A collaborative, interdisciplinary initiative for a smart cities innovation network

Villanueva-Rosales, Natalia; Cheu, Ruey Long; Gates, Ann Q.; Rivera, Norma; Mondragon, Oscar; Ferregut, Sergio Cabrera Carlos; Carrasco, Cesar; Nazarian, Soheil; Taboada, Heidi A.; Larios, Victor M.; Barbosa-Santillán, Liliana Ibeth; Svítek, Miroslav; Přibyl, Ondřej; Horák, Tomáš; Procházková, Dana

doi:10.1109/isc2.2015.7366179

Cited by 18 publications

(9 citation statements)

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“…In the case of INGER, a first step would be the promotion of workshops with the participation of INGER researchers and authorities aimed at building consensus on the research priorities of the institution, the main problems to solve, and the way the INGER scientist could collaborate with each other. In this regard, it has been proposed that the assistance of learning strategies combined with the use of ontology engineering could facilitate the construction of the interdisciplinary agreements [ 19 , 20 ]. The generation of multiple scenarios at the request of the workshop participants by taking advantage of the flexibility of our model in NetLogo would be useful to the planning of the research activities in the institution.…”

Section: Discussionmentioning

confidence: 99%

Team Assembly Mechanisms and the Knowledge Produced in the Mexico’s National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach

García-Peña¹,

Gutiérrez‐Robledo²,

Cabrera-Becerril

et al. 2019

Scientifica

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Mexico’s National Institute of Geriatrics (INGER) is the national research center of reference for matters related to human aging. INGER scientists perform basic, clinical, and demographic research which may imply different scientific cultures working together in the same specialized institution. In this paper, by a combination of text mining, coauthorship network analysis, and agent-based modeling, we analyzed and modeled the team assembly practices and the structure of the knowledge produced by scientists from INGER. Our results showed a weak connection between basic and clinical research and the emergence of a highly connected academic leadership. Importantly, basic and clinical-demographic researchers exhibited different team assembly strategies: basic researchers tended to form larger teams mainly with external collaborators, while clinical and demographic researchers formed smaller teams that very often incorporated internal (INGER) collaborators. We showed how these two different ways to form research teams impacted the organization of knowledge produced at INGER. Following these observations, we modeled, via agent-based modeling, the coexistence of different scientific cultures (basic and clinical research) exhibiting different team assembly strategies in the same institution. Three virtual experiments were run in our agent-based model. The three experiments kept similar values to the collaborating dynamics of INGER in terms of average team size and probabilities of choosing incumbents and external collaborators. The only difference among these experiments was the value of homophily defined as the trend to collaborate with research studies from the same field (14% corresponding to the 46% and 79%). The main result of these experiments is that by modulating just one variable (homophily), we could successfully reproduce the current situation of INGER (homophily of 79%) and simulate alternative scenarios in which interdisciplinary (46%) and transdisciplinary (14%) research could be done.

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Section: Discussionmentioning

confidence: 99%

Team Assembly Mechanisms and the Knowledge Produced in the Mexico’s National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach

García-Peña¹,

Gutiérrez‐Robledo²,

Cabrera-Becerril

et al. 2019

Scientifica

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“…The 6V (volume, variety, variability, veracity and value) challenges have been introduced in [28] and Zero-Emission Neighborhoods architecture has proposed for smart home data management. IoT hub is a new component in this architecture and other elements are the same as previous research of [23] and [27]. The final research of the series replaces IoT hub by the cloudlet layer [14], where a hybrid data management architecture combined with smart city scenario and ZEN center scenario are demonstrated.…”

Section: Literature Reviewmentioning

confidence: 97%

“…A smart city provides services and resources to its citizen. The services are classified in some branches such as living, governance, economy, environment, education, healthcare, safety, security [21,22,23]. Those services can be ensured by using resources like brainware, hardware, and software [21].…”

Section: Literature Reviewmentioning

confidence: 99%

IEF2C: A novel AI-powered framework for suspected COVID-19 patient detection and contact tracing in smart cities

Rahman

Mahmud

Rahman

2020

Preprint

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Novel coronavirus disease 2019 (COVID-19) has found as a climacteric pandemic that spreads from human-to-human through contiguity. It is not possible to control the virus properly because its vaccine has not been discovered yet. As of now, various contact tracing approaches are being taken to contain the spread of the virus by breaking its transmission chain. Recently, few technologies are being exploited for contact tracing such as geological location, Bluetooth and google maps. Governments of some countries have already launched contact tracing applications to identify the close contact people of COVID-19 positive patients.However, these methods may not be effective without a huge amount of testing facility since the tracing action starts when a COVID-19 testing result is found positive. In this paper, we have proposed a novel AI-powered data management framework for smart city named IoT-to-Edge-to-Fog-to-cloudlet-to-Cloud (IEF2C) data management framework which will be exploited to identify the COVID-19 suspected patients in smart cities and also be used to inform the surrounding people about the suspected patient. The proposed framework is isolated into five layers which have different tasks such as data collection, processing, authentication and storage. The framework will bring all the citizens under a common platform as well as will ensure the user and device mobility. It will mitigate the information gap by helping concerned authorities and users to take proper steps in such a pandemic era.

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“…A number of literature sources suggested that cities were always defined as complex systems [19], and thus it was not possible rationally to reduce it to a variety of dimensions, even if it was considered to be the best fitting one. The concept of a smart and sustainability-focused city is an even more complex notion [2,[20][21][22].…”

Section: Complexity and Adaptability In A Collaborative Smart City Modelmentioning

confidence: 99%

“…In this case we have a network with a high degree of centralization, in which very few nodes with a central position dominate (Nodes 15,17,19,21). The network will fragment very quickly into small, unconnected groups, if the nodes are destroyed/damaged or eliminated.…”

Section: Degree Centralitymentioning

confidence: 99%

A Pattern of Collaborative Networking for Enhancing Sustainability of Smart Cities

et al. 2020

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This paper represents a research response to the current vision on transformations regarding the capacity building of smart cities focused towards sustainability, by addressing the knowledge based urban development and collaborative tools that support the development, dissemination, and use of knowledge. The purpose of this paper is to develop a collaborative pattern of knowledge networking, focusing on sustainability goals within a smart city concept, using the logic of the Complex Adaptive System (CAS). The study was carried out in an innovation cluster in Romania; the Social Network Analysis (SNA) was used as a tool to perform the study. The results of this analysis, due to the suggested networking, have led to delimitation of the roles that Groups of Competences play to enhance the sustainability of smart cities in areas where the use of knowledge has the greatest impact. Results show that the success of the smart solutions’ implementation depends on how the social and competence structures of the network are shaped and whether it permanently adapts to fit the sustainability objectives in the considered areas.

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A collaborative, interdisciplinary initiative for a smart cities innovation network

Cited by 18 publications

References 1 publication

Team Assembly Mechanisms and the Knowledge Produced in the Mexico’s National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach

Team Assembly Mechanisms and the Knowledge Produced in the Mexico’s National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach

IEF2C: A novel AI-powered framework for suspected COVID-19 patient detection and contact tracing in smart cities

A Pattern of Collaborative Networking for Enhancing Sustainability of Smart Cities

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