NASA SpaceCube Next-Generation Artificial-Intelligence Computing for STP-H9-SCENIC on ISS

Recently, Artificial Intelligence (AI) and Machine Learning (ML) capabilities have seen an exponential increase in interest from academia and industry that can be a disruptive, transformative development for future missions. Specifically, AI/ML concepts for edge computing can be integrated into future missions for autonomous operation, constellation missions, and onboard data analysis. However, using commercial AI software frameworks onboard spacecraft is challenging because traditional radiation-hardened processors and common spacecraft processors cannot provide the necessary onboard processing capability to effectively deploy complex AI models. Advantageously, embedded AI microchips being developed for the mobile market demonstrate remarkable capability and follow similar size, weight, and power constraints that could be imposed on a space-based system. Unfortunately, many of these devices have not been qualified for use in space. Therefore, Space Test Program - Houston 9 - SpaceCube Edge-Node Intelligent Collaboration (STP-H9-SCENIC) will demonstrate inflight, cutting-edge AI applications on multiple space-based devices for next-generation onboard intelligence. SCENIC will characterize several embedded AI devices in a relevant space environment and will provide NASA and DoD with flight heritage data and lessons learned for developers seeking to enable AI/ML on future missions. Finally, SCENIC also includes new CubeSat form-factor GPS and SDR cards for guidance and navigation

Cognitive Radio Platforms For Disaster Response Networks, Survey

Either natural or man-made a disaster is defined as unexpected destructive event that causes damages and malfunction of existing systems and services all around the disaster area, these destructive effects are unfortunately beyond the capability of local authorities to recover and respond immediately, the disaster recovery plans are immediately initiated so that rescue and aid operations can help those who are trapped in disaster area to survive, those efforts need to be controlled and coordinated with reliable communication systems that are more likely partially or fully disabled due to the disaster, the capabilities of cognitive radio technology enables it to play a significant role in providing efficient communication services for the rescue teams and headquarters as well as trapped victims, in this paper, we survey the cognitive radio architectures that can replace the Software Defined Radio SDR in order to reduce the network expenses in terms of network size and network computational complexit

Spectrum-efficient Architecture for Cognitive Wireless Sensor Networks

Projecte realitzat en col.laboració amb el centre Université Libre de BruxellesHoy en día existe la creencia de que en unos pocos años las actuales Redes Inalámbricas de Sensores estarán presentes en muchas aplicaciones. Mientras estas sigan actuando en la banda sin licencia de ISM 2,4GHz, tendrán que coexistir con otras exitosas tecnologías como Wi-Fi o Bluetooth. En consecuencia, resulta obvio asegurar que la banda en cuestión estará superpoblada en un futuro próximo. Sin embargo y gracias a las nuevas técnicas de Radio Cognitiva, que permitirán la aplicación de un eficiente Acceso al Espectro Dinámico, se conseguirá una distribución racional, dentro del espectro disponible en ese momento y lugar, de las comunicaciones inalámbricas que se estén llevando a cabo. Esta actuación permitirá acceder a frecuencias menos pobladas para poder transmitir con menos interferencias e incluso con menos pérdidas de propagación. A lo largo de este trabajo se va a presentar una arquitectura eficiente, espectralmente hablando, para Redes Inalámbricas de Sensores y Cognitivas. Este esquema desarrolla un protocolo de recolección de datos, para una red con topología de árbol, totalmente escalable y con finalidades genéricas. A través de las pruebas realizadas, podemos afirmar que nuestro esquema, sin alterar el ciclo normal de recolección de datos, puede detectar la presencia de otras Redes Inalámbricas de Sensores y, consecuentemente, migrar la red a nueva frecuencia mientras que todas estas operaciones están ocultas al usuario final. También es eficiente a nivel de energía, ya que no se realizan comprobaciones redundantes de la presencia de otras redes. De esta manera, nuestra propuesta asegura un mejor comportamiento en caso de la existencia de una Red Inalámbrica de Sensores externa, sin realizar operaciones complicadas ni añadiendo más tráfico a la red

Sensor Networks in the Low Lands

This paper provides an overview of scientific and industrial developments of the last decade in the area of sensor networks in The Netherlands (Low Lands). The goal is to highlight areas in which the Netherlands has made most contributions and is currently a dominant player in the field of sensor networks. On the one hand, motivations, addressed topics, and initiatives taken in this period are presented, while on the other hand, special emphasis is given to identifying current and future trends and formulating a vision for the coming five to ten years. The presented overview and trend analysis clearly show that Dutch research and industrial efforts, in line with recent worldwide developments in the field of sensor technology, present a clear shift from sensor node platforms, operating systems, communication, networking, and data management aspects of the sensor networks to reasoning/cognition, control, and actuation

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa