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- W3200316478 abstract "Adaptation of modern computing strategies to solve various problems from other domains are increasing drastically. The domains such as education, medical science, and agriculture have seen rapid growth with the help of accelerated improvements of modern computing. These improvements can be observed in terms of sophisticated hardware and improved algorithms for analysis of data. One such improvement is IoT-based computing. The IoT devices-oriented solutions are adopted to a great extend for building smart agriculture processes. Nonetheless, the applicability of the IoT devices to solve these problems of various domains comes with few challenges. The primary challenges are the high cost of deployment, the capacity or sustainability of the deployed device sets due to the limitation of battery technology and lastly the maintainability of these devices remotely due to not so improved communication infrastructure for the IoT devices. Specifically, the adaptation of IoT solutions for agriculture has these mentioned problems to a greater extend. In the recent times, many parallel research outcomes can be observed, which all aim to solve these problems. Nonetheless, these parallel research outcomes or the existing solutions are criticised for not solving all the problems, rather just focused on one problem out of the three highlighted. Henceforth, this work identifies the need and the opportunity to build a framework to solve all the mentioned problems. The proposed strategy demonstrated in the work firstly develops an automatic process to analyse the agriculture field and proposed the most optimal plan to place the IoT devices. To find the most optimal and cost-effective deployment map or plan, this work demonstrates a novel application of the curve fitting method and a novel deployment of optimization method for range and power awareness. Secondly, this work also demonstrates a strategy to collect the sensor data optimally so that, any analytical engine can be built on this proposed architecture. The proposed framework has improved response time by 15% and the average churn rates by nearly 20% compared to the parallel research outcomes to increase sustainability of the network compared with the parallel research outcomes" @default.
- W3200316478 created "2021-09-27" @default.
- W3200316478 creator A5031701396 @default.
- W3200316478 date "2021-09-08" @default.
- W3200316478 modified "2023-09-23" @default.
- W3200316478 title "An Optimal Strategy for Sustainable IoT Device Placements for Agriculture with sensors" @default.
- W3200316478 hasPublicationYear "2021" @default.
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