The use of wireless and Internet of Things (IoT) devices is growing rapidly. Because of this expansion, nowadays, mobile apps are integrated into low-cost, low-power platforms. Low-power, inexpensive sensor nodes are used to facilitate this integration. Given that they self-organize, these systems qualify as IoT-based wireless sensor networks. WSNs have gained tremendous popularity in recent years, but they are also subject to security breaches from multiple entities. WSNs pose various challenges, such as the possibility of numerous attacks, their innate power, and their unfeasibility for use in standard security solutions. In this paper, to overcome these issues, we propose the secure encryption random permutation pseudo algorithm (SERPPA) for achieving network security and energy consumption. SERPPA contains a major entity known as a cluster head responsible for backing up and monitoring the activities of the nodes in the network. The proposed work performance is compared with other work based on secure IoT devices. The calculation metrics taken for consideration are energy, overheads, computation cost, and time consumption. The obtained results show that the proposed SERPPA is very significant in comparison to the existing works, such as GKA (Group Key Agreement) and MPKE (Multipath Key Establishment), in terms of data transfer rate, energy consumption and throughput.

The pest and insect affected crop is an important concern to cause damage to the agricultural sector. While identifying the pest in the crop, the camera placement is not supported in an inconsistent manner to capture the pest images. Hence, certain Internet of Things (IoT) devices are used to catch the pest images with its corresponding agriculture based sensor, yet it also faces some limitations to provide the accurate results. In order to alleviate the problem, an IoT-assisted pest identification and classification method is proposed. Initially, the IoT sensors are used to collect the required images. Subsequently, the input images are used to perform the object detection phase that is accomplished by the Yolov3, where the pest is detected significantly. Further, the detected images are fed into the model of “Convolutional Neural Network (CNN),” in which the deep features are fetched and finally given as input to the classifier model of “Convolution Neural Long Short-Term Memory (CNLSTM),” in turn some hyper parameters are optimally tuned by “Adaptive Honey Badger Algorithm (AHBA).” Hence, the experimental results prove that the recommended method achieves the better performance in terms of diverse metrics.

Several advanced features exist in fifth-generation (5G) correspondence than in fourth-generation (4G) correspondence. Centric cloud-computing architecture achieves resource sharing and effectively handles big data explosion. For data security problems, researchers had developed many methods to protect data against cyber-attacks. Only a few solutions are based on blockchain (BC), but are affected by expensive storage costs, network latency, confidence, and capacity. Things are represented in digital form in the virtual cyberspace which is the major responsibility of the communication model based on cybertwin. A novel cybertwin-based UAV 6G network architecture is proposed with new concepts such as cloud operators and cybertwin in UAV. Here, IoE applications have to be energy aware and provide scalability with less latency. A novel Compute first networking (CFN) framework named secure blockchain-based UAV communication (BC-UAV) is designed which offers network services such as computing, caching, and communication resources. The focus of the blockchain was to improve the security in the cloud using hashing technique. Edge clouds support core clouds to quickly respond to user requests.

The Accessing academic information like exam time tables, exam scores, syllabus and tutor information on institutional websites can be time consuming and tedious on behalf of the student. In this case, we will solve this dilemma by creating an intelligent and an automated Student Intent-based Educational Chatbot that integrates deep learning along with state-of-art optimization algorithms. Following the pre-processing of student inquiries with generic chatbot data sets, the system starts operating. The text data that were cleaned are represented as vectors through three high-performing language representations, i.e., BERT, TransformerNet, and Text CNN. Such vectors are subject to weighted selection of features and fusion where best features are selected and fused together by Averaging-based Driving Training -Barnacles Mating Optimizer (ADT -BMO) is a new hybrid metaheuristic optimization algorithm. ADT-BMO is smart when it comes to weighting the feature and optimization parameter to maximize the relevance of fused features. Thicken with Symmetric Convolution (AA-DTCN-SC) and the down-stream adaptive feature set refined above are fed to this network to achieve accurate intent recognition. ADT-BMO also improves AA-DTCN-SC model by optimizing the parameters hidden neurons, activation functions and epochs under which the classification accuracy is high. In testing the noted purpose, there is an automatic building of system responses to queries formulated using contextually right answers. The experimental simulations also illustrate the effectiveness of the superiority of this approach as the chatbot performs better than its baseline models of DTCN, RNN and Bi-LSTM by 4.44%, 3.3%, 10.59%, 11.9, respectively. The given research therefore presents a well-functioning, scalable and time-saving educational chatbot, which improves student engagement through provision of quick, precise, and pertinent scholarly assistance. • To help save the time and work required of students and administrators by replying to common academic questions through the creation of an automated intelligent chatbot that is able to find correct and instant answers based on deep learning and NLP approaches. • In order to eliminate the shortcomings of current chatbot platforms, i.e. irrelevant outputs and fabricated predictivity of user intent, through implementation of a different novel model (AA-DTCN-SC) on deep learning, which is optimized using a hybrid ADT-BMO algorithm to aid intent inference and user interaction in educational settings.