This book outlines the promise of the field of the Cognitive Internet of Things when it is applied to cognitive buildings. After an introduction, the authors discuss the goals of cognitive buildings such as operation in a more efficient, flexible, interactive, intuitive, and sustainable way. They go on to outline the benefits that these technologies promise to building owners, occupants, and their environments that range from reducing energy consumption and carbon footprint to promoting health, well-being, and productivity. The authors outline technologies that provide buildings and equipment with the ability to collect, aggregate, and analyze data and how this information can be collected by sensors and related to internal conditions and settings, energy consumption, user requests, and preferences to maintain comfort and save energy. This book is of interest to practitioners, researchers, students, and professors in IoT and smart cities.
Author(s): Franco Cicirelli, Antonio Guerrieri, Andrea Vinci, Giandomenico Spezzano
Series: Internet of Things
Publisher: Springer
Year: 2022
Language: English
Pages: 353
City: Cham
Preface
Contents
1 COGITO: A Platform for Developing Cognitive Environments
1.1 Introduction
1.2 The COGITO Platform
1.2.1 An Overview of the Platform
1.2.2 Developing an Application Over the COGITO Platform
1.3 Equipment Deployment
1.4 Cognitive Applications for Indoor Environments
1.4.1 Thermal Comfort
1.4.2 Occupancy Forecast
1.4.3 Air Quality
1.4.4 Smart Meeting Room
1.5 Cognitive Applications for Outdoor Environments
1.5.1 Smart Parking
1.5.2 Monitoring Weather Conditions
1.6 Conclusion
References
2 Cloud, Fog, and Edge Computing for IoT-Enabled Cognitive Buildings
2.1 Introduction
2.1.1 Background
2.2 Cloud Computing
2.2.1 Benefits of Cloud Computing
2.2.2 Cloud Computing Characteristics
2.2.3 Cloud Services Models
2.2.4 Cloud Deployment Models
2.2.5 Cloud Computing for Smart Building
2.3 Edge and Fog Computing
2.3.1 Edge Computing Characteristics
2.3.2 Fog Computing
2.3.2.1 Fog Computing Characteristics
2.3.2.2 Fog Computing Services and Deployment Models
2.3.3 Fog Computing Versus Edge Computing
2.3.4 Edge Computing Versus Cloud Computing
2.3.5 Edge and Fog Computing for Smart Buildings
2.4 IoT-Enabled Smart Buildings
2.4.1 IoT-Enabled Smart Building Model
2.4.2 IoT-Enabled Smart Building Components
2.4.3 IoT-Enabled Smart Building Design Framework
2.4.4 Sample IoT-Enabled Smart Building Scenarios
2.4.4.1 HVAC System
2.4.4.2 Health Monitoring System
2.4.4.3 Remote Monitoring System
2.5 Conclusion
References
3 Edge Caching in IoT Smart Environments: Benefits, Challenges, and Research Perspectives Toward 6G
3.1 Introduction
3.2 Content Distribution in Smart Environments
3.2.1 Peculiarities of IoT Contents and Devices
3.2.2 Benefits of Edge Caching
3.3 Conventional Edge Caching Designs
3.4 Disruptive Pervasive Edge Caching Solutions
3.4.1 NDN in a Nutshell
3.4.2 Popularity-Driven Approaches
3.4.3 Energy-Driven Solutions
3.4.4 Freshness-Driven Caching
3.4.4.1 Freshness-Based Only Solutions
3.4.4.2 Multi-Criteria Approaches
3.5 IoT Data Caching at the Edge Toward 6G
3.5.1 NDN and SDN Interplay
3.5.2 Joint Computing and Caching
3.5.3 AI-Based In-Network Caching Strategies
3.6 Conclusions
References
4 Needs Analysis, Protection, and Regulation of the Rights of Individuals and Communities for Urban and Residential Comfort in Cognitive Buildings
4.1 Introduction
4.2 New Technologies for Outdoor and Indoor Well-Being: The Legal Framework
4.3 Multiscalar Analysis of the City of Matera and the Demonstrators of the COGITO Project
4.4 An “Ideal” Technology: Reflections from Ethnographic Research
4.5 Need Assessment in the Design of a Cognitive System
4.6 Effectiveness of the GDPR in the Data Protection System Transformed by New Technologies
References
5 Real Case Studies Toward IoT-Based Cognitive Environments
5.1 Introduction
5.2 Wireless Sensor Networks and MANs for CoIoTEs
5.2.1 Wireless Sensor Networks
5.2.2 MAN
5.2.3 Applications and Design Challenges
5.3 Implementation of the Smart Street Network in the City of Cosenza
5.3.1 Description of the Communication Backbone
5.3.2 Implementation of the Final Solution
5.3.3 The Devices Involved in the Realization of the Smart Street
5.4 A VPN ``Hub and Spoke'' for Secure Interconnection of Geographically Distributed Sensor Networks
5.4.1 VPN Topology Overview
5.4.2 A Secure Sockets Layer VPN
5.4.3 Realization of the Peripheral Hub Nodes and the Master Server
5.5 Design and Implementation of an Intelligent Video Conferencing System for CoIoTEs
5.5.1 The Jitsi Video Conferencing System
5.5.2 A Web-Based File Manager
5.5.3 The Email Processing Component
5.6 Conclusions
References
6 Audio Analysis for Enhancing Security in Cognitive Environments Through AI on the Edge
6.1 Introduction
6.2 Approaches for Audio Recording
6.2.1 Array of Microphones
6.2.2 Recording Devices
6.3 Understanding Audio Recordings
6.4 AI in Audio Analysis
6.5 AI and Algorithms for Sample Normalization and Audio Understanding
6.6 Privacy Implications on Sensitive Data: Defining Minimum Information Content
6.7 Analyzing Hardware Devices
6.8 Reacting Based on the Information: Actuators
6.9 A Complete Implementation
6.10 Case Study
6.10.1 Free and Restricted Access Room
6.10.2 Residential Apartment
6.11 Further Improvements/Conclusions
References
7 Aggregate Programming for Customized Building Management and Users Preference Implementation
7.1 Introduction
7.2 Description of the Brescia Use Case
7.2.1 User Preferences and Feedback Collection
7.2.2 Sensor Integration Through IoT Paradigm
7.3 Aggregate Programming
7.4 Aggregate Programming for the Brescia Use Case
7.4.1 Users Localization
7.4.2 Porting Aggregate Programming to Embedded Systems
7.4.3 An AP Case Study Using RTLS
7.5 Simulation
7.6 Conclusions
References
8 IoT Control-Based Solar Shadings: Advanced Operating Strategy to Optimize Energy Savings and Visual Comfort
8.1 Introduction
8.2 Materials and Method
8.2.1 Sensors and Actuators
8.2.2 Operating Control Strategy for Venetian Blinds
8.2.2.1 Absence of Occupants
8.2.2.2 Presence of Occupants
8.3 Analysis of Results
8.3.1 Simulation Environment
8.3.2 Evaluation of Thermal Gains on an Hourly Basis
8.3.2.1 LED System
8.3.2.2 Fluorescent Lamps
8.3.3 Evaluation of Thermal Gains on a Monthly Basis
8.3.4 Evaluation of Annual Energy and Economic Savings
8.3.4.1 Energy Savings
8.3.4.2 Economic Savings
8.4 Conclusions
References
9 Room Occupancy Prediction Leveraging LSTM: An Approach for Cognitive and Self-Adapting Buildings
9.1 Introduction
9.2 Related Work
9.3 An Approach for Room Occupancy Prediction for Cognitive and Self-Adapting Building
9.3.1 Software Architecture
9.3.1.1 Components
9.3.1.2 Devices and Virtual Objects
9.3.1.3 Application Agents
9.3.2 Definition of the Prediction Tasks
9.3.3 Data Pre-processing
9.3.3.1 Transformation Approach for Non-homogeneous Time Series
9.3.4 Networks Training
9.3.4.1 LSTM Neural Network
9.3.5 Training
9.4 Experimental Results
9.4.1 Dataset
9.4.1.1 Dataset A: Occupancy Detection Dataset
9.4.1.2 Dataset B: Experimental Dataset
9.4.2 Evaluation Metrics
9.4.3 Imbalanced Classification Techniques
9.4.3.1 Focal Loss
9.4.3.2 Weight Balancing
9.4.4 Results
9.4.4.1 Task 1: Occupancy Detection
9.4.4.2 Task 2: Occupancy Prediction
9.5 Conclusion and Future Work
References
10 Edge Intelligence Against COVID-19: A Smart University Campus Case Study
10.1 Introduction
10.2 Background and Enabling Technologies
10.2.1 ACOSO-Meth
10.2.2 Uppaal
10.2.3 DHT11
10.2.4 Arduino Uno
10.2.5 QR Code
10.2.6 Raspberry Pi
10.2.7 Node-RED
10.2.8 MQTT (Message Queue Telemetry Transport)
10.2.9 Long Short-Term Memory (LSTM)
10.2.10 Docker
10.2.11 DigitalOcean
10.3 Related Works
10.3.1 Monitoring at the End-Device Layer
10.3.2 Monitoring at the Edge Layer
10.3.3 Monitoring at the Cloud Layer
10.4 Project Development
10.4.1 Analysis Phase
10.4.2 Design Phase
10.4.3 Verification and Validation
10.4.4 Implementation Phase
10.4.5 Deployment and Orchestration
10.5 Conclusions
References
11 Structural Health Monitoring in Cognitive Buildings
11.1 Introduction
11.2 Structural Monitoring Techniques
11.3 Cognitive Buildings
11.4 Case Study
11.5 Conclusions and Future Activities
References
12 Development of Indoor Smart Environments Leveraging the Internet of Things and Artificial Intelligence: A Case Study
12.1 Introduction
12.2 Related Work
12.3 Smart Management of Indoor Spaces
12.4 Smart Meeting Room Application Components
12.4.1 Smart Objects
12.4.2 Software Components
12.5 Management of the Conference System in Indoor Environments
12.5.1 Management of the Booking of the Smart Meeting Room
12.5.2 Event Management in the Pre and Start Phases
12.5.3 Event Management
12.6 Conclusion
References
13 Human-Centered Reinforcement Learning for Lighting and Blind Control in Cognitive Buildings
13.1 Introduction
13.2 Reinforcement Learning in Control Systems
13.3 A Human-Centered RL with a Satisfaction-Based Visual Comfort Model
13.4 An RL Model for the Management of the Visual Comfort
13.4.1 The State Variables
13.4.2 The Decision Variables
13.4.3 The Reward Function
13.4.4 Q-Learning
13.5 Case Study
13.6 Conclusions
References
14 Intelligent Load Scheduling in Cognitive Buildings: A Use Case
14.1 Introduction
14.2 Basic Concepts
14.2.1 The COGITO Platform
14.2.2 Reinforcement Learning
14.2.3 Markov Decision Process
14.2.4 The Load Scheduling
14.3 Integration Between the COGITO Platform and the Omnia Energia Equipment
14.4 The Case Study
14.4.1 The Case Study Equipment
14.4.2 The Functional Perspective
14.4.3 The Underpinning Software Infrastructure
14.4.4 Customization of the Omnia Meter
14.4.5 The Case Study Dashboard
14.5 Conclusion
References
15 Cognitive Systems for Energy Efficiency and Thermal Comfort in Smart Buildings
15.1 Introduction
15.2 Related Work
15.3 A DRL Model for the Management of Indoor Environments
15.3.1 Thermal Model
15.3.2 Behavioral Model
15.3.3 Objective Function and Reward
15.4 Experimental Results
15.5 Conclusions
References
Index
