Climate change, soil erosion, and degradation among others affect the growth and production of crops. Soil is suffering from intensive farming and unsustainable soil disturbance, leading to severe soil degradation. The Internet of Things (IoT) allows the monitoring of crucial environmental parameters such as soil nutrients, moisture, humidity, and temperature. A pre-understanding of these parameters allows agriculturists to use the optimum quantity of water and fertilizer for different types of soil. Soil fertility can be detected by using NPK sensors. The Internet of Things (IoT) brought a new face to the crop farming approach where conventional methods are automated and/or remotely controlled to improve crop farming. In this paper, a survey on IoT technologies for crop farming including sensors, communication, and network protocols in crop farming activities is considered. Additionally, applications of IoT technologies in soil management and monitoring, growth and yield estimation, and quality control mechanisms are presented.

Large-scale renewable energy plants such as solar photovoltaic (PV) farms are vital to the global transition to a green energy economy. They reduce greenhouse gas emissions, mitigate climate change, and promote sustainable and resilient energy. However, large-scale solar PV farms need adequate planning and site selection for optimal performance. This study presents a geographic information system (GIS)-based multi-criteria decision-making (MCDM) framework utilizing the analytic hierarchy process (AHP) to identify optimal sites for utility-scale photovoltaic (PV) farms in Ikorodu, Lagos State, Nigeria. By integrating critical environmental, technical, economic, and social factors, the model evaluates land suitability for solar energy projects across the study area. The finding indicates that 68.77% of the land is unsuitable for development, with only 17.78% classified as highly suitable and 12.67% as moderately suitable. Marginally suitable and most appropriate areas are minimal, at 0.73% and 0.04%, respectively. This study provides a replicable approach for stakeholders and policymakers aiming to implement sustainable energy solutions, aligning with national renewable energy targets. Future research could integrate dynamic factors such as community engagement, land use changes, and evolving environmental policies to enhance decision-making models. This framework offers valuable insights into renewable energy planning and contributes to advancing Nigeria’s transition to sustainable energy systems.

Emerging research has shown the significance of communication technologies in the development of embedded systems and devices. Since there are various communication technologies with varying advantages and disadvantages in embedded systems, decision makers in identifying the best alternative can be prejudiced by various features. Hence, making the selection of the most suitable wireless communication technology a multi-criteria problem. To rank the common wireless communication technologies relevant to the development of embedded systems, this paper presents a multi-criteria decision making (MCDM) approach for analyzing experts opinion on the most preferred wireless communication technologies for embedded devices. To accommodate the subjective nature of experts’ responses, a Fuzzy-TOPSIS MCDM approach is proposed for the ranking wireless communication technologies that can be adopted in the development of embedded systems. Based on the aggregation of responses from experts, the results show that the most preferred wireless communication alternative for embedded application is Wi-Fi with a closeness coefficient (𝐶𝐶𝑖) of 0.52, while the least preferred alternative is Long-Term Evolution (LTE) with a (𝐶𝐶𝑖) 0.32.

To securely address energy shortage and various environmental issues attributed to fossil fuel, the adoption of renewable energy is growing across the globe. However, wind and solar which form the bulk of the emerging renewable energy for micro-grid applications are intermittent and need energy storage device for backup. Due to its environmentally friendly nature, the use of hydrogen as storage mechanism is now being explored for micro-grid applications. However, due to the various technical criteria attributed to various fuel cell (FC) technologies used for hydrogen production, selecting the most suitable alternative remains a challenge. This study uses evaluation based on distance from average solution, a multicriteria decision making tool to rank FC technologies that can be used to produce of hydrogen energy storage in micro-grid applications. The analysis was based on 4 FC technologies and 6 technical criteria. The results of the study show that the most preferred FC technology for micro-grid application is the polymeric electrolyte membrane while the least preferred is molten carbonate FC. It is expected that future analysis would explore the inclusion of socio-economic criteria in the evaluation of the most preferred FC technology for micro-grid application.

Selecting the most preferred software for teaching power systems engineering at the undergraduate level is a complex problem in developing countries, and it requires making an informed decision by compromising on various criteria. This study proposes a multi-criteria framework to determine the most preferred software solution for instructing undergraduate power system modules using the Fuzzy-ARAS (additive ratio assessment) method and expert opinions. Twelve evaluation criteria were used to evaluate eight widely used software packages. A questionnaire was designed to capture views from professionals in academia and industry on the criteria weights and ranking of software options. Linguistic terms were used to represent the experts’ judgment, and weights were assigned to each criterion. The Fuzzy-ARAS multi-criteria decision approach was applied to obtain ratings for each software alternative. Based on the result, MATLAB emerged as the most preferred software for instructing power systems analysis, whereas MATPOWER (V 8.0) was rated as the least preferred choice. In addition, the Fuzzy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach was used, producing a separate ranking; the most preferred software was MATPOWER, while the least preferred software was NEPLAN (V 360 10.5.1). A new coefficient that combines the findings of the two approaches was suggested to reconcile the ranks. The combined ranking aligns with the result of the Fuzzy-TOPSIS method by returning MATLAB as the most preferred, while the least preferred software was NEPLAN. This study significantly contributes to the choice of software for undergraduate power systems analysis instruction by providing direction to educators and institutions looking for software solutions to improve undergraduate power systems analysis education.