An optimal strategy for sustainable IoT device placements for agriculture

Abstract

In recent years, there has been a significant increase in the adaptation of current computer methodologies to tackle issues from different fields. Education, medical research, and agriculture are just a few of the fields that have seen fast development as a result of the rapid advancements in contemporary computer technology. These advancements may be seen in the form of more complex technology as well as enhanced algorithms for data processing. One such advancement is the Internet of Things (IoT)-based computing. Smart agricultural processes are being built with the use of Internet of Things (IoT) device-oriented solutions, which are becoming more popular. Nonetheless, the application of IoT devices to tackle these issues across a wide range of fields is fraught with a number of difficulties. The primary challenges are the high cost of deployment, the capacity or sustainability of the deployed device sets due to the limitations of battery technology, and, finally, the maintainability of these devices remotely due to the lack of an adequate communication infrastructure for IoT devices, all of which are significant obstacles. In particular, the adaption of Internet of Things solutions for agriculture has these previously discussed issues to a higher extent. In recent years, a slew of parallel research outputs has emerged, all of which are geared at finding solutions to these issues. Nonetheless, these parallel study outputs or current remedies have been criticized for not addressing all of the issues, but rather for focusing on just one of the three issues that have been identified as problematic. Thus, this study indicates the need, and possibility for developing a framework that may be used to address all of the challenges that have been identified. To begin, the recommended method, which is proven in the work, provides an automated procedure to assess the farm field, and then proposes the most ideal design for placing the Internet of Things devices. This study exhibits a unique application of the curve fitting approach for range, and power awareness, as well as a novel deployment of an optimization method for range, and power awareness, in order to determine the most optimum, and cost-effective deployment map or plan. Second, this study provides a technique for collecting sensor data in the most efficient manner possible, allowing any analytical engine to be constructed on top of the suggested architecture. According to the suggested framework, response time has been reduced by 15%, and average churn rates have been reduced by almost 20% when compared to the results of parallel research, resulting in increased network sustainability when compared to the results of parallel research results.