No doubt in today’s era people are living in the age where there is a huge scarcity in between the production and demand of electrical energy and hence people are deriving electrical energy through renewable energy resources such that photovoltaic plates and high-tech wind turbines. These both have very high impact and potential to meet the above-mentioned scarcity gap. This paper produces a novel approach for prototyping the hybrid energy generation system using solar and wind energy. In addition to this paper proposes the maximum power point tracking (MPPT) using perturb and observe method to increase the efficiency. One may read about the design of this novel hybrid energy system, its hardware implementation with software simulations.

—Internet of things is like an umbrella that covers all connected things through the internet. The main objective behind all these connected devices is to share data, collect data and information, in the existing ecosystem (people, system, devices, etc.) to perform smartly and help to make human life better, easier, and comfortable. IoT is being used in multiple industries for different purposes such as manufacturing, healthcare, automation, vehicle transportation, etc. which is now called the Industrial Internet of Things (IIoT). The industrial revolution has made IoT very complex, crowded, and complicated. The recent technological developments of IIoT expect to create assorted applications in diverse domains of IIoT without human effort. Middleware is a system designed to be the intermediary between IoT devices and applications. The number of organizations depends on integrated solutions even these solutions are very complex for their requirements due to the successful communication among applications from various vendors. Generally, middleware provides ease in the development process through heterogeneous communications of devices and computing and supports interoperability among assorted services and applications. There has been a number of protocols and middleware in IIoT. This paper presents the review of IoT middleware used for diverse environments with respect to the various non - functional requirements.

Big data is a valuable asset for organisation as it analyses and help to understand the customers, changes within their business environment, market analysis and future trends. The big data is multifaceted (different data types and versatile), and mostly exists in unstructured formats. The extraction of value from this data is challenging. The usability and productivity of this multifaceted unstructured data is greatly compromised. A number of factors and associated reasons affect the usability of unstructured big data. The present research work investigates these factors and associated reasons behind the usability issues of multifaceted unstructured big data. The identification of these factors contribute to develop solutions to reduce the lack of usability of highly unstructured big data. A detailed study of existing literature followed by survey questionnaire has been conducted to identify the factors and their reasons. Descriptive statistics has been used to analyse and interpret the data and results.

In this paper, the image efficiency is improved by using hybrid model of wiener’s filter and fuzzy filter. It’s a challenging task to remove Gaussian noise (GN) from an image and to protect the picture edges. The Fuzzy - Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results presented that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.

Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machinelearning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.