Green Internet of Things: The Next Generation Energy Efficient Internet of Things

Thilakarathne, Navod Neranjan; Kagita, Mohan Krishna; Priyashan, W. D. Madhuka

doi:10.1007/978-981-16-2008-9_38

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…Thilakarathne et al, [8] presented the energy efficient computing using Green Internet of Things which can decrease the use of dangerous materials and provide energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

A Review on Waste Management in Green Computing

Bichaiah¹

2022

RRRJ

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Green Computing, a recent trend and developing industry, is working to create a more sustainable future. Reusable resources are provided through green computing, which is now employed by many different technologies. Green technology is sustainable and eco-friendly. Computer use is growing quickly, which means that computers are using more energy. As a result of higher energy consumption, more eco-harmful carbon footprints are being released into the environment. Due to this issue, many companies having proper way of waste management has started to implement the green computing technologies. This study reviews the different approaches of green computing like energy minimization, virtualization, reduction in use of hazardous substances, and cloud computing. This paper also explains the green computing and its needs. The concern of waste management, which is an approach of green computing can be achieved this way.

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“…Thilakarathne et al, [8] presented the energy efficient computing using Green Internet of Things which can decrease the use of dangerous materials and provide energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

A Review on Waste Management in Green Computing

Bichaiah¹

2022

RRRJ

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“…As a new technology, the internet has realized global connectivity, while massive data sharing between billions of Internet of Things (IoT) devices has created a large amount of energy demand, and DOI: 10.1002/adts.202300795 increased energy waste. [1][2][3] Additionally, it has led to the emergence of a new concept, Internet of Vehicle (IoV). [4] IoV is an integral part of the IoT, and it occupies a very vital position.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Zheng,

Huang,

Liu

et al. 2024

Advcd Theory and Sims

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As lithium‐ion batteries are the main power source of new energy vehicles, making accurate predictions of unknown State of Charge (SOC) during vehicle operation for vehicle data monitoring is vital to the advancement of intelligent new energy vehicles. In this manuscript, an expression tree‐based genetic programming regression model (ETGPR) is proposed to estimate the real‐time SOC of lithium‐ion batteries. The proposed model mainly adopts the symbolic regression technique. In addition to the current–voltage curves being fed into the model, an additional approach is designed to ensure real‐time model predictions in dynamic situations, which includes the previous moment's power in the input parameters. Different seed hyperparameters in the model are set, and the model automatically performs evolutionary calculations. Subsequently, each parameter of the model is optimally adjusted to obtain a set of regression expressions that accurately reflect the relationship between the SOC and each parameter after a specified number of iterations. Finally, the generated expression is proven to perform better in terms of its ability to capture the nonlinear relationship between SOC and battery variables. Also, the model demonstrates excellent robustness in the presence of notable noise from input‐independent features compared to other models, a root mean square error (RMSE) of less than 0.3% and a mean absolute error (MAE) of less than 0.2% are achieved. Furthermore, the potential of the model's implement‐ability under variable temperature and real driving data conditions is verified.

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“…The limits of wireless communications in the green data center make ensuring security problematic. The sustainable green smart computing platforms connected to the network are more exposed to security attacks than standard computing [5][6][7][8]. Regardless of how many security controls are in place, there will always be issues such as software flaws and vulnerabilities that hackers can exploit [9,10].…”

Section: Introductionmentioning

confidence: 99%

Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach

2024

IJIES

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Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the applying sigmoid fish swarm optimization (SiFSO) for early compromised device detection and subsequently alerting other network nodes. Additionally, our data center implements an innovative ant skyscape architecture (ASA) cooling mechanism, departing from traditional, unsustainable cooling strategies that harm the environment. To validate the effectiveness of these approaches, extensive simulations were conducted. The evaluations primarily revolved around the fish colony's ability to detect compromised devices, focusing on source tracing, realistic modelling, and an impressive 98% detection accuracy rate under ASA cooling solution with 0.16 ºC within 1,300 second. Compromised devices pose a substantial risk to green data centers, as attackers could manipulate and disrupt network equipment. Therefore, incorporating cyber enhancements into the green data center concept is imperative to foster more adaptable and efficient smart networks.

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Green Internet of Things: The Next Generation Energy Efficient Internet of Things

Cited by 17 publications

References 31 publications

A Review on Waste Management in Green Computing

A Review on Waste Management in Green Computing

A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach

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