29 research outputs found
Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services
[EN] This paper presents the design and validation of a traceability system, based on radio frequency identification (RFID) technology and Internet of Things (IoT) services, intended to address the interconnection and cost-implementation problems typical in traceability systems. The RFID layer integrates temperature sensors into RFID tags, to track and trace food conditions during transportation.
The IoT paradigm makes it possible to connect multiple systems to the same platform, addressing interconnection problems between different technology providers. The cost-implementation issues are addressed following the Data as a Service (DaaS) billing scheme, where users pay for the data they consume and not the installed equipment, avoiding the big initial investment that these high-tech solutions commonly require. The developed system is validated in two case scenarios, one carried out in controlled laboratory conditions, monitoring chopped pumpkin. Another case, carried out in a real scenario, monitors oranges sent from Valencia, Spain to Cork, Ireland.Urbano, O.; Perles, A.; Pedraza, C.; Rubio-Arraez, S.; Castelló Gómez, ML.; Ortolá Ortolá, MD.; Mercado Romero, R. (2020). Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services. Sensors. 20(4):1-19. https://doi.org/10.3390/s20041163119204Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172-184. doi:10.1016/j.foodcont.2013.11.007Bosona, T., & Gebresenbet, G. (2013). Food traceability as an integral part of logistics management in food and agricultural supply chain. Food Control, 33(1), 32-48. doi:10.1016/j.foodcont.2013.02.004Bechini, A., Cimino, M. G. C. A., Marcelloni, F., & Tomasi, A. (2008). Patterns and technologies for enabling supply chain traceability through collaborative e-business. Information and Software Technology, 50(4), 342-359. doi:10.1016/j.infsof.2007.02.017Badia-Melis, R., Mishra, P., & Ruiz-García, L. (2015). Food traceability: New trends and recent advances. A review. Food Control, 57, 393-401. doi:10.1016/j.foodcont.2015.05.005Timestrip Visual Indicators of Time and Temperaturehttps://timestrip.com/Storøy, J., Thakur, M., & Olsen, P. (2013). The TraceFood Framework – Principles and guidelines for implementing traceability in food value chains. Journal of Food Engineering, 115(1), 41-48. doi:10.1016/j.jfoodeng.2012.09.018Pizzuti, T., Mirabelli, G., Sanz-Bobi, M. A., & Goméz-Gonzaléz, F. (2014). Food Track & Trace ontology for helping the food traceability control. Journal of Food Engineering, 120, 17-30. doi:10.1016/j.jfoodeng.2013.07.017Landt, J. (2005). The history of RFID. IEEE Potentials, 24(4), 8-11. doi:10.1109/mp.2005.1549751Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarriá, D., & Menesatti, P. (2012). A Review on Agri-food Supply Chain Traceability by Means of RFID Technology. Food and Bioprocess Technology, 6(2), 353-366. doi:10.1007/s11947-012-0958-7Mainetti, L., Mele, F., Patrono, L., Simone, F., Stefanizzi, M. L., & Vergallo, R. (2013). An RFID-Based Tracing and Tracking System for the Fresh Vegetables Supply Chain. International Journal of Antennas and Propagation, 2013, 1-15. doi:10.1155/2013/531364Figorilli, S., Antonucci, F., Costa, C., Pallottino, F., Raso, L., Castiglione, M., … Menesatti, P. (2018). A Blockchain Implementation Prototype for the Electronic Open Source Traceability of Wood along the Whole Supply Chain. Sensors, 18(9), 3133. doi:10.3390/s18093133Aguzzi, J., Sbragaglia, V., Sarriá, D., García, J. A., Costa, C., Río, J. del, … Sardà, F. (2011). A New Laboratory Radio Frequency Identification (RFID) System for Behavioural Tracking of Marine Organisms. Sensors, 11(10), 9532-9548. doi:10.3390/s111009532Donelli, M. (2018). An RFID-Based Sensor for Masonry Crack Monitoring. Sensors, 18(12), 4485. doi:10.3390/s18124485De Souza, P., Marendy, P., Barbosa, K., Budi, S., Hirsch, P., Nikolic, N., … Davie, A. (2018). Low-Cost Electronic Tagging System for Bee Monitoring. Sensors, 18(7), 2124. doi:10.3390/s18072124Corchia, L., Monti, G., & Tarricone, L. (2019). A Frequency Signature RFID Chipless Tag for Wearable Applications. Sensors, 19(3), 494. doi:10.3390/s19030494Zuffanelli, S., Aguila, P., Zamora, G., Paredes, F., Martin, F., & Bonache, J. (2016). A High-Gain Passive UHF-RFID Tag with Increased Read Range. Sensors, 16(7), 1150. doi:10.3390/s16071150Monteleone, S., Sampaio, M., & Maia, R. F. (2017). A novel deployment of smart Cold Chain system using 2G-RFID-Sys temperature monitoring in medicine Cold Chain based on Internet of Things. 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). doi:10.1109/soli.2017.8120995Zou, Z., Chen, Q., Uysal, I., & Zheng, L. (2014). Radio frequency identification enabled wireless sensing for intelligent food logistics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2017), 20130313. doi:10.1098/rsta.2013.0313Azzarelli, J. M., Mirica, K. A., Ravnsbæk, J. B., & Swager, T. M. (2014). Wireless gas detection with a smartphone via rf communication. Proceedings of the National Academy of Sciences, 111(51), 18162-18166. doi:10.1073/pnas.1415403111Pies, M., Hajovsky, R., & Ozana, S. (2014). Wireless measurement of carbon monoxide concentration. 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014). doi:10.1109/iccas.2014.6987843Azzara, A., Bocchino, S., Pagano, P., Pellerano, G., & Petracca, M. (2013). Middleware solutions in WSN: The IoT oriented approach in the ICSI project. 2013 21st International Conference on Software, Telecommunications and Computer Networks - (SoftCOM 2013). doi:10.1109/softcom.2013.6671886Ribeiro, A. R. L., Silva, F. C. S., Freitas, L. C., Costa, J. C., & Francês, C. R. (2005). SensorBus. Proceedings of the 3rd international IFIP/ACM Latin American conference on Networking - LANC ’05. doi:10.1145/1168117.1168119Sulc, V., Kuchta, R., & Vrba, R. (2010). IQRF Smart House - A Case Study. 2010 Third International Conference on Advances in Mesh Networks. doi:10.1109/mesh.2010.17Porkodi, R., & Bhuvaneswari, V. (2014). The Internet of Things (IoT) Applications and Communication Enabling Technology Standards: An Overview. 2014 International Conference on Intelligent Computing Applications. doi:10.1109/icica.2014.73EPC Radio-Frequency Identity Protocols. Generation-2 UHF RFIDhttps://www.gs1.org/sites/default/files/docs/epc/uhfc1g2_2_0_0_standard_20131101.pdfUusitalo, M. (2006). Global Vision for the Future Wireless World from the WWRF. IEEE Vehicular Technology Magazine>, 1(2), 4-8. doi:10.1109/mvt.2006.283570Sung, J., Lopez, T. S., & Kim, D. (2007). The EPC Sensor Network for RFID and WSN Integration Infrastructure. Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW’07). doi:10.1109/percomw.2007.113Chunxiao Fan, Zhigang Wen, Fan Wang, & Yuexin Wu. (2011). A middleware of Internet of Things (IoT) based on ZigBee and RFID. IET International Conference on Communication Technology and Application (ICCTA 2011). doi:10.1049/cp.2011.0765Centenaro, M., Vangelista, L., Zanella, A., & Zorzi, M. (2016). Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios. IEEE Wireless Communications, 23(5), 60-67. doi:10.1109/mwc.2016.7721743Hai Liu, Bolic, M., Nayak, A., & Stojmenovic, I. (2008). Taxonomy and Challenges of the Integration of RFID and Wireless Sensor Networks. IEEE Network, 22(6), 26-35. doi:10.1109/mnet.2008.4694171Bertolini, M., Bevilacqua, M., & Massini, R. (2006). FMECA approach to product traceability in the food industry. Food Control, 17(2), 137-145. doi:10.1016/j.foodcont.2004.09.013Zhang, M., & Li, P. (2012). RFID Application Strategy in Agri-Food Supply Chain Based on Safety and Benefit Analysis. Physics Procedia, 25, 636-642. doi:10.1016/j.phpro.2012.03.137Engels, D. W., Kang, Y. S., & Wang, J. (2013). On security with the new Gen2 RFID security framework. 2013 IEEE International Conference on RFID (RFID). doi:10.1109/rfid.2013.6548148SINIEV: Un Centro Inteligente De Control De Tránsito Y Transporte Que Beneficiaría A Todo El Paíshttps://revistadelogistica.com/actualidad/siniev-un-centro-inteligente-de-control-de-transito-y-transporte-que-beneficiara-a-todo-el-pais/Tentzeris, M. M., Kim, S., Traille, A., Aubert, H., Yoshihiro, K., Georgiadis, A., & Collado, A. (2013). Inkjet-printed RFID-enabled sensors on paper for IoT and “Smart Skin” applications. ICECom 2013. doi:10.1109/icecom.2013.6684749Vega, F., Pantoja, J., Morales, S., Urbano, O., Arevalo, A., Muskus, E., … Hernandez, N. (2016). An IoT-based open platform for monitoring non-ionizing radiation levels in Colombia. 2016 IEEE Colombian Conference on Communications and Computing (COLCOM). doi:10.1109/colcomcon.2016.7516379Yang, K., & Jia, X. (2011). Data storage auditing service in cloud computing: challenges, methods and opportunities. World Wide Web, 15(4), 409-428. doi:10.1007/s11280-011-0138-0Alfian, G., Rhee, J., Ahn, H., Lee, J., Farooq, U., Ijaz, M. F., & Syaekhoni, M. A. (2017). Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering, 212, 65-75. doi:10.1016/j.jfoodeng.2017.05.008Chen, R.-Y. (2015). Autonomous tracing system for backward design in food supply chain. Food Control, 51, 70-84. doi:10.1016/j.foodcont.2014.11.004Song, J., Wei, Q., Wang, X., Li, D., Liu, C., Zhang, M., & Meng, L. (2018). Degradation of carotenoids in dehydrated pumpkins as affected by different storage conditions. Food Research International, 107, 130-136. doi:10.1016/j.foodres.2018.02.024Montesano, D., Rocchetti, G., Putnik, P., & Lucini, L. (2018). Bioactive profile of pumpkin: an overview on terpenoids and their health-promoting properties. Current Opinion in Food Science, 22, 81-87. doi:10.1016/j.cofs.2018.02.003Rubio-Arraez, S., Capella, J. V., Castelló, M. L., & Ortolá, M. D. (2016). Physicochemical characteristics of citrus jelly with non cariogenic and functional sweeteners. Journal of Food Science and Technology, 53(10), 3642-3650. doi:10.1007/s13197-016-2319-4Carmona, L., Alquézar, B., Marques, V. V., & Peña, L. (2017). Anthocyanin biosynthesis and accumulation in blood oranges during postharvest storage at different low temperatures. Food Chemistry, 237, 7-14. doi:10.1016/j.foodchem.2017.05.07
Smart Monetization - Telecom Revenue Management beyond the traditional invoice
Nowadays, there is a fast and unpredictable technological evolution, with new systems
constantly emerging on the market, with the capability of being monetized. However,
these systems are not always fully and flexibly explored. Many hardware distributors are
selling products without a clear view on sustainable business models for them, leaving
these as an afterthought. Communications Service Providers are suddenly under pres sure to modernize and expande their business models as to regain the ground claimed by
Over-the-top service providers, who make use of existing infrastructures to provide their
own services, which naturally may lead to substantial revenue loss from the actual infras tructure owners. The Smart Monetization project aims to explore this paradigm, with
the design and implementation of a reusable asset, making use of Big Data and Analytics
tools that can ingest and process usage and billing data from customers, detecting event
patterns and correlations that can be monetized, leading to improved and new service
experiences and ensuring, as well, greater transparency on the process of billing and
charging of these services
Prototyping and Evaluation of Sensor Data Integration in Cloud Platforms
The SFI Smart Ocean centre has initiated a long-running project which consists of developing a wireless and autonomous marine observation system for monitoring of underwater environments and structures. The increasing popularity of integrating the Internet of Things (IoT) with Cloud Computing has led to promising infrastructures that could realize Smart Ocean's goals. The project will utilize underwater wireless sensor networks (UWSNs) for collecting data in the marine environments and develop a cloud-based platform for retrieving, processing, and storing all the sensor data. Currently, the project is in its early stages and the collaborating partners are researching approaches and technologies that can potentially be utilized. This thesis contributes to the centre's ongoing research, focusing on the aspect of how sensor data can be integrated into three different cloud platforms: Microsoft Azure, Amazon Web Services, and the Google Cloud Platform. The goals were to develop prototypes that could successfully send data to the chosen cloud platforms and evaluate their applicability in context of the Smart Ocean project. In order to determine the most suitable option, each platform was evaluated based on set of defined criteria, focusing on their sensor data integration capabilities. The thesis has also investigated the cloud platforms' supported protocol bindings, as well as several candidate technologies for metadata standards and compared them in surveys. Our evaluation results shows that all three cloud platforms handle sensor data integration in very similar ways, offering a set of cloud services relevant for creating diverse IoT solutions. However, the Google Cloud Platform ranks at the bottom due to the lack of IoT focus on their platform, with less service options, features, and capabilities compared to the other two. Both Microsoft Azure and Amazon Web Services rank very close to each other, as they provide many of the same sensor data integration capabilities, making them the most applicable options.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO
Mining Heterogeneous Urban Data at Multiple Granularity Layers
The recent development of urban areas and of the new advanced services supported by digital technologies has generated big challenges for people and city administrators, like air pollution, high energy consumption, traffic congestion, management of public events. Moreover, understanding the perception of citizens about the provided services and other relevant topics can help devising targeted actions in the management. With the large diffusion of sensing technologies and user devices, the capability to generate data of public interest within the urban area has rapidly grown. For instance, different sensors networks deployed in the urban area allow collecting a variety of data useful to characterize several aspects of the urban environment.
The huge amount of data produced by different types of devices and applications brings a rich knowledge about the urban context. Mining big urban data can provide decision makers with knowledge useful to tackle the aforementioned challenges for a smart and sustainable administration of urban spaces. However, the high volume and heterogeneity of data increase the complexity of the analysis. Moreover, different sources provide data with different spatial and temporal references. The extraction of significant information from such diverse kinds of data depends also on how they are integrated, hence alternative data representations and efficient processing technologies are required.
The PhD research activity presented in this thesis was aimed at tackling these issues. Indeed, the thesis deals with the analysis of big heterogeneous data in smart city scenarios, by means of new data mining techniques and algorithms, to study the nature of urban related processes. The problem is addressed focusing on both infrastructural and algorithmic layers. In the first layer, the thesis proposes the enhancement of the current leading techniques for the storage and elaboration of Big Data. The integration with novel computing platforms is also considered to support parallelization of tasks, tackling the issue of automatic scaling of resources. At algorithmic layer, the research activity aimed at innovating current data mining algorithms, by adapting them to novel Big Data architectures and to Cloud computing environments. Such algorithms have been applied to various classes of urban data, in order to discover hidden but important information to support the optimization of the related processes.
This research activity focused on the development of a distributed framework to automatically aggregate heterogeneous data at multiple temporal and spatial granularities and to apply different data mining techniques. Parallel computations are performed according to the MapReduce paradigm and exploiting in-memory computing to reach near-linear computational scalability. By exploring manifold data resolutions in a relatively short time, several additional patterns of data can be discovered, allowing to further enrich the description of urban processes. Such framework is suitably applied to different use cases, where many types of data are used to provide insightful descriptive and predictive analyses.
In particular, the PhD activity addressed two main issues in the context of urban data mining: the evaluation of buildings energy efficiency from different energy-related data and the characterization of people's perception and interest about different topics from user-generated content on social networks. For each use case within the considered applications, a specific architectural solution was designed to obtain meaningful and actionable results and to optimize the computational performance and scalability of algorithms, which were extensively validated through experimental tests
Configuração automática de plataforma de gestão de desempenho em ambientes NFV e SDN
Mestrado em Engenharia de Computadores e TelemáticaWith 5G set to arrive within the next three years, this next-generation
of mobile networks will transform the mobile industry with a profound
impact both on its customers as well as on the existing technologies
and network architectures. Software-Defined Networking (SDN), together
with Network Functions Virtualization (NFV), are going to play
key roles for the operators as they prepare the migration from 4G to
5G allowing them to quickly scale their networks. This dissertation will
present a research work done on this new paradigm of virtualized and
programmable networks focusing on the performance management, supervision
and monitoring domains, aiming to address Self-Organizing
Networks (SON) scenarios in a NFV/SDN context, with one of the scenarios
being the detection and prediction of potential network and service
anomalies. The research work itself was done while participating in
a R&D project designated SELFNET (A Framework for Self-Organized
Network Management in Virtualized and Software Defined Networks)
funded by the European Commission under the H2020 5G-PPP programme,
with Altice Labs being one of the participating partners of
this project. Performance management system advancements in a 5G
scenario require aggregation, correlation and analysis of data gathered
from these virtualized and programmable network elements. Both opensource
monitoring tools and customized catalog-driven tools were either
integrated on or developed with this purpose, and the results show
that they were able to successfully address these requirements of the
SELFNET project. Current performance management platforms of the
network operators in production are designed for non virtualized (non-
NFV) and non programmable (non-SDN) networks, and the knowledge
gathered while doing this research work allowed Altice Labs to understand
how its Altaia performance management platform must evolve in
order to be prepared for the upcoming 5G next generation mobile networks.Com o 5G prestes a chegar nos próximos três anos, esta próxima geração
de redes móveis irá transformar a indústria de telecomunicações
móveis com um impacto profundo nos seus clientes assim como nas
tecnologias e arquiteturas de redes. As redes programáveis (SDN),
em conjunto com a virtualização de funções de rede (NFV), irão desempenhar
papéis vitais para as operadoras na sua migração do 4G
para o 5G, permitindo-as escalar as suas redes rapidamente. Esta
dissertação irá apresentar um trabalho de investigação realizado sobre
este novo paradigma de virtualização e programação de redes,
concentrando-se no domínio da gestão de desempenho, supervisionamento
e monitoria, abordando cenários de redes auto-organizadas
(SON) num contexto NFV/SDN, sendo um destes cenários a deteção
e predição de potenciais anomalias de redes e serviços. O trabalho de
investigação foi enquadrado num projeto de I&D designado SELFNET
(A Framework for Self-Organized Network Management in Virtualized
and Software Defined Networks) financiado pela Comissão Europeia
no âmbito do programa H2020 5G-PPP, sendo a Altice Labs um dos
parceiros participantes deste projeto. Avanços em sistemas de gestão
de desempenho em cenários 5G requerem agregação, correlação e
análise de dados recolhidos destes elementos de rede programáveis
e virtualizados. Ferramentas de monitoria open-source e ferramentas
catalog-driven foram integradas ou desenvolvidas com este propósito,
e os resultados mostram que estas preencheram os requisitos do projeto
SELFNET com sucesso. As plataformas de gestão de desempenho
das operadoras de rede atualmente em produção estão concebidas
para redes não virtualizadas (non-NFV) e não programáveis (non-
SDN), e o conhecimento adquirido durante este trabalho de investigação
permitiu à Altice Labs compreender como a sua plataforma de gestão
de desempenho (Altaia) terá que evoluir por forma a preparar-se
para a próxima geração de redes móveis 5G
The integration of lessons learned knowledge in Building Information Modelling (BIM)
Lessons learned systems are vital means for integrating construction knowledge into the various phases of the construction project life cycle. Many such systems are tailored towards the owner organisation’s specific needs and workflows to overcome challenges with information collection, documentation and retrieval. Previous works have relied on the development of conventional local and network/cloud-based database management systems to store and retrieve lessons gathered on projects. These lessons learned systems operate independently and have not been developed to take full advantage of the benefits of integration with emerging building information modelling (BIM) technology. As such construction professionals are faced with the shortcomings of the lack in efficient and speedy retrieval of context-focused information on lessons learned for appropriate utilization in projects. To tackle this challenge, we propose the integration of lesson learned knowledge management in BIM in addition to existing 2D-8D modelling of project information. The integration was implemented through the embedding of non –structured query system, NoSQL (MongoDB), in a BIM enabled environment to host lessons learned information linked to model items and 4D modelling project tasks of the digitised model. This is beyond existing conventional text-based queries and is novel. The system is implemented in .NET Frameworks and interfaced with a project management BIM tool, Navisworks Manage. The demonstration with a test case of a federated model from a pre-design school project suggests that lessons learned systems can become an integral part of BIM environments and contribute to enhancing knowledge reuse in projects
Heatwave Vulnerability in Hounslow
The purpose of this project was to aid the London Borough of Hounslow in improving their heatwave emergency plans and to assist in an experimental project that aims to determine if heatwave models can be used for emergency planning. We accomplished these tasks by first reviewing previously developed models in order to build an operational definition of vulnerability. We then began a large-scale review of data available in the borough that could be used for heat wave modelling and emergency planning. Finally, we created a set of recommendations for the borough and highlighted areas we found to be most at risk to heatwaves based on the data we identified. We also pinpointed key areas of our project that future projects may want to expand upon
Um Modelo Computacional para Cidades Inteligentes Assistivas
Este artigo apresenta o MASC, um modelo computacional para criação de cidades inteligentes assistivas. A aplicação da computação ubíqua na acessibilidade oportuniza soluções para pessoas com deficiências (PcDs). Diferente dos trabalhos relacionados, o MASC utiliza as interações das PcDs para composição de trilhas que são oferecidas como serviços. O modelo é genérico, auxiliando em diferentes tipos de deficiências. Além disso, o MASC foi projetado para viabilizar aplicações massivas. A implementação de um protótipo permitiu a avaliação do MASC, considerando-se desempenho e funcionalidade. A avaliação foi realizada com dados gerados por um simulador de contextos em uma região localizada na cidade São Leopoldo - RS. Os resultados apresentados nos testes indicam que os serviços oferecidos pelo modelo podem ser implantados nas cidades inteligentes para colaborar com acessibilidade, auxiliando PcDs, profissionais da saúde e administração pública
Design and Implementation of a Distributed Mobility Management Entity (MME) on OpenStack
Network Functions Virtualisation (NFV) involves the implementation of network functions, for example firewalls and routers, as software applications that can run on general-purpose servers. In present-day networks, each network function is typically implemented on dedicated and proprietary hardware. By utilising virtualisation technologies, NFV enables network functions to be deployed on cloud computing infrastructure in data centers.
This thesis discusses the application of NFV to the Evolved Packet Core (EPC) in Long Term Evolution (LTE) networks; specifically to the Mobility Management Entity (MME), a control plane entity in the EPC. With the convergence of cloud computing and mobile networks, conventional architectures of network elements need to be re-designed in order to fully harness benefits such as scalability and elasticity. To this end, we design and implement a distributed MME with a three-tier architecture common to web applications. We highlight design considerations for moving MME functionality to the cloud and compare our new distributed design to that of a standalone MME. We deploy and test the distributed MME on two separate OpenStack clouds. Our results indicate that the benefits of scalability and resilience can outweigh the marginal increase in latency for EPC procedures. We find that the latency is dependent on the actual placement of MME components within the data center. Also, we believe that extensions to the OpenStack platform are required before it can meet performance and availability requirements for telecommunication applications