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From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

The ability to gather, store, and make meaning from large amounts of sensor data is becoming a technological and financial reality for cities. Many of these initiatives are happening through deals brokered between vendors, developers, and cities. They are made manifest in the environment as infrastructure – invisible to citizens and communities. We assert that in order to have community-centered smart cities, we need to transform sensor data collection and usage from invisible infrastructure into visible and legible interface. In this chapter, we compare two different urban sensing initiatives and examine the methods used for feedback between sensors and people. We question how value gets produced and communicated to citizens in urban sensing projects and what kind of oversight and ethical considerations are necessary. Finally, we make a case for “seamful” interfaces between communities, sensors, and cities that reveal their inner workings for the purposes of civic pedagogy and dialogue.

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

Flexible hydrogenated amorphous silicon (a-Si:H) solar cells have many advantages, including lower weight, good flexibility and light sensitivity. Moreover, a-Si:H solar cells can be used as sensors, as indoor light sources and can also generate electricity. These solar cells are suitable for the design of portable systems and curved materials. The purpose of this study was to integrate flexible a-Si:H solar cells and wearable technology and to apply the dual functions of photovoltaics and photo sensors to smart clothing and eyewear.

The integration of flexible a-Si:H solar cells and tri-colour light-emitting diodes (LEDs) was used to develop smart auto-flashing clothing. In addition, we combined flexible a-Si:H solar cells and twisted nematic (TN) liquid crystal (LC) cells to design smart self-activation eyewear.

The maximum power resistance value of flexible a-Si:H solar cells was used to deduce the equation of solar cell voltage value generated by different percentages of SUN (100% SUN means 100 mW/cm²). A solar cell was used as a photo sensor that connects a resistor in a series to the Arduino to detect the voltage value, and then different percentages of SUN are calculated from the equation. Applying the deduced equation to the smart phone APP and Arduino code, we developed a human–machine interface (HMI) to facilitate user operation.

In this study, the flexible a-Si:H solar cell performs the function of not only photovoltaic power generation but also that of a photo sensor. The smart auto-flashing clothing is suitable for traffic guides, joggers and people engaging in other night activities. This smart self-activating eyewear can adjust to light and protect the eyes.

Sensors and transducers form the “front‐ends”, without which many modern electronic systems could not function. Such components are implemented extensively in industrial control systems and energy industry installations (e.g. the oil and gas production and distribution industries). They are also essential components within OEM products such as tape recorders and VCRs. In most of these systems digital electronics is pervasive and considerable advantages are obtained where the sensor is provided complete with extensive electronic circuitry.

The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.

SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.

It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.

In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.

This paper examines the use of intelligent technologies in buildings and whether the use of smart technologies impacts the circular economy performance of buildings in terms of energy and water consumption, their marginal cost and the management decision time and quality, for building management companies.

The study is initiated through the detailed build-up of the proposition that employs a systematic literature review and adopts the case study research design to make a cross-case analysis of the information extracted from data. The data are derived from the operating costs of two buildings in which most advanced smart technologies are used in Cape Town and interviews with their facility managers. These data provide two research case studies. The results of the investigation are then analysed and linked back to the literature.

The results of the research suggest that the implementation of smart technologies to create intelligent infrastructure is beneficial to the circular economy performance of buildings and the time taken for management decisions. The results of the study have proven that the impact of smart technologies on the circular economy performance of buildings is positive, as it lowers the cost of utilities and decreases the time required for management decisions.

The research reported in this paper is exploratory, and due to its limited sample size, its findings may not be statistically generalizable to the population of high-occupancy buildings in Cape Town, which incorporate smart infrastructure technologies within their building management systems (BMSs). Also, the empirical data collected were limited to the views and opinions of the interviewees, and the secondary data were obtained from the selected buildings.

The findings suggest that investment in smart technologies within buildings is of significant value and will improve the circular economy performance of buildings in terms of low energy and water use, and effective management decisions.

The results imply that there would be more effective maintenance decisions taken by facilities managers, which will enable the maintenance of equipment to be properly monitored, problems with the building services and equipment to be identified in good time and in improved well-being and user satisfaction.

The study provides evidence to support the concept that advanced smart technologies boost performance, the time required for management decisions and that they enable circularity in buildings. It supports the proposition that investment in the more advanced smart technologies in buildings has more positive rewards.

The purpose of this paper is to present a novel model for inhabitant prediction in smart houses based on daily life activities. It uses data provided by non intrusive sensors and devices to predict the house occupant. The authors' model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user. BCM was tested using real data and compared with a frequency based approach. In this paper the authors present also their approach to improve the accuracy of BCM using SVM feature selection algorithm.

The model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user.

BCM was tested using real data and compared with a frequency based approach. In this paper the authors' also present their approach to improve the accuracy of BCM using SVM feature selection algorithm.

The paper is based on blind user recognition in smart homes.