# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 7 Conclusions  
In this work, advances and associated challenges in IoT-based solar plant monitoring have been explored. Furthermore, an in-depth review and analysis of different computing paradigms (centralized and decentralized) employed has been performed. Underlying technologies (such as compute boards, sensors, communication and cloud platforms) employed as building blocks for these solutions have been reported and analyzed. This comparative analysis was undertaken based on cost-effectiveness, power efficiency, scalability, reliability, big data management, and operation under harsh environments. It has been concluded that a balance needs to be maintained between levels of monitoring (such as a solar panel, array, or inverter level) and cost incurred in terms of additional hardware requirements, thus increasing the overall monitoring system cost. It was observed that the proposed solutions in the existing literature are either expensive or too complicated for implementation. Furthermore, these solutions provide little support for customization or are designed to address only small-scale systems. In this context, an edge computing paradigm has been proposed, which offers greater scalability, big data support, improved monitoring tools, and enhanced security. Suggestions have been made for low-cost and power-optimized end and edge device fabrication. As meteorological parameters do not vary much from solar array to array, it has been proposed to integrate meteorological sensors at edge nodes instead of at individual panels. To keep overall design complexity and data transmission requirements low, it is proposed that data management should be implemented at edge nodes. As opposed to free and open-source cloud platforms, it was concluded that public AWS is a better choice. Salient contributions range from greater support and tools for IoT systems with enhanced security to a notification and alarm system to notify the user of any unpredictable behavior. As a future consideration, this work can be used for grid integration and smart metering, which will offer improved operating strategies, enhanced power plant scheduling, energy forecasting, and flexible generation and storage. It can play a vital role in a country's economy by filling energy demand gaps as well as mitigating GHG emissions and tackling climate change.  
[PERSON] prepared the structure of the paper with [PERSON] and wrote the primary draft with [PERSON]. [PERSON] has performed an in-depth literature review and conducted an analysis of the techniques proposed in the paper. [PERSON] and [PERSON] wrote the primary part of the paper and revised it with respect to comments from [PERSON] and [PERSON]. Moreover, [PERSON] also assisted in modelling and implementing the figures of the paper in an appropriate way. [PERSON] and [PERSON] have critically reviewed the paper, assisted the team in refining and finalizing the final draft, and suggested some changes. All the authors read, reviewed, and took part in different sections of the paper to make the final version. All the authors have approved the content of the paper to be submitted to this journal. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** In this article, explicit open-source data has not been used for processing or evaluation.  
**Conflicts of Interest:** We have checked first individually and later declared that all authors have no conflicts of interest to disclose.