Towards green computing for Internet of things: Energy oriented path and message scheduling approach

Farhan, Laith; Kharel, Rupak; Kaiwartya, Omprakash; Hammoudeh, Mohammad; Adebisi, Bamidele

doi:10.1016/j.scs.2017.12.018

Cited by 77 publications

(63 citation statements)

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“…Virtualization is a presentation process to users in which desktop's physical entities such as network, servers, and storage are accessible in a better mode than the existing configuration. The foremost advantage of having a virtualized system is that a number of heterogeneous operating platforms reside concurrently on the same host and can be independent among many running physical hosts . Three majorly discussed parts of virtualization solutions are full virtualization, para virtualization, and hardware‐assisted virtualization.…”

Section: Improvising Energy Cost Through Power‐saving Policies Of Grementioning

confidence: 99%

Power‐saving policies for annual energy cost savings in green computing

Sharma

Mittal

Obaidat

2019

Int J Communication

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The concept of green computing is a step toward advanced information technology-related data computations. There is an urgent need to develop sustainable mobile telecommunication networks subscribed by Telecommunication Regulatory Authority of India (TRAI). This paper aims at deploying major recommendations and directions in computing systems directed by regulating authorities. Green computing-based power-saving policies include virtualization technique, power-saving method, and recycling technique-type solution components that we tried to embed in existing systems. The energyefficient green computing reserves the reliability and power of informationdriven technology. This research paper provides a comprehensive approach toward applying green mechanism by spotting the difference between energy consumption of current academic scenario and after embedding power-saving policies of green computing. Moreover, an account has been maintained about the amount of energy preservation. The extraordinary energy-and cost-saving features of green computing make environment sustainable and lively for future generations.

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Section: Improvising Energy Cost Through Power‐saving Policies Of Grementioning

confidence: 99%

Power‐saving policies for annual energy cost savings in green computing

Sharma

Mittal

Obaidat

2019

Int J Communication

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“…Akram and Cho [27] considered the uncertainty of security attacks on sensor nodes, and adopted a fuzzy-based selection of the intermediate verification nodes for a WSN to optimize energy consumption. Also, there are many other recent work which have considered parameter optimization in WSN and the details can be found in [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

et al. 2018

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Wireless (smart) sensor networks (WSNs), networks made up of embedded wireless smart sensors, are an important paradigm with a wide range of applications, including the internet of things (IoT), smart grids, smart production systems, smart buildings and many others. WSNs achieve better execution efficiency if their energy consumption can be better controlled, because their component sensors are either difficult or impossible to recharge, and have a finite battery life. In addition, transmission cost must be minimized, and signal transmission quantity must be maximized to improve WSN performance. Thus, a multi-objective involving energy consumption, cost and signal transmission quantity in WSNs needs to be studied. Energy consumption, cost and signal transmission quantity usually have uncertain characteristics, and can often be represented by fuzzy numbers. Therefore, this work suggests a fuzzy simplified swarm optimization algorithm (fSSO) to resolve the multi-objective optimization problem consisting of energy consumption, cost and signal transmission quantity of the transmission process in WSNs under uncertainty. Finally, an experiment of ten benchmarks from smaller to larger scale WSNs is conducted to demonstrate the effectiveness and efficiency of the proposed fSSO algorithm.Energies 2018, 11, 2385 2 of 23 the WSN system fails because nodes deplete their limited battery power. In other words, a critical restriction in any WSN system is energy consumption. Therefore, numerous investigations of WSNs have had a primary focus on energy consumption.Optimizing energy efficiency (EE) in a WSN, which is defined as the ratio of output over energy consumption, has been the subject of a great number of studies. Mekonnen et al. [2] proposed a prototype of a WSN applied in a video surveillance system to optimize energy consumption. Trapasiya and Soni [11] addressed the goal of retransmission energy reduction in WSNs. Quang and Kim [12] proposed a gradient routing in an industrial WSN to optimize energy consumption. Setiawan et al. [13] came up with an energy management policy to maximize energy transfer efficiency for a WSN. Liu et al. [14] optimally designed a WSN to minimize energy consumption.The minimization of energy consumption in WSNs has also been investigated by many researches [15][16][17][18]. Chanak et al. [19] discussed the balance of energy consumption among deployed sensors in a WSN. The energy-optimal routing problem in WSNs has recently been studied by several works [20][21][22]. The improvement of energy consumption by clustering method in a WSN has also been discussed by some works [23,24].However, the measured values of energy consumption in real-life WSN systems are usually uncertain and imprecise. Fuzzy set theory can effectively resolve these uncertain and imprecise problems. In studies of energy consumption for WSNs, few researchers have presented fuzzy-based methods to solve uncertainty problems in such systems. Collotta et al. [25] considered the turning on/off of devices as the output of a Fuzz...

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“…Underwater networking is significantly challenging compared to traditional wireless networking due to the dynamic self-mobility of the medium of communication and constraints in signal propagation in the underwater environment [14][15][16]. In this constrained networking environment, the underwater network deployment-oriented challenges further complicate scientific investigations towards the development of an energy-centric green underwater network for various application domains [17][18][19].Towards enabling green underwater networking, several service and geolocation-centric techniques of varying quality have been suggested [20,21]. A heuristic approach has been suggested in underwater networking for solving the surface gateway deployment optimization problem, focusing on the quality of service [22].…”

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W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

Rathore

Sangwan

Mazumdar

et al. 2020

Sensors

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Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.Sensors 2020, 20, 1377 2 of 23 water-based transport applications [6,7], oil, and natural gas production applications [8,9], and developing fishing-centric industries [10,11]. In underwater networking, tiny sensor nodes are deployed underwater, as well as on the upper surface layer for monitoring the specific underwater area [12]. These underwater nodes communicate with the surface nodes, acting as access points or cluster heads for reaching the sink node of the network, which accumulates the information and communicates with the cloud-enabled computing resources [13]. Underwater networking is significantly challenging compared to traditional wireless networking due to the dynamic self-mobility of the medium of communication and constraints in signal propagation in the underwater environment [14][15][16]. In this constrained networking environment, the underwater network deployment-oriented challenges further complicate scientific investigations towards the development of an energy-centric green underwater network for various application domains [17][18][19].Towards enabling green underwater networking, several service and geolocation-centric techniques of varying quality have been suggested [20,21]. A heuristic approach has been suggested in underwater networking for solving the surface gateway deployment o...

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Towards green computing for Internet of things: Energy oriented path and message scheduling approach

Abstract: Abstract-Recently, energy efficiency in sensor enabled wire-

Cited by 77 publications

References 25 publications

Power‐saving policies for annual energy cost savings in green computing

Power‐saving policies for annual energy cost savings in green computing

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

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