Conference Paper

Automatic extraction of POIs in smart cities: Big data processing in ParticipAct

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Recent advances in sensor-equipped smartphones are opening brand new opportunities, such as automatically extracting Points Of Interest (POIs) and mobility habits of citizens in Smart Cities from the large amount of harvested data hotspots. At the same time, the high dynamicity and unpredictability of Smart Cities crowds, opportunistically collaborating toward these common crowdsensing tasks, introduces challenging issues due to the need for fast and continuous processing of these Big Data Streams in the backend of next generation crowdsensing platforms. This paper presents our practical experiences and lessons learnt in deploying the ParticipAct platform and living lab, an ongoing experiment at University of Bologna that involves 300 students for one year. Among all management issues addressed in ParticipAct, this article shows the integration of MongoDB in the ParticipAct backend, as a powerful NoSQL storage and processing engine to fasten the identification of POIs; the reported performance results confirm the feasibility of the approach by quantifying its advantages for city managers.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Noteworthy examples of solution-based research in the domain include the publications by [10,[24][25][26][27], whereas examples of experience research include the publications by [28][29][30][31][32]. Considering the analysis criteria for research question 2 as discussed in the section 2, the analysis by research type indicates that the current state of the research domain is semi-matured and gradually moving towards being a fully matured area of research as witnessed by the 33.33% growth in the production of evaluation and validation type research in 2017. ...
... There is a need to produce novel contributions such as models, tools, processes, and methodologies in research publications of type evaluation and validation. MongoDB as a storage and processing engine in the ParticipAct backend [28]. ...
...  Development of environmental awareness in smart cities [28]. ...
Article
Full-text available
Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain "Big Data in Smart Cities" by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored.
... Noteworthy examples of solution-based research in the domain include the publications by [10,[24][25][26][27], whereas examples of experience research include the publications by [28][29][30][31][32]. Considering the analysis criteria for research question 2 as discussed in the section 2, the analysis by research type indicates that the current state of the research domain is semi-matured and gradually moving towards being a fully matured area of research as witnessed by the 33.33% growth in the production of evaluation and validation type research in 2017. ...
... There is a need to produce novel contributions such as models, tools, processes, and methodologies in research publications of type evaluation and validation. MongoDB as a storage and processing engine in the ParticipAct backend [28]. ...
...  Development of environmental awareness in smart cities [28]. ...
Article
Full-text available
Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain "Big Data in Smart Cities" by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored.
... And through the systematic analysis of business districts and centers, together with relevant theoretical research, the features, impacts, and challenges of the distribution of commercial facilities can be identified and followed by suggestions for improvement. So far, however, no large-scale studies have been performed to investigate the structure of urban commercial spaces using such point-ofinterest (POI) data as commercial outlets, housing prices, and urban information [2]. Outside China, POI-data-based urban research tends to have a strong social impact as it examines employment and the influence of social behavior [1][2][3][4][5][6][7]. ...
... So far, however, no large-scale studies have been performed to investigate the structure of urban commercial spaces using such point-ofinterest (POI) data as commercial outlets, housing prices, and urban information [2]. Outside China, POI-data-based urban research tends to have a strong social impact as it examines employment and the influence of social behavior [1][2][3][4][5][6][7]. In China, however, studies of this kind focus on presenting statistics of phenomena themselves and are of less social value, given inaccessibility to platforms for urban public information and to urban societal data [8][9][10][11][12][13][14][15][16][17]. ...
Article
Full-text available
Big data has provided new opportunities, directions, and methods for research on urban commercial center systems. Based on a quantitative assessment of big data and public participation, the “big data + public feedback” evaluation model can objectively and scientifically quantify the scale and structural characteristics of urban commercial center systems. In this paper, socioeconomic and material spatial attributes were considered in the selection of four indexes, including commercial agglomeration centrality, commercial facility service level, commercial industry status, and industry attraction. Specifically, we based our selection on the big data of the point-of-interest network, housing price, and population. ArcGIS, SPSS, and other analytical tools were employed to conduct a comparative analysis, cluster analysis, spatial network analysis, and correlation analysis. Using these data, we constructed an assessment index system, which was then utilized to comprehensively evaluate the current commercial land use in Nanjing’s main urban area and measure the degree of commercialization. The commercial center system in the main urban area of Nanjing was found to be consistent with the spatial structure system of “one main core, five secondary cores, multiple district cores, three horizontal axes, and one vertical axis.” Meanwhile, a public questionnaire was used to evaluate the public’s perception of the commercialization level in Nanjing. Finally, the results obtained were used for comparison with the structure of the commercial center system of Nanjing commercial network planning. We discovered that the results of the public’s perception of the commercialization level in Nanjing were similar to those of the big data analysis, which confirmed the credibility of big data analysis results. In conclusion, the findings of this study provide a basis for developing urban commercial center-level positioning and propose a method for data-assisted planning research.
... -The variety concerns the fact that big data is generated from heterogeneous and structured sources such as databases, simulations, and medical records. [12] -Velocity is related to the speed of processing and response to demand [13], the analysis of data and the rapid processing of large data promptly guarantees the value of this data, the more time passes, the more the data loses its relevance. ...
Article
Full-text available
Digital technologies are occupying more and more a very important place in the industry, and more precisely with the 4th industrial revolution or what is called industry 4.0. In addition, digital transformation requires the implementation of two tools: Big data and the Internet of Things as the two starting tools, which continue to evolve gradually. Intending to explore on this area, this paper studies the literature to get a detailed understanding of Industry 4.0, as well as an overview of the two digitization tools namely big data and the Internet of Things used to improve the quality of processes in different areas. Through a systematic literature review (SLR), the study is an effort to provide an overview of existing big data and the Internet of Things in the literature and to study the existing studies to classify them by application domain and according to a developed architectural framework. The search identified 81 relevant articles. Analyses of the distribution of articles by publication year, domain, country, type, tool, and source are presented and discussed. A research agenda for future research are provided.
... However, in a sophisticated approach, must be explored information about potential participants and its mobile context of executionpeople geographical localisation, the frequency of visits on a specific area or commutation timecontinuously adapt the task assignment process more effectively. In other words, a modern crowdsensing system should have its focus on disseminating and minimising users load and be directed to willingly people to execute and share the workload in a fair way between all the participants, according to all involved policy (Corradi, Curatola, Foschini, Ianniello, & De Rolt, 2015). ...
Article
The use of information technology is one way to help in solving urban problems, aiming for the development of smart cities. Crowdsensing mechanism is an important tool in this process, exploring a collective intelligence and organising a collaboration of large groups of people. This work focuses mainly on the process of management of crowdsensing campaigns contributing to the theoretical framework regarding the theme. Through a crowdsensing system, collaborative data collection and sensor monitoring campaigns were executed, which allowed learning about the management of crowdsensing campaigns, with results such as adjustments in the computational platform by the insertion of new types of campaigns and the inclusion of feedback elements. Collaborative data collection campaigns were carried out in a monitored way that enabled learning about crowdsensing campaigns management, resulting in significant contributions to the improvement of the system and propositions of adjustments in the theoretical framework of management campaign models.
... As informações advindas dos sensores alimentam grandes bancos de dados (big data), dando suporte à gestão municipal. Diferentes tipos de equipamentos podem servir como sensores, incluindo os telefones celulares (dado o alcance e a facilidade de implementação destes) (CORRADI et al., 2015). ...
Article
Full-text available
Resumo Cidades inteligentes são aquelas onde se utiliza tecnologias de informação e comunicação visando à gestão eficiente no uso de recursos e uma maior participação cidadã. Nelas, sustentabilidade é um importante objetivo, levando pesquisadores a adotar a expressão “cidades inteligentes sustentáveis”. Para atingir esse objetivo, são necessários sistemas de avaliação por indicadores, direcionando políticas públicas e investimentos, comparações entre cidades e reprodução de bons exemplos. O mais importante desses sistemas é proposto pela norma ISO 37120:2018 “Sustainable cities and communities - Indicators for city services and quality of life”. Entretanto, seriam necessários indicadores específicos para adequada avaliação de cidades inteligentes, considerando suas características particulares e seu foco sustentável. Diante desse cenário, por meio de uma revisão bibliográfica, pesquisou-se por estudos anteriores que pudessem ser referência em indicadores sobre cidades inteligentes, complementares à ISO. As pesquisas consideradas nessa revisão possuem visão holística de cidades. Como resultado, foram obtidos indicadores com variáveis qualitativas e quantitativas ordinais, e alguns indicadores em sobreposição aos presentes na ISO. Ao final da pesquisa, são sugeridas melhorias com o incremento aos indicadores sugeridos e modificação de alguns segundo o conceito de cidades inteligentes.
... C'est le cas par exemple de Rae et al. [141], ou de Corradi et al. [34], qui se sont focalisés sur l'extraction de POI dans des documents textuels. L'enjeu dans tous ces travaux est uniquement d'extraire les propriétés ...
Thesis
Les récents développements des nouvelles technologies de l’information et de la communication fontdu Web une véritable mine d’information. Cependant, les pages Web sont très peu structurées. Parconséquent, il est difficile pour une machine de les traiter automatiquement pour en extraire des informationspertinentes pour une tâche ciblée. C’est pourquoi les travaux de recherche s’inscrivant dans lathématique de l’Extraction d’Information dans les pages web sont en forte croissance. Une fois extraites,ces informations sont généralement structurées et stockées dans des index. L’interrogation de ces index,pour répondre à des besoins d’information précis, correspond à la Recherche d’Information (RI). Notretravail de thèse se situe à la croisée de ces deux thématiques. Notre objectif principal est de concevoiret de mettre en oeuvre des stratégies permettant de scruter le web pour en extraire des Entités Nommées(EN) complexes (EN composées de plusieurs propriétés pouvant être du texte ou d’autres EN) detype entreprise ou de type événement, par exemple. Nous proposons ensuite des services d’indexation etd’interrogation pour répondre à des besoins d’informations.Ces travaux ont été réalisés au sein de l’équipe T2I du LIUPPA, et font suite à une commande del’entreprise Cogniteev, dont le coeur de métier est centré sur l’analyse du contenu du Web. Les problématiquesvisées sont, d’une part, l’extraction d’EN complexes sur le Web et, d’autre part, l’indexation et larecherche d’information intégrant ces EN complexes.Notre première contribution porte sur l’extraction d’EN complexes dans des textes. Pour cette contribution,nous prenons en compte plusieurs problèmes, notamment le contexte bruité caractérisant certainespropriétés (pour un événement par exemple, la page web correspondante peut contenir deux dates : ladate de l’événement et celle de mise en vente des billets). Pour ce problème en particulier, nous introduisonsun module de détection de blocs qui permet de focaliser l’extraction des propriétés sur les blocs detexte pertinents. Nos expérimentations montrent une nette amélioration du processus d’extraction dansun contexte bruité grâce à cette approche. Nous nous sommes également intéressés à l’extraction desadresses, où la principale difficulté découle du fait qu’aucun standard ne se soit réellement imposé commemodèle de référence. Nous proposons un modèle étendu et une approche d’extraction par patrons.Notre deuxième contribution porte sur le calcul de similarité entre EN complexes. Dans l’état de l’art,ce calcul se fait généralement en deux étapes : (i) une première calcule les similarités entre propriétéset, (ii) une deuxième agrège les scores obtenus pour le calcul de la similarité globale. En ce qui concernecette première étape, nous complétons l’état de l’art en proposant une fonction de calcul de similaritéentre EN spatiales, l’une représentée par un point et l’autre par un polygone. Notons que nos principalespropositions se situent au niveau de la deuxième étape. Ainsi, nous proposons trois techniques pourl’agrégation des scores intermédiaires. Les deux premières sont basées sur la somme pondérée des scoresintermédiaires (combinaison linéaire et régression logistique). La troisième exploite les arbres de décisionspour agréger les scores intermédiaires. Enfin, nous proposons une dernière approche basée sur le clusteringet le modèle vectoriel de Salton pour le calcul de similarité entre EN complexes. Son originalité vient dufait qu’elle ne nécessite pas de passer par le calcul de scores de similarités intermédiaires
... The server side provides management, storage, and analysis of crowdsensed data. At the highest level comprises two main parts: the back-end and the crowdsensing manager (Corradi et al. 2015). ...
Article
Full-text available
ICTs and social networks can contribute to the emergence of creating competent teams for emergency response. For example, during disasters such as Pakistan flood of 2010, Japan tsunami of 2011 and Thailand flood of 2011, On-Line Social Networks (OSNs) have served as a main technology for numerous people seeking to share information about their personal status, to request resources, or to report the status of their community. They can be used at least for three main functions: detecting emergencies, disseminating information, and managing emergencies. On the other hand, the combination of Internet and mobile technology generated smart devices with associated sensor technologies that are becoming crucial parts in delivering supports during disaster and emergency situation. However, such use of mobile devices usually requires a reliable support system from crowdsensing technologies and back-end intelligent systems. In this way, the mobile crowdsensing must be designed with focus on mechanisms to identify and localize survivors and first responders in an incident zone, as well as mechanisms for competence characterization provided by social networks to facilitate coordination for individuals as well as crowds. These main technologies, the crowdsensing and social networks combined with semantic web and ontologies can provide a complete emergency response system that is the purpose of the CO-SEMIWA platform. This platform permits an interaction among participants to create dynamically competent teams where all participants perform tasks and solve problems in a specific emergency context and situation
... From the more social point of view, there are many duties involved in people participate, such as the identification of people willing to participate in crowdsensing campaigns, how to keep them involved and how to foster their participation with active collaboration actions in some data collection campaigns ( Corradi et al., 2015). ...
Article
The massive amount of data generated in projects focused on smart cities creates a degree of complexity in how to manage all this information. In attention to solve this problem, several approaches have been developed in recent years. In this paper we propose an infrastructure model for big data for a smart city project. The goal of this model is to present the stages for the processing of data in the steps of extraction, storage, processing and visualisation, as well as the types of tools needed for each phase. To implement our proposed model, we used the ParticipACT Brazil, which is a project based in smart cities. This project uses different databases to compose its big data and uses this data to solve urban problems. We observe that our model provides a structured vision of the software to be used in big data server of ParticipACT Brazil.
... From the more social point of view, there are many duties involved in people participate, such as the identification of people willing to participate in crowdsensing campaigns, how to keep them involved and how to foster their participation with active collaboration actions in some data collection campaigns ( Corradi et al., 2015). ...
Article
The massive amount of data generated in projects focused on smart cities creates a degree of complexity in how to manage all this information. In attention to solve this problem, several approaches have been developed in recent years. In this paper we propose an infrastructure model for big data for a smart city project. The goal of this model is to present the stages for the processing of data in the steps of extraction, storage, processing and visualisation, as well as the types of tools needed for each phase. To implement our proposed model, we used the ParticipACT Brazil, which is a project based in smart cities. This project uses different databases to compose its big data and uses this data to solve urban problems. We observe that our model provides a structured vision of the software to be used in big data server of ParticipACT Brazil.
... From the more social point of view, there are many duties involved in people participate, such as the identification of people wiling to participate in crowdsensing campaigns, how to keep them involved and how to foster their participation with active collaboration actions in some data collection campaigns [10]. ...
Article
Full-text available
Today, smart cities represent an effective digital platform for facilitating our lives by shifting all stakeholders toward more sustainable behavior. Consequently, the field of smart cities has become an increasingly important research area. The smart city comprises a huge number of hybrid networks, with each network containing an enormous number of nodes that transmit massive amounts of data, thus giving rise to many network problems, such as delay and loss of connectivity. Decreasing the amount of such transmitted data is a great challenge. This paper presents a data overhead reduction scheme (DORS) for heterogeneous networks in smart city environments that comprise five different methods: median, nonlinear least squares, compression, data merging, and prioritization. Each method is applied according to the current status of quality of service. To measure the performance of the proposed model, a simulation environment is constructed for a smart city using network simulation package, NS2. The obtained results indicate that DORS has the capability to decrease the size of transmitted data in the simulated smart city environment while attaining a notable performance enhancement in terms of data reduction rate, end‐to‐end delay, packet loss ratio, throughput, and energy consumption ratio. This paper presents a data overhead reduction scheme (DORS) for heterogeneous networks in smart city environments that comprise five different methods: median, nonlinear least squares, compression, data merging, and prioritization. The obtained results indicate that DORS has the capability to decrease the size of transmitted data in the simulated smart city environment while attaining a notable performance enhancement in terms of data reduction rate, end‐to‐end delay, packet loss ratio, throughput, and energy consumption ratio.
Article
Full-text available
The emergence of smart cities aims at mitigating the challenges raised due to the continuous urbanization development and increasing population density in cities. To face these challenges, governments and decision makers undertake smart city projects targeting sustainable economic growth and better quality of life for both inhabitants and visitors. Information and Communication Technology (ICT) is a key enabling technology for city smartening. However, ICT artifacts and applications yield massive volumes of data known as big data. Extracting insights and hidden correlations from big data is a growing trend in information systems to provide better services to citizens and support the decision making processes. However, to extract valuable insights for developing city level smart information services, the generated datasets from various city domains need to be integrated and analyzed. This process usually referred to as big data analytics or big data value chain. Surveying the literature reveals an increasing interest in harnessing big data analytics applications in general and in the area of smart cities in particular. Yet, comprehensive discussions on the essential characteristics of big data analytics frameworks fitting smart cities requirements are still needed. This paper presents a novel big data analytics framework for smart cities called “Smart City Data Analytics Panel — SCDAP”. The design of SCDAP is based on answering the following research questions: what are the characteristics of big data analytics frameworks applied in smart cities in literature and what are the essential design principles that should guide the design of big data analytics frameworks have to serve smart cities purposes? In answering these questions, we adopted a systematic literature review on big data analytics frameworks in smart cities. The proposed framework introduces new functionalities to big data analytics frameworks represented in data model management and aggregation. The value of the proposed framework is discussed in comparison to traditional knowledge discovery approaches.
Conference Paper
Full-text available
Smart City (SC) is an emerging concept aiming at mitigating the challenges raised due to the continuous urbanization development. To face these challenges, government decision makers sponsor SC projects targeting sustainable economic growth and better quality of life for inhabitants and visitors. Information and Communication Technologies (ICT) is the enabling technology for smartening. These technologies yield massive volumes of data known as Big Data (BD). If spawned BD are integrated and analyzed, both city decision makers and citizens can benefit from valuable insights and information services. The process of extracting information and insights from BD is known as Big Data Analytics (BDA). Although BDA involves non-trivial challenges, it attracted academician and industrialist. Surveying the literature reveals the novelty and increasing interest in addressing BD applications in SCs. Although literature is replete with abundant number of articles about SCs applications harnessing BD, comprehensive discussion on BDA frameworks fitting SCs requirements is still needed. This paper attempts to fill this gap. It is a systematic literature review on BDA frameworks in SCs. In this review, we will try to answer the following research questions: what are the big data analytics frameworks applied in smart cities? what are the functional gaps in the current available frameworks? what are the conceptual guidelines of designing integrated scalable big data analytics frameworks for smart cities purposes? The paper concludes with a proposal for a novel conceptual analytics framework to serve SCs requirements. Additionally, open issues and further research directions are presented.
Article
Full-text available
Recent evolutions in smartphones, today provided with several sensors, have the strong processing capabilities needed to extract from raw sensed data sensor meaningful high-level views of the physical context around the user. A new promising research area called mobile sensing promotes completely decentralized sensing based on smartphone capabilities only. However, current mobile sensing solutions are not very mature; yet, because they are based on ad hoc software solutions tailored to one specific technical problem (e.g., power management, resource locking, etc.), they are difficult to reuse and integrate in different projects, and they do not focus on the performance efficiency of the monitoring support. To overcome those limitations, this paper proposes Mobile Sensing Framework (MSF), a flexible platform to ease the development of mobile sensing applications through the definition of a common set of facilities that mask all low-level technical details in reading and processing raw sensor data. MSF has been optimized also to enhance performances for Android-based systems, and we report an extensive set of experimental results that assess our architecture and quantitatively compare it with a selection of other mobile sensing systems by showing that MSF outperforms them by presenting lower CPU usage and memory footprints.
Article
Full-text available
Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.
Conference Paper
Full-text available
PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sam- pled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an ex- ample of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper doc- uments and evaluates the running PEIR system, which in- cludes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based ac- tivity classification (to determine transportation mode); au- tomatic location data segmentation into "trips"; lookup of traffic, weather, and other context data needed by the mod- els; and environmental impact and exposure calculation us- ing efficient implementations of established models. Addi- tionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic compo- nents developed based on experience with the system and un- dergoing testing for inclusion in PEIR, including: new map- matching and GSM-augmented activity classification tech- niques, and a selective hiding mechanism that generates be- lievable proxy traces for times a user does not want their real location revealed.
Article
The World Wide Web has succeeded in large part because its software architecture has been designed to meet the needs of an Internet-scale distributed hypermedia application. The modern Web architecture emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. In this article we introduce the Representational State Transfer (REST) architectural style, developed as an abstract model of the Web architecture and used to guide our redesign and definition of the Hypertext Transfer Protocol and Uniform Resource Identifiers. We describe the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, contrasting them to the constraints of other architectural styles. We then compare the abstract model to the currently deployed Web architecture in order to elicit mismatches between the existing protocols and the applications they are intended to support.
Article
The widespread availability of smartphones today equipped with several physical and virtual sensors allows to directly collect various information about surrounding physical and logical context for different purposes that range from detecting user's current physical activity and also user presence in a designated area, often referred to as geofencing, to determining current social pulse of individuals and entire communities. Mobile crowdsensing seems a promising solution for enabling the design/development and deployment of a wide range of advanced applications in various fields. In particular, public safety, transportation, and energy monitoring and management in urban environments can benefit from mobile crowdsensing in terms of advanced provisioned applications as well as savings of investments in the urban sensing infrastructure. However, enabling those advanced smart urban applications requires complex signal processing, machine learning, and resource management algorithms that are often beyond the skills of many mobile app developers. This paper describes the pivotal relevance of these facilities for mobile crowdsensing applications and presents our open-source solution, called Mobile Sensing Technology (MoST), for activity detection and geofencing, comparing it with the reference implementations provided by Google as part of the Google Play Services library. Experimental results within the testbed framework of a crowd-management application scenario validate MoST design guidelines and demonstrate the general-purpose, unintrusive, and power-efficient characteristics of MoST sensing capabilities.
Article
In recent years, the widespread availability of smartphones provided with sensors has enabled the possibility of harvesting large quantities of data in urban areas exploiting user devices, thus enabling so-called mobile crowd sensing (MCS). While many efforts have been made to improve specific techniques for MCS - spanning from signal processing to the assignment of data collection campaigns to users, and to the entire data processing spectrum - to the best of our knowledge, thus far there have been no active experiments of MCS that involve all these techniques in a large-scale real-world experiment. Based on these considerations, we started the ParticipAct Living Lab testbed, an ongoing experiment at the University of Bologna involving 300 students for one year in crowd sensing campaigns that can passively access smartphone sensors and also require active user collaboration. In this article we describe the guidelines behind the design of ParticipAct, as well as its features and architecture. Moreover, we report some of the seminal results gathered during the first three months of its deployment, including accuracy of the classifier provided by ParticipAct client and user inclination to successfully complete tasks depending on the level of active collaboration required to executing them.
Article
As a prominent subcategory of cyber-physical systems, mobile cyber-physical systems could take advantage of widely used mobile devices, such as smartphones, as a convenient and economical platform that facilitates sophisticated and ubiquitous mobile sensing applications between humans and the surrounding physical world. This paper presents Vita, a novel mobile cyber-physical system for crowdsensing applications, which enables mobile users to perform mobile crowdsensing tasks in an efficient manner through mobile devices. Vita provides a flexible and universal architecture across mobile devices and cloud computing platforms by integrating the service-oriented architecture with resource optimization mechanism for crowdsensing, with extensive supports to application developers and end users. The customized platform of Vita enables intelligent deployments of tasks between humans in the physical world, and dynamic collaborations of services between mobile devices and cloud computing platform during run-time of mobile devices with service failure handling support. Our practical experiments show that Vita performs its tasks efficiently with a low computation and communication overhead on mobile devices, and eases the development of multiple mobile crowdsensing applications and services. In addition, we present a mobile crowdsensing application, Smart City, developed on Vita to demonstrate the functionalities and practical usage of Vita.
Conference Paper
There is tremendous interest in exploiting smartphones as a "community of sensors". It is envisioned that this community-driven smartphone sensor network has unprecedented potential to sense heterogeneous phenomena ranging from sound pollution to urban social dynamics. However, designing smartphone-resident middleware for opportunistic and objective-oriented sensing of these phenomena is an open challenge. In this paper, we propose USense, a novel utility-driven smartphone middleware for executing community-driven sensing tasks. USense is different from other mobile phone sensing frameworks in the following ways: it is (i) Application aware, i.e., it adapts its operation based on demands of the application (ii) User aware, i.e., it incorporates preferences, policies as well as behavioral history of the user carrying the phone, and (iii) Situation aware, i.e. it considers resource dynamics on the phone at any given point. We argue that these three aspects are essentially decoupled in nature and combining them effectively is the key towards designing a re-usable and scalable middleware. Based on an extensible model for 'Sensing Moments', USense first allows application developers to easily create sensing tasks. Secondly, we propose a unified device middleware to simultaneously execute the sensing tasks at the right moments across multiple applications. We have implemented USense on the Android platform, and demonstrate its effectiveness through real-life data traces.
Conference Paper
Ubiquity of internet-connected media- and sensor-equipped portable devices is enabling a new class of applications which exploit the power of crowds to perform sensing tasks in the real world. Such paradigm is referred as crowd-sensing, and lies at the intersection of crowd-sourcing and participatory sensing. This has a wide range of potential applications such as direct involvement of citizens into public decision making. In this work we present Matador, a framework to embed context-awareness in the presentation and execution of crowd-sensing tasks. This allows to present the right tasks, to the right users in the right circumstances, and to preserve normal device functioning. We present the design and prototype implementation of the platform, including an energy-efficient context sampling algorithm. We validate the proposed approach through a numerical study and a small pilot, and demonstrate the ability of the proposed system to efficiently deliver crowd-sensing tasks, while minimizing the consumption of mobile device resources.
Article
This article investigates how and to what extent the power of collective although imprecise intelligence can be employed in smart cities. The main visionary goal is to automate the organization of spontaneous and impromptu collaborations of large groups of people participating in collective actions (i.e., participAct), such as in the notable case of urban crowdsensing. In a crowdsensing environment, people or their mobile devices act as both sensors that collect urban data and actuators that take actions in the city, possibly upon request. Managing the crowdsensing process is a challenging task spanning several socio-technical issues: from the characterization of the regions under control to the quantification of the sensing density needed to obtain a certain accuracy; from the evaluation of a good balance between sensing accuracy and resource usage (number of people involved, network bandwidth, battery usage, etc.) to the selection of good incentives for people to participAct (monetary, social, etc.). To tackle these problems, this article proposes a crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.
Article
The ever-increasing people demand for mobile communications has widely been satisfied by novel socially-enabled services and communication styles, starting from the advent of instant messaging to Voice over IP (VoIP), from Web 2.0 to social networks, and beyond. Most people agree that now the new challenge is to explore the potential of that immense information and service deposit already collected, ever-increasing, and easy-to-access to promote a socially-aware recommendation and to foster a more efficient delivery of mobile services. The article addresses the above issues, still unexplored in literature, by focusing on integration problems and describing a practical design experience derived from the development of a socio-technical aware support for the management of mobile services based on the widely diffused and interoperable IP Multimedia Subsystem (IMS) session control platform. Our proposal is organized along three main original technical guidelines: an innovative support to monitor and describe via social, physical, and computation information the sociotechnical context where services and user interact; a recommendation system to automate and facilitate dynamic selection of the mobile services most suitable from both final user and operator perspectives; and a new IMS-based management platform to ease the composition of existing IMS mobile services and the monitoring and control of their usage at runtime. We believe that we contribute with a first step to the understanding of how full awareness of socio and technical information allows improving overall Quality of Experience (QoE) for the final users.
Conference Paper
This paper presents a new model for understanding human behavior. In this model (FBM), behavior is a product of three factors: motivation, ability, and triggers, each of which has subcomponents. The FBM asserts that for a person to perform a target behavior, he or she must (1) be sufficiently motivated, (2) have the ability to perform the behavior, and (3) be triggered to perform the behavior. These three factors must occur at the same moment, else the behavior will not happen. The FBM is useful in analysis and design of persuasive technologies. The FBM also helps teams work together efficiently because this model gives people a shared way of thinking about behavior change.
Article
The World Wide Web has succeeded in large part because its software architecture has been designed to meet the needs of an Internet-scale distributed hypermedia application. The modern Web architecture emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. In this article we introduce the Representational State Transfer (REST) architectural style, developed as an abstract model of the Web architecture and used to guide our redesign and definition of the Hypertext Transfer Protocol and Uniform Resource Identifiers. We describe the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, contrasting them to the constraints of other architectural styles. We then compare the abstract model to the currently deployed Web architecture in order to elicit mismatches between the existing protocols and the applications they are intended to support.
  • G Cardone
  • A Cirri
  • A L Corradi
  • R Foschini
  • R Ianniello
  • Montanari
G. Cardone, A. Cirri, A. Corradi. L. Foschini, R. Ianniello, and R. Montanari, "Crowdsensing in Urban Areas for City-scale Mass Gathering Management: Geofencing and Activity Recognition," IEEE Sensors Journal, 2014.