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The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Smart agriculture is a recent trend in this era, which adopts smart technology to increase farmland productivity. IoT gives a new dimension in the area of smart farming and agriculture domain. This research contains main modules are such as system model with energy model & objective model, CH selection-based data aggregation and shortest path routing in IoT. The proposed Agriculture Monitoring System (ACM) is focused to ensure the accurate data aggregation and reduction of energy consumption over WSN based IoT. Initially, the system model is constructed along with energy model and objective model for efficient data aggregation. The Grey Wolf Optimization (GWO) technique is used to find the more possible solution node and provides the better performance of the data aggregation process. It also checks the location accuracy and minimization of localization error. Then the shortest path routing is ensured by using by Enhanced Ad-hoc on-demand Distance Vector Routing (EAODV) protocol which is used for fast data aggregation during data communication for Smart Agriculture application. EAODV discovers the best path between sensor nodes and it denotes the node contain minimum distance and faster packet transmission in IoT. Also, it avoids congestion hence packet loss and routing overhead is reduced prominently. In the proposed framework, data from the agriculture sensors are routed while using a trusted network towards the CH and further towards the BS. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using sensor nodes.
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