Green Computing In Mobile And Wireless Networks

DOI : 10.17577/NCRTCA-PID-031

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Green Computing In Mobile And Wireless Networks

[1]Harshitha L [2]Dr. Chandrika Murali

PG Scholar, Department of MCA Dayananda Sagar College of Engineering

Assistant Professor, Department of MCA Dayananda Sagar College of Engineering

Bengaluru, India Bengaluru, India

Abstract Green computing has surfaced as an essential exploration area due to the adding energy consumption and environmental impact of information and communication technology (ICT) systems. Mobile and wireless networks, which have come ubiquitous in ultramodern society, contribute significantly to energy consumption and carbon emigrations. This research paper aims to explore the concept of green computing in the environment of mobile and wireless networks, fastening on the various approaches and ways to minimize energy consumption, reduce environmental impact, and improve sustainability in these networks. This research paper aims to saving of energy or reduction of carbon footprint for mobile and other wireless networks. The report also looks at the potential advantages, difficulties, and prospective future research directions in this area.(Abstract)

Keywordsenergy-saving, environmental impact, energy reduction, carbon foot print, greenhouse gases, energy-saving solution


    The term "Green Computing" is also termed as energy- efficient computing or sustainable computing; it is the practice of creating, using, and disposing of computing systems and technology in an environmentally responsible way. It entails lowering the environmental harm caused by computing operations, such as energy usage, electronic waste, and resource efficiency. The increased use of mobile devices, such as smartphones, Laptops, and Internet of Things devices, as well as the quick expansion in their quantity demand for wireless access, has raised serious concerns about these networks energy consumption and environmental impact.

    Wireless and mobile networks serve a vital role in providing communication, transfer of data, and access to the data. However, the growth in use of energy triggered on by these networks creates problems for the environment, such as higher greenhouse gas emissions such as carbon dioxide and depletion of resource. In order to overcome these difficulties, green computing in mobile and wireless networks incorporates techniques and innovations that optimize energy effectiveness, lessen carbon footprint, and support sustainability. The goal of green computing in the context of mobile and wireless networks is to reduce energy usage without affecting user experience, performance of network, or quality of service. This includes a number of procedures, including developing of energy-conscious protocols and algorithms, the design of hardware components, improving the performance of network infrastructure, and the incorporation of renewable energy sources. Mobile and wireless networks may become

    more environmentally friendly, economically viable, and socially responsible by using green computing concepts.

    Future ICT (Information and Communication Technologies) are going to be more dependent on energy efficiency as the price and supply of energy keep on increasing. Energy- efficient ways that decrease the overall energy consumption of data processing, storage, and transmission are required due to the expanding use of ICT, rising energy prices, and the need to minimize emissions of greenhouse gases. The earliest and most compelling proof demonstrates that CO2 and other emissions are seriously harming the ecosystem and the world's climate. Therefore, protecting our precious planet is the highest priority and most significant for green computing technologist.

    Likewise, conservation of energy/ energy-saving techniques in wireless and mobile networks can offer economic advantages. Network providers and customers may save money by lowering energy costs and prolonging the battery life of mobile devices by optimizing energy use. The whole sustainability and duration of the mobile and wireless communication ecosystem may be enhanced via energy efficiency. In order to achieve the intended outcomes in green computing in mobile and wireless networks, experts are actively analyzing multiple domains and making notable efforts. Some of the primary areas of attention are:

    1. Energy-Efficient Protocols and Algorithms: For wireless and mobile networks, researchers are creating energy- efficient protocols and algorithms. These protocols aim to reduce the use of energy by optimizing transmission of data, routing and resource allocation processes. To increase energy efficiency, strategies including sleep mode functioning, adaptive modulation, and broadcast power management are being considered.

    2. Energy Harvesting and Wireless Charging: To enable mobile devices and network components to produce energy on their own, renewable energy approaches, such as solar, kinetic, and thermal energy harvesting, are being researched. In order to allow energy replenishment without the need for physical connections and lessen reliance on conventional power sources, researchers are also investigating wireless charging solutions.

    3. Green data centers and cloud computing: These are being investigated in an effort to reduce the energy used for storage of data, processing, and transmission. To maximize energy use in data centers, strategies including workload combining, server virtualization, and energy-aware resource management are being researched.

    4. Device Design and Energy-Aware Components: Researchers are concentrating on building energy- efficient hardware and mobile devices. To increase battery life and

    lower energy consumption in mobile devices, this involves the development of low-power CPUs, energy-efficient displays, power management systems, and battery technologies.


    1. The research paper titled "Statistical-QoS Driven Energy- Efficiency Optimization Over Green 5G Mobile Wireless Networks" explores ways to increase network energy efficiency while maintaining quality-of-service for multimedia wireless traffic. The paper presents a QoS-driven green power allocation scheme for SISO-channel based 5G wireless networks that converges to the depicting water-filling scheme when the QoS constraint becomes very loose. Additionally, the paper presents a statistical-QoS-driven green power allocation scheme for MIMO-channel based 5G wireless networks that can optimize the effective power efficiency (EPE) over 5G mobile wireless networks. The paper concludes that the developed schemes can enable the effective implementation of green 5G wireless networks [1].

    2. In the research paper Analysis on Improving Energy Efficiency in Green Cloud Computing for IOT Devices Summary Green Cloud computing is a cost-effective and agile paradigm that has converted the way businesses procure and use IT services. It presents new openings for IT service providers and outsourcing companies, who must acclimatize their strategies to incorporate Green Cloud services. To insure successful deployment, CIOs need to develop short- term and long- term plans, while involving all stakeholders and enforcing user training programs if necessary. herbage computing, including Green Cloud results, offers commercial benefits similar as cost savings, adaptability, and positive public relations. By embracing Green Computing practices at both system-wide and individual situations, companies and individualities can make a significant impact in te fight against climate change [5].

    3. In the research paper Green Computing: From Current to Future Trends provides several energy- saving techniques that can be implemented in enterprise systems. These techniques include power management, leveraging unused computer resources, and adopting fewer and more energy- efficient systems. Virtualization can also be used to consolidate servers and reduce energy consumption. Additionally, using energy- efficient hardware and implementing cooling systems that use outside air or water instead of air conditioning can help reduce energy consumption and greenhouse gas emissions. These techniques can help organizations save money, reduce their environmental impact, and promote sustainable computing practices.

    4. In the research paper titled Moving towards smart transportation with machine learning and Internet of Things (IoT): a review. This research paper explores the use of Internet of effects( IoT) and machine learning( ML) ways in transportation. It reviews the being literature and discusses different approaches and ML algorithms proposed by various

      authors. The research highlights the significant benefactions made by these approaches in making transportation smarter and addressing the challenges caused by increased business. The paper identifies the eventuality for farther ML content, particularly in the operation of business lights and parking systems, to handle the growing number of vehicles in metropolises. By combining IoT and ML, experimenters can address the challenges posed by the rapid-fire increase in vehicle figures compared to population growth. [6].


    Reduced Carbon Emissions: By lowering carbon emissions, green computing in wireless and mobile networks helps to lessen the impact on the environment. It is possible to lessen the dependence on fossil fuels for powering network infrastructure by maximizing energy use and using green energy sources. This aids in the endeavor to create a low- carbon future and battle climate change on a global scale.

    Cost reductions: In mobile and wireless networks, energy- saving techniques can result in significant price reductions. Network operators may lower their expenses for operations and cost of electricity by consuming less energy. Mobile device users can also gain from a longer lifespan for their batteries, which lowers the need for frequent charging or replacement of batteries.

    Energy Efficiency: Using energy-efficient techniques and tools in wireless and mobile networks results in lower energy usage. This leads to longer battery life for mobile devices, cheaper operational costs for service providers, and less carbon impact. Energy efficiency promotes a sustainable environment by conserving natural resources.

    Resource Optimization: Green computing methods maximize the use of network resources such processor speed, storage space, and bandwidth. Mobile and wireless networks can manage larger amounts of traffic and enhance overall performance by effectively assigning and handling these resources. Resource optimization helps to enhance scalability, lessen network traffic, and enhance customer service.


    There are also several limitations and challenges that researchers and practitioners need to address. Some of the key limitations include:

    Heterogeneous Network Environments: Devices, innovations, and network frameworks that make up mobile and wireless connections are extremely varied. In heterogeneous settings with different hardware capabilities, topologies of networks, and protocols for communications, energy efficiency is difficult to achieve. Green computing solutions needs to ensure scalability and performance.

    Minimal Battery Life of Mobile Devices: Batteries with low capacity are frequently used to power mobile devices like smartphones and tablets. These devices' usability and usefulness may be impacted the by cost-effective strategies used to prolong battery life, such as low-power modes or limiting computing capability. Energy optimization and user needs and expectations must be balanced when designing mobile devices.

    Privacy and Security Considerations: Green computing projects in mobile and wireless networks should take possible security and privacy consequences into account. Compression of data and configurable sleep modes are two energy-saving strategies that may expose vulnerabilities or risk user privacy. It is crucial to make sure that green technologies retain a high degree of security and safeguard user data.

    Economic viability: Although green computing solutions might result in long-term cost benefits, adoption may be limited by the high cost of the initial investment and ongoing deployment. It is important to carefully assess the economic feasibility of energy-efficient technology and practices, taking into account things like repayment times, regulatory incentives, and return on investment.

    Conflicts between Efficiency of Energy and Performance: In mobile and wireless networks, achieving great energy conservation sometimes involves tradeoffs with performance measures like latency, speed, and dependability. Energy- saving optimization may lead to a decline in bandwidth or experience for users. Designing energy-efficient protocols and algorithms continues to be difficult since performance goals must be balanced with energy economy.

    There are many possible directions for more study and development in the prospective future application of green computing in mobile and wireless networks. The following are some crucial fields that have excellent chances for advancement:

    Artificial Intelligence for Energy Optimization: In mobile and wireless networks, the use of artificial intelligence techniques has a huge potential for energy optimization. In order to allocate resources with the greatest possible energy efficiency, machine learning algorithms may be used to analyze network traffic patterns, forecast user demand, and dynamically alter resource allocation.

    Integration of Renewable Energy Sources: Research must be conducted in order to determine the most effective way to incorporate sources of sustainable energy into wireless and mobile networks. In order to lessen the dependency on traditional power sources, strategies like storage of energy, smart power system integration, and wind and solar power harvesting can be quite helpful. Network activities may become less harmful to the environment by creating energy management systems that efficiently harvest and distribute renewable energy.

    Green data centers and edge computing: Edge computing, which moves processing towards the network edge, presents potential clients for increasing energy efficiency. The development of management of resources algorithms, energy- conscious task transfer techniques, and energy- efficient computing edge architectures can all be the subject of research. The energy efficiency of mobile and wireless networks can also be improved by developments in green data Centre

    designs, such as the incorporation of renewable energy and energy-efficient cooling techniques.


This research paper has explored the concept of green computing in the context of mobile and wireless networks. The paper has stressed the significance of addressing energy consumption and environmental impact in these networks due to their wide use and significant donation to overall energy consumption and carbon emigrations.

Throughout the paper, various energy-effective ways, both at the network structure and mobile device position, have been discussed. These ways include energy-effective protocols and algorithms, power operation strategies, dynamic resource allocation, sleep modes, and cross-layer optimization. Also, the paper has explored the part of green computing in mobile operations, services, and data operation.

The experimental results of the proposed dynamic cloudlet- grounded mobile cloud computing ( DECM) mdel have demonstrated its effectiveness in enabling mobile users to address green IT challenges within a complex wireless terrain. Still, farther exploration is needed to assess the connection of DECM in different diligence with varying service conditions and to establish structured connections among Cloudlet Distribution Centers ( CDLs) to enhance communication between cloud servers and mobile devices.

The challenges and openings associated with enforcing green computing results in mobile and wireless networks have been bandied, encompassing specialized, profitable, policy, and societal considerations. The eventuality of machine literacy, artificial intelligence, Internet of effects( IoT), edge computing, and block chain for advancing green computing in these networks has also been stressed as unborn exploration directions.


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