Computación Urbana
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Urban computing extends ubiquitous computing by studying people's behavior on campus to develop context-aware applications. The document discusses constructing a semantic model of campus interactions and an algorithm using this model. It also covers developing security strategies for urban computing systems against attacks like DDos and sniffing. Finally, it describes organizing a testbed in Finland for developing and testing urban computing applications with students from many countries.
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Chap 1 libre
Ubiquitous computing will surround users
with a comfortable and convenient information environment and a smart
space that merges physical and computational infrastructures into an integrated
habitat. This habitat will feature a proliferation of hundreds or thousands of
computing devices and sensors that will provide new functionality, offer specialized
services, and boost productivity
and interaction among the devices and
the
users.
New Research Articles 2020 July Issue International Journal of Software Engin...
The document summarizes how an agile software startup in Brazil dealt with uncertainties caused by the COVID-19 pandemic. The startup used an action research methodology over six sprints during quarantine. Practices like remote work, daily standup meetings, planning poker, and fun retrospectives helped the teams adapt to changes and remain productive. Lessons were shared about how agile methods can help software startups effectively manage uncertainties in a crisis environment.
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This document proposes applying big data techniques to smart cities to better utilize vast amounts of data from various sensors. It presents a general IoT architecture with four layers and describes two scenarios: 1) Using data from campus sensors to improve energy efficiency and comfort in smart buildings. This achieved 23% average energy savings per month. 2) Analyzing transportation card data to detect travel patterns and improve planning for public tram services. The ongoing work aims to incorporate human behavior data to further optimize smart city services.
Citymatter: UX / UI Design
Citymatter is a media start-up designed to improve engagement and connections between citizens, their cities, and their local governments. To do this, it aggregates local data from open government portals, private databases, APIs, and media content. I developed this concept during my 2014-15 Knight Fellowship at Stanford University.
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This document discusses fog computing and its applications for smart cities. Fog computing involves distributing storage, communication and other functions to peripheral devices and networks located near users. This helps realize the potential of the internet of things. Fog computing can enable applications like smart grids, traffic systems and building controls. It also discusses the importance of citizen involvement and open source approaches to ensure privacy, security, and democratic decision making in developing smart city infrastructure and governance models.
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Smart cities are expected to significantly improve people's quality of life, promote sustainable development, and enhance the efficiency of operations. With the implementation of many smart devices, c problems have become a serious challenge that needs strong treatments, especially the cyber-attack, which most countries suffer from it.
My study focuses on the security of smart city systems, which include equipment like air conditioning, alarm systems, lighting, and doors. Some of the difficulties that arise daily may be found in the garage. This research aims to come up with a simulation of smart devices that can be and reduce cyber attach. Use of Cisco Packet tracer Features Simulated smart home and c devices are monitored. Simulation results show that smart objects can be connected to the home portal and objects can be successfullymonitored which leads to the idea of real-life implementation and see. In my research make manysolutions for attachingissues,which was great, and apply some wirelessprotocol.
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J017555559
This document proposes a web-based location-aware system architecture to combat electoral crimes in Nigeria. It would allow the Independent National Electoral Commission (INEC), police, and public to exchange information about crimes in real-time. The system uses a client-server model, with GPS sensors on electoral devices to track their location if stolen. The public could anonymously report crimes or missing devices via a mobile app. INEC could then locate stolen devices on a server map. This is proposed to improve communication between authorities and the public to better detect and respond to electoral crimes.
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Ambient intelligence (AmI) refers to digital environments that are aware of a person's presence and context and can respond accordingly. Key aspects of AmI include systems and technologies that are embedded, context-aware, personalized, adaptive, and anticipatory. AmI aims to improve people's quality of life while also benefiting the environment through more efficient energy usage and waste reduction. Some applications of AmI include smart homes, health monitoring, transportation, education, emergency services, and production facilities. However, challenges remain regarding issues like limited sensor battery life, modeling multiple users, self-testing software, and privacy/security concerns.
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docx
The document describes a platform called RADICAL that aggregates and analyzes big data from Internet of Things devices and social networks for smart city applications. It collects millions of data points from IoT sensors and social media reports in several European cities. The platform stores this data in a SQL database and performs sentiment analysis and other analytics. It aims to improve data processing efficiency to reduce retrieval times for large datasets from smart city services.
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docx
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as.
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docx
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as ...
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Computación Urbana
- 2. Urban computing is a extention of ubiquitous computing it shares common requirements with its predecessor, like context awareness, user centricity, composabilit y and dynamicity.
- 3. Study of the behavior of a group of people on campus. Analyzed data and constructed a semantic model with the variables that make interactions on campus. Construction of the variables proposed algorithm with the semantic model. Implementation of the model in a framework to provide services.
- 4. Developing security strategies for urban computing systems. Attacks UrbComp systems include: DDos, Sniffing, Sham server, Malicious Context, Routing Attack. Urban Life Scenario. Safety tips such as encryption, access lists, authentication between devices, etc.
- 5. Organization of a testbed in Oulu, Finland to develop urban computing environments. First event: Students from at least 20 countries interacting with the city. Second event: Innovative development of applications for the use of these applications in the community.