A novel Bluetooth low energy based system for spatial exploration in smart cities

Smart Cities are employing information and communication technologies in the quest for sustainable economic development and the fostering of new forms of collective life. They facilitate connections between citizens and organizations that are of paramount importance for their long-term sustainability. As cities become more complex and their communities more dispersed, questions such as ‘where can I find …’ are increasingly pertinent. In this paper, we introduce NomaBlue, a new vision of spatial recognition in smart cities, the proposed system is based on an intelligent nomadic data collection and users' collaboration using smart Bluetooth technology. We demonstrate using two case-studies that our approach is capable of proposing an efficient spatial recognition service while supporting a range of users’ constraints, our system is disconnected from the internet, it can operate in any indoor/outdoor area, it doesn't require pre-defined geographic databases and uses a new concept of nomadic data collection and sharing to speed-up the circulating information in smart cities.     

Understanding Skout users’ mobility patterns on a global scale: a data-driven study

Location-based social apps, such as Skout, have been widely used by millions of users for sharing their location information. In this work, we collected all the location information published by over 1.2 million Skout users during December 2012 and June 2016. Based on the collected information, we model the inter-city mobility of Skout users with a global city network, and analyze the evolution of the network based on its structural characteristics. Moreover, we look into Skout users’ mobility patterns by discovering the most popular inter-city routes, destinations, and tightly connected city groups, and analyze the impact on the mobility patterns from geographical distances, languages and cultures. Finally, we leverage machine learning techniques to build a model for identifying the most influential cities in the world according to the Skout data. The results are able to assist individuals, governors and business leaders in making better decisions regarding traveling, immigrating, measuring city improvements and cooperation with cities.

     

Quality and the efficiency of data in “Smart-Cities”

As a new form of sustainable development, the concept “Smart Cities” knows a large expansion during the recent years. It represents an urban model, refers to all alternative approaches to metropolitan ICTs case to enhance quality and performance of urban service for better interaction between citizens and government. However, the smart cities based on distributed and autonomous information infrastructure contains millions of information sources that will be expected more than 50 billion devices connected by using IoT or other similar technologies in 2020. In Information Technology, we often need to process and reason with information coming from various sources (sensors, experts, models). Information is almost always tainted with various kinds of imperfection: imprecision, uncertainty, ambiguity, we need a theoretical framework general enough to allow for the representation, propagation and combination of all kinds of imperfect information. The theory of belief functions is one such Framework. Real-time data generated from autonomous and distributed sources can contain all sorts of imperfections regarding on the quality of data e.g. imprecision, uncertainty, ignorance and/or incompleteness. Any imperfection in data within smart city can have an adverse effect over the performance of urban services and decision making. In this context, we address in this article the problem of imperfection in smart city data. We will focus on handling imperfection during the process of information retrieval and data integration and we will create an evidential database by using the evidence theory in order to improve the efficiency of smart city. The expected outcomes from this paper are (1) to focus on handling imperfection during the process of information retrieval and data integration (2) to create an evidential database by using the evidence theory in order to improve the efficiency of smart city. As experimentation we present a special case of modeling imperfect data in the field of Healthcare. An evidential database will be built which will contain all the perfect and imperfect data. These data come from several Heterogeneous sources in a context of Smart Cities. Imperfect aspects in the evidential database expressed by the theory of beliefs that will present in this paper.     

微信小程序用户授权登录机制研究和实现

微信小程序的出现,一方面缓解了用户手机安装大量APP浪费手机存储资源并导致手机速度变慢的问题,另一方面,也减轻了开发者为不同手机操作系统(Android,iOS)分别开发程序的工作负担。微信小程序应用开发是以MVC模式的JSON作为数据交换格式的以WEB开发为基础的开发技术,但是也有很多不同于以往WEB开发的地方,尤其是用户授权登录方面,用户认证信息需要在微信小程序、开发者服务器和微信接口服务器之间传递,这个过程中要考虑用户认证信息传递的流程和数据安全问题。文章研究了这两个问题并在一个应用中做了具体实现。     

A service-oriented city portal framework and collaborative development platform

Cities are being equipped with multiple information systems to provide public services for city officials, officers, citizens, and tourists. There have been concerns with efficient service implementation and provision, e.g., data islands and function overlaps between systems and applications. Service-oriented portals are efficient at facilitating information sharing and collaborative work between city systems and users. The goal of this research is to make cities responsive, agile and to provide composite services efficiently and cost efficiently. A service-oriented framework for city portals is proposed to design, integrate and streamline city systems and applications. A model driven collaborative development platform of the proposed framework was developed for service-oriented digital portals. The architecture and implementation issues of the platform are discussed. The service identification policies are discussed within the framework. A case study has been developed and evaluated on the platform to provide a composite service, i.e., a traffic search service on a city portal.     

Mining Urban Data (Part C)

Modern cities generate a flood of rich and varied data. New information sources like public transport and wearable devices provide opportunities for novel applications that will improve citizens׳ quality of life by reducing transportation time, enhancing city planning, and improving air quality to name a few applications. From a data science perspective, data emerging from smart cities give rise to a lot of challenges that constitute a new interdisciplinary field of research. This article introduces the third part of a special issue on the topic ‘Mining Urban Data’ published in the journal Information Systems.     

Green Bear - A LoRaWAN-based Human-in-the-Loop case-study for sustainable cities

Citizens are a crucial factor in the sustainability of the cities because they are one of the pillars for balancing their environment. Smart city projects can offer citizens an opportunity to understand how they contribute to their city’s sustainability. Furthermore, an interactive smart city system can create synergies that benefit both the citizens and the city itself. This paper presents the Green Bear smart city platform, an IoT Human-in-the-Loop system that uses LoRaWAN nodes to gather information on city green spaces, bike lanes, and recycling depots. A user-centered mobile application that allows for active user participation, feedback, and incentivization is used to close the loop with humans. This system allows citizens to evaluate their participation in aspects that improve the city’s sustainability through a gamification scheme, obtaining points for different activities in the city’s public spaces and personal activities to improve their quality of life. This solution is being implemented in the city of Coimbra, Portugal. After an overview of the system, the paper describes each main system module. The Green Bear prototype was subject to functional and technological assessment, and the results are presented and discussed.     

Sensitive-by-distance: quasi-health data in the algorithmic era

This article offers a new perspective on the boundaries between health and non-health data in the age of ‘Quantified-Self’ apps: the ‘data-sensitiveness-by-computational-distance’ approach-or, more simply, the ‘sensitive-by-distance’ approach. This approach takes into account two variables: the intrinsic sensitiveness (a static variable) of personal data and the computational distance (a dynamic variable) between some kinds of personal data and pure health (or sensitive) data, which depends upon computational capacity. From an objective perspective, computational capacity depends on the level of development of data retrieval technologies at a certain moment, the availability of ‘accessory data’, and the applicable legal restraints on processing data. From a subjective perspective, computational capacity depends on the specific data mining efforts (or the ability to invest in them) taken by a given data controller: economic resources, human resources, and the use of accessory data. A direct consequence of the expansion of augmented humanity in collecting and inferring personal data is the increasing loss of health data processing ‘legibility’ for data subjects. In order to address this issue, we propose exploiting the existing legal tools in the General Data Protection Regulation to empower data subjects (the right to data access, the right to know the logic involved in automated decision-making, data portability, etc.).     

‘Working Well, Together’: Arts-Based Research and the Cultural Future of Small Cities

This article offers a research update on a 3-year programme initiated by the Kamloops Art Gallery and the University College of the Cariboo in Kamloops, British Columbia. The programme is supported by a ‘Community–University Research Alliance’ grant from the Social Sciences and Humanities Research Council of Canada, and the collaboration focuses on the cultural future of small cities – on how cultural and arts organisations work together (or fail to work together) in a small city setting. If not by definition, then certainly by default, ‘culture’ is associated with big city life: big cities are equated commonly with ‘big culture’; small cities with something less. The Cultural Future of Small Cities research group seeks to provide a more nuanced view of what constitutes culture in a small Canadian city. In particular, the researchers are exploring notions of social capital and community asset building: in this context, ‘visual and verbal representation’, ‘home’, ‘community’ and the need to define a local ‘sense of place’ have emerged as important themes. As the Small Cities programme begins its second year, a unique but key aspect has become the artist-as-researcher. Correspondence and offprint requests to: L. Dubinsky, Kamloops Art Gallery, 101–465 Victoria Street, Kamloops, BC V2C 2A9 Canada. Tel.: 250-828-3543; Email: ldubinsky@museums.ca     

Intelligent services for Big Data science

Cities are areas where Big Data is having a real impact. Town planners and administration bodies just need the right tools at their fingertips to consume all the data points that a town or city generates and then be able to turn that into actions that improve peoples’ lives. In this case, Big Data is definitely a phenomenon that has a direct impact on the quality of life for those of us that choose to live in a town or city. Smart Cities of tomorrow will rely not only on sensors within the city infrastructure, but also on a large number of devices that will willingly sense and integrate their data into technological platforms used for introspection into the habits and situations of individuals and city-large communities. Predictions say that cities will generate over 4.1 terabytes per day per square kilometer of urbanized land area by 2016. Handling efficiently such amounts of data is already a challenge. In this paper we present our solutions designed to support next-generation Big Data applications. We first present CAPIM, a platform designed to automate the process of collecting and aggregating context information on a large scale. It integrates services designed to collect context data (location, user’s profile and characteristics, as well as the environment). Later on, we present a concrete implementation of an Intelligent Transportation System designed on top of CAPIM. The application is designed to assist users and city officials better understand traffic problems in large cities. Finally, we present a solution to handle efficient storage of context data on a large scale. The combination of these services provides support for intelligent Smart City applications, for actively and autonomously adaptation and smart provision of services and content, using the advantages of contextual information.     

Design challenges in motivating change for sustainable urban mobility

In recent years, the design and deployment of persuasive interventions for inducing sustainable urban mobility behaviors has become a very active research field, leveraging on the pervasive usage of social media and mobile apps by citizens in their daily life. Several challenges in designing and assessing motivational features for effective and long-lasting behavior change in this area have also been identified, such as the focus of most solutions on targeting and prescribing individual (versus collective) mobility choices, as well as a general lack of large-scale evaluations on the impact of these solutions on citizens’ life. This paper reports lessons learnt from three parallel and complementary user studies, where motivational features for sustainable urban mobility, including social influence strategies delivered through social media, were prototyped, tested and refined. By reflecting on our results and design experiences so far, we aim to provide better guidance for future development of more effective solutions supporting citizens’ adoption of sustainable mobility behaviors in urban settings.     

Land‐use dynamic discovery based on heterogeneous mobility sources

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport‐network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time‐invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.     

Discovering and understanding android sensor usage behaviors with data flow analysis

Today’s Android-powered smartphones have various embedded sensors that measure the acceleration, orientation, light and other environmental conditions. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can read data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users’ privacy. In this work, we are motivated to provide an overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in the apps collected from both a Chinese app. market called “AppChina” and the official market called “Google Play”. To fulfill this goal, We develop a tool called “SDFDroid” to identify the used sensors’ types and to generate the sensor data propagation graphs in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation graphs. We apply our method on 22,010 apps collected from AppChina and 7,601 apps from Google Play. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage behaviors in the third-party libraries. Our results show that the accelerometer is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are still some third-party libraries registering all the types of sensors recklessly. These results call for more attentions on better regulating the sensor usage in Android apps.     

DynamiCITY: Revealing city dynamics from citizens social media broadcasts

Social media and mobile devices have revolutionized the way people communicate and share information in various contexts, such as in cities. In today’s “smart” cities, massive amounts of multiple forms of geolocated content is generated daily in social media, out of which knowledge for social interactions and urban dynamics can be derived. This work addresses the problem of detecting urban social activity patterns and interactions, by modeling cities into “dynamic areas”, i.e., coherent geographic areas shaped through social activities. Social media users provide the information on such social activities and interactions in cases when they are on the move around the city neighborhoods. The proposed approach models city places as feature vectors which represent users visiting patterns (social activity), the time of observed visits (temporal activity), and the context of functionality of visited places category. To uncover the dynamics of city areas, a clustering approach is proposed which considers the derived feature vectors to group people’s activities with respect to location, time, and context. The proposed methodology has been implemented on the DynamiCITY platform which demonstrates neighborhood analytics via a Web interface that allows end-users to explore neighborhoods dynamics and gain insights for city cross-neighborhood patterns and inter-relationships.     

浅谈无障碍设计在老年人住宅居住环境中的重要性

本文主要针对无障碍设计在老年人住宅居住环境的重要性进行了探讨,希望更多的人能去多思考一些老年人目前的生活状态,能从生活最细微之处去关心他们、爱护他们、尊重他们。如果生活中实现了无障碍,那么我们的社会会更和谐,老年人的生活也会更加美好。无障碍设计会让更多人体会到社会的关爱,给予他们心灵上的更多的帮助和支持。     

A secure cloud-assisted urban data sharing framework for ubiquitous-cities

With the accelerated process of urbanization, more and more people tend to live in cities. In order to deal with the big data that are generated by citizens and public city departments, new information and communication technologies are utilized to process the urban data, which makes it more easier to manage. Cloud computing is a novel computation technology. After cloud computing was commercialized, there have been lot of cloud-based applications. Since the cloud service is provided by the third party, the cloud is semi-trusted. Due to the features of cloud computing, there are many security issues in cloud computing. Attribute-based encryption (ABE) is a promising cryptography technique which can be used in the cloud to solve many security issues. In this paper, we propose a framework for urban data sharing by exploiting the attribute-based cryptography. In order to fit the real world ubiquitous-cities utilization, we extend our scheme to support dynamic operations. In particular, from the part of performance analysis, it can be concluded that our scheme is secure and can resist possible attacks. Moreover, experimental results and comparisons show that our scheme is more efficient in terms of computation.     

Personalized app recommendation based on app permissions

With the development of science and technology, the popularity of smart phones has made exponential growth in mobile phone application market. How to help users to select applications they prefer has become a hot topic in recommendation algorithm. As traditional recommendation algorithms are based on popularity and download, they inadvertently fail to recommend the desirable applications. At the same time, many users tend to pay more attention to permissions of those applications, because of some privacy and security reasons. There are few recommendation algorithms which take account of apps’ permissions, functionalities and users’ interests altogether. Some of them only consider permissions while neglecting the users’ interests, others just perform linear combination of apps’ permissions, functionalities and users’ interests to implement top-N recommendation. In this paper, we devise a recommendation method based on both permissions and functionalities. After demonstrating the correlation of apps’ permissions and users’ interests, we design an app risk score calculating method ARSM based on app-permission bipartite graph model. Furthermore, we propose a novel matrix factorization algorithm MFPF based on users’ interests, apps’ permissions and functionalities to handle personalized app recommendation. We compare our work with some of the state-of-the-art recommendation algorithms, and the results indicate that our work can improve the recommendation accuracy remarkably.     

Crowds,citizens and sensors: process and practice for mobilising learning

Participatory sensing is an emerging field in which citizens are empowered by technologies to monitor their own environments. Harvesting and analysing data gathered in response to personal or local enquiries can be seen as an antidote to information provided by official sources. Democratising sensing means that ordinary people can learn about and understand the world around them better and can be a part of the decision-making in improving environments for all. In this paper, we review and describe participatory sensing and discuss this in relation to making a series of prototype tools and applications for mobile users—Located Lexicon, Where’s Fenton? and Tall Buildings. In the first of these projects, Located Lexicon, we wanted to find out whether a lexicon of terms derived from user-generated content could enable the formation of Twitter like groups that allow users to engage in finding out more about their location. In the second project, Where’s Fenton? we made a publicly available app that involves users in counting the abundance and logging the location of deer in a park. This project focused specifically on anonymity of the user in collecting data for a specific enquiry. In the last project, Tall Buildings, we experimented with using dimensions of altitude, distance and speed to encourage users to physically explore a city from its rooftops. In all of these projects, we experiment with the pedestrian as a human sensor and the methods and roles they may engage in to make new discoveries. The underlying premise for our work is that it is not possible to calibrate people to be identical, so experimenting with crowd-sourced data opens up thinking about the way we observe and learn about the physical environment.     

Quantify city-level dynamic functions across China using social media and POIs data

Location-aware big data from social media have been widely used to study functions of different zones in a city but not across a city as a whole. In this study, a novel framework is proposed to quantify city-level dynamic functions of 200 cities in China from a perspective of collective human activities. The random forest model was used to determine the temporal variations in the proportions of different urban functions by examining the relationship between Points-of-Interest (POIs) and Tencent Location Request (TLR) data. We then hierarchically clustered and analyzed the structures and distribution patterns of the dynamic urban functions of 200 Chinese cities at different temporal scales. In the end, we calculated an urban functional equilibrium index based on the urban functional proportion and then mapped spatial distribution patterns of the indexes across mainland China. Results show that on a daily scale when the cities were grouped into two clusters, they are either dominated by the work/education and commerce or residence functions. The cities in the former cluster are mainly the provincial capitals and located within major urban agglomerations. When the cities were grouped into four clusters, the clusters are dominated their commerce, work, residence, and balanced multiple functions, respectively. For each of the 200 cities, its urban functions change dynamically from the daybreak to the evening in terms of human activities. Besides, the equilibrium indexes show a power-law relationship with their rankings. Our research shows that city-level dynamic function can be quantified from the perspective of variations in human activities by using social media big data that otherwise could not be achieved in the conventional urban functions’ studies.     

Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city

In his classic book “The Image of the City” Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated areas such as planning, work with user generated content has largely ignored these ideas. In this paper, we address this gap, using streets to aggregate filtered user generated content related to more than 1 million images and 60,000 individuals and explore similarity between more than 3000 streets in London across three dimensions: user behaviour, time and semantics. To perform our study we used two different sources of user generated content: (1) a collection of metadata attached to Flickr images and (2) street network of London from OpenStreetMap. We first explore global patterns in the distinctiveness and spatial autocorrelation of similarity using our three dimensions, establishing that the semantic and user dimensions in particular allow us to explore the city in different ways. We then used a Processing tool to interactively explore individual patterns of similarity across these four dimensions simultaneously, presenting results here for four selected and contrasting locations in London. Before drilling into the data to interpret in more detail, the identified patterns demonstrate that streets are natural units capturing perception of cities not only as paths but also through the emergence of other elements of the city proposed by Lynch including districts, landmarks and edges. Our approach also demonstrates how user generated content can be captured, allowing bottom-up perception from citizens to flow into a representation.