-Star Algorithm
		3.1 Preliminaries
		3.2 Description
	4 Network Model
		4.1 Assumption
		4.2 Energy Model
	5 Protocol Design
		5.1 Discovery Phase
		5.2 Operational Phase and Security Analysis
	6 Simulation and Discussion
	7 Conclusion
	References
Edge Clustering and Communication Efficiency with GNNs in Internet of Vehicles
	1 Introduction
	2 Related Work
		2.1 Traditional Intelligent Vehicular Networks
		2.2 Graph Neural Networks-Based Intelligent Vehicular Networks
	3 Problem Statement
	4 GNN-Based Vehicular Edge Clustering
		4.1 VANET Graphs
		4.2 GNN Architecture
		4.3 Clustering
	5 Experiments
		5.1 Data
		5.2 Settings
		5.3 Results
			5.3.1 Model Training Results
			5.3.2 Average Distance to Centroid
			5.3.3 Mean Squared Error and Sum of Squared Error
			5.3.4 Silhouette Score
			5.3.5 Davies–Bouldin Index
			5.3.6 Area Under the Curve and Average Precision
	6 Conclusion
	References
Discrete Planar Two-Watchtower Problem for k-Visibility
	1 Introduction
		1.1 Preliminaries
		1.2 Problem Definition
		1.3 Related Work
	2 Main Result
		2.1 Algorithm
	3 A Terrain Guarded by Two Watchtowers with 2-Visibility
	4 Conclusion and Future Work
	References
Towards Intra-cluster Data Prediction in IoT for Efficient Energy Consumption
	1 Introduction
	2 Related Work
	3 Energy Prediction Model for Data Aggregation
		3.1 Problem Statement
		3.2 Network Model
		3.3 Prediction and Data Transmission Approach
			3.3.1 Cluster Formation
			3.3.2 Election of the Cluster Head
			3.3.3 Predicting the Data to Be Sent
			3.3.4 Energy Model
			3.3.5 Intra-cluster Prediction
	4 Experimental Results
		4.1 Comparative Analysis
	5 Conclusion
	References
Location-Based Clustering Approach for Next-Hop Selection in Opportunistic Networks
	1 Introduction
	2 Related Works
	3 System Model
		3.1 Methods Used and Their Roles
	4 Simulation and Evaluation
	5 Conclusion
	References
Dungeons, Dragons, and Data Breaches: Analyzing AI Attacks on Various Network Configurations
	1 Introduction
	2 Background
		2.1 Reinforcement Learning
		2.2 Q-Learning
		2.3 Deep-Q-Learning
		2.4 Related Work
	3 Methodology
		3.1 Environments
			3.1.1 Chain Network
			3.1.2 Toy Network
			3.1.3 Custom Network
	4 Performance Evaluation Results
		4.1 Cumulative Reward per Epoch Generated by the Chain Network
		4.2 Cumulative Reward per Epoch Generated by the Toy Network
		4.3 Cumulative Reward per Epoch Generated by the Custom Network
		4.4 Average Time Generated by the Chain Network
		4.5 Average Time Generated by the Toy Network
		4.6 Average Time Generated by the Custom Network
	5 Conclusion
	References
Q-Learning-Based Underwater Sensor Networks Routing Protocol for Pollution Monitoring
	1 Introduction
		1.1 Organization
	2 Background
		2.1 Reinforcement Learning
		2.2 Q-LEARNING Technique
		2.3 Acoustic Communication
		2.4 Magnetic Induction Communication
	3 Proposed Q-Learning for UWSNs
	4 Performance Evaluation
	5 Conclusion
	References
Flood Forecasting in the Far-North Region of Cameroon: A Comparative Study of Machine Learning and Deep Learning Methods
	1 Introduction
		1.1 Background
		1.2 Contribution of the Authors
		1.3 Organization of the Paper
	2 Related Works
	3 ML and DL Models
		3.1 1D-CNN
		3.2 LSTM
		3.3 MLP
	4 Study Area and Data
		4.1 Study Area
		4.2 Data
	5 Design and Comparison of Models
		5.1 Methods
		5.2 Model Performance Evaluation
		5.3 Comparison of Models for Short-Term Flood Forecasting
		5.4 Comparison of Models for Long-Term Flood Forecasting
	6 Results and Discussion
	7 Conclusion
	References
A Multifactorial Approach to Explain Risk Features for Predicting Survival Rate of Heart Failure
	1 Introduction
	2 Related Works
	3 Descriptive Statistics on Clinical Heart Failure Dataset
	4 Feature Selection
	5 Conclusion
	References
Performance Evaluation of Data Stream Clustering Algorithm on Parameter Specification
	1 Introduction
	2 Clustering Techniques
		2.1 Partitioning-Based
		2.2 Hierarchical-Based Clustering
		2.3 Grid-Based Clustering
		2.4 Density-Based Clustering
		2.5 Model-Based Clustering
	3 Clustering Performance Metrics
	4 Massive Online Analysis Framework for Clustering
		4.1 Massive Online Analysis Graphical User Interface
		4.2 Massive Online Analysis Clustering
		4.3 Datasets
		4.4 Evaluation Platform Setup
		4.5 Experimental Approach
	5 Experimental Results
		5.1 DenStream with Ticked EvaluateMicroClustering on Synthetic Data
		5.2 DenStream with Ticked EvaluateMicroClustering on Real-World Datasets
		5.3 Discussion
	6 Conclusion
	References
Detecting DDoS Attacks in the Internet of Medical Things Through Machine Learning-Based Classification
	1 Introduction
	2 Related Works
	3 Descriptive Analysis on MedibotDDoS Dataset
	4 Experimental Evaluation
	5 Conclusion
	References
SmishShield: A Machine Learning-Based Smishing Detection System
	1 Introduction
	2 Related Works
	3 Methodology
		3.1 Data Collection
		3.2 Text Processing and Encoding
		3.3 Feature Extraction and Vectorizaton
		3.4 Model Training
			3.4.1 Test and Training Size
		3.5 Evaluation
			3.5.1 Accuracy
			3.5.2 Precision
			3.5.3 AUC
	4 Results and Discussion
		4.1 Message Length Analysis
		4.2 Relevant Features
		4.3 Model Performance
	5 Conclusion
	References
Index