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

A Crowd-Assisted Real-time Public Transport Information Service: No More Endless Wait

Many passengers have expressed frustration in waiting for public bus endlessly without knowing the estimated ar- rival time. In many developing countries, requiring bus operators to invest in the installation of a GPS unit on every bus in order to track the bus location and subsequently predicting the bus arrival time can be costly. This paper proposes passenger-assisted sharing of bus location to provide an estimation of bus arrival time. Our scheme aims to exploit the availability and capability of passenger mobile phones to share location information of the travelling buses in order to collect transportation data, at the same time provide an estimation of bus arrival time to the general public. A mobile app is developed to periodically report bus location to the cloud service, and it can detect location spoofing by malicious users. The preliminary results of the field tests suggest that the proposed system is viable and the predicated ETA falls within three minutes of the bus actual arrival time

Smart Timetable Service Based on Crowdsensed Data

The rapid technological development and the introduction of smart services make it possible for modern cities to offer an enhanced perception of city life for their inhabitants. For instance, a smart timetable service of the city’s public transportation lines updated in real-time can decrease unnecessary waiting times at stops and increase the efficiency of travel planning. However, the implementation of such a service in a traditional way requires the deployment and maintenance of some costly sensing and tracking infrastructure. Fortunately, mobile crowdsensing, when the crowd of passengers and their mobile devices are used to gather data, can be a viable and almost free of charge alternative for implementing sensing based smart city services. In this chapter, we put the emphasis on the introduction of a crowdsensing based smart timetable service, which has been developed as a prototype smart city application. The front-end interface of this service is called TrafficInfo. It is a simple and easy-to-use Android application which visualizes public transport information of the given city on Google Maps in real-time. The live updates of transport schedule information rely on the automatic stop event detection of public transport vehicles. TrafficInfo is built upon an Extensible Messaging and Presence Protocol (XMPP) based communication framework which was designed to facilitate the development of crowd assisted smart city applications. The chapter introduces this generic framework shortly, then describes the prototype smart timetable service

An IoT-based contribution to improve mobility of the visually impaired in Smart Cities

The Internet of Things envisions that objects of everyday life will be equipped with sensors, microcontrollers, transceivers for digital communication and suitable protocol which communicates among them and with users, becoming an integral part of Internet. Due to the growing developments in digital technologies, Smart Cities have been equipped with different electronic devices based on IoT and several applications are being created for most diverse areas of knowledge making systems more efficient. However, Assistive technology is a field that is not enough explored in this scenario yet. In this work, an integrated framework with an IoT architecture customized for an electronic cane (electronic travel aid designed for the visually impaired) has been designed. The architecture is organized by a five-layer architecture: edge technology, gateway, Internet, middleware and application. This new feature brings the ability to connect to environment devices, receiving the coordinates of their geographic locations, alerting the user when it is close to anyone of these devices and sending those coordinates to a web application for smart monitoring. Preliminary studies and experimental tests with three blind users of the Cane show that this approach would contribute to get more spatial information from the environment improving mobility of visually impaired people.This research was supported by the Brazilian National Council of Scientific & Technological Development—CNPq, Grant Number 315338/2018-0, and Fundação de Amparo a Pesquisa no Estado de Santa Catarina -FAPESC, (Programa Sinapse da Inovação Operação SC III)

IoTRec: The IoT Recommender for Smart Parking System

This paper proposes a General Data Protection Regulation (GDPR)-compliant Internet of Things (IoT) Recommender (IoTRec) system, developed in the framework of H2020 EU-KR WISE-IoT (Worldwide Interoperability for Semantic IoT) project, which provides the recommendations of parking spots and routes while protecting users’ privacy. It provides recommendations by exploiting the IoT technology (parking and traffic sensors). The IoTRec provides four-fold functions. Firstly, it helps the user to find a free parking spot based on different metrics (such as the nearest or nearest trusted parking spot). Secondly, it recommends a route (the least crowded or the shortest route) leading to the recommended parking spot from the user’s current location. Thirdly, it provides the real-time provision of expected availability of parking areas (comprised of parking spots organized into groups) in a user-friendly manner. Finally, it provides a GDPR-compliant implementation for operating in a privacy-aware environment. The IoTRec is integrated into the smart parking use case of the WISE-IoT project and is evaluated by the citizens of Santander, Spain through a prototype, but it can be applied to any IoT-enabled locality. The evaluation results show the citizen’s satisfaction with the quality, functionalities, ease of use and reliability of the recommendations/services offered by the IoTRec

Program your city: Designing an urban integrated open data API

Cities accumulate and distribute vast sets of digital information. Many decision-making and planning processes in councils, local governments and organisations are based on both real-time and historical data. Until recently, only a small, carefully selected subset of this information has been released to the public – usually for specific purposes (e.g. train timetables, release of planning application through websites to name just a few). This situation is however changing rapidly. Regulatory frameworks, such as the Freedom of Information Legislation in the US, the UK, the European Union and many other countries guarantee public access to data held by the state. One of the results of this legislation and changing attitudes towards open data has been the widespread release of public information as part of recent Government 2.0 initiatives. This includes the creation of public data catalogues such as data.gov.au (U.S.), data.gov.uk (U.K.), data.gov.au (Australia) at federal government levels, and datasf.org (San Francisco) and data.london.gov.uk (London) at municipal levels. The release of this data has opened up the possibility of a wide range of future applications and services which are now the subject of intensified research efforts. Previous research endeavours have explored the creation of specialised tools to aid decision-making by urban citizens, councils and other stakeholders (Calabrese, Kloeckl & Ratti, 2008; Paulos, Honicky & Hooker, 2009). While these initiatives represent an important step towards open data, they too often result in mere collections of data repositories. Proprietary database formats and the lack of an open application programming interface (API) limit the full potential achievable by allowing these data sets to be cross-queried. Our research, presented in this paper, looks beyond the pure release of data. It is concerned with three essential questions: First, how can data from different sources be integrated into a consistent framework and made accessible? Second, how can ordinary citizens be supported in easily composing data from different sources in order to address their specific problems? Third, what are interfaces that make it easy for citizens to interact with data in an urban environment? How can data be accessed and collected

Smart-phone based spatio-temporal sensing for annotated transit map generation

City transit maps are one of the important resources for public navigation in today's digital world. However, the availability of transit maps for many developing countries is very limited, primarily due to the various socio-economic factors that drive the private operated and partially regulated transport services. Public transports at these cities are marred with many factors such as uncoordinated waiting time at bus stoppages, crowding in the bus, sporadic road conditions etc., which also need to be annotated so that commuters can take informed decision. Interestingly, many of these factors are spatio-temporal in nature. In this paper, we develop CityMap, a system to automatically extract transit routes along with their eccentricities from spatio-temporal crowdsensed data collected via commuters' smart-phones. We apply a learning based methodology coupled with a feature selection mechanism to filter out the necessary information from raw smart-phone sensor data with minimal user engagement and drain of batt

Unsupervised annotated city traffic map generation

Public bus services in many cities in countries like India are controlled by private owners, hence, building up a database for all the bus routes is non-trivial. In this paper, we leverage smart-phone based sensing to crowdsource and populate the information repository for bus routes in a city. We have developed an intelligent data logging module for smartphones and a server side processing mechanism to extract roads and bus routes information. From a 3 month long study involving more than 30 volunteers in 3 different cities in India, we found that the developed system, CrowdMap, can annotate bus routes wit