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

✍ Scribed by Dinesh Peter (editor), Amir H Alavi (editor), Bahman Javadi (editor), Steven L. Fernandes (editor)

• Publisher: Academic Press
• Year: 2020
• Tongue: English
• Leaves: 198
• Series: Intelligent Data-Centric Systems: Sensor Collected Intelligence
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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