Diseases related to poor water and sanitation conditions have over 200 million cases reported annually, causing 5-10 million deaths worldwide. Water quality monitoring has thus become essential to the supply of clean and safe water. Conventional monitoring processes involve manual collection of samples from various points in the distribution network, followed by laboratory testing and analysis. This process has proved to be ineffective since it is laborious, time consuming and lacks real-time results to promote proactive response to water contamination. Wireless sensor networks (WSN) have since been considered a promising alternative to complement conventional monitoring processes. These networks are relatively affordable and allow measurements to be taken remotely, in real-time and with minimal human intervention. This work surveys the application of WSN in environmental monitoring, with particular emphasis on water quality. Various WSN based water quality monitoring methods suggested by other authors are studied and analyzed, taking into account their coverage, energy and security concerns. The work also compares and evaluates sensor node architectures proposed the various authors in terms of monitored parameters, microcontroller/microprocessor units (MCU) and wireless communication standards adopted, localization, data security implementation, power supply architectures, autonomy and potential application scenarios.

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The rapid development of wireless technology has sparked interest in multi-band reconfigurable antennas as devices and satellites are innovating toward miniaturization. With limited space, reliable and efficient high bandwidth antenna systems are needed for current and next-generation wireless technology as well as for the revolutionary small satellites. The fifth generation of mobile communication technology promises high data rates, low latency and good spectrum efficiency. One of the key enablers of this technology is the integration of satellite technology-particularly CubeSats with terrestrial communication technologies. Next-generation antennas that can meet functional requirements for 5G and CubeSat applications are therefore of fundamental importance. These antenna systems should have large bandwidth, high gain and efficiency and be compact in size. Reconfigurable antennas can provide different configurations in terms of the operating frequency, radiation pattern and polarization. Tuning reconfigurable antennas can be done by changing the physical parameters of the antenna elements through electronic switches, optical switches and the use of meta-materials. The most popular implementation method for

The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models. To further enhance the comparison and provide more insights into the advancement in the area, we simulate the performance of different ML methods used in solar PV power forecasting and, finally, a discussion on the results of the work.