On the Location of Fog Nodes in Fog-Cloud Infrastructures

Silva, Rodrigo A. C. da; Fonseca, Nelson L. S. da

doi:10.3390/s19112445

Cited by 38 publications

(20 citation statements)

References 23 publications

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“…Kem et al described an FC architecture [17], where end users provide household fog nodes and receive incentives for serving nodes. More recent work [10,12,16,17,23,24,27,33,38] has studied the influence of fog nodes connected to other network devices in an FC architecture. However, the abovementioned authors have not addressed the impact of fog node deployment on different physical locations.…”

Section: Motivationmentioning

confidence: 99%

“…The fog computing (FC) architecture migrates services (i.e., computing, processing, and storage) from the cloud to the network edge or devices located near end users by implementing these services at the routers, switches, WiFi access points (APs), smart devices, etc. The devices that host these services are named fog nodes [15,27]. The purpose of FC is to minimize the average end-to-end delay for time-critical applications and reduce the core network's bandwidth usage.…”

Section: Introductionmentioning

confidence: 99%

“…Then, fog nodes make the resource-constrained IoT devices virtually limitless and provide better services in terms of latency. Nevertheless, ubiquitous fog node deployment is still unrealistic in the near future because of cost issues [27,38]. Accordingly, in this work, we consider a scenario in which IoT devices offload tasks to fog nodes via multihop wireless networks.…”

Section: Introductionmentioning

confidence: 99%

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SOSW: scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems

et al. 2021

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In a fog computing (FC) architecture, cloud services migrate towards the network edge and operate via edge devices such as access points (AP), routers, and switches. These devices become part of a virtualization infrastructure and are referred to as "fog nodes". Recently, software-defined networking (SDN) has been used in FC to improve its control and manageability. The current SDN-based FC literature has overlooked two issues: (a) fog nodes' deployment at optimal locations and (b) SDN best path computation for data flows based on constraints (i.e., end-to-end delay and link utilization). To solve these optimization problems, this paper suggests a novel approach, called scalable and optimal near-sighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems (SOSW). First, the SOSW model uses singular-value decomposition (SVD) and QR factorization with column pivoting linear algebra methods on the traffic matrix of the network to compute the optimal locations for fog nodes, and second, it introduces a new heuristic-based traffic engineering algorithm, called the constraint-based shortest path algorithm (CSPA), which uses ant colony optimization (ACO) to optimize the path computation process for task offloading. The results show that our proposed approach significantly reduces average latency and energy consumption in comparison with existing approaches.

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Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SOSW: scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems

et al. 2021

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“…However, the dynamic traffic characteristics, and heterogeneous computation capabilities of fog nodes and the cloud center present many challenges for workload allocation. First, the tasks are generated stochastically and the amount of computation also varies for different tasks and over time [12]. An online algorithm would thus be required to solve a workload allocation.…”

Section: Introductionmentioning

confidence: 99%

Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay

Guo

et al. 2019

Sensors

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Fog computing has recently emerged as an extension of cloud computing in providing high-performance computing services for delay-sensitive Internet of Things (IoT) applications. By offloading tasks to a geographically proximal fog computing server instead of a remote cloud, the delay performance can be greatly improved. However, some IoT applications may still experience considerable delays, including queuing and computation delays, when huge amounts of tasks instantaneously feed into a resource-limited fog node. Accordingly, the cooperation among geographically close fog nodes and the cloud center is desired in fog computing with the ever-increasing computational demands from IoT applications. This paper investigates a workload allocation scheme in an IoT–fog–cloud cooperation system for reducing task service delay, aiming at satisfying as many as possible delay-sensitive IoT applications’ quality of service (QoS) requirements. To this end, we first formulate the workload allocation problem in an IoT-edge-cloud cooperation system, which suggests optimal workload allocation among local fog node, neighboring fog node, and the cloud center to minimize task service delay. Then, the stability of the IoT-fog-cloud queueing system is theoretically analyzed with Lyapunov drift plus penalty theory. Based on the analytical results, we propose a delay-aware online workload allocation and scheduling (DAOWA) algorithm to achieve the goal of reducing long-term average task serve delay. Theoretical analysis and simulations have been conducted to demonstrate the efficiency of the proposal in task serve delay reduction and IoT-fog-cloud queueing system stability.

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“…The objective of the current work is to create a system that automatically detects and provides notifications about traffic congestion in a timely manner to suppress the number of casualties, by giving prompt medical aid to the victims of the road accident. This can be accomplished by utilizing the Internet of Things (IoT) [5,6] combined with Vehicular Ad hoc Networks (VANETs). As the node (vehicle) in VANET has a limited processing capability, it therefore might create latency if the processing of the captured information through the camera is carried out by the node itself.…”

mentioning

confidence: 99%

Road Accidents Detection, Data Collection and Data Analysis Using V2X Communication and Edge/Cloud Computing

et al. 2019

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With the improvement in transportation infrastructure and in-vehicle technology in addition to a meteoric increase in the total number of commercial and non-commercial vehicles on the road, traffic accidents may occur, which usually cause a high death toll. More than half of these deaths occur due to a delayed response by medical care providers and rescue authorities. The chances of survival of an accident victim could increase drastically if immediate medical assistance is provided at an accident location. This work proposes a low-cost accident detection and notification system, which utilizes a multi-tier IoT-based vehicular environment; principally, it uses V2X Communication and Edge/Cloud computing. In this work, vehicles are equipped with an On-Board Unit (OBU) in addition to mechanical sensors (accelerometer, gyroscope) for reliable accident detection along with a Global Positioning System (GPS) module for identification of accident location. In addition to this, a camera module is implanted on the vehicle to capture the moment when an accident takes place. In order to facilitate inter-vehicle communication (IVC), OBU in each vehicle incorporates a wireless networking interface. Once an accident occurs, a vehicle detects it and generates an alert message. It then sends the message along with the accident location to an intermediate device, placed at the edge of the vehicular network, and therefore called an edge device. Upon receiving the notification, this edge device finds the nearest hospital and makes a request for an ambulance to be dispatched immediately. It also performs some preprocessing of data and effectively acts as a bridge between the sensors installed inside the vehicle and the distant server deployed in the cloud. A significant issue that the traffic authorities are currently facing is the real-time visualization of data obtained through such environments. Wireless interfaces are usually capable of forwarding real-time sensor data; however, this feature is not yet commercially available in the OBU of the vehicle; therefore, practical implementation is carried out using the Internet of things (IoT) in order to create a network among the vehicles, the edge node, and the central server. By performing analysis on the adequate acquired data of road accidents, the constructive plans of action can be devised that may limit the death toll. In order to assist the relevant authorities in performing wholesome analysis of refined and reliable data, a dynamic front-end visualization is proposed, which is hosted in the cloud. The generated charts and graphs help the personnel at relevant organizations to make appropriate decisions based on the conclusive analysis of processed and stored data.

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On the Location of Fog Nodes in Fog-Cloud Infrastructures

Cited by 38 publications

References 23 publications

SOSW: scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems

SOSW: scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems

Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay

Road Accidents Detection, Data Collection and Data Analysis Using V2X Communication and Edge/Cloud Computing

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