The multihop underwater acoustic sensor network (M-UASN) collects oceanographic data at different depths. Due to the harsh underwater environment, the route is a major research problem. In this article, the routing path from source to sink is adapted by the vector-based forwarding (VBF) protocol. In VBF, based on the vector size, the packets are transmitted within the pipe from hop to hop. The limitation is that every node inside the pipe vector receives the same packets. That results in a waste of battery energy and, in turn, reduces the lifetime of the acoustic node. To enhance, in this article, it is divided into two parts. The first part is that the first hop nodes from the source are optimally divided into subsets such that all the second hop nodes will receive packets from each subset. This optimal route cover subset is identified with an evolutionary memetic algorithm. The election of subset is done through a voltage reference model, and the battery voltage is modeled mathematically and the role of the nodes is given based on the voltage profile and Markov probability approach. This method enhances the lifetime of the underwater acoustic network when compared with the VBF algorithm. The proposed model also provides improved throughput and equal load sharing. The results are compared with VBF, quality-of-service aware evolutionary routing protocol (QERP), and multiobjective optimized opportunistic routing (BMOOR).

A color fundus image is a photograph obtained using a fundus camera of the inner wall of the eyeball. In the image, doctors may see changes in the retinal vessels, which can be used to diagnose various dangerous disorders such as arteriosclerosis, some macular degeneration related to age, and glaucoma. To diagnose certain disorders as early as possible, automatic segmentation of retinal arteries is used to help the doctors. Also, it is a challenge for the medical community to analyze the image with the right procedure to diagnose the disorders with high accuracy. Furthermore, this will help the doctor to make the right decision on effective treatment. Hence, the authors have implemented an enhanced architecture called U-Net to segment retinal vessels in this paper. The proposed conventional U-Net permits using all the accessible spatial setting information by adding the multiscale input layer and a thick square to the conventional U-Net in terms of improving the accuracy level of image segmentation. It achieved 95.6% accuracy with a comparatively traditional U-Net model. Moreover, the segmentation results have proved that the proposed approach outperformed in detecting most complex low-contrast blood vessels even when they are very thin. The task of segmenting vessels in retinal images is known as retinal vessel segmentation. Blood vessel density can be assessed using dense pixel values. Data augmentation and analytics play a major role in building the true value of eye blood vessels for medical diagnosis. The proposed method is very promising in the automatic segmentation of retinal arteries.

One of the major issues facing the world is the resource of safe water, which is decreasing rapidly due to climatic changes, contamination, and pollution. The most affected living beings are underwater life forms as they eventually take these toxins in and are thus prone to death, making continuously checking water quality a quintessential task. But traditional systems for checking water quality are energy-consuming, involving the initial collection of water samples from different locations and then testing them in the lab. One emerging technology, the Internet of Things (IoT), shows great promise related to this field. This paper presents a detailed review of various water quality monitoring systems (WQSN), using IoT, that have been proposed by various researchers for the past decade (2011–2020). In this instance, new calculations are made for potential clients to analyze the concerned area of research. This review acknowledges key accomplishments concerning quality measures and success indicators regarding qualitative and quantitative measurement. This study also explores the key points and reasons behind lessons learned and proposes a roadmap for impending findings.

The Internet of Vehicles (IoV) is an important idea in developing intelligent transportation systems and self-driving cars. Vehicles with various wireless networking options can communicate both inside and outside the vehicles. IoVs with cognitive radio (CR) enable communication between vehicles in a variety of communication scenarios, increasing the rate of data transfer and bandwidth. The use of CR can meet the future need for quicker data transport between vehicles and infrastructure (V2I). Vehicles with CR capabilities on VANET have a different appearance than regular VANET vehicles. This paper aims to develop effective spectrum management for CR-equipped automobiles. An improved channel decision model has been proposed with proven outcomes to boost the pace of transmission, eliminate end-to-end delays, and minimize the number of handoffs. Many high-bandwidth channels will be used in the near future to communicate large-sized multimedia content between vehicles and roadside units (RSU) for both entertainment and safety purposes. Co-operative sensing promotes energy-constrained CR vehicles for sensing a wide spectrum, resulting in high-quality communication channels for requesting vehicles. Our research on the CR-VANET focuses on channel decision instead of spectrum sensing and it differs from previous studies. We used the DAHP–TOPSIS model under multi-criteria decision analysis (MCDA), a sub-domain of operations research, to boost profits, i.e., transmission rate with less computing time. We constructed a test-bed in MATLAB and carried out several analyses to demonstrate that the suggested model performs better than other parallel MCDA models because there has been a limited amount of research work conducted with CR-VANET

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