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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
AMBIENT INTELLIGENCE AND INTERNET OF THINGS
The book explores long-term implementation techniques and research paths of ambient intelligence and the Internet of Things that meet the design and application requirements of a variety of modern and real-time applications.
Working environments based on the emerging technologies of ambient intelligence (AmI) and the Internet of Things (IoT) are available for current and future use in the diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them to make our lives easier. It is asserted that Am I can successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques and that it will transparently and proactively change the environment to conform to the requirements of the user. Over time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.
The 13 chapters in Ambient Intelligence and Internet of Things: Convergent Technologies provide a comprehensive knowledge of the fundamental structure of innovative cutting-edge AmI and IoT technologies as well as practical applications.
Audience
The book will appeal to researchers, industry engineers, and students in artificial and ambient intelligence, the Internet of Things, intelligent systems, electronics and communication, electronics instrumentations, and computer science.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 Ambient Intelligence and Internet of Things: An Overview
1.1 Introduction
1.2 Ambient Intelligent System
1.3 Characteristics of AmI Systems
1.4 Driving Force for Ambient Computing
1.5 Ambient Intelligence Contributing Technologies
1.6 Architecture Overview
1.7 The Internet of Things
1.8 IoT as the New Revolution
1.9 IoT Challenges
1.10 Role of Artificial Intelligence in the Internet of Things (IoT)
1.11 IoT in Various Domains
1.12 Healthcare
1.13 Home Automation
1.14 Smart City
1.15 Security
1.16 Industry
1.17 Education
1.18 Agriculture
1.19 Tourism
1.20 Environment Monitoring
1.21 Manufacturing and Retail
1.22 Logistics
1.23 Conclusion
References
2 An Overview of Internet of Things Related Protocols, Technologies, Challenges and Application
2.1 Introduction
2.2 Messaging Protocols
2.3 Enabling Technologies
2.4 IoT Architecture
2.5 Applications Area
2.6 Challenges and Security Issues
2.7 Conclusion
References
3 Ambient Intelligence Health Services Using IoT
3.1 Introduction
3.2 Background of AML
3.3 AmI Future
3.4 Applications of Ambient Intelligence
3.5 COVID-19
3.6 Coronavirus Worldwide
3.7 Proposed Framework for COVID-19
3.8 Hardware and Software
3.9 Mini Breadboard
3.10 Node MCU
3.11 Advantages
3.12 Conclusion
References
4 Security in Ambient Intelligence and Internet of Things
4.1 Introduction
4.2 Research Areas
4.3 Security Threats and Requirements
4.4 Security Threats in Existing Routing Protocols that are Designed With No Focus on Security in AmI and IoT Based on Sensor Networks
4.5 Protocols Designed for Security Keeping Focus on Security at Design Time for AmI and IoT Based on Sensor Network
4.6 Introducing Hybrid Model in Military Application for Enhanced Security
4.7 Conclusion
References
5 Futuristic AI Convergence of Megatrends: IoT and Cloud Computing
5.1 Introduction
5.2 Methodology
5.3 Artificial Intelligence of Things
5.4 AI Transforming Cloud Computing
5.5 Conclusion
References
6 Analysis of Internet of Things Acceptance Dimensions in Hospitals
6.1 Introduction
6.2 Literature Review
6.3 Research Methodology
6.4 Data Analysis
6.5 Discussion
6.6 Conclusion
References
7 Role of IoT in Sustainable Healthcare Systems
7.1 Introduction
7.2 Basic Structure of IoT Implementation in the Healthcare Field
7.3 Different Technologies of IoT for the Healthcare Systems
7.4 Applications and Examples of IoT in the Healthcare Systems
7.5 Companies Associated With IoT and Healthcare Sector Worldwide
7.6 Conclusion and Future Enhancement in the Healthcare System With IoT
References
8 Fog Computing Paradigm for Internet of Things Applications
8.1 Introduction
8.2 Challenges
8.3 Fog Computing: The Emerging Era of Computing Paradigm
8.4 Related Work
8.5 Fog Computing Challenges
8.6 Fog Supported IoT Applications
8.7 Summary and Conclusion
References
9 Application of Internet of Things in Marketing Management
9.1 Introduction
9.2 Literature Review
9.3 Research Methodology
9.4 Discussion
9.5 Results
9.6 Conclusions
References
10 Healthcare Internet of Things: A New Revolution
10.1 Introduction
10.2 Healthcare IoT Architecture (IoT)
10.3 Healthcare IoT Technologies
10.4 Community-Based Healthcare Services
10.5 Cognitive Computation
10.6 Adverse Drug Reaction
10.7 Blockchain
10.8 Child Health Information
10.9 Growth in Healthcare IoT
10.10 Benefits of IoT in Healthcare
10.11 Conclusion
References
11 Detection-Based Visual Object Tracking Based on Enhanced YOLO-Lite and LSTM
11.1 Introduction
11.2 Related Work
11.3 Proposed Approach
11.4 Evaluation Metrics
11.5 Experimental Results and Discussion
11.6 Conclusion
References
12 Introduction to AmI and IoT
12.1 Introduction
12.2 AmI and the IoT and Environmental and Societal Sustainability: Dangers, Challenges, and Underpinnings
12.3 Role of AmI and the IoT as New I.C.T.s to Conservational and Social Sustainability
12.4 The Environmental Influences of AmI and the IoT Technology
12.5 Conclusion
References
13 Design of Optimum Construction Site Management Architecture: A Quality Perspective Using Machine Learning Approach
13.1 Introduction
13.2 Literature Review
13.3 Proposed Construction Management Model Based on Machine Learning
13.4 Comparative Analysis
13.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 CoAP techniques.
Chapter 3
Table 3.1 Ambient Intelligence using Sensitive (S), Responsive (R) , Adaptive ...
Table 3.2 Worldwide COVID-19 stats till June 2021.
Table 3.3 Specification of Node MCU.
Chapter 4
Table 4.1 Techniques and approaches for routing and their impact on security.
Table 4.2 Fuzzy-logic in clustering sensor nodes.
Chapter 5
Table 5.1 Highlights of some of the most well-known AI and IoT techniques.
Table 5.2 A brief description of the mentioned AI and Cloud Computing popular ...
Chapter 6
Table 6.1 Demographics of the respondents.
Table 6.2 Parameters of factor analysis, Cronbach alpha, CR, and AVE.
Table 6.3 Model fit measures for the CFA for the TC, OC, and EC context.
Table 6.4 Discriminant validity matrix for TC, OC, EC.
Table 6.5 Path analysis results for TC, OC, and EC.
Chapter 8
Table 8.1 Cloud v/s fog computing.
Table 8.2 Description of layers in fog computing architecture.
Table 8.3 Related research works.
Table 8.4 Description of cryptographic attacks.
Chapter 9
Table 9.1 IoT System applications in enterprise [20].
Table 9.2 Benefits of IoT in BPM.
Table 9.3 Benefits of IoT for PA and RA for MMgnt [5].
Table 9.4 Benefits of AmI in MMgnt [40].
Table 9.5 Research proposals with their descriptions.
Table 9.6 Development of sectors with IoT applications [4].
Table 9.7 IoT favoring SCo for costs and risk [12, 13].
Table 9.8 Factors affecting the shape and length of PLC [8, 19].
Table 9.9 Types of Internet-based model BPM [16].
Chapter 11
Table 11.1 Model parameters of YOLO-Lite.
Table 11.2 Precision and recall-based evaluation of EYL for object detection.
Table 11.3 The processing speed of various models on OTB-2015.
Table 11.4 Comparison of AUC scores of various trackers with the proposed fram...
Table 11.5 Comparison between various trackers with proposed tracker based on ...
Table 11.6 Performance comparison of the proposed tracker with another tracker...
Chapter 13
Table 13.1 Constructed area v/s customer feedback.
Table 13.2 Cost efficiency v/s constructed area.
Table 13.3 Deadline overshoot vs constructed area.
List of Illustrations
Chapter 1
Figure 1.1 Ambience intelligence system.
Figure 1.2 Ambience intelligence architecture.
Figure 1.3 Different sources of data growth (https://www.slideshare.net/bjorna...
Figure 1.4 IoT applications.
Chapter 2
Figure 2.1 Various application domains of IoT.
Figure 2.2 Characteristics of IoT-enabled environment.
Figure 2.3 MQTT publish-subscribe model.
Figure 2.4 A wireless sensor network model.
Figure 2.5 Cloud computing-enabled IoT.
Figure 2.6 Cloud computing in IoT.
Figure 2.7 Four-layered architecture of IoT.
Figure 2.8 Perception layer.
Figure 2.9 Network layer.
Figure 2.10 Data management layer.
Figure 2.11 Application layer.
Figure 2.12 IoT application areas.
Chapter 3
Figure 3.1 Association of AML with existing technologies.
Figure 3.2 AmI features.
Figure 3.3 AML solution.
Figure 3.4 Camera sensor used to measure social distancing.
Figure 3.5 Thermographic cameras used for finding the temperature of the patie...
Figure 3.6 Corona virus.
Figure 3.7 COVID-19 tests, confirmed cases, and death.
Figure 3.8 Confirmed COVID deaths till June 2021.
Figure 3.9 Cumulative confirmed COVID-19 deaths and cases, world.
Figure 3.10 Proposed framework.
Figure 3.11 Heartbeat sensor.
Figure 3.12 Temperature sensor.
Figure 3.13 Blood pressure sensor module.
Figure 3.14 Mini breadboard.
Figure 3.15 Node MCU board.
Chapter 4
Figure 4.1 Classification of routing techniques in the network.
Figure 4.2 System architecture [76].
Figure 4.3 Comparisons on throughput [76].
Figure 4.4 Comparative analysis on P.D.R. [76].
Figure 4.5 Consumption of energy comparative analysis [76].
Figure 4.6 Malicious nodes 10 and 19 are found correctly.
Chapter 5
Figure 5.1 Problem and factors why human intelligence seems comparatively less...
Figure 5.2 Futuristic AI—convergence of megatrends: cloud computing and IoT.
Figure 5.3 Current and future scenarios of AI-based technology.
Figure 5.4 Flow for obtaining final set for writing review.
Figure 5.5 Number of articles in diverse fields covered by the database.
Figure 5.6 Percentage of papers included in various fields by IoT and cloud co...
Figure 5.7 Application areas of IoT technologies.
Figure 5.8 Application areas of cloud computing.
Chapter 6
Figure 6.1 CFA for the latent variables for the TC context.
Figure 6.2 CFA for the latent variables for the OC context.
Figure 6.3 CFA for the latent variables for the EC context.
Figure 6.4 Final measurement model for TC context.
Figure 6.5 Final measurement model for OC context.
Figure 6.6 Final measurement model for EC context.
Chapter 7
Figure 7.1 Entities of IoT used for various purposes in the healthcare system.
Figure 7.2 Application and examples of IoT in healthcare systems.
Figure 7.3 Overview of a healthcare system.
Figure 7.4 (a) Heart monitoring band. (b) Complete fitness belt. (c) Hand move...
Figure 7.5 Structure diagram of the system.
Figure 7.6 (a) Front and backside view of a pulse rate sensor. (b) PPG-based p...
Figure 7.7 Breathing process cycle in a human being.
Figure 7.8 IoT-based BT measurement.
Figure 7.9 IoT-based BP measurement system.
Figure 7.10 Blood oxygen measurement.
Chapter 8
Figure 8.1 Abstract view of IoT.
Figure 8.2 Growth of IoT.
Figure 8.3 Generic architecture of IoT.
Figure 8.4 IoT protocol stack.
Figure 8.5 Current cloud computing model.
Figure 8.6 Fog computing architecture.
Figure 8.7 Fog helps to address new IoT challenges.
Figure 8.8 Fog nodes.
Figure 8.9 Open fog consortium.
Figure 8.10 Fog-supported IoT application.
Chapter 9
Figure 9.1 Transformation from TP to SP [2].
Figure 9.2 IoT system [3, 4, 12].
Figure 9.3 Scope of CRM [10].
Figure 9.4 PLC phases in terms of time and revenue with their descriptions [18...
Figure 9.5 Business process management (BPM) with five steps [7, 11].
Figure 9.6 Ambient intelligence [12].
Figure 9.7 Benefits of IoT and CRM on firms functionality with their descripti...
Figure 9.8 Changes in medium of communication [2].
Figure 9.9 Criteria for IoTP for PDS [22].
Figure 9.10 Shapes of PLC in IoT environment [18].
Chapter 10
Figure 10.1 Architecture of healthcare IoT.
Figure 10.2 Different healthcare IoT technologies.
Figure 10.3 Smart watch.
Figure 10.4 Wearables for health monitoring [91, 92].
Figure 10.5 TV control using wearables [93].
Figure 10.6 Intel Quark X1000 SoC.
Figure 10.7 IoT-Communication using RFID.
Figure 10.8 IoT communication using bluetooth.
Figure 10.9 IoT-Communication using ZigBee.
Figure 10.10 IoT-Communication using Wi-Fi.
Figure 10.11 (a) HIoT—direct satellite communication. (b) HIoT—satellite via g...
Figure 10.12 Rapid growth in healthcare IoT (reconstructed from [90]).
Chapter 11
Figure 11.1 Enhanced YOLO-Lite module.
Figure 11.2 Traditional spatial pyramid pooling network.
Figure 11.3 Hybrid spatial pyramid pooling network.
Figure 11.4 A single cell of the LSTM network.
Figure 11.5 Block diagram of the proposed framework.
Figure 11.6 Tracking results using the proposed tracker on a few frames of OTB...
Chapter 13
Figure 13.1 Construction management flow [20].
Figure 13.2 The overall flow of the proposed system.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Ambient Intelligence and Internet of Things
Convergent Technologies
Edited by
Md Rashid MahmoodRohit RajaHarpreet KaurSandeep KumarandKapil Kumar Nagwanshi
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-82123-6
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
Working environments based on the emerging technologies of ambient intelligence (AmI) and the internet of things (IoT) are available for current and future use in a diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them for the purpose of making our lives easier. It is asserted that AmI is able to successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques, and that it will transparently and proactively change the environment to conform to the requirements of the user. Over a long period of time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.
Ambient Intelligence and Internet of Things: Convergent Technologies provides comprehensive knowledge of AmI and the IoT along with practical applications. Since this book focuses on the fundamental structure of innovative cutting-edge AmI and IoT technologies, it will be of interest and use to students, academicians, researchers and industry professionals in the domain of AI, AmI and IoT. It will be a better option compared to the majority of books that are now available on the market because older publications rarely touch on contemporary applications of AmI and IoT.
We would like to thank all of the contributing authors who made a significant contribution to the creation of this peer-reviewed edited volume by giving of their time, effort, and insightful recommendations. The editors are also thankful to Scrivener Publishing and their team members for the opportunity to publish this volume. Lastly, we thank our family members for their love, support, encouragement, and patience during the entire period of this work.
Dr. Md Rashid Mahmood
Dr. Rohit Raja
Dr. Harpreet Kaur
Dr. Sandeep Kumar
Dr. Kapil Kumar Nagwanshi
October 2022
1Ambient Intelligence and Internet of Things: An Overview
Md Rashid Mahmood1*, Harpreet Kaur1, Manpreet Kaur1, Rohit Raja2 and Imran Ahmed Khan3
1Department of ECE, Guru Nanak Institutions Technical Campus, Hyderabad, India
2Department of IT, Guru Ghasidas Vishwavidyala, Bilaspur, India
3Department of ECE, Jamia Millia Islamia, New Delhi, India
Abstract
Ambient intelligence (AmI) is the ability of technology to make judgments and act on our behalf. AmI is a cutting-edge technology that has the potential to fundamentally alter the way we interact with machines and electronics in our environment. It does not ask the user questions but rather understands the context in which the user is operating. Ambient intelligence (AmI) uses sensors and devices in our homes and offices to gather information about the environment. The AmI system then makes inferences based on proximity, intent, and behavioral patterns. It reacts to the user via a smart device’s elegantly built natural interface. The Internet of Things (IoT) is a network of web-connected smart gadgets that collect data from their surroundings and use it to make decisions about their own lives. Ambient intelligence refers to what occurs when various devices connect, and more specifically, what they learn from one another. Ambient computing is a new kind of relationship between computers and employees. It gathers information for us when we ask for it, or even before we ask. Ambient intelligence aims to improve the way people and their environment interact with one another. Ambient intelligence (AI) is a subset of artificial intelligence (AI). Artificial intelligence mimics human cognitive processes such as perceiving, interpreting, and learning, among others. AmI is interlinked with the Internet of Things (IoT).
Keywords: Ambient intelligence, Internet of Things, artificial intelligence, human computer interaction
1.1 Introduction
Ambient intelligence, often known as AmI, is the ability of technology to make judgments and act on our behalf while taking our preferences into consideration depending on the data accessible to it from all of the linked sensors and devices surrounding the user. AmI is a highly intelligent, widespread, and intuitive system. It does not ask the user questions but rather understands the context in which the user is operating. It does not make its physical presence known but instead performs actions that are suited to the user’s needs. AmI is a cutting-edge technology that has the potential to fundamentally alter the way we interact with the machines and electronics in our environment. Ambient intelligence (AmI) is a term that is frequently used in conjunction with artificial intelligence (AI), the Internet of things (IoT), big data, machine learning (ML), networks, human-computer interaction (HCI), and pervasive, ubiquitous computing. On the other hand, artificial intelligence owes its success to the amazing growth of information and communication technology (ICTs) [1].
Intelligence is defined as the capacity to acquire knowledge and use it in novel settings. “Artificial” is anything created by humans, whereas “ambience” is what surrounds us. Additionally, we prefer to think of ambient intelligence (AmI) is an artificial construct since the mechanisms underlying natural AmI are the focus of biology and sociology. Numerous artificial intelligence technologies developed by computers are based on the concept of replicating brain functioning and human intellect.
Everyday life is made up of a combination of hardware, software, user experience, and machine/human-machine interaction and learning. In other words, it is the act of employing a computer, a device having far-field communication capabilities, or an internet-enabled gadget without necessarily being aware of doing so. For example, we no longer need to use a desktop computer in order to operate a computer. They are unseen to us, function in sync with us, and provide an overall seamless experience.
AmI uses a variety of IoT sensors and devices in our homes and workplaces to gather information about the environment and user context. The acquired data is then processed by the AmI system. The processing and analysis of collected data are used to identify user proximity, state, intent and behavioral patterns in the AmI system. Thereafter, it makes inferences based on what it has learned so far, what it has seen before, and any patterns it notices. Once it has determined the appropriate course of action, it reacts to the user via the smart device’s elegantly built natural interface.
There are countless ways in which ambient intelligence may improve our lives. Regardless of where we are in the office, living room, shopping mall, or driving, we should always be mindful of our surroundings. Technology will serve as a constant companion. Our health monitoring devices can measure our blood pressure, so it tells us not to eat those high-cholesterol food items. It can be inconvenient to divert the route because it knows that there was an accident on our regular way to work. As soon as we get home from work on a hot summer evening, it turns on the air conditioner to keep us cool.
Consider the following scenario: Peter returns home after a hectic day at work, and AmI systems assist him in relaxing.
At his front entrance, Peter’s car is recognized by the system, and the parking door will open to allow him to enter it.
At the next level, Peter is recognized by a facial recognition system, which allows him to enter his house.
Peter’s facial expressions are captured by the AmI system, and the system determines that he is under stress.
When Peter walks into the living room, the system automatically adjusts the lighting to suit his mood.
AmI plays relaxing music from Peter’s music library, according to his preferences.
The blinds and curtains are closed by AmI to keep the light from coming in from the windows.
As soon as Peter gets on the couch, AmI plays a very important message from his wife. She says she will be home a little later from work.
When I look at Peter’s calendar, it informs me that the conference call at 8.30 pm has been moved to 9:00 am tomorrow.
AmI reminds Peter that his favourite reality show is on TV tonight by reading the TV schedule and asking if he wants to set a reminder for it.
Ambient intelligence (AmI) is interlinked with the Internet of Things (IoT). IoT refers to smart lighting, smart transportation, smart homes, smart villages, smart grids, etc., among other things, and the way these items communicate. Ambient intelligence refers to what occurs when various devices connect, and more specifically, what they learn from one another.
The Internet of Things (IoT) is a network of web-connected smart gadgets that collect data from their surroundings and use it to make decisions about their own lives. Interactions between Internet of Things devices and a gateway or other cutting-edge devices transmit sensitive data that may be analyzed remotely or on-site. These gadgets communicate with one another and respond to each other’s data. While people can communicate with robots, machines are capable of doing the majority of jobs without the need for human intervention.
It is expected that the Internet of Things will have an impact on society, the economy, and technology as it grows. Sensors and other ordinary items, as well as consumer devices, are becoming increasingly capable of storing and processing data. Despite this, there are a number of significant challenges that could hinder the Internet of Things from realizing its potential. The general public is well aware of the risks associated with Internet-connected gadgets, hacking, surveillance, and privacy violations. There is a new set of policy, legal, and development challenges that have evolved in recent years. The increasing use of Internet of Things (IoT) devices has the potential to transform our lives.
With the Internet of Things (IoT) devices such as Internet-enabled appliances, home automation components, and energy management gadgets, we are getting closer to having a smart house. In addition to other Internet-of-Things-enabled medical equipment, wearable fitness and health monitoring devices are transforming the way healthcare is delivered. The disabled and the elderly will benefit the most from this technology, as it will increase their freedom and quality of life while simultaneously cutting their expenditures [2].
To exchange data and manage message traffic, an Internet of Things device connects directly to a cloud service, such as an application service provider, through a secure connection. When an Internet of Things device connects to a cloud service through an application-layer gateway (ALG), the device-to-gateway model is utilized. On local gateway devices, features like data translation and protocol encoding are accessible.
Using this paradigm, smart devices can communicate with one another without using the Internet Protocol (IP). A gateway is required for IPv4 devices and services to function effectively. This strategy is most frequently used to incorporate new smart gadgets into current parental control systems. In order to conduct an analysis, data from various sources can be integrated with smart object data from the cloud service. A business can even benefit from ambient computing. It can help it work more efficiently, remove unnecessary steps in processes, and collect, analyze, and actively learn from data.
When computers are used in the workplace, a new kind of relationship between employees and computers develops. It gathers information for us when we ask for it or even before we ask. Ambient computing already delivers sophisticated services like voice-assisted systems, chatbots, etc. [3].
1.2 Ambient Intelligent System
Figure 1.1 illustrates the ambience intelligence system for some specific application areas; it exhibits several smart and intelligent systems that surround the user and make use of AI and ML technology. In this way, AmI is not a specific technology but rather a user’s experience with the services supplied by such systems. Typically, the cost functions employed to optimize AmI systems are related to subjective human experience, which can be quantified only to a limited extent. As a result, in order to ensure the success of AI and AmI, we must find the optimal objective-cost functions that accurately capture the subjective human experience in AmI.
Figure 1.1 Ambience intelligence system.
AI, also known as “machine intelligence” (MI), is the intelligence demonstrated by machines as opposed to the natural intelligence (NI) exhibited by humans and other animals. AI is a subset of artificial intelligence (AI). As a result, artificial intelligence mimics human cognitive processes such as perceiving, interpreting environmental input and learning, among others. Whenever robots demonstrate intelligence in their immediate environment, this is referred to as “ambient intelligence.” Because of its emphasis on humans and the environment, AmI is much more than a collection of diverse artificial intelligence application domains; rather, it is a network of disparate areas that function together.
Ambient intelligence is an interdisciplinary topic of study that aims to improve the way people and their environments interact with one another. Ultimately, the goal of the area is to make our houses and places of employment more favourable to us. The concept of smart houses is just one example; it may be applied to hospitals, public transportation systems, industries, and a variety of other settings. Unlike the concept of a disappeared computer, AmI is compatible with it. Context awareness, human-centered computer interface design, and other aspects of pervasive and ubiquitous computing are all intertwined with the field of artificial intelligence [4].
1.3 Characteristics of AmI Systems
Invisible: Ambient computing occurs in the background. For example, in a commercial conference room setting, an AI system can gather data and take notes on a conversation without the participants’ being aware of it. Simultaneously, the camera and a speaker system can be adjusted to optimize image quality and focus on the person speaking.
Easy to Implement: Ambient computing is straightforward and simple to implement. Intelligent technologies are becoming more common in today’s communication environment. Now that consumers can connect via voice or text, they can select the mode of communication that is most convenient for them.
Context-aware: They identify a user and, if possible, the user’s present state and situational context. Systems and applications that are aware of the context in which they work can gather and interpret information about the people, roles, activities, times, places, devices, and software that make up the current situation and then act in a way that fits the situation. Such behavior could include giving personalized or structured information, as well as taking action to avoid something that could be bad. As computers become more intelligent and more aware of their surroundings, they could change how we interact with them in the future.
Integration: Ambient computing allows for the seamless integration of tools, which is considerably more useful than information from non-integrated systems. They are completely incorporated into their surroundings; they are “invisible.”
Personalized: The system can be customized by the user to match their specific requirements. They are frequently adapted to the user’s specific requirements.
Adaptive: Components of the system may change according to individual requirements. AmI systems adjust to changes in the user’s physical or mental condition.
Anticipatory: Capable of anticipating user wishes in the absence of cognitive mediation.
Unobtrusive: Discreet, delivering just the information essential to other devices and humans about the user.
Noninvasive: They do not need the user to take action; rather, they act on their own.
AmI is a type of future intelligent computing (IC) in which sensors and processors are embedded in everyday objects, allowing the environment to dynamically adapt to the user’s needs and desires. These AI systems will employ the contextual data collected by these embedded sensors, as well as AI algorithms, to interpret and forecast the desires of their users. The technology will be simple to use and designed with the user in mind.
AmI is the capacity of technology to make judgments and act on our behalf while taking our preferences into account, based on data collected from all of the user’s linked sensors and devices. AmI is a highly intelligent system that is widely distributed and intuitive. It does not pose questions to the user, but rather recognizes and understands the context in which the user is acting. It does not make its physical presence known but instead executes activities that are tailored to the demands of the individual user. An emerging technology, ambient intelligence (AmI), has the potential to profoundly modify how humans interact with machines and other devices in their surroundings. In its interdisciplinary nature, ambient intelligence (AmI) is a technology that functions at the confluence of many technologies, such as the IoT, AI, big data, pervasive-ubiquitous computing, networks, and human-computer interaction (HCI).
Intelligent digital systems put in our homes or offices, among other things, that employ sensors and gadgets from the IoT to perceive the environment and user context are used to accomplish this sensing and contextualization. This is followed by the AmI system itself processing the data it has collected from these other systems in the following step. In addition to processing and evaluating the data, the AmI system performs analysis on the data in order to ascertain the vicinity of the person in question as well as his or her condition, purpose, and behavior. Intuition follows after that, and it is produced from insights derived from current facts, previous learning, and pattern recognition. It then calculates the most suitable course of action to take and interacts with the user through a natural interface that can be readily developed on a smartphone or tablet computer.
When it comes to making our lives easier and more pleasurable, AmI opens the door to a myriad of possibilities. It makes no difference what we are doing, whether it is in our living room, kitchen, or place of employment. At the backdrop of our activities, whether we are in the store, in the automobile, or in the hospital, technology will be there to assist us with our tasks. All sorts of things have happened, from our cell phones warning us not to eat that ice cream because it can detect our blood sugar levels from health monitoring equipment to our work computers informing us that there was an accident on our regular way to work. When we go home from work on hot summer evenings, it will automatically switch on the air conditioner to chill our homes before we arrive home.
It is important to know that privacy will be a big issue with AmI, even though the technology has a lot of promise. It is possible that AmI systems will know almost everything about the lives of the people they follow. If their communications are read by people who are not supposed to be reading them, they could cause a lot of problems. When people ask about how their data is used, how it is kept private, and how it is kept safe, they need to get more detailed answers. Systems need to be built with trust at the heart of their architecture.
AmI has a lot of potentials to improve the quality of life for everyone, as well as their comfort and safety. Houses that have technology that is more focused on people will make many common tasks much easier. As the population ages, providing the right care for the elderly will become more important in this situation. Even when we are not at home, AmI will be used in a wide range of fields, including retail, healthcare, manufacturing, smart cities, and more [5].
1.4 Driving Force for Ambient Computing
A group of technologies is assisting in the development of ambient intelligence.
User Interface: The use of machines that respond to speech, touch, movement, and biometrics in place of a computer screen and keyboard (speech recognition, retina recognition, face recognition, fingerprints, etc.).
Artificial Intelligence (AI): Automated systems that can read documents, analyze data, make decisions, and translate languages are becoming more common.
Machine Learning (ML): It is the ability of electronic equipment to learn new skills and improve performance at specific tasks without being explicitly programmed to do so.
Natural language processing (NLP): Natural language processing (NLP) is a technology that allows computers to interpret and respond to human speech.
Edge Computing (EC): “Edge computing” is a term that refers to the practice of moving data processing away from a “centralized” processing center (often in the cloud) to smaller processing centers that are closer in proximity to the data source.
Maintain ongoing connectivity as users of digital devices move from one location to another. Mesh networks have the capability of facilitating smooth movement among a wide range of devices, applications, places, and individual networks, among others.
1.5 Ambient Intelligence Contributing Technologies
Sensing:
This could be done through a wired or wireless connection. Sensors can be either stand-alone or integrated into an electronic device.
Reasoning:
Systems will be able to “think,” and they will be able to do things like help people, such as:
Recognizing and predicting activity
The ability to identify and evaluate the context of each activity.
Proposing a plan of action or decision
Centralized data transmission and computation vs. distributed sensing and computation
Acting:
Changing an environment’s attributes. For example, a robot vacuuming or a user notification asking them to make a decision.
Interacting:
Users will be able to interact with ambient intelligence in a variety of ways. The Web, mobile, and wearable gadgets, as well as home appliances and natural user interfaces, are among them.
The basic goal of AmI is to create systems that adapt the surrounding environment to the demands of users, whether expressed consciously or not, while also attaining other system-driven goals, such as reducing global energy usage. The widespread deployment of sensor and actuator devices in accordance with ubiquitous computing is an inherent necessity.
The ultimate goal of ambient intelligence is to have the user’s preferences fully integrated into the system. When it comes to AmI systems, the key requirement is the presence of unobtrusive and ubiquitous sensors, which are necessary for context-aware reasoning in order to respond to user inputs and act on the environment. A ubiquitous digital intelligent environment, then, is one that puts the human user at the center by allowing them to control their surroundings.
A wireless sensor network (WSN) is made up of a lot of small computational units that can be programmed, are self-sufficient, and can communicate wirelessly with each other. These small sensor nodes can also be equipped with sensors that can measure a wide range of environmental characteristics, and ad-hoc sensors for specific tasks can be made.
A WSN can do low-level processing of sensed data, which makes it easier to choose only important data from a lot of measurements. Basically, wireless sensor networks (WSNs) are just one part of a bigger architecture that tries to figure out how to deal with a lot of data without overwhelming the person who is trying to figure out what to do with it.
The various physical or environmental conditions are monitored by WSNs, A node-to-node network enables each sensor to send data to the next terminal. Nodes in WSNs are small and inexpensive and can be put in a wide range of locations. With the help of energy harvesting, which uses energy from external sources such as kinetic and wind energy, as well as sound and electromagnetic radiation, WSN nodes can readily run on low-power batteries.
Using pervasive sensory infrastructure, it is possible to collect information about the surrounding environment, which may subsequently be utilized to run artificial reasoning algorithms on a central server.
The WSN is equipped with commercially accessible sensors that monitor temperature, pressure, humidity, ambient light, etc. [7].
1.6 Architecture Overview
Pictured in Figure 1.2 is a schematic representation of the architecture of the ambient intelligence system (AIS). The physical layer includes all of AmI functions’ sensors and actuators, as well as those required by the end-user in case they are not already included in the AmI software. Exporting higher-level abstractions that identify the primary monitored components and address basic connectivity issues between gateways are two of the main functions of the physical abstraction interface. It will be possible to consolidate functions such as message transmission, physical infrastructure health monitoring and control, and system reconfiguration due to physical infrastructure changes.
Figure 1.2 Ambience intelligence architecture.
There are core AmI features defined in a middleware toolkit for generating intelligent services over available hardware, and these features are housed in the middleware layer where final developer AmI applications are produced.
In addition to the AMI modules and their interfaces with AMI-hosted applications, several other components contribute to the middleware in the system. There is a thin layer of middleware over distant sensor nodes that allows them to respond to system commands, but most of the services are delivered by remote gateways. There are both wireless and wired sensors and actuators in each of the remote networks that have been placed in a particular location.
According to the system, it is possible to gain access to the room through the use of a specific gateway node, referred to as the Local Gateway (LG), which acts as a link between physical devices and the system as a whole. In network technology, a local gateway (LG) is a network technology that facilitates the connection between various network technologies while also providing the higher layers with a homogeneous representation of data that has been created by heterogeneous sensor technologies.
In addition to the central AmI Server, several top-level gateways (TLGs) are connected to it, each of which handles a unique environment. Local Gateways, which are connected to the Central AmI Server and, in turn, connected to the Top Level Gateways, are responsible for providing fine-grained monitoring.
Remote LGs could be run on low-power computers, such as micros-erver nodes connected to a wireless sensor and actuator network. Data can be temporarily stored in their local database and processed more finely before being sent to a remote processing unit on these devices, which are more powerful than basic wireless nodes. In order to construct a communication backbone within the regulated premises, a top-level gateway collects data from all of the LGs and acts as a coordinator for the Local Gateways Network.
The Top-Level Gateway also provides programming interfaces to some components, like sensors and actuators, which are not exclusive to a single building. The Central AmI Server is the only way for AmI applications to access the physical layer’s functionality; the system’s intelligent software components are not directly tied to the hardware, making ad-hoc application creation easier and more generalizable. WSNs represent the proposed system’s primary sensory infrastructure. Because of the standard abstraction layer, the system has been built to be easily adaptable with additional sensors.
Various connected elements rely on a variety of physically diverse media, which may be wired or wireless, to communicate with the Local Gateway, and the presence of this protocol on board of the Local Gateway prevents the data management and communication software operations on board from being constrained by information about the data formats and communication modalities specific to each of those technologies.
There are a lot of choices when it comes to the sensors and platforms for environmental monitoring. The AmI application under testing decides which sensors and which platforms to use. However, supplementary sensors are also used for specific testing purposes so that AmI applications can get more information about how to save energy so that they can make the right decisions.
Tracking energy consumption at various resolutions is possible, and the energy consumption of entire buildings or individual devices can be studied. An energy monitor that has multiple functions can be used to keep track of the overall energy use of a certain room. It was connected to the monophase power line that supplied power to the room under observation, and it enabled us to collect data on voltage, current, and active and reactive power in real-time. The energy consumption of each individual gadget is monitored by specialized “energy sensor nodes,” which measure the energy consumption of every device connected to the sensor’s power outlet.
A middleware layer that provides the essential functionality that enables the development of specialized modules on top of a hardware substrate is referred to as a hardware substrate. Thus, these modules were developed in accordance with the multi-tier knowledge representation schema, which classifies them as Level 0; some of them perform only low-level data processing, and as a result, they are classified as sub-symbolic in nature, while others perform a type of high-level processing, and as a result, they are classified as symbolic in nature. Several of these modules can be used in conjunction with one another to offer support for a specific AmI application.
Logically, the primary goal of middleware is to decouple the programmes from a specific choice in terms of the underlying hardware, allowing the developer to devote his or her attention solely to the challenges pertaining to AmI features.
1.7 The Internet of Things
The Internet of Things (IoT) is a new technology that establishes a global network of devices and objects that can connect and share data via the Internet. It is important to distinguish between the Internet and the IoT. The IoT can produce, analyze, and make judgments on linked items; in other words, it is wiser than the Internet. Interconnected devices include security cameras, autos, sensors, buildings, and software.
In the IoT, any object that can connect to the Internet but is not a mobile device or computer qualifies as a “thing.” Wearables, digital and mechanical machines, and even animals are examples of “things.” An object must have two components in order to be classified as an IoT device: the object itself and an Internet connection [8].
However, simply connecting an object to the Internet does not guarantee that it will be more useful, so manufacturers typically incorporate one or both of the following:
sensors that collect information about an object or its surroundings,
actuators are machines that perform physical actions in the real world.
In other words, an IoT device is a nontraditional object that is connected to the Internet and must include at least one sensor or actuator in order to profit from the connection.
1.8 IoT as the New Revolution
In the industrial industry, the use of automation and data interchange is referred to as “Industrie 4.0.” In the words of the Boston Consulting Group, “Industrie 4.0” is comprised of nine major technologies: autonomous robots, horizontal and vertical system integration, simulation, the industrial Internet of things, cybersecurity, big data and analytics in the cloud additive manufacturing, and augmented reality. Using these technologies, a “smart factory” is being built in which machines, systems, and humans communicate with one another in order to coordinate and monitor progress on the assembly line. It is the networked devices that supply the sensor data, and they are controlled digitally. Microsoft has named the IoT the “fourth industrial revolution,” alongside the Digital Revolution, mechanical production, mass production, and science. In 2017, IoT spending exceeded $800 billion, with the industry’s impact estimated to range between $3.9 and $11.1 trillion by 2025 [9].
Since 2000, digital disruption has resulted in the loss of 52% of Fortune 500 corporations. The IoT will have a similar impact on people who are left standing in the future. By 2020, 80% of firms expect their industry to be disrupted, and it is easy to understand why: two-thirds of customers aim to buy connected gadgets for their homes by 2019, and Gartner forecasts that 95% of all electronic products will have built-in IoT technology by 2020.
As if that were not enough, the US Department of Transportation estimates 76% of car accidents can be prevented with that vehicle-to-vehicle communication. South Korea’s new “smart city” has reduced per capita energy consumption by 40%, and advances in IoT healthcare are expected to prevent 50,000 preventable deaths per year in just a small area–hospital errors.
Between the late 1700s and the early 1800s, the world underwent its first industrial revolution. During this time period, manufacturing evolved, with a strong emphasis on physical labour performed by people and supplemented by work animals. This includes the use of water and steam-powered engines, as well as various other types of machine tools and other methods of production.
The introduction of steel and the widespread use of electricity in commercial companies in the early twentieth century marked the beginning of the world’s second industrial revolution. Following the introduction of electricity, manufacturers were able to increase production while also making factory machinery more mobile. As a means of enhancing productivity, mass manufacturing techniques were developed during this period.
Manufacturers progressively began to incorporate more electrical and, subsequently, computer technology into their operations in the late 1950s, heralding the beginning of the third industrial revolution. The manufacturing industry began to shift its emphasis toward digital and automation software around this time period.
The fourth industrial revolution (also known as Industry 4.0) has made major advances in the last few decades, particularly in the United States. The Internet of Things (IoT), cyber-physical systems, and access to real-time data are all available in addition to interconnectivity through the Internet of Things (IoT). From past decades, Industry 4.0 has elevated the importance of digital technology to a whole new level. Manufacturers in the Fourth Industrial Revolution (Industry 4.0) will need to be more integrated, comprehensive, and all-inclusive in their operations. It serves as a transitional layer between the digital and physical worlds, allowing for improved communication and access among departmental teams and products, as well as suppliers, partners, and individuals. In the fourth industrial revolution, CEOs will have a better understanding of and control over all aspects of their organizations, and they will be able to harness real-time data to increase efficiency, streamline procedures, and drive development [10].
1.9 IoT Challenges
It is expected that the Internet of Things will get more sophisticated as the number of real-time applications that demand smart connectivity between themselves increases. A few examples of these problems are described below.
1. Smart connectivityThe Internet of Things architecture may require sensors and devices to update their trends or features to keep up with changes in their surroundings. In essence, the Internet of Things (IoT) is a data processing and decision-making system that constantly seeks to improve itself. It also has the ability to change the trends or characteristics of connected devices in order to react to changes in the environment. Smart technology, such as the Internet of Things (IoT), allows all linked devices to update themselves in response to changes in their environment, as well as adapt and perform with great precision in any unexpected situation. Because of this, smart-linked systems can be produced provided that a smart infrastructure is adequately constructed to properly treat the data acquired from devices and make the necessary decisions.
2. High security and privacyConnecting billions of devices around the world is the primary objective of the Internet of Things. The Internet of Things is expected to connect 50 billion devices by 2020. Strong security measures are required to avoid fraud and provide high levels of data protection when connecting such a large number of devices. Getting organizations and consumers to trust the IoT enough to share their data is therefore a significant hurdle.
3. The treatment of big dataThe exponential growth in data exchange between connected devices is the most important drawback of the IoT. Social media like Facebook, weblogs, and email, as well as physical equipment connections like microphones, sensors, and cameras, are all major sources of data, but databases used in corporate processes are also important. The last 2 years have seen the creation of 90% of the world’s data. Because of this, it is becoming increasingly difficult for IoT infrastructure builders to handle the exponential expansion of data.
The most major issue with utilizing IoT is the massive increase in data sent between linked devices. As shown in Figure 1.3, the three main sources of data are (1) the database used in the business process; (2) human everyday activities, such as Facebook, email, and weblogs; and (3) the connection of physical equipment such as microphones and cameras. It is worth noting that 90% of all the data on the planet has been created in the last 2 years. This makes it increasingly difficult for IoT infrastructure builders to deal with the exponential expansion of produced data.
While the IoT architecture connects a large number of devices, the amount of data sent between them grow quickly. This will cause some data delivery latency or delay among the connected devices. This introduces a new challenge for the Internet of Things: reducing latency in order to offer a stable Internet of Things infrastructure.
Figure 1.3 Different sources of data growth (https://www.slideshare.net/bjorna/big-data-in-oil-and-gas).
1. Lowering bandwidth and energy consumptionThe number of devices connecting to the Internet of Things, speaking with one another, and sharing data with one another has expanded tremendously, as has the amount of bandwidth and electricity they consume. Therefore, while developing an Internet of Things architecture, it is important to consider both bandwidth and power usage. The current tendency is for connected devices to be smaller in size, which results in decreased power usage. Due to the high amount of data that is shared among devices, the communication data rate is still an issue that needs to be addressed.
2. ComplexityTo connect devices and share data via the IoT, several layers and levels of software and hardware, as well as some standard protocols, can be employed. As the volume of shared data and linked devices grows significantly, so will the sophistication of the software, hardware, and standard protocols that are employed. As a result, as the number of connected devices grows, it becomes increasingly difficult to reduce the complexity of the Internet of Things (IoT) technology.
1.10 Role of Artificial Intelligence in the Internet of Things (IoT)
There are several issues that arise while using the Internet of Things (IoT). The most significant issue is that the use of the Internet of Things (IoT) has increased dramatically in recent years, which has resulted in an increase in concerns about cyber security. It is artificial intelligence (AI) that is at the forefront of cybersecurity, as it is utilized to develop complex algorithms to defend networks and systems (including IoT technology). However, cyber-attackers have figured out how to exploit artificial intelligence and have even begun using hostile AI to launch cyber-attacks against organizations and individuals. Aiming to present and summarise significant work in these disciplines, this review study collects data from various different surveys and research papers on the Internet of Things, artificial intelligence, and AI-based assaults and counter-attacks, as well as the relationship between these three issues.
The corporate world is rapidly altering as a result of IoT implementation. The IoT is helpful in the huge collection of data from a variety of sources. On the other hand, wrapping one’s head around the avalanche of data coming in from countless IoT devices makes data gathering, processing, and analysis tough.
A lot of money will have to be spent on new technology to see the future and full potential of IoT devices. There is a chance that AI and the Internet of Things (IoT) could have a big impact on how businesses and economies work in the future. With little or no help from humans, the IoT powered by AI creates intelligent technologies that act like smart people and help people make smart decisions.
The Internet of Things (IoT) is concerned with devices talking with one another over the Internet, whereas artificial intelligence (AI) is concerned with devices learning from data and experience. The Internet of Things (IoT) is being used in a variety of applications these days [11].
1.11 IoT in Various Domains
The below-given Figure 1.4 depicts applications based on IoT. There are many more applications that can be combined with IoT.
Figure 1.4 IoT applications.
1.12 Healthcare
The Internet of Things (IoT), a new revolution, offers enormous potential in a variety of areas, including healthcare. The full use of this technology in healthcare is a common goal since it enables medical service providers to work more efficiently while offering better patient care. There are various benefits to implementing this technology-based healthcare plan, which could increase treatment quality and efficiency, as well as improve the health of seniors and other patients.
1.13 Home Automation
Home automation is indeed a notion that attempts to put management of common household electrical equipment at your fingertips, providing consumers with more cost-effective lighting options, increased energy conservation, and efficient energy usage. Apart from lights and the establishment of a centralized home entertainment system, the concept also includes complete control over your home security and much more. Home automation systems based on the Internet of Things (IoT) seek to control all of the devices in your smart home using internet protocols or cloud computing, as the name implies. IoT-based systems have numerous advantages over traditional wired systems, including simplicity of use, ease of installation, lack of complications caused by running wires or faulty electrical connections, and reduced difficulty in detecting and reacting to faults.
