The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems

Description

The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems discusses the recent, rapid development of Internet of things (IoT) and its focus on research in smart cities, especially on surveillance tracking systems in which computing devices are widely distributed and huge amounts of dynamic real-time data are collected and processed. Efficient surveillance tracking systems in the Big Data era require the capability of quickly abstracting useful information from the increasing amounts of data. Real-time information fusion is imperative and part of the challenge to mission critical surveillance tasks for various applications. This book presents all of these concepts, with a goal of creating automated IT systems that are capable of resolving problems without demanding human aid. The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems Copyright Contents List of Contributors 1 Reliable Surveillance Tracking System based on Software Defined Internet of Things 1.1 Introduction 1.2 Surveillance Tracking System 1.2.1 Classification of the Surveillance 1.2.1.1 Audio surveillance 1.2.1.2 Video surveillance 1.2.1.3 Internet surveillance 1.2.2 Applications 1.2.2.1 Corporate surveillance 1.2.2.2 Public health surveillance 1.2.2.3 Vehicular surveillance 1.2.3 Challenges 1.2.3.1 Dynamic processing 1.2.3.2 Visual processing 1.2.3.3 Data management 1.2.3.4 Security and privacy 1.3 Wireless Communication Technologies 1.4 Software Defined Networking 1.5 Software Defined Surveillance Tracking System 1.5.1 Traffic Engineering 1.5.2 Proposed Traffic Engineering Framework 1.6 Conclusion References 2 An Efficient Provably Secure Identity-Based Authenticated Key Agreement Scheme for Intervehicular Ad Hoc Networks 2.1 Introduction 2.1.1 Related Work 2.2 Preliminaries 2.2.1 Hardness Assumptions 2.2.2 Desirable Security Attributes of Authenticated Key Agreement Protocols 2.3 Security Model 2.3.1 Participants 2.3.2 Session 2.3.3 Adversary 2.3.4 Fresh Session 2.3.5 Security Experiment 2.3.6 Definition 1 (eCK Security of Identity-Based Authenticated Key Agreement Protocol) 2.4 Provably Secure Identity-Based Authenticated Key Agreement Protocol for V2V Communications 2.4.1 Setup Phase 2.4.2 Entity Registration Phase 2.4.3 Key Agreement Phase 2.5 Security Analysis 2.5.1 Event W ^ F1a 2.5.1.1 Simulation 2.5.2 Event W ^ F1b 2.5.3 Event W ^ F2a 2.5.4 Event W ^ F2b 2.5.5 Event W ^ F2c 2.5.6 Event W ^ F2d 2.6 Analysis of Dang et al.’s Identity-Based Authenticated Key Agreement Protocol 2.6.1 Key Compromise Impersonation Attack Against Dang et al.’s Protocol 2.6.2 Flaws in the Security Proof 2.7 Efficiency Analysis 2.8 Conclusion Acknowledgment References 3 Dynamic Self-Aware Task Assignment Algorithm for an Internet of Things-Based Wireless Surveillance System 3.1 Introduction 3.2 Related Works 3.2.1 Factors Affecting the Wireless Surveillance System 3.3 Self-Aware Dynamic Task Assignment Algorithm 3.3.1 Wireless Surveillance System Framework 3.3.2 Technique for Order of Preference by Similarity to Ideal Solution 3.3.3 Self-Aware Dynamic Task Assignment 3.4 Simulation Analysis and Results 3.4.1 Simulation Setup 3.4.2 Bandwidth Analysis 3.4.3 Energy Consumption 3.5 Conclusion References 4 Smart Vehicle Monitoring and Tracking System Powered by Active Radio Frequency Identification and Internet of Things 4.1 Related Works 4.2 Need for Smart Vehicle Monitoring System 4.3 Design of Smart Vehicle Monitoring System 4.4 Evaluation of SVM-ARFIoT 4.5 Conclusion References 5 An Efficient Framework for Object Tracking in Video Surveillance 5.1 Introduction 5.1.1 Objectives 5.2 Related Works 5.3 Proposed Work 5.4 Proposed Phases 5.4.1 Preprocessing 5.4.2 Object Detection 5.4.3 Feature Extraction 5.4.4 Object Segmentation 5.4.5 Object Tracking 5.5 Results and Discussions 5.5.1 Analysis Parameters 5.5.1.1 Precision 5.5.1.2 Recall 5.5.1.3 F-Measure(F) 5.5.1.4 Success and failure rate 5.6 Conclusion Acknowledgment References Further Reading 6 Development of Efficient Swarm Intelligence Algorithm for Simulating Two-Dimensional Orthomosaic for Terrain Mapping Usin... 6.1 Introduction 6.2 Literature Review 6.2.1 Efficient Three-Dimensional Placement of a Unmanned Aerial Vehicle Using Particle Swarm Optimization 6.2.2 API Development for Cooperative Airborne-Based Sense and Avoid in Unmanned Aircraft System 6.2.3 Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods 6.2.4 Flocking Algorithm for Autonomous Flying Robots 6.2.5 A Ground Control Station for a Multiunmanned Aerial Vehicle Surveillance System 6.2.6 Multiunmanned Aerial Vehicle Control With the Paparazzi System 6.3 Related Works 6.3.1 Cooperative Unmanned Aerial Vehicle Methods 6.3.2 Path Planning 6.3.3 Collision Avoidance 6.4 Proposed Architecture 6.4.1 DroneKit-Python 6.4.1.1 Installation 6.4.2 DroneKit-Python Software in the Loop 6.4.2.1 Installation 6.4.2.2 Running software in the loop 6.4.3 MAVLink 6.4.4 ArduPilot 6.4.5 Mission Planner 6.4.6 Two-Dimensional Orthomosaics 6.5 Simulation of the DroneKit Software in the Loop 6.6 Collision Avoidance and Path Planning 6.7 Applications 6.8 Conclusion Further Reading 7 Trends of Sound Event Recognition in Audio Surveillance: A Recent Review and Study 7.1 Introduction 7.2 Nature of Sound Event Data 7.2.1 Nature of Data 7.3 Feature Extraction Techniques 7.3.1 Feature Selection 7.3.2 Feature Extraction 7.4 Sound Event Recognition Techniques 7.4.1 Nonprobabilistic Linear Classifier 7.4.1.1 Support vector machines 7.4.1.2 Hidden-Markov model 7.4.2 Deep Learning Methodologies 7.4.2.1 Neural networks 7.4.2.2 Convolutional neural networks 7.4.2.3 Recurrent neural network 7.5 Experimentation and Performance Analysis 7.5.1 Data Set 7.5.2 Comparative Study on Related Work 7.6 Future Directions and Conclusion References Further Reading 8 Object Classification of Remote Sensing Image Using Deep Convolutional Neural Network 8.1 Introduction 8.2 Related Works 8.3 VGG-16 Deep Convolutional Neural Network Model 8.4 Data Set Description 8.5 Experimental Results and Analysis 8.5.1 Classification of Results for Various Hyperparameters 8.6 Conclusion References 9 Compressive Sensing-Aided Collision Avoidance System 9.1 Introduction 9.2 Theoretical Background 9.2.1 Sparsity 9.2.2 Compressed Sensing Problem Statement 9.2.3 Recovery 9.2.4 Quality Measurement 9.3 System 9.3.1 Signal Acquisition 9.3.2 Image Processing 9.3.3 Analysis 9.4 Result 9.5 Conclusion References 10 Review of Intellectual Video Surveillance Through Internet of Things 10.1 Introduction 10.1.1 Internet of Things Environmental Taxonomy 10.2 Video Surveillance—Internet of Things 10.2.1 Sensing and Monitoring 10.2.1.1 Sensor-based motion detection 10.2.1.1.1 Discrete sensing platform 10.2.1.1.2 Collaborative sensing platform 10.2.1.1.3 Wearable body sensors 10.2.1.2 Algorithm-based motion detection 10.2.1.3 Intelligent front-end devices 10.2.2 Internet of Things Data Analytics 10.2.3 Communication 10.2.3.1 Short-range communication 10.2.3.2 Medium-range communication 10.2.3.3 Long-range communication 10.2.4 Data Warehousing 10.2.4.1 Cloud 10.2.4.2 Fog and edge 10.2.4.3 Hybrid technologies 10.2.5 Application-Oriented Design 10.3 Conclusion References 11 Violence Detection in Automated Video Surveillance: Recent Trends and Comparative Studies 11.1 Introduction 11.2 Feature Descriptors 11.2.1 Histogram of Oriented Gradients 11.2.2 Space–Time Interest Points 11.2.3 Histogram of Oriented Optical Flow 11.2.4 Violence Flow Descriptor 11.3 Modeling Techniques 11.3.1 Supervised Models 11.3.1.1 Shallow models 11.3.1.1.1 Support vector machine 11.3.1.2 Deep models 11.3.1.2.1 Artificial neural networks 11.3.1.2.2 Convolutional neural networks 11.3.1.2.3 Long short-term memory 11.3.2 Unsupervised Models 11.3.2.1 Shallow models 11.3.2.1.1 Principal component analysis 11.3.2.2 Deep models 11.3.2.2.1 Generative adversarial network 11.3.2.2.2 Autoencoders Convolutional autoencoder 3D Autoencoder 11.4 Experimental Study and Result Analysis 11.4.1 Data Sets 11.4.2 Comparative Study on Related Work 11.4.3 Our Baseline Study 11.5 Conclusion References 12 FPGA-Based Detection and Tracking System for Surveillance Camera 12.1 Introduction 12.2 Prior Research 12.3 Surveillance System Tasks and Challenges 12.4 Methodology 12.5 Conclusion References Further Reading Index