Energy Efficient Fog Servers for Internet of Things Information Piece Delivery (IoTIPD) in a Smart City Vehicular Environment

Igder, Samaneh; Bhattacharya, Samya; Elmirghani, Jaafar M. H.

doi:10.1109/ngmast.2016.17

Cited by 24 publications

(13 citation statements)

References 7 publications

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“…The communication power in the edge, on the other hand, is solely consumed by the access point, while for cloud the communication power is the power consumed in the routers and switches leading to it. For the vehicles the same approach in [21] is followed where a typical type of computer is said to dedicate 58% of its power consumption to processing and 21% to communication, and the rest to storage. The model is evaluated for a varied number of requests ranging from 1 to 10 requests of equal demands initiated by vehicles.…”

Section: Optimisation Model and Resultsmentioning

confidence: 99%

Energy Efficient Distributed Processing in Vehicular Cloud Architecture

Behbehani

Musa

El-Gorashi

et al. 2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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Facilitating the revolution for smarter cities, vehicles are getting smarter and equipped with more resources to go beyond transportation functionality. On-Board Units (OBU) are efficient computers inside vehicles that serve safety and non-safety based applications. However, much of these resources are underutilised. On the other hand, more users are relying now on cloud computing which is becoming costly and energy consuming. In this paper, we develop a Mixed Integer linear Programming (MILP) model that optimizes the allocation of processing demands in an architecture that encompasses the vehicles, edge and cloud computing with the objective of minimizing power consumption. The results show power savings of 70%-90% compared to conventional clouds for small demands. For medium and large demand sizes, the results show 20%-30% power saving as the cloud was used partially due to capacity limitations on the vehicular and edge nodes. INTROUCTIONEnd users are growing more dependent on cloud services and data centers [1]. As the demand on cloud services grows higher, the data centers, as expected, tend to grow even bigger and more expensive in term of both monetary cost and energy consumptions. The energy consumption of clouds and data centers is contributing much to the total cost and power consumption in the Information and Communication Technology (ICT) field. That is why a lot of effort is being put forward now to explore alternatives that are more energy efficient and still as powerful [2][3][4][5][6][7][8][9][10][11]. One approach that is being actively evaluated is distributed service providers or the installation of mini data centers close to end users' level. In [12] data processing is done at different layers of the network and not only in the core cloud through optimized placement of Virtual Machines (VM) in IoT devices. Comparison between centralized data centers and nano data centers, to show the validity of the small data centers and its impacting factors, was carried out in [13]. The work in [14] analysed the energy consumption and latency of computation offloading in mobile clouds.Modern vehicles are increasingly being viewed as smart machines with plenty of computing resources. Research in the area of vehicular networks is very promising and it varies from Internet of Vehicles (IoV) [15] to Vehicular Clouds to VaaR (vehicle as a Resource) [16]. Our work presents an end-to-end architecture that uses vehicular and edge computing as the first level of processing resources. It compares this architecture with conventional clouds from an energy consumption point of view. For the remainder or the paper, Section 2 presents the proposed architecture. Section 3 discusses the optimization model and its results, and in Section 4 the paper is concluded.

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Section: Optimisation Model and Resultsmentioning

confidence: 99%

Energy Efficient Distributed Processing in Vehicular Cloud Architecture

Behbehani

Musa

El-Gorashi

et al. 2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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“…The authors in [29] considered analytics based big data processing of sensor data. The author in [30] considered big data analytics to enhance actuation efficiency in the network. The authors of [31], [32] discussed greening these big data networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Resilient Service Embedding in IoT Networks

et al. 2020

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The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency.

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“…Another study [15,16] have proposed energy-efficient fog servers which deliver the information to mobile users (in vehicle). They used renewable energy, non-renewable energy, and also the combination of both.…”

Section: Related Workmentioning

confidence: 99%

Energy efficient edge-of-things

Toor

Islam²,

Ahmed

et al. 2019

J Wireless Com Network

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Edge-of-Things (EoT) emerged as a novel computing and storage paradigm to overcome the limitations of IoT-cloud environment by providing cloud-like services at edge of the network. EoT offers a vast area for research and development as the invention has laid out great opportunities to experiment the possibilities for handling large data sets produced by the growing Internet-of-Things (IoT). The EoT offers a framework that lies between the cloud-to-end to perform the processing and cater the storage demands of the IoT applications. However, the exponential increase in EoT infrastructure resulted into extreme energy consumption. This paper finds the opportunity to address the issue of energy consumption in IoT-EoT environment by introducing dynamic speed scaling mechanism in EoT devices. The proposed approach is rigorously evaluated, and the verification is acquired through the simulations carried out on the simulator, iFogSim. The results show significant improvement in energy conservation by dynamically scaling the processor frequency of EoT devices according to the load variations in IoT traffic.

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Energy Efficient Fog Servers for Internet of Things Information Piece Delivery (IoTIPD) in a Smart City Vehicular Environment

Cited by 24 publications

References 7 publications

Energy Efficient Distributed Processing in Vehicular Cloud Architecture

Energy Efficient Distributed Processing in Vehicular Cloud Architecture

Resilient Service Embedding in IoT Networks

Energy efficient edge-of-things

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