IoT (Internet of Things) usage in industrial and scientific domains is progressively increasing. Currently, IoTs are utilized in numerous applications in different domains, similar to communication technology, environmental monitoring, agriculture, medical services, and manufacturing purposes. But, the IoT systems are vulnerable against various intrusions and attacks in the perspective on the security view. It is essential to create an intrusion detection model to detect and secure the network from different attacks and anomalies that continually happen in the network. In this paper, the anomaly detection model for an IoT network using deep neural networks (DNN) with chicken swarm optimization (CSO) algorithm was proposed. Presently, the DNN has demonstrated its efficiency in different fields that are applicable to its usage. Deep learning is the type of algorithm based on machine learning which used many layers to gradually extricate more significant features of level from the raw inputs. The UNSW-NB15 dataset was utilized to evaluate the anomaly detection model. The proposed model obtained 94.85% accuracy and 96.53% detection rate which is better than other compared techniques like GA-NB, GSO, and PSO for validation. The DNN-CSO model has performed well in detecting most of the attacks, and it is appropriate for detecting anomalies in the IoT network.

Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. The main objective of this paper is to detect the most appropriate plant development parameters. This paper has the concept of reducing the hazards in agriculture and promoting intelligent farming. Advancement in agriculture is not new, but the AI-based wireless sensor will push intelligent agriculture to a new standard. The research goal of this work is to improve the prediction state using image processing-based machine learning techniques. The main objective of the paper, as described above, is to detect and control cotton leaf diseases. This paper comprises several aspects, including leaf disease detection, remote monitoring system depending on the server, moisture and temperature sensing, and soil sensing. Insects and pathogens are typically responsible for plant diseases that reduce productivity if not timely. This paper presents a method to monitor the soil quality and prevent cotton leaf diseases. The proposed system suggested uses a regression technique of artificial intelligence to identify and classify leaf diseases. The information would be delivered to farmers through the Android app after infection identification. The Android app also allows soil parameter values like moisture, humidity, and temperature to be displayed along with the chemical level in a container. The relay may be on/off to regulate the motor and chemical sprinkler system as required by using the Android app. In the proposed system, the SVM algorithm delivers the best accuracy in detecting various diseases and demonstrates its efficiency in the detection and control by the improvement of cultivation for the farmers.

The satellite communication is embellished constantly by providing information, ensuring security, and enables the communication among huge at a particular time efficiently. The satellite navigation helps in determining the people’s location. Global development, natural disasters, change in climatic conditions, agriculture crop growth, etc., are monitored using satellite observation. Hence, the satellite includes detailed information data, and it must be protected confidentially. The field of the satellite is enhanced at an astonishing pace. Satellite data play an important role in this modern world; hence, the onboard-satellite data must secure through the proper selection of error detection and estimation schema. Lightweight deep learning algorithm based on Extended Kalman Filter (KFK) is proposed to detect and estimate onboard pointing error such as an error in attitude and orbit. The Extended Kalman Filter (EKF) is widely used in the satellite system. EKF is utilized in this proposed model to detect the onboard pointing error such as attitude and orbit determination. An autonomous estimation of orbit position is possible through space-borne gravity. The information obtained through the observation of satellite data is compared with the accurate gravity model in detecting the error. The utilization of EKF reduces the dependence of the ground tracking system in satellite determination. The orbital altitude and orbital position are the most important challenges faced in the satellite determination system. The satellite model using the Extended Kalman Filter is an optimum method in estimating the orbital parameters. The errors in the linearization process are detected, and this can be overcome through the proper selection of linear expansion point with the EKF algorithmic model with the Jacobian matrix calculation. The results show that the EKF implementation helps in attaining better accuracy than other methodologies. Its contribution is enormous to many space missions, autonomous rendezvous and docking for manned and unmanned missions (e.g., ISS operations and beyond, in-orbit servicing, and in-orbit refueling), routine satellite OD operations, orbital debris removal systems, Space Situational Awareness (SSA) operations, and others.

The diffusion bonding (DB) method is used in this investigation to connect high-temperature dissimilar materials. The existence of difficult-to-remove oxide coatings on the titanium surfaces, as well as the arrangement of breakable metallic interlayers and oxide enclosures inside the bond region, provides the most significant challenges during the transition from AISI304 to Ti-6Al-4V alloying. In addition, an effort was made to advance DB processing maps for the operational connection of Ti-6Al-4V to AISI304 alloys to improve their performance. Joints had been created by combining several process factors, such as bonding temperature (T), bonding pressure (P), and holding time (t), to create diverse designs. Based on the findings, database processing maps were created. This set of processing maps may be used as a rough guideline for selecting appropriate DB process parameters for generating virtuous excellent bonds between Ti-6Al-4V and AISI304 alloys. The maximum lap shear strength (LSS) was achieved at 800°C, 15 MPa, and 45 min.

In mobile nodes in the network, they are unstable, so single-path communication does not provide sufficient results. In the alternative single path, communication is very difficult to handle the heavy load. The poor connectivity among the mobile node makes the uncertainty of packet loss; the path link is not measured in this network. The communication cost is also focused to achieve valid packet transmission. Because the high distance path selected for packet transmission causes a high cost for communication. It increases energy consumption and packet loss rate. So, the proposed dispersed path selection for communication (DPAC) method is constructed to obtain the best minimum distance routing path, this path operates with the help of queue variation that handled the data packet’s maintenance, and the time slot exceeds its limit. Packets are kept waiting, to increase the packet broadcasting efficiency. The multipath jamming detection algorithm is constructed to provide link-based path packet overload detection scheme to identify the packet overload. Also, separate the path based on its characteristics, to control overload. It reduces energy consumption and packet loss rate.