Internet of Things and Big Data Analytics for Smart and Connected Communities

This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy

A tutorial on the internet of things: from a heterogeneous network integration perspective

The days when the Internet was the only focus of the information society have already gone, and innovative network paradigms such as IoT, cloud computing, smartphone networks, social networks, and industrial networks are gaining popularity and establishing themselves as indispensable ingredients of the future smart universe. Among them, IoT is the most widespread one, envisioned to involve all things in the world. However, its potential will never be fully explored before the complete formation of cyberspace, where humans, computers, and smart objects are pervasively interconnected. Therefore, one of the most important development trends of IoT is its integration with existing network systems. In this tutorial, we provide a detailed analysis of this issue. In particular, the latest achievements, technical solutions, and influential ongoing projects are described, and possible visions and open challenges are also discussed

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

Understanding human-machine networks: A cross-disciplinary survey

© 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends

A public safety, participatory crowdsourcing smart city model for a developing country

Worldwide the population in cities is increasing. It is the responsibility of local government to provide public safety services in order to ensure the safety of their citizens and, yet, the local government often have inadequate resources to do this. ‘Smart Cities’ is a new and innovative concept that has emerged during the past few years and which involves using current infrastructure and resources more effectively and efficiently. One of the methods used to collect data in a smart city is participatory crowdsourcing but, in order to ensure effectiveness and efficiency, it is essential that a large amount of data be collected from the participants in such a project, who are generally citizens residing in the city. This study was conducted in the city of East London, which is part of the Buffalo City Metropolitan Municipality (BCMM). The study made use of a Design Science approach with a mixed method data collection method. The quantitative data collection comprised a questionnaire that was completed by 394 participants, while the qualitative data collection included a detailed literature review, conversational analysis and observations arising from the building of the crowdsourcing system prototype. The design artefact produced by this research is a model based on the literature, conversational analysis and the principles and concepts learnt from the prototype. Thus, this model represents what must be incorporated in the prototype to assist with the implementation of a public safety, participatory crowdsourcing smart city in a developing country. The model includes three areas ‒ the crowdsourcing system, the city (Buffalo City Metropolitan Municipality) and the citizens of East London. The crowdsourcing system incorporates factors of information security, specifically the CIA triad, and the usability of the crowdsourcing system. Usability includes characteristics such as the quality of the system and interface, as well as the usefulness of the public safety, participatory crowdsourcing system which was used to measure the confidence of the East London citizens in the system. Three steps were identified in the literature as being necessary for the implementation of a smart city project by a city. These steps include the planning, development and delivery of the smart city project. Finally, the trustworthiness of the public safety participatory crowdsourcing system is determined by the ability, reliability and benevolence of the system. These three characteristics were included in the citizen factor of the model

A public safety, participatory crowdsourcing smart city model for a developing country

Worldwide the population in cities is increasing. It is the responsibility of local government to provide public safety services in order to ensure the safety of their citizens and, yet, the local government often have inadequate resources to do this. ‘Smart Cities’ is a new and innovative concept that has emerged during the past few years and which involves using current infrastructure and resources more effectively and efficiently. One of the methods used to collect data in a smart city is participatory crowdsourcing but, in order to ensure effectiveness and efficiency, it is essential that a large amount of data be collected from the participants in such a project, who are generally citizens residing in the city. This study was conducted in the city of East London, which is part of the Buffalo City Metropolitan Municipality (BCMM). The study made use of a Design Science approach with a mixed method data collection method. The quantitative data collection comprised a questionnaire that was completed by 394 participants, while the qualitative data collection included a detailed literature review, conversational analysis and observations arising from the building of the crowdsourcing system prototype. The design artefact produced by this research is a model based on the literature, conversational analysis and the principles and concepts learnt from the prototype. Thus, this model represents what must be incorporated in the prototype to assist with the implementation of a public safety, participatory crowdsourcing smart city in a developing country. The model includes three areas ‒ the crowdsourcing system, the city (Buffalo City Metropolitan Municipality) and the citizens of East London. The crowdsourcing system incorporates factors of information security, specifically the CIA triad, and the usability of the crowdsourcing system. Usability includes characteristics such as the quality of the system and interface, as well as the usefulness of the public safety, participatory crowdsourcing system which was used to measure the confidence of the East London citizens in the system. Three steps were identified in the literature as being necessary for the implementation of a smart city project by a city. These steps include the planning, development and delivery of the smart city project. Finally, the trustworthiness of the public safety participatory crowdsourcing system is determined by the ability, reliability and benevolence of the system. These three characteristics were included in the citizen factor of the model

A distributed middleware for IT/OT convergence in modern industrial environments

The modern industrial environment is populated by a myriad of intelligent devices that collaborate for the accomplishment of the numerous business processes in place at the production sites. The close collaboration between humans and work machines poses new interesting challenges that industry must overcome in order to implement the new digital policies demanded by the industrial transition. The Industry 5.0 movement is a companion revolution of the previous Industry 4.0, and it relies on three characteristics that any industrial sector should have and pursue: human centrality, resilience, and sustainability. The application of the fifth industrial revolution cannot be completed without moving from the implementation of Industry 4.0-enabled platforms. The common feature found in the development of this kind of platform is the need to integrate the Information and Operational layers. Our thesis work focuses on the implementation of a platform addressing all the digitization features foreseen by the fourth industrial revolution, making the IT/OT convergence inside production plants an improvement and not a risk. Furthermore, we added modular features to our platform enabling the Industry 5.0 vision. We favored the human centrality using the mobile crowdsensing techniques and the reliability and sustainability using pluggable cloud computing services, combined with data coming from the crowd support. We achieved important and encouraging results in all the domains in which we conducted our experiments. Our IT/OT convergence-enabled platform exhibits the right performance needed to satisfy the strict requirements of production sites. The multi-layer capability of the framework enables the exploitation of data not strictly coming from work machines, allowing a more strict interaction between the company, its employees, and customers