An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)

During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect of the field. Therefore, WSNs are insecure because of this. With so much riding on WSN applications, accuracy in replies is paramount. Technology that can swiftly and continually analyse internet data streams is essential for spotting breaches and assaults. Without categorization, it is hard to simultaneously reduce processing time while maintaining a high level of detection accuracy. This paper proposed using a Multi-Layer Perceptron (MLP) to enhance the classification accuracy of a system. The proposed method utilises a feed-forward ANN model to generate a mapping for the training and testing datasets using backpropagation. Experiments are performed to determine how well the proposed MLP works. Then, the results are compared to those obtained by using the Hoeffding adaptive tree method and the Restricted Boltzmann Machine-based Clustered-Introduction Detection System. The proposed MLP achieves 98% accuracy, which is higher than the 96.33% achieved by the RBMC-IDS and the 97% accuracy achieved by the Hoeffding adaptive tree

Control of pests and diseases in plants using IOT Technology

The term ''smart agriculture'' describes how farming is carried out in the modern day as technology develops. Application of diverse insect protection and agricultural production tactics is crucial for crop monitoring. The structure as it is now has problems. A particular core Graphical Processing Unit (GPU) is used to manage the numerous sensors connected for crop surveillance and pest management. A Parallel and Distributed Simulation Framework (PDSF) with the Internet of Things (IoT) is proposed for pest management and agricultural monitoring tools. It lessens the pressure on a certain GPU, evenly distributes the workload over all available GPUs at simultaneously, and delivers data to the dashboards even when it's broken. The procedure will decrease system performance. In the PDSF multi-threading paradigm, each GPU unit distributes workloads to specific additional cores. To carry out the various tasks, the four levels of this system—crop management, pest identification and control, output activities, and input functional areas—are distributed among them. The information is processed simultaneously and handled in an efficient and controlled manner. The proposed system improves the performance measures of 98.65%