Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions

[EN] The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management. Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents. Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry. This necessitates the need for DP tools that intelligently and cost-effectively prioritize a large variety of data for detecting abnormal events and hence effectively manage them, thereby making the current smart cities greener. In this article, we first carry out an in-depth investigation of the recent approaches and trends of DP for data of different natures, genres, and domains of two decades in green smart cities. Next, we propose an energy-efficient DP framework by intelligent integration of the Internet of Things, artificial intelligence, and big data analytics. Experimental evaluation on real-world surveillance data verifies the energy efficiency and applicability of this framework in green smart cities. Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart citiesThis work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2016R-1A2B4011712).Muhammad, K.; Lloret, J.; Baik, SW. (2019). Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions. IEEE Communications Magazine. 57(2):60-65. https://doi.org/10.1109/MCOM.2018.1800371S606557

Delivery of Personalized and Adaptive Content to Mobile Devices:A Framework and Enabling Technology

Many innovative wireless applications that aim to provide mobile information access are emerging. Since people have different information needs and preferences, one of the challenges for mobile information systems is to take advantage of the convenience of handheld devices and provide personalized information to the right person in a preferred format. However, the unique features of wireless networks and mobile devices pose challenges to personalized mobile content delivery. This paper proposes a generic framework for delivering personalized and adaptive content to mobile users. It introduces a variety of enabling technologies and highlights important issues in this area. The framework can be applied to many applications such as mobile commerce and context-aware mobile services

Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review

Internet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, providing several opportunities for numerous IoT applications, particularly healthcare systems. Despite all the advantages, there are still several open issues that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information security, and privacy. IoT provides important characteristics to healthcare systems, such as availability, mobility, and scalability, that o er an architectural basis for numerous high technological healthcare applications, such as real-time patient monitoring, environmental and indoor quality monitoring, and ubiquitous and pervasive information access that benefits health professionals and patients. The constant scientific innovations make it possible to develop IoT devices through countless services for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced living environments (ELEs). This paper reviews the current state of the art on IoT architectures for ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities, open-source platforms, and operating systems. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions.info:eu-repo/semantics/publishedVersio

ICOPER Project - Deliverable 4.3 ISURE: Recommendations for extending effective reuse, embodied in the ICOPER CD&R

The purpose of this document is to capture the ideas and recommendations, within and beyond the ICOPER community, concerning the reuse of learning content, including appropriate methodologies as well as established strategies for remixing and repurposing reusable resources. The overall remit of this work focuses on describing the key issues that are related to extending effective reuse embodied in such materials. The objective of this investigation, is to support the reuse of learning content whilst considering how it could be originally created and then adapted with that ‘reuse’ in mind. In these circumstances a survey on effective reuse best practices can often provide an insight into the main challenges and benefits involved in the process of creating, remixing and repurposing what we are now designating as Reusable Learning Content (RLC). Several key issues are analysed in this report: Recommendations for extending effective reuse, building upon those described in the previous related deliverables 4.1 Content Development Methodologies and 4.2 Quality Control and Web 2.0 technologies. The findings of this current survey, however, provide further recommendations and strategies for using and developing this reusable learning content. In the spirit of ‘reuse’, this work also aims to serve as a foundation for the many different stakeholders and users within, and beyond, the ICOPER community who are interested in reusing learning resources. This report analyses a variety of information. Evidence has been gathered from a qualitative survey that has focused on the technical and pedagogical recommendations suggested by a Special Interest Group (SIG) on the most innovative practices with respect to new media content authors (for content authoring or modification) and course designers (for unit creation). This extended community includes a wider collection of OER specialists. This collected evidence, in the form of video and audio interviews, has also been represented as multimedia assets potentially helpful for learning and useful as learning content in the New Media Space (See section 4 for further details). Section 2 of this report introduces the concept of reusable learning content and reusability. Section 3 discusses an application created by the ICOPER community to enhance the opportunities for developing reusable content. Section 4 of this report provides an overview of the methodology used for the qualitative survey. Section 5 presents a summary of thematic findings. Section 6 highlights a list of recommendations for effective reuse of educational content, which were derived from thematic analysis described in Appendix A. Finally, section 7 summarises the key outcomes of this work

Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption

[EN] This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B4011712). Paper no. TII-17-2066.Muhammad, K.; Hamza, R.; Ahmad, J.; Lloret, J.; Wang, H.; Baik, SW. (2018). Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption. IEEE Transactions on Industrial Informatics. 14(8):3679-3689. https://doi.org/10.1109/TII.2018.2791944S3679368914

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

A New Competence-based Approach for Personalizing MOOCs in a Mobile Collaborative and Networked Environment

Massive Open Online Courses (MOOCs) are a new disruptive development in higher education that combines openness and scalability in a most powerful way. They have the potential to widen participation in higher education. Thus, they contribute to social inclusion, the dissemination of knowledge and pedagogical innovation and also the internationalization of higher education institutions. However, one of the critical elements for a massive open language learning experience to be successful is to empower learners and to facilitate networked learning experiences. In fact, MOOCs are designed for an undefined number of participants, thus serving a high heterogeneity of profiles, with diverse learning styles and prior knowledge, and also contexts of participation and diversity of online platforms. Personalization can play a key role in this process. The iMOOC pedagogical model introduced the notion of diversity to MOOC design, allowing for a clear differentiation of learning paths and also virtual environments. In this article, the authors present a proposal based on the iMOOC approach for a new framework for personalizing and adapting MOOCs designed in a collaborative, networked pedagogical approach by identifying each participant's competence profile and prior knowledge, as well as the respective mobile communication device used to generate matching personalized learning. This article also shows the results obtained in a laboratory environment after an experiment has been performed with a prototype of the framework. It can be observed that creating personalized learning paths is possible and the next step is to test this framework with real experimental groups.Los cursos en línea masivos y abiertos (MOOC) son una nueva tendencia rompedora en la educación superior. Estos cursos combinan la propiedad de ser abiertos con la posibilidad de ser escalables de una forma muy potente. Tienen el potencial de permitir la participación en la educación superior para todas las personas, a todos los niveles. Por lo tanto, contribuyen a la inclusión social, la difusión del conocimiento y la innovación pedagógica, así como la internalización de las instituciones de educación superior. Sin embargo, uno de los elementos críticos para que tenga éxito una experiencia de aprendizaje de forma abierta y masiva es potenciar y facilitar una red de aprendizaje. De hecho, los MOOC no están diseñados para un número predefinido de participantes por lo que sirven para un alto número de perfiles heterogéneos, con diversidad de estilos de aprendizaje y conocimientos previos, pero también contextos de participación y diversidad de plataformas online. La personalización puede desempeñar un papel clave en este proceso. El modelo pedagógico iMOOC introdujo el principio de diversidad en el diseño de MOOC, permitiendo una clara diferenciación de caminos de aprendizaje y también entornos virtuales. En este artículo los autores presentan una propuesta basada en el enfoque de iMOOC, sobre un nuevo sistema para la personalización y adaptación de MOOC diseñados en un enfoque colaborativo y en una red pedagógica. El mecanismo es identificar cada competencia del perfil de los participantes, el conocimiento previo que estos tienen así como detectar sus respectivos dispositivos móviles, y se genera un camino de aprendizaje personalizado en base a estos parámetros. Este artículo también muestra los resultados obtenidos en un entorno de laboratorio después de un experimento llevado a cabo con un prototipo del sistema. Se puede observar que es posible crear caminos de aprendizaje personalizados y que el siguiente paso es probar este sistema con grupos experimentales reales

Is Meat the New Tobacco? Regulating Food Demand in the Age of Climate Change

Switching from a meat-heavy to a plant-based diet is one of the highest-impact lifestyle changes for climate mitigation and adaptation. Conventional demand-side energy policy has focused on increasing consumption of efficient machines and fuels. Regulating food demand has key advantages. First, food consumption is biologically constrained, thus switching to more efficient foods avoids unintended consequences of switching to more efficient machines, like higher overall energy consumption. Second, food consumption, like smoking, is primed for norm- shifting because it occurs in socially conspicuous environments. While place-based bans and information regulation were essential in lowering the prevalence of smoking, the same strategies may be even more effective in reducing meat demand. Several policy reforms can be implemented at the federal level, from reform of food marketing schemes to publicly subsidized meal programs