Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

Meetings of the Advisory Technical Committees of the International Electrotechnical Commission were held in Brussels March 27th to April 1st. The meetings were attended by delegates from eight national committees — Belgium, France, Great Britain, Holland, Italy, Spain, Switzerland, and the United States. There were about 40 delegates in all.

https://ieeexplore.ieee.org/iel5/5308791/5321388/05321395.pdf

The International Electrotechnical Commission

Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents

The wireless sensor networks (WSNs) are used for the real-life implementation of the Internet of Things (IoT) in smart agriculture, smart buildings, smart cities, and online industrial monitoring applications. Generally, traditional WSN nodes are powered by limited energy capacity, non-rechargeable batteries. The WSN lifetime (days) depends upon, duty cycle, type of application deployment, and battery state of charge (SoC) level. We propose an innovative solution to the limited energy availability design problem by utilizing the ambient solar energy harvesting for battery charging of WSN nodes. However, there are many challenges in solar energy harvesting like intermittency of available power, solar energy prediction, thermal issues, solar panel conversion efficiency, and other environmental issues. The objective of this research work is to maximize the WSN network lifetime using solar energy harvesting technique. From our simulation results, it is proved that the sensor network lifetime is increased from 5.75 days to 115.75 days @ 25% duty cycle and higher, ideally up to infinite network lifetime. Furthermore, the network throughput is also increased from 100 K bits/s to 160 K bits/s. in SEH-WSNs.

Maximization of Wireless Sensor Network Lifetime using Solar Energy Harvesting for Smart Agriculture Monitoring

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.255

Fault diagnosis of Photovoltaic Modules

Solar energy harvesting that provides an alternative power source for an energy-constrained wireless sensor network (WSN) node is completely a new idea. Several developed countries like Finland, Mexico, China, and the USA are making research efforts to provide design solutions for challenges in renewable energy harvesting applications. The small size solar panels suitably connected to low-power energy harvester circuits and rechargeable batteries provide a loom to make the WSN nodes completely self-powered with an infinite network lifetime. Recent advancements in renewable energy harvesting technologies have led the researchers and companies to design and innovate novel energy harvesting circuits for traditional battery powered WSNs, such as Texas Instruments Ultra Low Energy Harvester and Power Management IC bq25505 [see https://store.ti.com/BQ25505 for Texas Instruments (TI) Ultra Low Power Boost Charger IC bq25505 with Battery Management and Autonomous Power Multiplexor for Primary Battery in Energy Harvester Applications datasheets (2015).]. In modern days, the increasing demand of smart autonomous sensor nodes in the Internet of Things applications (like temperature monitoring of an industrial plant over the internet, smart home automation, and smart cities) requires a detailed literature survey of state of the art in solar energy harvesting WSN (SEH-WSN) for researchers and design engineers. Therefore, we present an in-depth literature review of Solar cell efficiency, DC-DC power converters, Maximum Power Point Tracking algorithms, solar energy prediction algorithms, microcontrollers, energy storage (battery/supercapacitor), and various design costs for SEH-WSNs. As per our knowledge, this is the first comprehensive literature survey of SEH-WSNs.

Solar energy harvesting wireless sensor network nodes: A survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

https://www.mdpi.com/2079-9292/10/9/1012/pdf

Ahteshamul Haque

Papers

Maximization of Wireless Sensor Network Lifetime using Solar Energy Harvesting for Smart Agriculture Monitoring

Fault diagnosis of Photovoltaic Modules

Solar energy harvesting wireless sensor network nodes: A survey

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging