Green computing in IoT: Time slotted simultaneous wireless information and power transfer

Jaiswal, Ajay; Kumar, Sushil; Kaiwartya, Omprakash; Prasad, Mukesh; Kumar, Neeraj; Song, Houbing

doi:10.1016/j.comcom.2020.12.024

Cited by 20 publications

(5 citation statements)

References 42 publications

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“…5,7,8 Unfortunately, cloud servers are often located far from IoT edge devices, thus introducing latency and bandwidth challenges between users and computing nodes. [9][10][11][12] Latency in time-sensitive applications, such as smart grids or unmanned vehicle management can result in major problems. [13][14][15][16] A fog network can use the available local computing and storage resources near the IoT edge devices and can thus provide necessary computations to the IoT edge devices, providing an opportunity to reduce the latency and bandwidth requirements due to being closer to IoT edge devices than cloud servers.…”

Section: Introductionmentioning

confidence: 99%

“…The cloud‐centric IoT network has been designed to alleviate the constraints of IoT devices, which are due to the limitations of computational and memory resources, by providing centralized control of IoT devices 5,7,8 . Unfortunately, cloud servers are often located far from IoT edge devices, thus introducing latency and bandwidth challenges between users and computing nodes 9‐12 . Latency in time‐sensitive applications, such as smart grids or unmanned vehicle management can result in major problems 13‐16 .…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani

Sajjad

Alghamdi

et al. 2022

Trans Emerging Tel Tech

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A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani

Sajjad

Alghamdi

et al. 2022

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the IoT enabled connected environment, balancing the usage of sensor's energy for longevity and maximizing the throughput of the sensor-enabled smart services is major concern [4,5]. In next generation wireless systems, relay assisted wireless communication along with optimal transmit power has come out to be a favorable solution [6]. Nevertheless, proper relay allocation as forwarding sensors for data transmission is a crucial job as selection of relay might be led to lower throughput and higher energy consumption as compare to direct link transmission.…”

Section: Introductionmentioning

confidence: 99%

Quantum Learning-Enabled Green Communication for Next-Generation Wireless Systems

Jaiswal

Kumar

Kaiwartya

et al. 2021

IEEE Trans. on Green Commun. Netw.

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Next generation wireless systems have witnessed significant R&D attention from academia and industries to enable wide range of applications for connected environment around us. The technical design of next generation wireless systems in terms of relay and transmit power control is very critical due to the ever-reducing size of these sensor enabled systems. The growing demand of computation capability in these systems for smart decision making further diversified the significance of relay and transmit power control. Towards harnessing the benefits of Quantum Reinforcement Leaning (QRL) in the design of next generation wireless systems, this paper presents a framework for joint optimal Relay and transmit Power Selection (QRL-RPS). In QRL-RPS, each sensor node learns using its present and past local state's knowledge to take optimal decision in relay and transmit power selection. Firstly, RPS problem is modelled as a Markov Decision Process (MDP), and then QRL optimization aspect of the MDP problem is formulated focusing on joint optimization of energy consumption and throughput as network utility. Secondly, a QRL-RPS algorithm is developed based on Grover's iteration to solve the MDP problem. The comparative performance evaluation attests the benefit of the proposed framework as compared to the stateof-the-art techniques.

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“…To amicably handle the energy scarcity issue of sensor nodes, there is a need for an alternative energy source to compensate for the energy requirement in case of battery drain out [8,9]. One of the best resolutions to this issue is the use of an energy harvesting technique in which energy from natural sources available in the environment is transformed into power to resuscitate the batteries [10,11]. The new advancements in energy harvesting techniques for WSNs have become a prominent research field.…”

Section: Introductionmentioning

confidence: 99%

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Rathore

Sangwan

Kaiwartya

et al. 2021

Wireless Communications and Mobile Computing

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Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era.

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Green computing in IoT: Time slotted simultaneous wireless information and power transfer

Cited by 20 publications

References 42 publications

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Quantum Learning-Enabled Green Communication for Next-Generation Wireless Systems

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

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