Dr. Thippa Reddy Gadekallu, Vellore Institute of Technology, India.
Dr. Mamoun Alazab, Charles Darwin University, Australia.
Dr. Praveen Kumar Reddy M, Vellore Institute of Technology, India.

Summary

A green IoT energy-aware network plays an important role in sensing technology. IoT growth influences many applications such as e-health care, smart cities, vehicle systems, and industrial engineering in today's era. Some of the compromise solutions occur during the design and development of IoT-based applications such as security, resources, routing, node deployment, etc. The rapid increase in sensor nodes results in increased power consumption. Therefore, the reduction of environmental impact in green media networks is a crucial challenge for academic and industrial researchers. One of the IoT-based applications' problems is improving power efficiency and network longevity by miniature size, limited battery life, and dynamic motion of the sensor nodes in industrial applications. Energy optimization and resource management in these networks remain a challenge, Therefore for efficient power management and necessary optimization for IoT applications, Artificial Intelligence (AI), Deep Learning (DL) and other Neural Network (NN) based approaches will come up as a solution for green communication.

In addition, recent literature covered machine learning; deep learning algorithms can provide energy efficiency solutions, predictions on batteries, network monitoring, etc. This special issue seeks submissions of high-quality and unpublished articles to address the technical problems and challenges of green communications networks. In particular, we seek submissions that efficiently integrate new AI, DL approaches, focusing on the assessment of IoT ecosystem performance across existing green communication solutions. Theoretical and experimental studies for such scenarios are encouraged.

We solicit papers covering various topics of interest that include, but are not limited to, the following.

• Resource optimization in IoT applications.

• Machine learning approaches for green IoT.

• Quality of service in smart green communication networks for the IoT ecosystem.

• Architectures and models for smart green communication networks for IoT.

• Green communication network designs and implementations for IoT ecosystem.

• Innovative green communications technologies and protocols suitably designed for the IoT.

• Smart energy harvesting/charging and power management techniques using ML techniques.

• Energy-efficient sensing techniques.

• Experimental results and test-beds for smart green computing systems for IoT network models.

Keywords

Internet of Things, Energy Efficiency, Green Internet of Things, Machine Learning, Deep Learning

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ARTICLE

An Efficient Internet Traffic Classification System Using Deep Learning for IoT
Muhammad Basit Umair, Zeshan Iqbal, Muhammad Bilal, Jamel Nebhen, Tarik Adnan Almohamad, Raja Majid Mehmood
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 407-422, 2022, DOI:10.32604/cmc.2022.020727
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper… More >

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ARTICLE

Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing
Mohd Anul Haq, Mohd Abdul Rahim Khan, Talal AL-Harbi
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1769-1788, 2022, DOI:10.32604/cmc.2022.018708
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Intrusion Detection System (IDS) plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices. However, anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection. Also, we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks. Given this background, we designed a solution to detect intrusions using the Convolutional Neural Network (CNN) for Enhanced Data rates for GSM Evolution (EDGE) Computing. We created two separate categories to handle the attack and non-attack events in the system. The findings of this study indicate that this approach was… More >

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ARTICLE

Efficient Resource Allocation in Fog Computing Using QTCS Model
M. Iyapparaja, Naif Khalaf Alshammari, M. Sathish Kumar, S. Siva Rama Krishnan, Chiranji Lal Chowdhary
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2225-2239, 2022, DOI:10.32604/cmc.2022.015707
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available… More >

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ARTICLE

Measuring End-to-End Delay in Low Energy SDN IoT Platform
Mykola Beshley, Natalia Kryvinska, Halyna Beshley, Orest Kochan, Leonard Barolli
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 19-41, 2022, DOI:10.32604/cmc.2022.018579
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract In this paper, we developed a new customizable low energy Software Defined Networking (SDN) based Internet of Things (IoT) platform that can be reconfigured according to the requirements of the target IoT applications. Technically, the platform consists of a set of low cost and energy efficient single-board computers, which are interconnected within a network with the software defined configuration. The proposed SDN switch is deployed on Raspberry Pi 3 board using Open vSwitch (OvS) software, while the Floodlight controller is deployed on the Orange Pi Prime board. We firstly presented and implemented the method for measuring a delay introduced by… More >

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ARTICLE

Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization
Jagannath Paramguru, Subrat Kumar Barik, Ajit Kumar Barisal, Gaurav Dhiman, Rutvij H. Jhaveri, Mohammed Alkahtani, Mustufa Haider Abidi
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2863-2882, 2021, DOI:10.32604/cmc.2021.016002
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue. Various non-linearity are added to make the fossil fuel-based power systems more practical. In order to achieve an accurate economical schedule, valve point loading effect, ramp rate constraints, and prohibited operating zones are being considered for realistic scenarios. In this paper, an improved, and modified version of conventional particle swarm optimization (PSO), called Oscillatory PSO (OPSO), is devised to provide a cheaper schedule with optimum cost. The conventional PSO is improved by deriving a mechanism… More >

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ARTICLE

RSS-Based Selective Clustering Technique Using Master Node for WSN
Vikram Rajpoot, Vivek Tiwari, Akash Saxena, Prashant Chaturvedi, Dharmendra Singh Rajput, Mohammed Alkahtani, Mustufa Haider Abidi
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3917-3930, 2021, DOI:10.32604/cmc.2021.015826
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Wireless sensor networks (WSN) are designed to monitor the physical properties of the target area. The received signal strength (RSS) plays a significant role in reducing sensor node power consumption during data transmission. Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span. This paper introduces the RSS-based energy-efficient selective clustering technique using a master node (RESCM) to improve energy utilization using a master node. The master node positioned at the center of the network area and base station (BS) is placed outside the network area. During… More >

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ARTICLE

EA-RDSP: Energy Aware Rapidly Deployable Wireless Ad hoc System for Post Disaster Management
Ajmal Khan, Mubashir Mukhtar, Farman Ullah, Muhammad Bilal, Kyung-Sup Kwak
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1725-1746, 2021, DOI:10.32604/cmc.2021.017952
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract In post disaster scenarios such as war zones floods and earthquakes, the cellular communication infrastructure can be lost or severely damaged. In such emergency situations, remaining in contact with other rescue response teams in order to provide inputs for both headquarters and disaster survivors becomes very necessary. Therefore, in this research work, a design, implementation and evaluation of energy aware rapidly deployable system named EA-RDSP is proposed. The proposed research work assists the early rescue workers and victims to transmit their location information towards the remotely located servers. In EA-RDSP, two algorithms are proposed i.e., Hop count Assignment (HCA) algorithm… More >

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ARTICLE

Deep Neural Networks Based Approach for Battery Life Prediction
Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Iyapparaja Meenakshisundaram, Thippa Reddy Gadekallu, Sparsh Sharma, Mohammed Alkahtani, Mustufa Haider Abidi
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2599-2615, 2021, DOI:10.32604/cmc.2021.016229
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract The Internet of Things (IoT) and related applications have witnessed enormous growth since its inception. The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain. Although the applicability of these applications are predominant, battery life remains to be a major challenge for IoT devices, wherein unreliability and shortened life would make an IoT application completely useless. In this work, an optimized deep neural networks based model is used to predict the battery life of the IoT systems. The present study uses the Chicago Park Beach dataset collected from the publicly available data… More >

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Interference Mitigation in D2D Communication Underlying Cellular Networks: Towards Green Energy
Rana Zeeshan Ahamad, Abdul Rehman Javed, Shakir Mehmood, Mohammad Zubair Khan, Abdulfattah Noorwali, Muhammad Rizwan
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 45-58, 2021, DOI:10.32604/cmc.2021.016082
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Device to Device (D2D) communication is emerging as a new participant promising technology in 5G cellular networks to promote green energy networks. D2D communication can improve communication delays, spectral efficiency, system capacity, data off-loading, and many other fruitful scenarios where D2D can be implemented. Nevertheless, induction of D2D communication in reuse mode with the conventional cellular network can cause severe interference issues, which can significantly degrade network performance. To reap all the benefits of induction of D2D communication with conventional cellular communication, it is imperative to minimize interference’s detrimental effects. Efficient power control can minimize the negative effects of interference… More >

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Multimodal Medical Image Registration and Fusion for Quality Enhancement
Muhammad Adeel Azam, Khan Bahadar Khan, Muhammad Ahmad, Manuel Mazzara
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 821-840, 2021, DOI:10.32604/cmc.2021.016131
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal image registration while Discrete Wavelet… More >

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ARTICLE

Energy-Efficient Transmission Range Optimization Model for WSN-Based Internet of Things
Md. Jalil Piran, Sandeep Verma, Varun G. Menon, Doug Young Suh
CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2989-3007, 2021, DOI:10.32604/cmc.2021.015426
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract With the explosive advancements in wireless communications and digital electronics, some tiny devices, sensors, became a part of our daily life in numerous fields. Wireless sensor networks (WSNs) is composed of tiny sensor devices. WSNs have emerged as a key technology enabling the realization of the Internet of Things (IoT). In particular, the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities, smart homes, smart healthcare, security applications, environmental monitoring, etc. However, the limitations of energy, communication range, and computational resources are bottlenecks to the widespread applications… More >

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ARTICLE

A Novel Green IoT-Based Pay-As-You-Go Smart Parking System
Andrea Sant, Lalit Garg, Peter Xuereb, Chinmay Chakraborty
CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3523-3544, 2021, DOI:10.32604/cmc.2021.015265
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract The better management of resources and the potential improvement in traffic congestion via reducing the orbiting time for parking spaces is crucial in a smart city, particularly those with an uneven correlation between the increase in vehicles and infrastructure. This paper proposes and analyses a novel green IoT-based Pay-As-You-Go (PAYG) smart parking system by utilizing unused garage parking spaces. The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’ pricing portfolio with a garage’s current demand. Malta, the world’s fourth-most densely populated country, is considered as a case of a… More >

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ARTICLE

Position Vectors Based Efficient Indoor Positioning System
Ayesha Javed, Mir Yasir Umair, Alina Mirza, Abdul Wakeel, Fazli Subhan, Wazir Zada Khan
CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1781-1799, 2021, DOI:10.32604/cmc.2021.015229
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract With the advent and advancements in the wireless technologies, Wi-Fi fingerprinting-based Indoor Positioning System (IPS) has become one of the most promising solutions for localization in indoor environments. Unlike the outdoor environment, the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efficient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things (IoTs) and green computing. In this paper, we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors. Initially, in the database development phase, Motley Kennan propagation… More >

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ARTICLE

Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication
Abdul Rehman Javed, Rabia Abid, Bakhtawar Aslam, Hafiza Ammara Khalid, Mohammad Zubair Khan, Omar H. Alhazmi, Muhammad Rizwan
CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1933-1950, 2021, DOI:10.32604/cmc.2021.015272
（This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract With the popularity of green computing and the huge usage of networks, there is an acute need for expansion of the 5G network. 5G is used where energy efficiency is the highest priority, and it can play a pinnacle role in helping every industry to hit sustainability. While in the 5G network, conventional performance guides, such as network capacity and coverage are still major issues and need improvements. Device to Device communication (D2D) communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques. The issue of energy utilization in the IoT based… More >

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Green IoT Networks using Machine Learning, Deep Learning Models

Guest Editors

Summary

Keywords

Published Papers

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2204

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2016

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1434

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1977

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1109

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1

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1952

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1029

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2001

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1347

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2398

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1599

Cited by

7

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3311

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1964

Cited by

4

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3347

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1842

Cited by

4

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2438

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1562

Cited by

2

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2878

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2103

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2269

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1564

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