AIoT Technologies and Applications for Smart Environments

In dealing with the impacts of our changing climate, the sun, air, and water are the three main factors that will affect our ability to maintain a healthy and livable living environment. The two United Nations' Sustainable Development Goals (SDGs) of "health welfare" and "sustainable city" relate closely to how societies face climate change. Applying smart technologies such as the Artificial Intelligence of Things (AIoT), big data, blockchains, and the Internet of Things (IoT), cloud calculation, and network management allows designers to access information on relational and adaptive environmental designs. Moreover, these technologies help us learn evolutionary computation information in order to provide advanced mechanisms. Models that help promote the implementation of smart neighborhoods and cities integrate smart technologies and IoT in order to improve air quality and living convenience and achieve living environments that are livable and healthy. This article primarily addresses the impacts of climate change on our living environment and how we may use green and smart buildings to ameliorate the effects of this change on daily life, promote the efficient use of water resources, and make living spaces significantly more environmentally friendly. In addition, we hope to apply the idea of smart IoT and big data analysis to design "passive toughness adaptation" and "automatic sensing prevention" into healthy living environments, which may facilitate our ability to handle the problems of super-ageing societies and to adapt to the diminishing birthrate. An intelligent and resilient environment that is appropriate for all age groups may provide a valuable path forward for preparing effectively for the impacts of climate change.

The rapid development of contemporary artificial intelligence and Internet of Things (IoT) technologies has given rise to the emerging paradigm of the AIoT (Artificial Intelligence of Things), which is profoundly impacting human life and driving the digital transformation of industries and society. The AIoT not only enhances product functionality and convenience but also accelerates the achievement of the United Nations Sustainable Development Goals (SDGs). However, the widespread adoption of these technologies still poses challenges related to social inclusivity, particularly regarding insufficient accessibility for elderly users, which may exacerbate the digital divide and social inequality, contradicting SDG 10 (reducing inequality). This study integrates AIoT product development processes based on sustainability and universal design principles using methods such as Quality Function Deployment, the Analytic Hierarchy Process, the Scenario Method, the Entropy Weight Method, and Fuzzy Comprehensive Evaluation. The features of this process include ease of operation and high flexibility, making it suitable for cross-departmental product development while prioritizing the needs of diverse age groups throughout the development process. The research findings indicate that the AIoT product concepts proposed can meet the needs of diverse users, contributing to the realization of age-friendly products. This study provides a reference point for future AIoT product development, promoting the inclusive and sustainable development of smart technology.

In recent times, a massive amount of smart devices or objects are connected that enhances the scale of the digital world. These smart objects are referred as “things” or physical devices that have the potential to sense the real-world physical objects, collect the data, and network with others. The objects are connected through the internet, which crafts the terminology of Internet of Things (IoT). IoT has been developed and become the center of consideration due to the novelty of embedded device and a rapid enhancement in its number. This increase is resulting in the creative applications of smart environments. Smart transportation is a central stake for the quality of life of citizens in smart environment. Smart transportation involves the use of devices and sensors in the control system of vehicle; for example navigation system of cars, traffic signal management system, number recognition system and speed monitoring system. In this research article, we propose architecture for smart transportation system using Big Data analytics, in order to achieve real time processing and facilitate a friendly communication in the environment of IoT based smart transportation. The proposed architecture is a 3-phase scheme which is responsible for the organization and management of Big Data, real-time processing of Big Data and service management. The proposed architecture is a generic solution for the smart transportation planning using real time Big Data processing. The proposed scheme is realized using Spark over single node Hadoop setup with various input libraries. A huge amount of data from different authentic and reliable sources is measured to validate the proposed architecture. In addition, the effectiveness of proposed scheme also highlighted with regard to throughput.

Pervasive or ubiquitous computing environments have become very common nowadays. The vision of pervasive computing is given by Mark Weiser in 1991. Pervasive computing offers such computing environments in which users are offered their desired services without requiring their dedicated or substantial attention. It supports users to achieve their desired goals/task with minimal user interaction. The applications of pervasive computing can be seen in various sub-domains including mobile and ad hoc networks, wireless sensor networks, location-aware systems, context-aware systems and smart environments (e.g. smart offices and smart homes). Sensing physical phenomena, human activity recognition, understanding and analysis can substantially play its role in realizing smart environments. In recent years, the domain of human activities recognition has got significant attention from researchers due its potential applications in smart environments. In the literature of smart environments, it is envisioned that a system could offer the best services to its users only if it is able to understand and recognize the actions/activities being performed by the user while present in the smart environment. Consequently, in this paper a low-cost system to recognize human activities is proposed. The proof-of-concept implementation of the proposed system is carried out and its usability is also conducted. The usability study results show that the proposed system is user friendly and significantly improves the user experience of smart home users.

Internet of Things (IoT) and artificial intelligence (AI) technology are integrated under the umbrella title "Artificial Intelligence of Things" (AIoT). AIoT seeks to improve data management and analytics, increase human-machine interactions, and streamline IoT operations. The IoT is an interconnected system of computing devices, mechanical and digital machines, and items that may send data over a network without requiring human or computer-to-human interaction. Any device that can be assigned an Internet Protocol address and communicate data across a network is considered an IoT item. Examples include an implanted heart monitor or a car with built-in sensors that alert the driver when the tyre pressure is small. As AI enhances IoT through connectivity, signaling, and data exchange while IoT enhances AI through machine learning capabilities and enhanced decision-making processes, AIoT is a game-changer and mutually beneficial technology for both types of technology. The IoT may look up businesses and their services by adding value to the data they produce. Using AI, the IoT device may assess, learn from, and make judgments without the assistance of a human. The current chapter presents two new approaches towards the AIoT and IoT applications: AIoT-based water management system and IoT-based smart irrigation system which are very successful in smart agriculture system.

The severe resource restrictions of computer-augmented everyday artifacts imply substantial problems for the design of applications in smart environments. Some of these problems can be overcome by exploiting the resources, I/O interfaces, and computing capabilities of nearby mobile devices in an ad-hoc fashion. We identify the means by which smart objects can make use of handheld devices such as PDAs and mobile phones, and derive the following major roles of handhelds in smart environments: (1) mobile infrastructure access point; (2) user interface; (3) remote sensor; (4) mobile storage medium; (5) remote resource provider; and (6) weak user identifier. We present concrete applications that illustrate these roles, and describe how handhelds can serve as mobile mediators between computer-augmented everyday artifacts, their users, and background infrastructure services. The presented applications include a remote interaction scenario, a smart medicine cabinet, and an inventory monitoring application.

We are in a time when a large number of electronic devices influence human interaction with the environment. The physical place where these various devices interact with each other to provide useful services for end-users gives birth to the concept of a Smart Environment. The interaction of the multiple devices that make up a smart environment produces a large amount of information or Big Data problems, so the insertion of the Internet of Things (IoT) technology in the environment is essential. IoT integration and cloud computing technology are needed as the impact of evolution on the next generation of smart environments. Trends in research related to IoT and Smart Environments are increasing rapidly beyond the trend of Smart City that first emerged. This paper reviewed current trends about smart environments, architectural concepts, frameworks, and applications of IoT-based Smart Environments.

In Internet of Things (IoT), there are many future applications in smart environments. This paper first presents a possible scenario of a family making use of the IoT in their daily living, and then work leading to integration of unobtrusive sensors in the smart environment is discussed. In the applications discussed, Bluetooth connections linked and connected the sensors that were used in the wireless sensor network as an intuitive sensor communication platform. The paper presents results of a methodology and experimentation for human identification using sensors and microcontrollers and discusses how this can lead to integration and increased personalization within an IoT smart environment using only unobtrusive sensors.

Nowadays, the increasing in the use of Internet of Things (IoT) devices is growing the realization of pervasive Smart Environments (SEs) and Smart Urban Ecosystems, where all the data gathered by the “Things” can be elaborated and used to improve the livability, the safety and the security of the environment, and to make inhabitants lives easier. Many efforts have been already done in the direction of SEs development and in the implementation of platforms specifically designed for SE realization. Anyway, such efforts miss of solutions regarding the interoperability among the realized SEs and other third-part “Things”. This chapter gives an overview of iSapiens, which is a Java-based platform specifically designed for the development and implementation of SEs. iSapiens tries to overcome the interoperability issue by leveraging the Social Internet of Things (SIoT) paradigm that allows to dynamically include in an SE the new “Things” that can appear in an environment without requiring interventions from humans. iSapiens provides tools for the realization of pervasive SEs and relies on the edge computing paradigm. Such paradigm is extremely important in a distributed system since it allows to use distributed storage and computation at the edge of a network, so reducing latencies with respect to move all the executions and storages in the cloud. Moreover, the chapter will review some SE applications realized by exploiting iSapiens concepts.

Smart Technologies with Wireless Sensor Networks

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

Human activity recognition is an important problem in health care, ambient-assisted living, surveillance-based security, etc. and has crucial applications in smart environment. A non-invasive, automated system for monitoring human activity using array of heterogeneous ultrasonic sensors has been proposed in this work. Ultrasonic sensors are widely used for distance measurement in many applications. In the proposed system experiments have been conducted using ten volunteers in a controlled laboratory environment. The data collection unit has two kinds of setups of ultrasonic sensors: the former with five HC-SR04 sensors, and the latter with four HC-SR04 ultrasonic sensors and an LV-MaxSonar-EZ0 sensor. The proposed method is found capable of detecting standing, sitting and falling of a person, and also the movements in different directions. Based on the collected data, we have performed classification analysis using multiple machine learning algorithms. The experimental results show 81% to 90% correct detection of different activities of the volunteers.

One of the challenges faced by developers of applications for smart environments is the diversity of contexts of use. Applications in smart environments must cope with continuously changing context of use, so the developers need to prepare them for a possibly broad range of situations. Since the developer has no access to all environments, in which her application will be executed, it must be possible to simulate different environments and evaluate the behavior of the application at design time. In our demonstration the designer has the possibility to simulate and modify a runtime context model and observe as her application adapts on the fly. In the underlying runtime architecture applications, defined as sets of models, are adapted automatically on the basis of the information held in the runtime context model. A visual tool enables the user interface developer to access and modify the models at any time and immediately observe the behavior of the application.

This study developed an efficient object detection model for indoor environments, addressing common challenges such as occlusions, varying lighting, and cluttered scenes. We evaluated several YOLOv8 variants—ranging from nano to extra-large—and introduced an optimized YOLOv8x model. Our approach combines structured pruning, quantization-aware training, and advanced data preprocessing techniques, including augmentation and noise reduction, to improve model performance while reducing computational demands. The models were developed and evaluated using a carefully selected indoor object detection dataset featuring ten common categories. Performance was measured through key metrics like precision, recall, and mean average precision (mAP). Among them, the fine-tuned YOLOv8x clearly outshined the baseline models, reaching a training precision of 0.577, a recall of 0.572, and an mAP@0.50 of 0.537. When tested on new data, it demonstrated even better generalization, delivering a precision of 0.502, a recall of 0.528, and an mAP@0.50 of 0.480—proving robustness and reliability in real-world scenarios. These results demonstrate that pruning and quantization can significantly reduce model complexity without sacrificing accuracy, which helps to detect indoor objects. In essence, it is optimized for indoor object detection, offering promising applications in smart environments, surveillance, and robotics.

A consistent user experience in combination with proactive assistance may improve the user performance while interacting with heterogeneous data sources as e.g., occurring in business decision making. We describe our approach which is based on inferring the user intentions from sensory inputs, providing a situation aware information assistance, and controlling the environment proactively by anticipating future goals. Our system has been realized within a smart meeting room and has in parts been evaluated. In this paper, we describe the core ideas underlying our approach and report on first findings from the evaluation.