68,400 research outputs found
Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case
[EN] During the past few decades, the combination of flourishing maritime commerce and
urban population increases has made port-cities face several challenges. Smart Port-Cities of the
future will take advantage of the newest IoT technologies to tackle those challenges in a joint fashion
from both the city and port side. A specific matter of interest in this work is how to obtain reliable,
measurable indicators to establish port-city policies for mutual benefit. This paper proposes an IoTbased software framework, accompanied with a methodology for defining, calculating, and
predicting composite indicators that represent real-world phenomena in the context of a Smart PortCity. This paper envisions, develops, and deploys the framework on a real use-case as a practice
experiment. The experiment consists of deploying a composite index for monitoring traffic
congestion at the port-city interface in Thessaloniki (Greece). Results were aligned with the
expectations, validated through nine scenarios, concluding with delivery of a useful tool for
interested actors at Smart Port-Cities to work over and build policies upon.This research was funded, by the European Commission, via the agency INEA, under the H2020-project PIXEL, grant number 769355, and, when applicable, by the H2020-project DataPorts, grant number 871493, via the DG-CONNECT agency.Lacalle, I.; Belsa, A.; Vaño, R.; Palau Salvador, CE. (2020). Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case. Sensors. 20(15):1-41. https://doi.org/10.3390/s20154131S1412015Urban Population Growthhttps://www.who.int/gho/urban_health/situation_trends/urban_population_growth_text/en/Smart Port Cityhttps://maritimestreet.fr/smart-port-city/The Worldâs 33 Megacitieshttps://www.msn.com/en-us/money/realestate/the-worlds-33-megacities/ar-BBUaR3vDocksTheFuture Project Maritime Traffic Analysis and Forecast Review-Key Resultshttps://www.docksthefuture.eu/wp-content/uploads/2020/04/Attachment_0-2019-09-09T135818.886-1.pdfHamburg Port Authority: SmartPORThttps://www.hamburg-port-authority.de/en/hpa-360/smartport/Guo, H., Wang, L., Chen, F., & Liang, D. (2014). Scientific big data and Digital Earth. Chinese Science Bulletin, 59(35), 5066-5073. doi:10.1007/s11434-014-0645-3AIVP Agenda 2030 for Sustainable Port-Citieshttps://www.aivpagenda2030.com/Urban Transport Challengeshttps://transportgeography.org/?page_id=4621Passenger Cars in the EUhttps://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EUAverage CO2 Emissions from New Cars and Vans Registered in Europe Increased in 2018, Requiring Significant Emission Reductions to Meet the 2020 Targetshttps://ec.europa.eu/clima/news/average-co2-emissions-new-cars-and-vans-registered-europe-increased-2018-requiring-significant_en7 Smart City Solutions to Reduce Traffic Congestionhttps://www.geotab.com/blog/reduce-traffic-congestion/The Port and the CityâThoughts on the Relation between Cities and Portshttps://theportandthecity.wordpress.com/Yau, K.-L. A., Peng, S., Qadir, J., Low, Y.-C., & Ling, M. H. (2020). Towards Smart Port Infrastructures: Enhancing Port Activities Using Information and Communications Technology. IEEE Access, 8, 83387-83404. doi:10.1109/access.2020.2990961Two Projects Led by Valenciaport Win the IAPH World Port Sustainability Awards 2020âValenciaporthttps://www.valenciaport.com/en/two-projects-led-by-valenciaport-win-the-iaph-world-port-sustainability-awards-2020/Ahlgren, B., Hidell, M., & Ngai, E. C.-H. (2016). Internet of Things for Smart Cities: Interoperability and Open Data. IEEE Internet Computing, 20(6), 52-56. doi:10.1109/mic.2016.124Inkinen, T., Helminen, R., & Saarikoski, J. (2019). Port Digitalization with Open Data: Challenges, Opportunities, and Integrations. Journal of Open Innovation: Technology, Market, and Complexity, 5(2), 30. doi:10.3390/joitmc5020030Analytical Report 4: Open Datain Citieshttps://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n4_-_open_data_in_cities_v1.0_final.pdfAnalytical Report 6: Open Datain Cities 2https://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n6_-_open_data_in_cities_2_-_final-clean.pdfINTER-IoT Deliverableshttps://inter-iot.eu/deliverablesActivage Project D3.1 Report on IoT European Platformshttps://www.activageproject.eu/docs/downloads/activage_public_deliverables/ACTIVAGE_D3.1_M3_ReportonIoTEuropeanPlatforms_V1.0.pdfThe Open Source Platform for Our Smart Digital FutureâFIWAREhttps://www.fiware.org/FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/index.htmlApache Kafkahttps://kafka.apache.org/FIWARE Orion Context Brokerhttps://fiware-orion.readthedocs.io/en/master/Saborido, R., & Alba, E. (2020). Software systems from smart city vendors. Cities, 101, 102690. doi:10.1016/j.cities.2020.102690Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. S. (2018). Software Platforms for Smart Cities. ACM Computing Surveys, 50(6), 1-37. doi:10.1145/3124391Smart Citieshttps://www.fiware.org/community/smart-cities/Araujo, V., Mitra, K., Saguna, S., & Ă
hlund, C. (2019). Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities. Journal of Parallel and Distributed Computing, 132, 250-261. doi:10.1016/j.jpdc.2018.12.010Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019). Smart cities: Advances in researchâAn information systems perspective. International Journal of Information Management, 47, 88-100. doi:10.1016/j.ijinfomgt.2019.01.004Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Alavi, A. H., Jiao, P., Buttlar, W. G., & Lajnef, N. (2018). Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement, 129, 589-606. doi:10.1016/j.measurement.2018.07.067Samih, H. (2019). Smart cities and internet of things. Journal of Information Technology Case and Application Research, 21(1), 3-12. doi:10.1080/15228053.2019.1587572Lanza, J., SĂĄnchez, L., GutiĂ©rrez, V., Galache, J., Santana, J., Sotres, P., & Muñoz, L. (2016). Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case. Energies, 9(9), 719. doi:10.3390/en9090719A Novel Architecture for Modelling, Virtualising and Managing the Energy Consumption of Household Appliances|AIM Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224621Intelligent Use of Buildingsâ Energy Information|IntUBE Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224286Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of Things: applications and challenges in smart cities. A case study of IBM smart city projects. Business Process Management Journal, 22(2). doi:10.1108/bpmj-05-2015-0074Molavi, A., Lim, G. J., & Race, B. (2019). A framework for building a smart port and smart port index. International Journal of Sustainable Transportation, 14(9), 686-700. doi:10.1080/15568318.2019.1610919Moustaka, V., Vakali, A., & Anthopoulos, L. G. (2019). A Systematic Review for Smart City Data Analytics. ACM Computing Surveys, 51(5), 1-41. doi:10.1145/3239566Alam, M., Dupras, J., & Messier, C. (2016). A framework towards a composite indicator for urban ecosystem services. Ecological Indicators, 60, 38-44. doi:10.1016/j.ecolind.2015.05.035PIXEL Project D5.1 Environmental Factors and Mapping to Pilotshttps://pixel-ports.eu/wp-content/uploads/2020/05/D5.1-Environmental-aspects-and-mapping-to-pilots.pdfEconomic Sentiment IndicatorâEurostathttps://ec.europa.eu/eurostat/web/products-datasets/product?code=teibs010Human Development Index (HDI)|Human Development Reportshttp://hdr.undp.org/en/content/human-development-index-hdiCOIN|Competence Centre on Composite Indicators and Scoreboardshttps://composite-indicators.jrc.ec.europa.eu/CITYkeys Projecthttp://www.citykeys-project.eu/citykeys/homeCITYkeys D1-4 Indicators for Smart City Projects and Smart Citieshttp://nws.eurocities.eu/MediaShell/media/CITYkeysD14Indicatorsforsmartcityprojectsandsmartcities.pdfMake Healthy Choices Easier OptionsâScientific Americanhttps://www.scientificamerican.com/podcast/episode/make-healthy-choices-easier-options-12-09-20/FIWARE E Interoperabilidad Para Smart Citieshttps://www.apegr.org/images/descargas/J7OctESMARTCITY/2PresentacionFIWARE.pdfChen, G., Govindan, K., & Yang, Z. (2013). Managing truck arrivals with time windows to alleviate gate congestion at container terminals. International Journal of Production Economics, 141(1), 179-188. doi:10.1016/j.ijpe.2012.03.033Patel, N., & Mukherjee, A. B. (2015). Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index. Bulletin of Geography. Socio-economic Series, 30(30), 123-134. doi:10.1515/bog-2015-0039Aimsun Live: Model Every Movement at Every Momenthttps://www.aimsun.com/aimsun-live/PTV Vissim: Traffic Simulation Softwarehttps://www.ptvgroup.com/en/solutions/products/ptv-vissim/IBM Traffic Prediction Toolhttps://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=1248Veins: The Open Source Vehicular Network Simulation Frameworkhttps://veins.car2x.org/Mena-Yedra, R., GavaldĂ , R., & Casas, J. (2017). Adarules: Learning rules for real-time road-traffic prediction. Transportation Research Procedia, 27, 11-18. doi:10.1016/j.trpro.2017.12.106PIXEL Projecthttps://pixel-ports.euReference Architectural Model Industrie 4.0 (rami 4.0)https://www.plattform-i40.de/PI40/Navigation/EN/Home/home.htmlSethi, P., & Sarangi, S. R. (2017). Internet of Things: Architectures, Protocols, and Applications. Journal of Electrical and Computer Engineering, 2017, 1-25. doi:10.1155/2017/9324035Containers & ContainerizationâThe Pros and Conshttps://spin.atomicobject.com/2019/05/24/containerization-pros-cons/Pyngsi Frameworkhttps://github.com/pixel-ports/pyngsiPIXEL Project D6.2 PIXEL Information System Architecture and DesignâVersion 2https://pixel-ports.eu/wp-content/uploads/2020/05/D6.2-PIXEL-Information-System-architecture-and-design-v2.pdfApache Hivehttps://hive.apache.org/MySQLhttps://www.mysql.com/MariaDB Serverhttps://mariadb.org/Elasticsearchhttps://www.elastic.co/elasticsearch/MongoDBhttps://www.mongodb.com/Node-REDhttps://nodered.org/Swarm Mode Overview | Docker Documentationhttps://docs.docker.com/engine/swarm/Kuberneteshttps://kubernetes.io/PIXEL Project D6.3 PIXEL Data Acquisition, Information Hub and Data Representation v1https://pixel-ports.eu/wp-content/uploads/2020/05/D6.3_PIXEL-data-acquisition-information-hub-and-data-representation-v1.pdfOverview of Docker Compose|Docker Documentationhttps://docs.docker.com/compose/Kibana: Explore, Visualize, Discover Datahttps://www.elastic.co/kibanaGrafana: The Open Observability Platformshttps://grafana.com/Vue.jshttps://vuejs.org/PIXEL Project D5.2 PEI Definition and Algorithms v1https://pixel-ports.eu/wp-content/uploads/2020/05/D5.2-PEI-Definition-and-Algorithms-v1.pdfKeyPerformanceIndicatorâFIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/KeyPerformanceIndicator/doc/spec/index.htmlWhat Is a Container?|App Containerization|Dockerhttps://www.docker.com/resources/what-containerGarcia-Alonso, L., Moura, T. G. Z., & Roibas, D. (2020). The effect of weather conditions on port technical efficiency. Marine Policy, 113, 103816. doi:10.1016/j.marpol.2020.103816TrafficThessâLIVE Traffic in Thessaloniki, Greecehttps://www.trafficthess.imet.gr/National Observatory of AthensâMeteoâStationsâ Live Data and Databasehttp://stratus.meteo.noa.gr/frontHow to Use Smart Data Models in Your ProjectsâFIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/howto/index.htmlGan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491-502. doi:10.1016/j.ecolind.2017.05.068Wilson, M. C., & Wu, J. (2016). The problems of weak sustainability and associated indicators. International Journal of Sustainable Development & World Ecology, 24(1), 44-51. doi:10.1080/13504509.2015.1136360Kumar, S. V., & Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 7(3). doi:10.1007/s12544-015-0170-8Prophet: Forecastig at Scalehttps://facebook.github.io/prophet/PIXEL Project D4.4 PredictiveAlgorithms v2https://pixel-ports.eu/wp-content/uploads/2020/05/PIXEL_D4.4_Predictive-Algorithms_v2.0_Final.pdfProject Jupyterhttps://jupyter.org/FIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/NGSIElasticsearchSinkâFIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/cygnus-ngsi/flume_extensions_catalogue/ngsi_elasticsearch_sink/index.htmlNode.jshttps://nodejs.org/Elasticsearch Node.js Client [7.x]https://www.elastic.co/guide/en/elasticsearch/client/javascript-api/current/index.htmlApache HTTP Server Projecthttps://httpd.apache.org/Everything You Need to Know about Min-Max Normalization: A Python Tutorialhttps://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79OpenStreetMaphttps://www.openstreetmap.org/LeafletâA JavaScript Library for Interactive Mapshttps://leafletjs.com/AmCharts: JavaScript Charts & Mapshttps://www.amcharts.com/FIWARE Cataloguehttps://www.fiware.org/developers/catalogue/Findlow, S. (2019). âCitizenshipâ and âDemocracy Educationâ: identity politics or enlightened political participation? British Journal of Sociology of Education, 40(7), 1004-1013. doi:10.1080/01425692.2019.1656910Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges. IEEE Communications Surveys & Tutorials, 20(1), 416-464. doi:10.1109/comst.2017.277115
Sensing as a Service Model for Smart Cities Supported by Internet of Things
The world population is growing at a rapid pace. Towns and cities are
accommodating half of the world's population thereby creating tremendous
pressure on every aspect of urban living. Cities are known to have large
concentration of resources and facilities. Such environments attract people
from rural areas. However, unprecedented attraction has now become an
overwhelming issue for city governance and politics. The enormous pressure
towards efficient city management has triggered various Smart City initiatives
by both government and private sector businesses to invest in ICT to find
sustainable solutions to the growing issues. The Internet of Things (IoT) has
also gained significant attention over the past decade. IoT envisions to
connect billions of sensors to the Internet and expects to use them for
efficient and effective resource management in Smart Cities. Today
infrastructure, platforms, and software applications are offered as services
using cloud technologies. In this paper, we explore the concept of sensing as a
service and how it fits with the Internet of Things. Our objective is to
investigate the concept of sensing as a service model in technological,
economical, and social perspectives and identify the major open challenges and
issues.Comment: Transactions on Emerging Telecommunications Technologies 2014
(Accepted for Publication
Internet of things
Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing todayâs pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efïŹcient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identiïŹed synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth
Securing the Participation of Safety-Critical SCADA Systems in the Industrial Internet of Things
In the past, industrial control systems were âair gappedâ and
isolated from more conventional networks. They used
specialist protocols, such as Modbus, that are very different
from TCP/IP. Individual devices used proprietary operating
systems rather than the more familiar Linux or Windows.
However, things are changing. There is a move for greater
connectivity â for instance so that higher-level enterprise
management systems can exchange information that helps
optimise production processes. At the same time, industrial
systems have been influenced by concepts from the Internet
of Things; where the information derived from sensors and
actuators in domestic and industrial components can be
addressed through network interfaces. This paper identifies a
range of cyber security and safety concerns that arise from
these developments. The closing sections introduce potential
solutions and identify areas for future research
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things
The number of connected sensors and devices is expected to increase to billions in the near
future. However, centralised cloud-computing data centres present various challenges to meet the
requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput
and bandwidth constraints. Edge computing is becoming the standard computing paradigm for
latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related
to centralised cloud-computing models. Such a paradigm relies on bringing computation close to
the source of data, which presents serious operational challenges for large-scale cloud-computing
providers. In this work, we present an architecture composed of low-cost Single-Board-Computer
clusters near to data sources, and centralised cloud-computing data centres. The proposed
cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT
workload requirements while keeping scalability. We include an extensive empirical analysis to
assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data
centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud
architectures, and evaluate them through extensive simulation. We finally show that acquisition costs
can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de EconomĂa y Competitividad TIN2017-82113-C2-1-RMinisterio de EconomĂa y Competitividad RTI2018-098062-A-I00European Unionâs Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Cyber security investigation for Raspberry Pi devices
Big Data on Cloud application is growing rapidly. When the cloud is attacked, the investigation relies on digital forensics evidence. This paper proposed the data collection via Raspberry Pi devices, in a healthcare situation. The significance of this work is that could be expanded into a digital device array that takes big data security issues into account. There are many potential impacts in health area. The field of Digital Forensics Science has been tagged as a reactive science by some who believe research and study in the field often arise as a result of the need to respond to event which brought about the needs for investigation; this work was carried as a proactive research that will add knowledge to the field of Digital Forensic Science.
The Raspberry Pi is a cost-effective, pocket sized computer that has gained global recognition since its development in 2008; with the wide spread usage of the device for different computing purposes. Raspberry Pi can potentially be a cyber security device, which can relate with forensics investigation in the near future. This work has used a systematic approach to study the structure and operation of the device and has established security issues that the widespread usage of the device can pose, such as health or smart city. Furthermore, its evidential information applied in security will be useful in the event that the device becomes a subject of digital forensic investigation in the foreseeable future. In healthcare system, PII (personal identifiable information) is a very important issue. When Raspberry Pi plays a processor role, its security is vital; consequently, digital forensics investigation on the Raspberry Pies becomes necessary
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