Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.

Unmanned Aerial Vehicles (UAVs) equipped with remote sensing (RS) abilities have revolutionized agriculture practices, especially in the area of plant disease detection, with the incorporation of deep learning (DL) techniques. Using the UAVs to capture high-resolution images of agricultural regions, RS information becomes widely accessible for monitoring and identifying crop health and signs of diseases. DL models, specifically convolutional neural networks (CNNs), are employed to analyze these images, enabling automated recognition and detection of plant diseases with enhanced accuracy. This integration of UAV-based RS and DL models facilitates early disease detection, allowing farmers to implement timely interventions, such as targeted pesticide application or crop management strategies, ensuring food security and reducing yield losses. Moreover, this model’s scalability and efficiency improve its value for precision agriculture, optimizing resource usage and promoting sustainable farming practices. This manuscript proposes an Optimal Fuzzy Deep Neural Networks-based Plant Disease Detection and Classification (OFDNN-PDDC) technique on UAV-based Remote Sensed Data. The purpose of the OFDNN-PDDC technique is to proficiently detect and classify the distinct kinds of plant diseases. The OFDNN-PDDC technique follows a three-stage process to enhance plant disease detection performance. At the primary level, the OFDNN-PDDC technique employs an improved ShuffleNetv2 model for learning complex and intrinsic feature patterns on the RS data. Besides, the OFDNN-PDDC technique utilizes a fuzzy restricted Boltzmann machine (FRBM) model to detect plant diseases. Finally, the hyperparameter selection of the OFDNN-PDDC technique is performed by the tent chaotic salp swarm algorithm (TCSSA) model. To illustrate the superior outcomes of the OFDNN-PDDC technique, detailed experiments were accomplished by utilizing APD and CPD datasets. The experimental validation of the OFDNN-PDDC technique portrayed a superior accuracy value of 96.18% and 98.85% over existing techniques under the used datasets.

A numbers of distributed autonomous sensor nodes are deployed in a wireless sensor network environment for carrying out multiple tasks. Data packets transmitted in wireless sensor network needs to be protected. Security keys can be generated and used to transmit data securely. These security key generation task needs to be properly managed in WSN. Efficient key management assists in providing confidentiality, integrity to the transmitted information. The security keys, if properly managed, blocks unauthorized users from accessing the transmitted data. Numerous key management mechanisms have been proposed in research literature. This proposed work presents a detailed survey performed on the current set of widely practiced key management techniques in WSNs. Both asymmetric encryption and hybrid type of encryption techniques are applied in key management. The survey presented analyzes the various key management techniques pertaining to computational overhead, communication complexity, energy consumption, robustness and scalability. The survey will be helpful for researchers in selecting the appropriate key management technique specific to their application needs.