Protect Nature and Reduce the Carbon Footprint With an Application of Blockchain for IIoT

Protect Nature and Reduce the Carbon Footprint With an Application of Blockchain for IIoT

Pawan Whig, Arun Velu, Ashima Bhatnagar Bhatia
DOI: 10.4018/978-1-6684-3733-9.ch007
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The cumulative amount of greenhouse gases that are shaped by our actions is a carbon footmark. In the US, the total carbon footmark of a humanoid is 16 tonnes, one of the largest amounts in the world. The average is closer to 4 tonnes worldwide. The average universal carbon footmark per year requirements is to drop below 3 tonnes by 2050 to have the utmost chance of stopping a 2°C point rise in worldwide temperature. Rahul et al. already predicted that the carbon footprint reduced by 17% with the use of IoT-enabled services. In this research study a novel approach to reduce carbon footprint using IoT with reinforcement AI learning is presented, which further reduced carbon footprint by 5% when using and nearly 7% when it is done using Q-Learning. The detailed findings are included to demonstrate the result.
Chapter Preview
Top

Introduction

A carbon footmark is the quantity of greenhouse vapours emitted by a single humanoid operation into the atmosphere, mainly carbon dioxide (Whig et al., 2022). A carbon footprint may be a large measure that can be attributed to an individual's behaviour, a family, a case, an entity, or even a country as a whole (Anand et al., 2022). Tons of CO2 corresponding gases, counting methane, nitrous oxide, and other greenhouse vapours, are typically measured as tonnes of CO2 produced each year, an amount that can be augmented with tonnes of CO2 corresponding vapours (Alkali et al., 2022; Chopra & WHIG, 2022).

Many variables are taken into account when assessing a carbon footprint some are shown in Figure.1. Pouring to the grocery supply, for instance, burns a sure quantity of gasoline, and the main foundations of greenhouse gases are fossil fuels (Chopra & Whig, 2022b, 2022a; Madhu & WHIG, 2022). Yet the grocery shop is operated by gas, and the workers have probably gone to work, meaning the store has a carbon footprint of its own. In addition, all the items that the store offers have been delivered there, meaning that the overall carbon emissions must also be taken into account (Bhargav & Whig, 2021; George et al., 2021; Khera et al., 2021; Mamza, 2021; Pawar, 2021; Whig & Rupani, 2020). In comparison, the berries, potatoes and essences produced by the supermarket were all cultivated or raised on plantations, a methane producing operation that has a greenhouse effect 25 times greater than CO2 (Arun Velu, 2021; Reddy, 2019; Velu & Whig, 2021; verma, 2019; Whig, 2019b, 2019a). The carbon foot print is well defined by Figure 1.

Figure 1.

Carbon foot print

978-1-6684-3733-9.ch007.f01

As of Dec 2020, carbon dioxide (CO2) makes up 411 ppm of the Earth's heaven, according to NASA. The United States receives about 81 percent of its overall electricity from the combustion of fossil fuels, according to the National Academy of Sciences (chouhan, 2019; Mathurkar et al., 2021; Nadikattu et al., 2020b, 2021; Ruchin & Whig, 2015; Sharma et al., 2016; Shrivastav et al., n.d.). One of the key reasons that green energy still needs to take off in the United States, or internationally, is that it is still impossible to store renewable energy sources. IoT has the ability to transform electricity grids by smart metering and forecasts to be powered by renewable sources such as wind and solar power. By converting from fossil fuels to clean energy sources, big cities are expected to reduce their carbon dioxide emissions by more than half (Chopra & Whig, 2021; Nadikattu et al., 2020a; Velu & Whig, n.d.; Whig2*, 2020). New technologies, in Industry 4.0 like AI, IoT and ML have been seen as a boon to reducing Carbon footprint. IoT Plays an important role in reducing Carbon Footprint (S. N. Ahmad & Whig, 2011; Pawan Whig 2 Anupam Priyam3, 2018; Whig & Ahmad, 2018).

Key Terms in this Chapter

IoT: The term IoT, or Internet of Things, refers to the collective network of connected devices and the technology that facilitates communication between devices and the cloud, as well as between the devices themselves.

Markov Decision: Markov decision process (MDP) is a discrete-time stochastic control process.

Reinforcement Learning: Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones.

IIoT: The industrial internet of things (IIoT) is the use of smart sensors and actuators to enhance manufacturing and industrial processes.

Carbon Footprint: A carbon footprint is the total amount of greenhouse gases (including carbon dioxide and methane) that are generated by our actions.

Machine Learning: Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Q-learning: Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state.

Complete Chapter List

Search this Book:
Reset