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Bulk micromachining in Si (110) wafer is an essential process for fabricating vertical microstructures by wet chemical etching. We compared the anisotropic etching properties of potassium hydroxide (KOH), tetra-methyl ammonium hydroxide (TMAH) and ethylene di-amine pyro-catechol (EDP) solutions. A series of etching experiments have been carried out using different etchant concentration and temperatures. Etching at elevated temperatures was found to improve the surface quality as well as shorten the etching time in all the etchants. At 120°C, we get a smooth surface (Ra = 21.2 nm) with an etching rate 12.2 lm/min in 40wt% KOH solution. At 125°C, EDP solution (88wt%) was found to produce smoothest surface (Ra = 9.4 nm) with an etch rate of 1.8 lm/min. In TMAH solution (25wt%), the best surface roughness was found to be 35.6 nm (Ra) at 90°C with an etch rate of 1.18 lm/min. The activation energy and preexponential factor in Arrhenius relation are also estimated from the corresponding etch rate data.

Waste has a direct impact on human health and the surrounding environment. Apart from the health aspect, many industries' growth is effected by waste material such as the food industry. Waste management authorities are interested in reducing the cost of waste management operations and searching for sustainable waste management solutions. For effective planning of waste management, reliable data analysis is required to produce results that can facilitate the planning process. Data mining and machine learning-based data analysis over the waste data can produce a more detailed, and in-time waste information generation, which can lead to effectively manage the waste amount of specific area. In this paper, a descriptive data analysis approach, along with predictive analysis, is used to produce in-time waste information. The performance of the proposed approach is evaluated using a real waste dataset of Jeju Island, South Korea. Waste bins are virtualized on its actual location on the Jeju map in Quantum Geographic Information Systems(QGIS) software. The performance results of the predictive analysis models are evaluated in terms of Mean Absolute Error(MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). Performance results indicate that predictive analysis models are reliable for the effective planning and optimization of waste management operations.INDEX TERMS Waste monitoring, QGIS, descriptive analytics, predictive analytics, waste management, data analysis.

The exponentially growing population, urbanization, and economic development have led to the rising generation of municipal solid waste. Municipal solid waste management is thus a significant hurdle for urban societies as it consumes a large chunk of public funds, and, when mishandled, it can lead to environmental and social hazards. Some of the prerequisites required for effective waste management are the monitoring of bins, timely collection of bins, and prioritization of those areas which produce more solid waste. In this paper, we propose an optimal route recommendation system for waste carriers vehicles to effectively collect solid waste based on the profile of a particular area. This article contributes with a multi-objective optimization approach to generate a route by minimizing the route distance and maximizing the amount of waste. Then, a family of evolutionary methods is employed to solve the proposed objective function and find the optimal route for waste carrier vehicles. The experiment is carried out on the real-world solid waste data of Jeju Island, South Korea. The data is processed to predict the behavior of people of a specified grid location in terms of waste disposal. Therefore, the recommendation system caters to the predicted waste across a set of bins inside the area, and considering the constraints such as total allowed distance and time, proposes a route that is best in terms of distance (fuel consumption) and waste collection. Different use cases are illustrated to signify the proposed system, and results indicate that it can be a step forward for the implementation of smart cities, which is the goal of Jeju Island. INDEX TERMS Waste management, route optimization, smart cities, sustainable development, green projects, Jeju Island.

Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient’s vital signs data in the IoT-based e-Health domain.

Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

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In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.