Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection

Processing and Fusion of Transport platooning sensor data

In this thesis we are considering a data set which a group of researchers gathered, while conducting a transport platooning field experiment. We are assisting in the processing and fusion of the data, gathered during this experiment. We are investigating which sensors were used and what data gathering setup was used. We are investigating methods of extracting the data, formatting it and fusing it into a coherent data set, in a standardised format. We then explore different methods for filtering and extracting the data into a convenient format. We are also analysing the quality of the gathered data, using various techniques, and providing feedback on what can be improved if a similar experiment is conducted in the future. Finally we also develop some simple visualizations of the data.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO

Visualizing the Motion Flow of Crowds

In modern cities, massive population causes problems, like congestion, accident, violence and crime everywhere. Video surveillance system such as closed-circuit television cameras is widely used by security guards to monitor human behaviors and activities to manage, direct, or protect people. With the quantity and prolonged duration of the recorded videos, it requires a huge amount of human resources to examine these video recordings and keep track of activities and events. In recent years, new techniques in computer vision field reduce the barrier of entry, allowing developers to experiment more with intelligent surveillance video system. Different from previous research, this dissertation does not address any algorithm design concerns related to object detection or object tracking. This study will put efforts on the technological side and executing methodologies in data visualization to find the model of detecting anomalies. It would like to provide an understanding of how to detect the behavior of the pedestrians in the video and find out anomalies or abnormal cases by using techniques of data visualization

A REVIEW ON INTERNET OF THINGS ARCHITECTURE FOR BIG DATA PROCESSING

The importance of big data implementations is increased due to large amount of gathered data via the online gates. The businesses and organizations would benefit from the big data analysis i.e. analyze the political, market, and social interests of the people. The Internet of Things (IoT) presents many facilities that support the big data transfer between various Internet objects. The integration between the big data and IoT offer a lot of implementations in the daily life like GPS, Satellites, and airplanes tracking. There are many challenges face the integration between big data transfer and IoT technology. The main challenges are the transfer architecture, transfer protocols, and the transfer security. The main aim of this paper is to review the useful architecture of IoT for the purpose of big data processing with the consideration of the various requirements such as the transfer protocol. This paper also reviews other important issues such as the security requirements and the multiple IoT applications. In addition, the future directions of the IoT-Big data are explained in this paper

Road Traffic Management using Vehicle-to- Everything (V2X) Communication

Traffic congestion is the primary concern in dense cities; while the increased number of automobiles is becoming uncontrollable in some cities, it is more challenging to manage or change how people use cars. To contribute to solving traffic congestion in cities, this project examines the study of transferring vehicles to be competent in a way that can help the government entities analyze the received vehicles’ data and for better decisions on reducing traffic congestion as well as the real-time monitoring of traffic wherever it is located using the Vehicle-to-Everything (V2X) communication methodology. This study proposes a hardware “system” that can be attached to any vehicle to collect real-time data from vehicles and communicate with the Road and Transportation Authority. The hardware system, however, is connected to the cars through a wireless On-Board Diagnostics (OBD) connection in favor of collecting all the necessary information from the vehicle, such as the car speed and Revolutions Per Minute (RPM) data. On the other hand, a GPS sensor is used to inquire about the vehicle’s location, a GSM module to make sure the device is always connected to the internet for data transmission, a LiDAR sensor for distance and safety measurement, and a camera module accessed only by the driver for object detection such as cars, pedestrians, traffic signs, damaged roads, and road hazards. Moreover, system updates and maintenance can be done remotely to reduce the number of visits to the traffic department since all devices are to be connected to a single platform. As a result, it was possible to create a prototype for a single vehicle, including the sensors mentioned above, returning valuable data that include vehicle speed and exact location, which will help future researchers develop an application platform to monitor and track traffic congestion in real time

Visual analysis of multi-labelled temporal noise data from multiple sensors

Environmental noise pollution is a problem for cities’ inhabitants, that can be especially severe in large cities. To implement measures that can alleviate this problem, it is necessary to understand the extent and impact of different noise sources. Although gathering data is relatively cheap, processing and analyzing the data is still complex. Besides the lack of an automatic method for labelling city sounds, maybe more important is the fact that there is not a tool that allows domain experts to analytically explore data that has been manually labelled. To solve this problem, we have created a visual analytics application that facilitates the exploration of multiple-labelled temporal data captured at four different corners of a crossing in a populated area of Barcelona, the Eixample neighborhood. Our tool consists of a series of linked interactive views that facilitate top-down (from noise events to labels) and bottom-up (from labels to time slots) exploration of the captured data.This project has been supported by grants TIN2017-88515-C2-1-R (GEN3DLIVE) from the Spanish Ministerio de Economía y Competitividad, by 839 FEDER (EU) funds, and PID2021-122136OB-C21 from MCIN/AEI/10.13039/501100011033/FEDER, EU.Peer ReviewedPostprint (published version