Abstract Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter‐class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid‐Convolutional Support Machine (H‐CSM) based on ‘Support Vector Machine (SVM)’, ‘Convolutional Neural Network (CNN)’ and ‘Convolutional Block Attention Module (CBAM)’ is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method.

The development of underwater wireless sensor networks (UWSNs) has attracted great interest from many researchers and scientists to detect and monitor unfamiliar underwater domains. To achieve this goal, collecting data with an underwater network of sensors is primordial. Moreover, real-time information transmission needs to be achieved through efficient and enabling technologies for node deployment and data collection in UWSN. The Internet of Things (IoT) helps in real-time data transmission, and it has great potential in UWSN, i.e., the Internet of Underwater Things (IoUT). The IoUT is a modern communication ecosystem for undersea things in marine and underwater environments. Intelligent boats and ships, automatic maritime transportation, location and navigation, undersea discovery, catastrophe forecasting, and avoidance, as well as intelligent monitoring and security are all intertwined with the IoUT technology. In this article, the enabling technologies of UWSN along with several fundamental key aspects are scrupulously explained. The study aims to inquire about node deployment and data collection strategies, and then encourages researchers to lay the groundwork for new node deployment and advanced data collection techniques that enable effective underwater communication techniques. Besides different types of communication media, applications of UWSNs are also part of this article. Various existing data collection protocols based on the deployment models are simulated using network simulator (NS 2.30) to analyze and compare the performance of state-of-the-art techniques.

Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction.

Abstract Sensors in underwater wireless sensor networks (UWSNs) can drift up to 3 m/s due to ocean currents, marine organisms, or passing vessels. In existing node deployment and localization techniques, node mobility and network disconnections are not taken into account. This article proposes a dynamic topology control algorithm for node deployment (DTCND) in mobile UWSN. This work aims to monitor the node mobility to predict nodes' location for ensuring coverage and connectivity. The sensor nodes are deployed randomly at different depths. The anchor nodes observe the signal quality index, energy drain rate, and node density at every time interval and detect node disconnections based on the variations in these observed metrics. After receiving the beacon messages from the anchor nodes, the courier nodes incline to move toward the target region for satisfying the coverage and connectivity constraints. Simulation results show that the proposed algorithm attains 5% higher connectivity when compared to energy‐efficient localization algorithm (EELA) and 9% higher connectivity when compared to adaptive triangular deployment algorithm (ATDA). The residual energy is also higher by 3% and 18% when compared to EELA and ATDA, respectively. The deployment cost and delay of DTCND also decrease when compared to EELA and ATDA, which results into efficient data collection.