An optimal energy utilization model for precision agriculture in WSNs using multi-objective clustering and deep learning

Wireless Sensor Networks (WSNs) play a crucial role in Precision Agriculture by providing real-time data on various environmental parameters like temperature, humidity, soil moisture, etc. However, the efficient utili-zation of energy in the sensor nodes of WSNs is a major challenge that needs to be addressed. To address this issue, a new multi-objective clustering approach is introduced in this work for grouping the sensor nodes of WSNs. Moreover, a multi-objective hybrid optimisation technique called Election based Aquila Optimizer (EAO) which is the combination of Aquila Optimizer (AO) and the Election-Based Optimisation Algorithm (EBOA) is proposed in this work to make sure that the Cluster Head (CH) selection process in WSNs to identify the best CH. In addition, the proposed method incorporates the newly developed optimization technique with convolutional neural network (CNN) as an Optimized CNN (O-CNN) to improve the clustering algorithm's precision and also enhance the training accuracy and testing accuracy. The proposed approach is evaluated through experiments and proved as better than other approaches by obtaining 99.23% as classification accuracy, 76.92% as throughput, 99% as packet delivery ratio, 98.24% as network lifetime and 50% as maximum energy consumption and it resolves a significant difficulty in precision agriculture.

Automated summary

Wireless Sensor Networks (WSNs) are crucial in Precision Agriculture for real-time data collection. Efficient energy utilization and Cluster Head (CH) selection processes are addressed using a multi-objective clustering approach and a hybrid optimization technique. The proposed method also improves clustering algorithm precision and training accuracy by combining optimization techniques with convolutional neural networks.