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Top1. Introduction
At present the world population is approximately 7.6 billion (Worldometers, 2018) and according to the current growth rate of 1.5% per year, it is likely to reach 10 billion by the year 2040. This excessive number is alarming and calls for some actions concerning resource consumption and management. Humans use more ecological resources and services than nature can regenerate. This is the high time which humans must start making ecological limits and change their behavior to find new ways to live well and save ecological resources. This demands advances in technology and infrastructure that will teach us how to function with limited resources.
Continuous development in Information and Communication Technology (ICT) contribute to the better life quality, reduced consumption of energy and decreased hazardous emissions and pollutions (Ecological footprint). It has been observed that the green Internet of Things (G-IoT) will initiate major changes and would support to achieve the vision of “green ambient intelligence” (G- IoT). Since the last decade efficiency improvements in the Internet of Things (IoT) have been globally advocated by the researchers (Zanellaetal, 2014). CISCO report published in 2008 gave an eye-opening statement that devices connected to the Internet already exceeded the human population living on earth and this is just the beginning. It also added that these devices would reach the limit of 50 billion by 2020. These things interact and communicate with each other with the help of the internet–and we call it the Internet of Things (IoT) (CISCO, 2008). The literature review revealed that not much effective work has been done in the direction of modeling of G-IoT enablers and establishing interdependence among these enablers; these gaps motivated the author’s to carry out the current research. The core contribution of this study is to provide modeling of the G-IoT enablers and implementation of G-IoT using these enablers. The proposed structural model will help to know the interdependence of these enablers of G-IoT. The key objectives of this research are:
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Identification of G-IoT enablers
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Establishing Interdependence among these enablers
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Development of structural model using Interpretive Structural Modeling (ISM) technique.
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Performing cross-impact matrix multiplication applied to classification (Matriced’Impacts croises-multiplication appliquean classment, abbreviated as MICMAC analysis).
The layout of the paper is as follows: An introduction is given in the section 1. The literature review is presented in section 2 followed by section 3 which details about questionnaire development and data collection. ISM Methodology is explained in section 4 followed by MICMAC analysis and formation of ISM based Model in section 5 and 6 respectively. Discussion is done in 7th section and lastly 8th section details about limitation and future direction of research.
Top2. Identification Of Enablers Ofg-Iot
2.1. Smart Infrastructure
(Talari et al., 2017) IoT is empowered by the expansion of several things and communication equipment. (Arshad et al., 2017) categorize different models for G-IoT on the basis of technologies used, for example; software-based G-IoT enablers like cloud computing and virtualization, hardware-based G-IoT enablers like sensor based Radio-Frequency Identification (RFID), Wireless Sensor Networks (WSN), etc., policy based G-IoT enablers like smart things/devices/cities, awareness-based enablers are awareness campaign, smart metering and finally changing habits to enable G-IoT, it includes decision for better habits, tracking habits and Recycling enablers for G-IoT includes phone recycling, sensor recycling etc.