scholarly journals Large-Scale Mobile Sensing Enabled Internet-of-Things Testbed for Smart City Services

2015 ◽  
Vol 11 (8) ◽  
pp. 785061 ◽  
Author(s):  
Jorge Lanza ◽  
Luis Sánchez ◽  
Luis Muñoz ◽  
José Antonio Galache ◽  
Pablo Sotres ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart City ◽  
Large Scale ◽  
Mobile Sensing

scholarly journals LoRa (Long-Range) High-Density Sensors for Internet of Things

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Alexandru Lavric
Keyword(s):  
Internet Of Things ◽  
Smart City ◽  
High Performance ◽  
Large Scale ◽  
Geographic Area ◽  
High Density ◽  
Monitoring And Control ◽  
Spreading Factor ◽  
Transfer Of Information ◽  
And Control

Over the past few years, there has been a growing awareness regarding the concept of Internet of Things (IoT), which involves connecting to the Internet various objects surrounding us in everyday life. The main purpose of this concept closely connected to the smart city issue is increasing the quality of life by contributing to streamlining resource consumption and protecting the environment. The LoRa communication mechanism is a physical layer of the LoRaWAN protocol, defined by the LoRa Alliance. Compared to other existing technologies, LoRa is a modulation technique enabling the transfer of information over a range of tens of kilometers. The main contribution this paper brings to the field is analyzing the scalability of the LoRa technology and determining the maximum number of sensors which can be integrated into this type of monitoring and control architecture. The sensor architecture is specific to the smart city concept that involves the integration of a large number of high-density sensors distributed on a large-scale geographic area. The reason behind this study is the need to assess the scalability of the LoRa technology, taking into consideration other factors, such as the packet payload size, the duty circle parameter, the spreading factor, and the number of nodes. The experimental results reveal that the maximum number of LoRa sensors that can communicate on the same channel is 1,500; furthermore, in order to obtain a high performance level, it is necessary to schedule and plan the network as carefully as possible. The spreading factor must be allocated according to the distance at which the sensor is placed from the gateway.

scholarly journals The Potential of Microservice Architecture for Internet of Things (IoT) in Smart City, a Literature Review

Kursor ◽  
2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Hatma - Suryotrisongko ◽  
Prasasti Karunia Farista Ananto
Keyword(s):  
Internet Of Things ◽  
Literature Review ◽  
Smart City ◽  
Large Scale ◽  
Service Oriented Architecture ◽  
Distributed Applications ◽  
Community Resources ◽  
Service Oriented ◽  
Microservice Architecture

A smart city is a term thatis often used in a literature that refers to a city's intelligence. The visions of smart city are to utilizeits community resources, improving the quality of its services, and reducing the costs for public administration operations. Internet of Things (IoT) is one of the main keys. To accomplish the visions, IoT needs to standardize the technology and web by developing the platform on a large scale. It is a challenge for the city to build widely distributed applications and platforms on the Web. The Microservice Architecture style comes up and offers a lot of convenience and it becomes a new trend for developing Smart City IoT platforms. Before Microservice Architecture, Service Oriented Architecture was previously widely used. To find out Microservice Architecture’s simplicity and potentials, there are two steps to follow: (1) Setting up a Search Strategy on literature review to collect subset of papers with Google Scholar, (2) synthesizing and compiling a subset of literature review to extract data and information to be a literature view. After some reviews of the literatures, most of them agree with the use of microservices architecture in creating or developing a smart city of IoT because the microservices architecture offers a lot and can help the work of  IoT smart city developers.

scholarly journals Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Aluizio Rocha Neto ◽  
Thiago P. Silva ◽  
Thais Batista ◽  
Flávia C. Delicato ◽  
Paulo F. Pires ◽  
...  
Keyword(s):  
Distributed System ◽  
Smart City ◽  
Large Scale ◽  
Facial Recognition ◽  
Public Spaces ◽  
Hardware Accelerators ◽  
Video Streams ◽  
Video Analytics ◽  
Learning Tasks ◽  
Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

scholarly journals Vehicular Crowdsourcing for Congestion Support in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 662-685
Author(s):  
Stephan Olariu
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Adaptive Traffic Signal Control ◽  
Traffic Conditions ◽  
Direct Benefits ◽  
Traffic Signal Control Systems ◽  
Computational Resources

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

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