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In this chapter, an attempt has been made to develop a security-based hardware system using an 8-bit single-chip microcontroller in conjunction with some sensor technology and lighting and alarming actuators. The proposed system aims to ensure the security and privacy of a dedicated area in terms of unauthorized human intrusion, hazardous gas leakage, extreme prolonged temperature changes, atypical smoke or vapor content in space and abrupt drop in illumination. The proposed system is capable of detecting any type of physical intervention and hazardous anomalies in the environment of the reserved space. In order to define the operation of the system, programs written in C++ with the special rule of code structuring have been deployed on the microcontroller using the Arduino Integrated Development Environment. The system is like a small container, enclosing all the respective sensor modules, microcontroller board and open connections for actuators. The proposed system is easy to use hardware and does not demand any human intervention for its functioning and can be installed with almost no changes in the infrastructure.

This paper describes the application of, enhancements to, and use of surface fire spread models in predicting and mitigating fire risk in the Wildland–Urban Interface (WUI). Research and fire management strategies undertaken in the East Bay Hill region (containing the 1991 Tunnel Fire) of the San Francisco Bay area over the past decade are reported. We ascertain that surface fire spread modeling has impacted policy and decision making, resulting in a regional strategic plan where large landowners and public agencies are able to implement fire mitigation practices. Although these practices involve extensive fuel management within a buffer zone between the wildland and residential properties, the residential property owners are still at risk, as no strategy within neighborhoods can be accurately mapped using the current scale of the data and models. WUI fires are eventually extinguished by fire fighters on the ground, up close, and at the backyard scale. We argue that large-scale (backyard scale) mapping and modeling of surface fire spread is necessary to engage the individual homeowner in a fuels management strategy. We describe our ongoing research and strategies, and suggest goals for future research and development in the area of large-scale WUI fire modeling and management.

This chapter presents the Smart Irrigation system using the Internet of Things (IoT). IoT Technology is a network of physical objects that are connected with sensors, software, etc. This chapter concludes the project based on the agriculture field that automates the irrigation process and on the agriculture field that automates the irrigation process and solves the challenge of water consumption in those areas. We have developed the system using different sensors like (1) Soil Moisture sensor, which measures the moisture present in the soil, (2) Humidity and Temperature Sensor (DHT11), which traces the temperature change. All these sensors are connected to the Node MCU ESP8266 microcontroller, which is also a Wi-Fi module. It uploads the data to the cloud and displays it in the form of readings detected by the Blynk Application. This sensor's reading values control the pump for emergency purposes, such as stopping the pump for irrigation. Thus, this project can automate the irrigation process by analyzing soil moisture and climatic conditions, covering essential aspects like less labor, power consumption, reliability, and cost.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

The performance of thermal comfort utilising machine learning and its acceptability by students and other users at the Professor Sir Edouard Lim Fat Engineering Tower at the University of Mauritius are evaluated in this study. Students and building occupants were asked to fill out surveys on-site as data was gathered from sensors throughout the structure. The Thermal Sensation Vote (TSV) and other important data were collected through the surveys, including the effect of wind on thermal comfort. An adaptive model incorporating solar and wind effects was evaluated using multiple linear regression techniques and RStudio. Three models were used to evaluate thermal comfort, including the adaptive one. Numerous models were compared and evaluated in order to select the best one. It was found that the adaptive model (Model 1) was deemed to be the best model for its application. It was also found that Fanger's PMV/PPD (Model 2) was a very good approach to determining thermal comfort. Through thorough analysis, it was concluded that the range of air temperature and wind speed for thermal comfort was 25.830°C–28.0°C and 0.26 m/s to 0.42 m/s, respectively. In order for cities to remain secure, resilient and sustainable, it will be important to manage thermal comfort and reduce populations' exposure to heat stress (SDG 11). The achievement of income and productivity goals will be hampered if measures to protect populations from heat stress are not taken (SDG 8). Thermal regulation is also necessary for the provision of numerous health services (SDG 3).

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Natural flood disasters frequently happen in Malaysia especially during monsoon season and Kuala Kangsar, Perak, is one of the cities with the frequent record of natural flood disasters. Previous flood disaster faced by this city showed the failure in notifying the citizen with sufficient time for preparation and evacuation. The authority in charge of the flood disaster in Kuala Kangsar depends on the real-time monitoring from the hydrological sensor located at several stations along the main river. The real-time information from hydrological sensor failed to provide early notification and warning to the public. Although many hydrological sensors are available at the stations, only water level sensors and rainfall sensors are used by authority for flood monitoring. This study developed a flood prediction model using artificial intelligence to predict the incoming flood in Kuala Kangsar area based on artificial neural network (ANN). The flood prediction model is expected to predict the incoming flood disaster by using information from the variety of hydrological sensors. The study finds that the proposed ANN model based on nonlinear autoregressive network with exogenous inputs (NARX) has better performance than other models with the correlation coefficient that is equal to 0.98930. The NARX model of flood prediction developed in this study can be referred to as the future flood prediction model in Kuala Kangsar, Perak.

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.