scholarly journals Low-latency and energy-efficient scheduling in fog-based IoT applications

2019 ◽  
pp. 1406-1427 ◽  
Author(s):  
DADMEHR RAHBARI ◽  
MOHSEN NICKRAY
Keyword(s):  
Intelligent Transportation Systems ◽  
Data Transfer ◽  
Smart Cities ◽  
Fog Computing ◽  
Transportation Systems ◽  
Healthcare Applications ◽  
Iot Applications ◽  
Priority Algorithms ◽  
Processing Power ◽  
Resource Management Algorithms

In today’s world, the internet of things (IoT) is developing rapidly. Wireless sensor network (WSN) as an infrastructure of IoT has limitations in the processing power, storage, and delay for data transfer to cloud. The large volume of generated data and their transmission between WSNs and cloud are serious challenges. Fog computing (FC) as an extension of cloud to the edge of the network reduces latency and traffic; thus, it is very useful in IoT applications such as healthcare applications, wearables, intelligent transportation systems, and smart cities. Resource allocation and task scheduling are the NP-hard issues in FC. Each application includes several modules that require resources to run. Fog devices (FDs) have the ability to run resource management algorithms because of their proximity to sensors and cloud as well as the proper processing power. In this paper, we review the scheduling strategies and parameters as well as providing a greedy knapsack-based scheduling (GKS) algorithm for allocating resources appropriately to modules in fog network. Our proposed method was simulated in iFogsim as a standard simulator for FC. The results show that the energy consumption, execution cost, and sensor lifetime in GKS are better than those of the first-come-first-served (FCFS), concurrent, and delay-priority algorithms.

scholarly journals On-demand multi-hop forwarding for sustainable vehicular data transfer network

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110151
Author(s):  
Ayoub el Bendali ◽  
Anis Ur Rahman ◽  
Asad Waqar Malik ◽  
Muazzam Ali Khan ◽  
Sri Devi Ravana
Keyword(s):  
Internet Of Things ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Data Transfer ◽  
Smart Cities ◽  
Transportation Systems ◽  
Vital Role ◽  
Vehicle Speed ◽  
Coverage Area ◽  
Smart Hospitals

Smart cities play a vital role to develop a sustainable infrastructure with efficient management of the Internet of things devices. The infrastructure is used to support various applications for smart hospitals, smart factories, and intelligent transportation systems. With the extensive deployment of Internet of things devices, unprecedented growth in data has lead to capacity and transfer issues. In this article, we proposed an efficient data transfer mechanism based on self-sustainable networks over the vehicular environment. Depending on whether the network is connected with vehicles available to support direct connection from the source to destination, we propose end-to-end and hop-by-hop forwarding for vehicular networks that are inherently disconnected. The evaluation results demonstrate that the lifetime of the discovered paths depends on the coverage area, vehicle mobility, and vehicle speed. Therefore, at times redundant disjoint paths are selected for communication. In the proposed work, selected vehicles are used to reach the destination.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

scholarly journals A Review of Machine Learning and IoT in Smart Transportation

2019 ◽  
Vol 11 (4) ◽  
pp. 94 ◽  
Author(s):  
Fotios Zantalis ◽  
Grigorios Koulouras ◽  
Sotiris Karabetsos ◽  
Dionisis Kandris
Keyword(s):  
Machine Learning ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Route Optimization ◽  
Smart Transportation ◽  
Smart Lighting ◽  
Iot Applications ◽  
Smart Parking ◽  
Lighting Systems ◽  
Accident Detection

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

scholarly journals Eco-Efficient Mobility in Smart City Scenarios

2020 ◽  
Vol 12 (20) ◽  
pp. 8443
Author(s):  
Ramon Sanchez-Iborra ◽  
Luis Bernal-Escobedo ◽  
José Santa
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Smart Cities ◽  
Low Cost ◽  
Transportation Systems ◽  
Area Network ◽  
Wide Area Network ◽  
Fully Integrated ◽  
Personal Mobility ◽  
Traffic Efficiency

Cooperative-Intelligent Transportation Systems (C-ITS) have brought a technological revolution, especially for ground vehicles, in terms of road safety, traffic efficiency, as well as in the experience of drivers and passengers. So far, these advances have been focused on traditional transportation means, leaving aside the new generation of personal vehicles that are nowadays flooding our streets. Together with bicycles and motorcycles, personal mobility devices such as segways or electric scooters are firm sustainable alternatives that represent the future to achieve eco-friendly personal mobility in urban settings. In a near future, smart cities will become hyper-connected spaces where these vehicles should be integrated within the underlying C-ITS ecosystem. In this paper, we provide a wide overview of the opportunities and challenges related to this necessary integration as well as the communication solutions that are already in the market to provide these moving devices with low-cost and efficient connectivity. We also present an On-Board Unit (OBU) prototype with different communication options based on the Low Power Wide Area Network (LPWAN) paradigm and several sensors to gather environmental information to facilitate eco-efficiency services. As the attained results suggest, this module allows personal vehicles to be fully integrated in smart city environments, presenting the possibilities of LoRaWAN and Narrow Band-Internet of Things (NB-IoT) communication technologies to provide vehicle connectivity and enable mobile urban sensing.

Future Aspects and Research Perspectives of the Internet of Things

2021 ◽  
pp. 1-18
Author(s):  
Harshit Bhardwaj ◽  
Pradeep Tomar ◽  
Aditi Sakalle ◽  
Taranjeet Singh ◽  
Divya Acharya ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
The Internet ◽  
Literature Analysis ◽  
Healthcare Applications ◽  
Web Based ◽  
Iot Applications ◽  
Research Perspectives ◽  
Software Production ◽  
The Internet Of Things

Fog computing has latency, particularly for healthcare applications, which is of the utmost importance. This research aims to be a comprehensive literature analysis of healthcare innovations for fog computing. All of these components involved special abilities. In sequence, developers must be qualified to write stable, healthy IoT programs in four distinct fields of software production: embedded, server, tablet, and web-based. Furthermore, the distributed results, IoT structure essence, dispersed abilities in programming play a deciding position. This chapter discusses the difficulties in creating the IoT method and summarizing findings and observations. Experiences of the need for and co-presence of various kinds of skills in software creation in the construction of IoT applications are discussed.

scholarly journals Blockchain for the Internet of Vehicles: A Decentralized IoT Solution for Vehicles Communication Using Ethereum

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3928 ◽  
Author(s):  
Rateb Jabbar ◽  
Mohamed Kharbeche ◽  
Khalifa Al-Khalifa ◽  
Moez Krichen ◽  
Kamel Barkaoui
Keyword(s):  
Intelligent Transportation Systems ◽  
Secure Communication ◽  
Data Exchange ◽  
Smart Cities ◽  
Transportation Systems ◽  
Smart Devices ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Computing Platform ◽  
Blockchain Technology

The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every “thing” to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities. The IoV has the potential to solve various traffic and road safety problems effectively in order to prevent fatal crashes. However, a particular challenge in the IoV, especially in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, is to ensure fast, secure transmission and accurate recording of the data. In order to overcome these challenges, this work is adapting Blockchain technology for real time application (RTA) to solve Vehicle-to-Everything (V2X) communications problems. Therefore, the main novelty of this paper is to develop a Blockchain-based IoT system in order to establish secure communication and create an entirely decentralized cloud computing platform. Moreover, the authors qualitatively tested the performance and resilience of the proposed system against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications such as security, centralization, and lack of privacy. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.

scholarly journals Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6309
Author(s):  
Mohammad Peyman ◽  
Pedro J. Copado ◽  
Rafael D. Tordecilla ◽  
Leandro do C. Martins ◽  
Fatos Xhafa ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Edge Computing ◽  
Analytical Models ◽  
Current Status ◽  
Ride Sharing ◽  
The City

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

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