Abstract
This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things (G‐IoT) is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G‐IoT. The gathered information will facilitate forecasting, decision‐making, and other activities related to smart urban services and then contribute to the incessant development of G‐IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G‐IoT approach and the knowledge gained through big data (BD) analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power‐Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data.
Highlights
Current works of literature predict that the world’s population will reach over 11 billion by 2045 compared to the unknown population of 7.6 billion. erefore, smart technologies must prevail, e.g., for a productive economy to thrive on both local and global levels, interconnectedness and innovation must prevail through environmental conversation, green house buildings, natural energy usage, and green urban planning, among others
We propose a new family of statistical models to provide the best description of the life span of the Wireless Sensor Networks (WSN)
The Complexity vision of a smart green city is becoming a reality. It is made possible by the implementation of modern technologies in the environment that can interact with the Internet of things (IoT). e IoT allows the wireless connectivity of anything from anywhere, anytime. is connectivity plays a very important role in the development of numerous fields, such as health, transport, industry, smart education, and waste management
Summary
Current works of literature predict that the world’s population will reach over 11 billion by 2045 compared to the unknown population of 7.6 billion. erefore, smart technologies must prevail, e.g., for a productive economy to thrive on both local and global levels, interconnectedness and innovation must prevail through environmental conversation, green house buildings, natural energy usage, and green urban planning, among others. Significant enhancement in information and communication technology (ICT) has enhanced living standards, decreased the use of energy, products, and services, and limited dangerous pollution. E study examines big data via a precise analysis of the rules that make a smart and feasible city through decreasing environmental pollution and contamination, diminishing energy requests, and effectively using resources [2]. Is can be achieved by deploying intelligent appliances and smart actuators that can autonomously observe energy needs and adjust Such energy sensors deployed in the environment can produce more products and services than the use of traditional methods by 30%–50%. Green manufacturing involves environmentally friendly operations within manufacturing It is the “greening” of manufacturing, in which workers use fewer natural resources, reduce pollution and waste, recycle and reuse materials, and moderate emissions throughout their processes. Information is presented in a way that is easy to use and understand and typically involves
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
