Water pollution has been an increasing problem over the last few years. Water personal satisfaction may be a standout amongst those primary variables with control wellbeing and the state for sicknesses "around kin what's more animals. Lakes and waterways would those fundamental wellsprings about drinking water, which impressively rely on upon water personal satisfaction (refers of the physical, chemical, What's more living aspects about water). The objective of this water quality monitoring system using internet of things is to find the quality of the water i.e. how the pH content varies and sending message to the corresponding authorities. We are going to implement this project at municipal water tanks and drinking water reservoir. For that we are using an Arduino board for finding pH value and GSM module for message technique. We use a led display to have continuous observation on water parameters. Finally the user gets message of pH value of water Further we extend this project by sending the sensor data to cloud for global monitoring of water quality.

Federated Learning (FL) enabled the reliability and robustness of 5G communication networks for wireless edge computing to provide collaborative Deep Learning (DL) of complex models while protecting privacy for healthcare systems. Wireless end devices are more susceptible to corruption due to the vulnerability offered by open network settings, however, this creates security issues and lessens the effectiveness of DL-based security models for healthcare systems. Furthermore, disaster reliability in communication networks has garnered unprecedented attention from governments and companies, particularly during the current COVID-19 pandemic scenario. In this work, a novel reliable personalized Federated Learning-based Customized Inequality-Aware Federated Learning (CusIAFL) technique is proposed for securing color images while communicating with a wireless network. The proposed technique adjusts each data sampling to the local target during optimization using knowledge of client-label availability. The work that is being presented uses a hybrid technique to maintain consistency in the time-series data. and a novel Pix2Pix Generative Adversarial Network (GAN) technique is used to generate realistic images. This novel work is tested on different non-medical and medical images. The experimental results have been evaluated using performance metrics, namely accuracy, entropy, PSNR, HD95, SSIM, and MSE. Furthermore, the accuracy varies from 89 to 93 percent with different datasets outperforming well with existing SOTA techniques. The outcomes demonstrate that the proposed CusIAFL-based scheme is more effective than the State-Of-The-Art (SOTA) models.

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\%), decreased node shutdown time (45 %), and less infrastructure cost (48\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

The continuous growth in wireless communication, the demand for sophisticated, simple and low-cost solutions are also increasing. The demand motivated the researchers to indulge into inventing suitable network solutions ranging from wireless sensor networks to wireless ad-hoc networks to Internet of Things (IoT). With the inventions coming from the researchers, the demand for further improvements into the existing researchers have also growth upbound. Initially the network protocols were the demand for research and further improvements. Nevertheless, the IoT devices are started getting used in various fields and started gathering a huge volume of data using complex application. This invites the demands for research on load balancing for IoT networks. Several research attempts were made to overcome the communication overheads caused by the heavy loads on the IoT networks. Theses research attempts proposed to manage the loads in the network by equally distributing the loads among the IoT nodes. Nonetheless, in the due course of time, the practitioners have decided to move the data collected by the IoT nodes and the applications processing those data in to the cloud. Hence, the challenge is to build an algorithm for cloud-based load balancer matching with the demands from the IoT network protocols. Hence, this work proposes a novel algorithm for managing the loads on cloud integrated IoT network frameworks. The proposed algorithm utilizes the analytics of loads on cloud computing environments driven by the physical host machines and the virtual environments. The major challenge addressed by this work is to design a load balancer considering the low availability of the energy and computational capabilities of IoT nodes but with the objective to improve the response time of the IoT network. The proposed algorithm for load balancer is designed considering the low effort integrations with existing IoT framework for making the wireless communication world a better place.