Guide to Ambient Intelligence in the IoT Environment

Many cities around the world have embarked on ambitious programmes towards creating Smart Cities where information, diverse digital opportunities, and collective intelligence can be harnessed ubiquitously. Smart Cities are conceptualized using citywide smart and intelligent architectures informed by the context in which they are implemented. These architectures make it possible to access information and intelligence anywhere and at any time. Information processing and computing is embedded within the urban infrastructures to a point where immovable city entities such as traffic lights are more intelligent to make real-time decisions based on the happenings in the environment in which they are deployed. Advanced development of ambient computing within the realm of Smart Cities will further culminate into possibilities such as vehicle-to-vehicle communication (V2V) and mobile-to-mobile (M2M) communication. Using extensive and critical literature review, this chapter specifically focusses on the design of information architectures that will ultimately support the enshrining of spatial intelligence within Smart City environments hinged on the internet of things (IoT) and cloud/fog computing. The chapter presents latest trends in the research and practice of ambient intelligence (AmI) linked to the realization of the key principles of Smart Cities from the information topology and architecture point of view. A conceptual ambient intelligence architecture that highlights the building blocks of any ambient intelligence architecture as deployed in Smart City environments is also proposed. The proposed conceptual architecture can be used as a blueprint in the design of ambient intelligence topologies and architectures in different contextual settings.

The advent of the Fourth Industrial Revolution has brought about the drive toward integrating systems and processes through the Internet of Things (IoT) in industrial environments. The major objective of introducing IoT into these environments is to realize dynamic optimization of productivity and efficiency and to mitigate the risks affecting financial loss and safety of personnel. Recent proliferation of sensors and smart embedded devices capable of communicating with each other offers industry the possibility of ubiquitous computing. Other distributed computing paradigms such as cloud computing are also helping to achieve the said objective. The next evolutionary step for ubiquitous computing in relation to industrial environments is the deployment of ambient intelligence (AmI) within the smart devices to bring about seamless integration of the personnel and environmental factors as well as equipment in the workplace. In this chapter, we look at how the application of AmI in the industrial sector has sought progress, and attempt to extricate the past and current challenges in terms to context, architecture, security, and uptake of relevant technologies. A bottom-up approach is taken in reviewing the key technological constituencies of AmI in Industrial IoT (IIoT) from the sensing technologies and communication to overall architectural developments and limitations. Some examples of future application scenarios of AmI in mining, manufacturing, and construction are also presented that offer high-level depictions of how the different aspects of AmI could potentially be brought together to benefit these industrial settings. Ultimately, this work aims to provide stakeholders with an understanding of the possibilities of AmI in IIoT environments through equipping them with knowledge of the state-of-the-art, technological limitations/barriers, and future developments encompassing this application area.

Ambient intelligence (AmI) is an emerging paradigm bringing intelligence into our lives with the help of intelligent interfaces and smart environments. AmI has the potential to affect our business environments significantly. With the help of AmI, we can find better ways to serve our customers and increase productivity. Internet of things (IoT) is a key enabling technology that provides the necessary infrastructure for ambient intelligence. In addition, ambient intelligence paradigm enhances the use and capabilities of IoT devices. As a result, businesses those want to benefit from this new paradigm and the relevant technologies need to build the necessary IoT infrastructure. In this study, our goal is to help the business and technical managers by developing an AmI enhanced business vision and managing an effective IoT transformation. In this chapter, we discuss an existing implementation of ambient intelligence in the business environment. Furthermore, we envision various future uses of AmI in business environments. We also present issues related to IoT technology transformations. In addition, we provide a set of guidelines, strategies, and best practices for business and IT managers for a successful IoT transformation leading to an ambient intelligence enhanced business environment. We divide the transformation issues into three categories: management issues, technical issues, and social issues. These issues are discussed in detail.

Technological improvements of the Internet and connected devices cause increased user expectations. People want to be offered different services in nearly every aspect of their lives. It is a key point that these services can be reached seamlessly and should be dynamically available conforming to the active daily life of today’s people. This can be achieved by having intelligent environments along with smart appliances and applications. The concept of ambient intelligence arises from this need to react with users at runtime and keep providing real-time services under changing conditions. This chapter introduces a component-oriented ontology-based approach to develop runtime adaptable ambient intelligence systems. In this approach, the adaptability mechanism is enabled through a component-oriented method with variability-related capabilities. The outcome supports the find-and-integrate method from the idea formation to the executable system, and thus reducing the need for heavy processes for development. Intelligence is provided through ontology modeling that supports repeatability of the approach in different domains, especially when used in interaction with component variability. In this context, an example problem exploiting the variability in the density of a smart stadium network is used to illustrate the application of the component-driven approach.

WiFi-Based Positioning Systems (WBPS) play a key role in indoor navigation, but further development of these systems continues to this day. WBPS have been applied to different tasks including mobility tracking and behavior analysis. Mobility tracking allows detecting a user in the environment even if one does not use positioning services. Tracking enables sensing the human presence in different environments, including occupancy detections in smart homes, geofencing, enhanced security and many other scenarios. One of the basic performance criteria of a positioning system is its precision. The general rule states that precision grows with the increase of the number of reference signals used for positioning. However, it is unclear how much information is required to estimate the location of a person reliably. This chapter overviews the current research in the area of Received Signal Strength Indicator (RSSI) based positioning and evaluates a positioning system for localizing a person in an indoor environment, taking into account the number of Access Points (APs) available for estimating the location. We conduct performance analysis of an indoor positioning system based on measurements from a real walk. Additionally, we conduct a simulation, where we analyze the impact of the noise on the positioning quality.

The automotive industry is shifting its focus from performance and features to safety, entertainment, and driver comfort. In this regard, driver assistance and autonomous driving technology are gaining more attention. Such technology has the potential to reduce road accidents, traffic congestion, and fuel usage. However, vehicles cannot become fully autonomous, until they are able to sense their context efficiently (context sensing), and to use ambient learning to respond appropriately and within short timescales to the data they have sensed. Context sharing will also become essential, because a single vehicle will not be able to gain a holistic view of its context without cooperation from other nearby vehicles and from the roadside infrastructure. Indeed, there are further advantages when a group of vehicles make intelligent decisions based on a common understanding of their context. This chapter highlights the significance of ambient intelligence for next-generation connected and autonomous vehicles, describes its current state of the art, and also shows how its potential might be achieved. One of the main challenges refers to how to provision and coordinate cloud-based services to meet the needs of real-time (low latency) data-intensive (high data rate) ambient intelligence, particularly for safety-critical vehicular safety applications. It indicates how autonomous or semi-autonomous vehicles are likely to make seamless use of any available wireless networking technologies to improve both coverage and reliability and, where feasible, to cache critical content near the network edge so as to minimize the number of network hops and hence service latencies. Both of these approaches should improve the network quality of service afforded to driving applications.

Carbon Monoxide (CO) is a ubiquitous product of partial burning of materials containing carbon. It is a poisonous gas, inhalation of which causes headache, nausea, dizziness which may sometimes lead to death. Thus, safety and security of human being from CO is of paramount significance. Designing a carbon monoxide detection system has, therefore, become very much essential to prevent such serious incidents. This chapter discusses the design and implementation of a secure and cost-effective real-time carbon monoxide detection and control system for living environments (e.g., air-conditioned rooms, factory spaces, and automobiles) by using embedded intelligent control mechanisms. The chapter provides a basic overview of the carbon monoxide gas, its sources and effects, related carbon monoxide gas sensors, and embedded intelligent controllers. The chapter also illustrates a review of various accidental cases due to exposure to CO and discusses a mathematical model for the embedded intelligent controllers. Lastly, a brief description of the software and hardware implementation of the embedded intelligence in an IoT platform has also been discussed. The chapter concludes with the significant contribution of this system by suggesting future research opportunities in this field.

The aim of this book chapter is to provide a comprehensive assessment of the ambient intelligence (AmI) microcontrollers suitable for low-power Internet of things (IoT) applications. The current challenges and trends in the evolution of low-power and high-performance microcontroller are also explored. The key focus is on the performance analysis of such devices as they facilitate the IoT vision with increased reliability. A detailed discussion of various microcontrollers, their architectures, low-power modes, and available temperature monitoring systems is also provided. In this context, design and architecture of a low-powered microcontroller is proposed and TCAD simulations are carried out for a better understanding of the suggested system. The intended audience is expected to be research and scientific community working in the field of IoT-based smart and intelligent microcontrollers for environmental study applications. The book chapter could be used for a course of higher education and for researchers in the fields of computer science, microelectronics, nanotechnology, and VLSI design. The microcontroller features and content related to IoT, as presented in this contribution, will hopefully be most valuable to the readers to understand the underlying concepts and to develop advanced high-performance circuits and systems. Illustrations, tables, and figures are also provided to supplement the text.

New technology trends including Ambient Intelligence (AmI), Machine Learning (ML), Internet of Things (IoT), Cloud and Edge computing all have an important role to play in further developing the tax administration processes. The main challenge is to effectively reform the Revenue Administration (RA) services in today’s electronic age. Business activities have become more global and digitalized; and revenue administrations’ traditional services are also developing fast through the use of smart devices and smart software applications in the IoT-distributed computing environment. In this digital world, tax service providers also have a big responsibility, in fact, a requirement, to use and adopt revenue administrations’ smart e-services built with some intelligence to improve automation. Understanding the need of the hour, tax service providers are attempting to develop IoT environments with AmI for fulfilling citizens’ tax obligations. It is understandable and should be acceptable that in doing so and through increased smart automation, their workload is going to decrease drastically. In this chapter, it is aimed to examine RA tax services provision in the IoT environment in some leading countries and discuss, in some detail, how the role of tax service providers is changing in this improving smart taxation environment.

The possibilities of ambient intelligence in the healthcare sector are multifaceted, ranging from supporting physical to mental wellbeing in various ways. Ambient intelligence can play an important role in supporting emotional wellbeing and reducing discomfort. Real-time capability in systems to provide support during discomfort can be useful in scenarios which are traditionally neglected. Absence of concern about wellbeing among commercial vehicle drivers during stressful driving situations may lead to accidents and poor lifestyle. Ambient intelligence can play a role in determining such situations to support the drivers when it is required. The availability of low-cost Internet of Thing (IoT) based components has opened up opportunities in areas where resources are constrained. In the current chapter, the focus is on improving the wellbeing of commercial vehicle drivers in a low-income setting. The chapter focuses on understanding the concepts of discomfort and wellbeing through a detailed qualitative study followed by a possible solution approach to address the ongoing challenges. A low-cost wearable IoT-enabled system along with a long-term analytic support is proposed to improve the wellbeing of drivers using ambient intelligence. The entire system is built up using a connectivity framework. The low-cost IoT device would enable support for discomfort for community who traditionally do not receive such support. Wellbeing of drivers is important for improved driving quality and better traffic management. A system in place to support drivers in real time, named Bap re Bap is presented here in the context of Bangladesh.

Elderly monitoring systems are the major applications of care for elderly and the disabled who live alone. Falls are the leading factor to be detected in the elderly monitoring system to avoid serious injuries and even death. The detection systems often use ambient sensors, wearable sensor, and vision-based technologies. In case of sensor-based devices, the elderly are required to wear the detection devices, however, quite often, they forget to wear these or do not wear them correctly. Moreover, the sensors need to be charged and maintained regularly. Also, the ambient sensors need to be installed in all the rooms to cover the whole actuation. The additional difficulty is that they are complex in circuitry and sensitive to temperature. Vision-based devices are the only plausible solution that can replace the aforementioned sensors. Besides, the cost of vision-based implementation is much lower and related devices are better than wearable devices in activity recognition. Much like Ambient sensors, cameras can also be installed in all the rooms; the cost and maintenance of these are less as compared to ambient sensors. This chapter proposes a vision-based posture monitoring system using infrared cameras connected to a digital video recorder and a fall detection mechanism to classify the falls. In the chapter, we observe the behavior of the elderly through the specially designed clothing fabricated with retroreflective radium tape (red in color) for posture identification. The proposed fall detection technique comprises various modules of operations such as image segmentation, rescaling, and classification. The infrared cameras observe the movement of the elderly people and signals are transmitted to a digital video recorder. The digital video recorder snaps only the motion frames from the signal. The motion images are segmented to red band using image segmentation and further rescaled for better classification using k-Nearest Neighbor and decision tree classifiers. The tests have been conducted on 10 different subjects to identify the falls during various motions such as supine, sitting, sitting with knee extension, and standing. We have shown a detection rate of 94% for the proposed model with k-nearest neighbor classifier.

Ambient Technologies, such as beacons, sensors, and other similar smart devices, can be used in work places such as offices to determine everything from whether an employee is in the building, to where they are located, and whether a booked conference room is actually in use. This is part of a larger smart office strategy involving digital facilities management solutions that respond to modern methods and manners of working, as well as smart building technologies providing digital ecosystems that allow workers empowerment through personalization and automation. This new data-driven environment contributes to energy efficiency, optimized space utilization, enhanced workplace experience and occupants’ comfort. However, all of this requires standards for data interoperability and seamless networking. Facilities managers are also now taking on a different role as to how they visualize new smarter office spaces, where it is expected that new environments would support their inhabitants intelligently by promoting easier management, better efficiency, increased productivity, and enabling the buildings to be part of the creation process for design and project development. There are obviously numerous sensitivity issues with respect to gathering, storing, maintaining, and processing of the ambient environment data in terms of user privacy, security, and possibility of potential data misuse. In this chapter, we discuss the new approaches to facilities management in terms of developing smarter office spaces, embedded with devices employing Ambient Intelligence (AmI) . We also articulate cases and examples of ambient technologies implementation.