Due to advances in technology, research in healthcare using a cyber-physical system (CPS) opens innovative dimensions of services. In this paper, the authors propose an energy- and service-level agreement (SLA)-efficient cyber physical system for E-healthcare during data transmission services. Furthermore, the proposed phenomenon will be enhanced to ensure the security by detecting and eliminating the malicious devices/nodes involved during the communication process through advances in the ad hoc on-demand distance vector (AODV) protocol. The proposed framework addresses the two security threats, such as grey and black holes, that severely affect network services. Furthermore, the proposed framework used to find the different network metrics such as average qualifying service set (QSS) paths, mean hop and energy efficiency of the quickest path. The framework is simulated by calculating the above metrics in mutual cases i.e., without the contribution of malevolent nodes and with the contribution of malevolent nodes over service time, hop count and energy constraints. Further, variation of SLA and energy shows their expediency in the selection of efficient network metrics.

The Internet of Things (IoT) is a new-fashioned technology that is the future of the next era of the internet which connect various physical objects that communicate with each other without the aid of human interactions. Security plays important role in network to prevent the unauthorized access, misuses of data, monitoring and data, modification etc. All layer in IoT architecture security considered as extremely important from viewpoint of designing criteria from bottom label to top label. IoT application is becoming important in day to day lifestyle such as healthcare, smart grid, smart home, smart parking. IoT application is useful to people but if the IoT system can't protect the user data from hacker, attacks, and vulnerabilities. Lightweight encryption is a sector of a classical cryptographic algorithm Sthat is pertinent for resource constrained devices in IoT. Related work for lightweight techniques used for secure data transmission is described in this paper.

Precision agriculture system is an arising idea that refers to overseeing farms utilizing current information and communication technologies to improve the quantity and quality of yields while advancing the human work required. The automation requires the assortment of information given by the sensors such as soil, water, light, humidity, temperature for additional information to furnish the operator with exact data to acquire excellent yield to farmers. In this work, a study is proposed that incorporates all common state-of-the-art approaches for precision agriculture use. Technologies like the Internet of Things (IoT) for data collection, machine Learning for crop damage prediction, and deep learning for crop disease detection is used. The data collection using IoT is responsible for the measure of moisture levels for smart irrigation, n, p, k estimations of fertilizers for best yield development. For crop damage prediction, various algorithms like Random Forest (RF), Light gradient boosting machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN) are used. Subsequently, Pre-Trained Convolutional Neural Network (CNN) models such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop was tainted with some illness or not.