Energy Efficient Fog Computing with Architecture of Smart Traffic Lights System

Luo, Yawen; Chen, Yuhua; Wu, Junchao

doi:10.1109/eiconcit50028.2021.9431873

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…The results demonstrate that the fog-based solution achieves significantly lower latency (around 14.74 ms with 30 cars and 15.64 ms with 60 cars) in contrast to the TLC-based architecture (104.43 ms with 30 cars and 108.32 ms with 60 cars), highlighting the potential of the fog-based approach in achieving efficient traffic light optimization. In another study [10], the authors propose an energy-efficient fog computing architecture for smart traffic lights. Their objective is to optimize energy consumption by leveraging fog computing techniques.…”

Section: Full Stack Simulatorsmentioning

confidence: 99%

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

Khattak,

Khan

2023

Sci. Eng. Technol.

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The Internet-of-Things (IoT) constructs a vast, intricate, and perpetually evolving ecosystem exerting profound societal implications. This labyrinthine nature often culminates in errors that directly impact human lives. A significant domain where this complexity materializes is Intelligent Transportation Systems (ITS). Present tools and methodologies inadequately accommodate the complex task of testing and validation, underscoring the urgency for comprehensive review and enhancement. This study aims to present a broad analysis of existing simulators utilized for ITS simulations. It delves into the role and effectiveness of such simulation tools, highlighting their limitations and proposing research directions. This paper scrutinizes both commercial and research-oriented IoT simulators for ITS, evaluating their features and simulation environment tools. We have detailed various ITS scenarios simulated within these frameworks, intending to gauge their readiness for real-world ITS applications and to elaborate on the challenges involved in ITS infrastructure implementation. The findings suggest that despite numerous simulators aiding the evolution of solutions for IoT challenges in recent years, their utility in actual ITS implementations remain uncertain. Consequently, we explore public cloud platforms offering IoT simulation capabilities, focusing particularly on the capabilities provided by the Amazon Web Services (AWS) IoT simulation for this study. Our research outlines the pressing challenges in this field, while proposing potential solutions and flagging opportunities for further research. This study paves the way towards improving the reliability and accuracy of IoT simulators in the context of ITS, which has immense potential to enhance the quality of human life.

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Section: Full Stack Simulatorsmentioning

confidence: 99%

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

Khattak,

Khan

2023

Sci. Eng. Technol.

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“…Whenever the fog node is not processing any task, it is said to be in idle mode, and when it is executing any task, it is said to be in busy mode. The fog device's energy consumption is calculated in iFogSim using Equation (3). The energy consumption of the fog nodes (EC) is calculated using the current energy consumption E, the current time T c , the updated time during the last utilization T r , and the host power utilization HP r .…”

Section: Problem Formulationmentioning

confidence: 99%

“…Cloud computing platforms for storing, processing, and analyzing the data generated from a large number of connected devices became a great innovation for the IoT, but this has brought many challenges with respect to their centralized architecture [1,2]. Due to the huge demand of IoT devices and location-aware services, applications are generating amounts of data that are almost inconceivable [3]. It has been estimated that the cloud alone may not be sufficient to handle the big data generated by IoT devices with respect to time-critical or emergency situations.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures

Sharma¹,

Rathee

Kerrache

et al. 2023

Applied Sciences

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The connection of many devices has brought new challenges with respect to the centralized architecture of cloud computing. The fog environment is suitable for many services and applications for which cloud computing does not support these well, such as: traffic light monitoring systems, healthcare monitoring systems, connected vehicles, smart cities, homes, and many others. Sending high-velocity data to the cloud leads to the congestion of the cloud infrastructure, which further leads to high latency and violations of the Quality-of-Service (QoS). Thus, delay-sensitive applications need to be processed at the edge of the network or near the end devices, rather than the cloud, in order to provide the guaranteed QoS related to the reduced latency, increased throughput, and high bandwidth. The aim of this paper was to propose a two-stage optimal task scheduling (2-ST) approach for the distribution of tasks executed within smart homes among several fog nodes. To effectively solve the task scheduling, this proposed approach uses a naïve-Bayes-based machine learning model for training in the first stage and optimization in the second stage using a hyperheuristic approach, which is a combination of both Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In addition, the proposed mechanism was validated against various metrics such as energy consumption, latency time, and network usage.

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“…Edge computing is quite similar to fog computing but very few differences in terms of high security and a large volume of data transfer make fog computing so viable to use with UAVs. 10 It helps to provide a way for IoT systems to communicate and process a large volume of data at a time by offloading it via devices to fog nodes or devices to UAVs. UAVs have their grand contribution to military, civil, commercial, and industrial areas.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with other computing paradigms, fog computing has numerous benefits such as better connectivity, ultra‐low latency, security, data‐offloading, and cost. Edge computing is quite similar to fog computing but very few differences in terms of high security and a large volume of data transfer make fog computing so viable to use with UAVs 10 . It helps to provide a way for IoT systems to communicate and process a large volume of data at a time by offloading it via devices to fog nodes or devices to UAVs.…”

Section: Introductionmentioning

confidence: 99%

A survey on green unmanned aerial vehicles‐based fog computing: Challenges and future perspective

Gupta

2022

Trans Emerging Tel Tech

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In the area of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT), enormous technological advancement has transformed the way we choose to live. UAVs link everything in the modern world, and it is an attractive area for academia to focus on. UAVs are proven to be a boon for the whole world as it provides flying services in critical and medical emergencies. UAVs are futuristic technology that enriches human needs by providing numerous benefits or services to proceed toward the smart world. In UAV‐assisted traditional networks, multiple sensors and flying nodes consume a lot of energy resources, and Fifth Generation (5G)/Beyond 5G enabled devices to excrete high radiations, which may cause damage to our environment as well as our society. So, it is very important to maintain a balance between the environment and modern technologies for a better lifestyle. To maintain this, many steps need to be put further to reduce the energy consumption of nodes, consume fewer data resources, and encourage effective energy‐use techniques. Green UAV‐based fog computing is a promising solution to balance environmental issues and provide energy‐efficient data computation and aerial to ground network communication for building an intelligent world with improved Quality of Services (QoS). This article provides a comprehensive survey on green technologies and applications for UAV‐Fog. We also highlight research issues, current challenges, lessons learned, and the future direction of the UAV‐Fog network.

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Energy Efficient Fog Computing with Architecture of Smart Traffic Lights System

Cited by 3 publications

References 12 publications

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures

A survey on green unmanned aerial vehicles‐based fog computing: Challenges and future perspective

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