The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.

The role of firewalls and security principles in resource‐limited wireless networks and Wireless Sensor Networks (WSN) is more expected than in any dedicated network environment. The advanced wireless technologies and emerging fashion of autonomous wireless network nodes are mostly expecting resilient firewall services with multi‐level security policies. Wireless networks are classified under sensor networks, Internet of Things (IoT) mobile networks, and so forth. According to the expectations, security frameworks are gradually invented around the communication platform using various ideologies. The novel ideology and respective implementation effort open better solutions against wireless network attacks. Anyhow, the minimal production of time complexity, energy complexity, and computation overhead from any novel security approach is always considered under the best practices. The complexity levels directly affect the wireless node's energy consumption ability and operational spans severely. The energy optimization techniques and load‐balancing techniques integrated into multi‐class firewall rules are extremely useful solutions for resource‐limited wireless networks. This article has been motivated to analyze the recent firewall techniques and secure data communication mechanisms used for securing wireless networks. Consequently, the practice of comparative literature analysis helps to improve the current limitations identified under the classified categories of security mechanisms such as energy‐optimized security principles and load‐balanced security principles. The establishment of secure and energy‐optimized multi‐class firewall rules in each wireless node assures a healthy focus on next‐generation networks. The experiment section shows the benefits of energy‐optimized secure network communication in terms of better accuracy and the benefits of load‐balanced secure network communication through minimal overhead in computing platforms.

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques.