Unmanned aerial vehicle based wireless sensor network for marine-coastal environment monitoring

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effectiveWireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario

Rapid Response Command and Control (R2C2): a systems engineering analysis of scaleable communications for Regional Combatant Commanders

Includes supplementary materialDisaster relief operations, such as the 2005 Tsunami and Hurricane Katrina, and wartime operations, such as Operation Enduring Freedom and Operation Iraqi Freedom, have identified the need for a standardized command and control system interoperable among Joint, Coalition, and Interagency entities. The Systems Engineering Analysis Cohort 9 (SEA-9) Rapid Response Command and Control (R2C2) integrated project team completed a systems engineering (SE) process to address the military’s command and control capability gap. During the process, the R2C2 team conducted mission analysis, generated requirements, developed and modeled architectures, and analyzed and compared current operational systems versus the team’s R2C2 system. The R2C2 system provided a reachback capability to the Regional Combatant Commander’s (RCC) headquarters, a local communications network for situational assessments, and Internet access for civilian counterparts participating in Humanitarian Assistance/Disaster Relief operations. Because the team designed the R2C2 system to be modular, analysis concluded that the R2C2 system was the preferred method to provide the RCC with the required flexibility and scalability to deliver a rapidly deployable command and control capability to perform the range of military operations

Applications of Prediction Approaches in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) collect data and continuously monitor ambient data such as temperature, humidity and light. The continuous data transmission of energy constrained sensor nodes is a challenge to the lifetime and performance of WSNs. The type of deployment environment is also and the network topology also contributes to the depletion of nodes which threatens the lifetime and the also the performance of the network. To overcome these challenges, a number of approaches have been proposed and implemented. Of these approaches are routing, clustering, prediction, and duty cycling. Prediction approaches may be used to schedule the sleep periods of nodes to improve the lifetime. The chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in scheduling the sleep/wake-up periods of sensor nodes

Underwater Sensor Nodes and Networks

Sensor technology has matured enough to be used in any type of environment. The appearance of new physical sensors has increased the range of environmental parameters for gathering data. Because of the huge amount of unexploited resources in the ocean environment, there is a need of new research in the field of sensors and sensor networks. This special issue is focused on collecting recent advances on underwater sensors and underwater sensor networks in order to measure, monitor, surveillance of and control of underwater environments. On the one hand, from the sensor node perspective, we will see works related with the deployment of physical sensors, development of sensor nodes and transceivers for sensor nodes, sensor measurement analysis and several issues such as layer 1 and 2 protocols for underwater communication and sensor localization and positioning systems. On the other hand, from the sensor network perspective, we will see several architectures and protocols for underwater environments and analysis concerning sensor network measurements. Both sides will provide us a complete view of last scientific advances in this research field.Lloret, J. (2013). Underwater Sensor Nodes and Networks. Sensors. 13(9):11782-11796. doi:10.3390/s130911782S1178211796139Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682-1690. doi:10.1049/iet-com.2010.0654Martinez, J. J., Myers, J. R., Carlson, T. J., Deng, Z. D., Rohrer, J. S., Caviggia, K. A., … Weiland, M. A. (2011). Design and Implementation of an Underwater Sound Recording Device. Sensors, 11(9), 8519-8535. doi:10.3390/s110908519Ardid, M., Martínez-Mora, J. A., Bou-Cabo, M., Larosa, G., Adrián-Martínez, S., & Llorens, C. D. (2012). Acoustic Transmitters for Underwater Neutrino Telescopes. Sensors, 12(4), 4113-4132. doi:10.3390/s120404113Baronti, F., Fantechi, G., Roncella, R., & Saletti, R. (2012). Wireless Sensor Node for Surface Seawater Density Measurements. Sensors, 12(3), 2954-2968. doi:10.3390/s120302954Mànuel, A., Roset, X., Rio, J. D., Toma, D. M., Carreras, N., Panahi, S. S., … Cadena, J. (2012). Ocean Bottom Seismometer: Design and Test of a Measurement System for Marine Seismology. Sensors, 12(3), 3693-3719. doi:10.3390/s120303693Jollymore, A., Johnson, M. S., & Hawthorne, I. (2012). Submersible UV-Vis Spectroscopy for Quantifying Streamwater Organic Carbon Dynamics: Implementation and Challenges before and after Forest Harvest in a Headwater Stream. Sensors, 12(4), 3798-3813. doi:10.3390/s120403798Won, T.-H., & Park, S.-J. (2012). Design and Implementation of an Omni-Directional Underwater Acoustic Micro-Modem Based on a Low-Power Micro-Controller Unit. Sensors, 12(2), 2309-2323. doi:10.3390/s120202309Sánchez, A., Blanc, S., Yuste, P., Perles, A., & Serrano, J. J. (2012). An Ultra-Low Power and Flexible Acoustic Modem Design to Develop Energy-Efficient Underwater Sensor Networks. Sensors, 12(6), 6837-6856. doi:10.3390/s120606837Shin, S.-Y., & Park, S.-H. (2011). A Cost Effective Block Framing Scheme for Underwater Communication. Sensors, 11(12), 11717-11735. doi:10.3390/s111211717Kim, Y., & Park, S.-H. (2011). A Query Result Merging Scheme for Providing Energy Efficiency in Underwater Sensor Networks. Sensors, 11(12), 11833-11855. doi:10.3390/s111211833Llor, J., & Malumbres, M. P. (2012). Underwater Wireless Sensor Networks: How Do Acoustic Propagation Models Impact the Performance of Higher-Level Protocols? Sensors, 12(2), 1312-1335. doi:10.3390/s120201312Zhang, G., Hovem, J. M., & Dong, H. (2012). Experimental Assessment of Different Receiver Structures for Underwater Acoustic Communications over Multipath Channels. Sensors, 12(2), 2118-2135. doi:10.3390/s120202118Ramezani, H., & Leus, G. (2012). Ranging in an Underwater Medium with Multiple Isogradient Sound Speed Profile Layers. Sensors, 12(3), 2996-3017. doi:10.3390/s120302996Lloret, J., Sendra, S., Ardid, M., & Rodrigues, J. J. P. C. (2012). Underwater Wireless Sensor Communications in the 2.4 GHz ISM Frequency Band. Sensors, 12(4), 4237-4264. doi:10.3390/s120404237Gao, M., Foh, C. H., & Cai, J. (2012). On the Selection of Transmission Range in Underwater Acoustic Sensor Networks. Sensors, 12(4), 4715-4729. doi:10.3390/s120404715Gómez, J. V., Sandnes, F. E., & Fernández, B. (2012). Sunlight Intensity Based Global Positioning System for Near-Surface Underwater Sensors. Sensors, 12(2), 1930-1949. doi:10.3390/s120201930Han, G., Jiang, J., Shu, L., Xu, Y., & Wang, F. (2012). Localization Algorithms of Underwater Wireless Sensor Networks: A Survey. Sensors, 12(2), 2026-2061. doi:10.3390/s120202026Moradi, M., Rezazadeh, J., & Ismail, A. S. (2012). A Reverse Localization Scheme for Underwater Acoustic Sensor Networks. Sensors, 12(4), 4352-4380. doi:10.3390/s120404352Lee, S., & Kim, K. (2012). Localization with a Mobile Beacon in Underwater Acoustic Sensor Networks. Sensors, 12(5), 5486-5501. doi:10.3390/s120505486Mohamed, N., Jawhar, I., Al-Jaroodi, J., & Zhang, L. (2011). Sensor Network Architectures for Monitoring Underwater Pipelines. Sensors, 11(11), 10738-10764. doi:10.3390/s111110738Macias, E., Suarez, A., Chiti, F., Sacco, A., & Fantacci, R. (2011). A Hierarchical Communication Architecture for Oceanic Surveillance Applications. Sensors, 11(12), 11343-11356. doi:10.3390/s111211343Zhang, S., Yu, J., Zhang, A., Yang, L., & Shu, Y. (2012). Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation. Sensors, 12(1), 373-390. doi:10.3390/s120100373Climent, S., Capella, J. V., Meratnia, N., & Serrano, J. J. (2012). Underwater Sensor Networks: A New Energy Efficient and Robust Architecture. Sensors, 12(1), 704-731. doi:10.3390/s120100704Min, H., Cho, Y., & Heo, J. (2012). Enhancing the Reliability of Head Nodes in Underwater Sensor Networks. Sensors, 12(2), 1194-1210. doi:10.3390/s120201194Yoon, S., Azad, A. K., Oh, H., & Kim, S. (2012). AURP: An AUV-Aided Underwater Routing Protocol for Underwater Acoustic Sensor Networks. Sensors, 12(2), 1827-1845. doi:10.3390/s120201827Caiti, A., Calabrò, V., Dini, G., Lo Duca, A., & Munafò, A. (2012). Secure Cooperation of Autonomous Mobile Sensors Using an Underwater Acoustic Network. Sensors, 12(2), 1967-1989. doi:10.3390/s120201967Wu, H., Chen, M., & Guan, X. (2012). A Network Coding Based Routing Protocol for Underwater Sensor Networks. Sensors, 12(4), 4559-4577. doi:10.3390/s120404559Navarro, G., Huertas, I. E., Costas, E., Flecha, S., Díez-Minguito, M., Caballero, I., … Ruiz, J. (2012). Use of a Real-Time Remote Monitoring Network (RTRM) to Characterize the Guadalquivir Estuary (Spain). Sensors, 12(2), 1398-1421. doi:10.3390/s120201398Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s12020146

Redes autónomas e inteligentes para la monitorización de variables ambientales

El entendimiento de nuestro entorno, ya sea urbano o natural, es un tema de constante interés en la sociedad, tanto por razones de mejora de calidad de vida como preservación ecológica. En las últimas décadas, la tecnología ha sido la principal aliada para lograr este objetivo, siendo uno de los principales contribuyentes las redes de sensores inalámbricos, o WSN por sus siglas en inglés. No obstante, sigue existiendo una fuerte necesidad de monitorización en distintas temáticas, además que los avances tecnológicos recientes permiten profundizar en el conocimiento en algunas áreas de estudio. En este sentido, este trabajo pretende evaluar la tecnología de WSN reciente con el fin de diseñar y desarrollar sistemas que aporten soluciones a problemáticas reales. Por consiguiente, con el conocimiento obtenido a partir de lo anterior, se busca también contribuir a las WSN en un sentido científico literario. Dicho lo anterior, la presente tesis realiza aportaciones en dos campos: el tecnológico y el metodológico. Desde una perspectiva técnica, se presenta la implementación de un sistema autónomo para monitorización en viviendas y un sistema de monitorización no supervisado para zonas ecológicas marinas protegidas. El primero busca cubrir una necesidad de estimación del consumo energético-térmico de los sistemas de calefacción, con el cual poder gestionar de mejor manera este recurso. Para ello se desarrolló el prototipo de un nodo sensor WiFi de bajo consumo energético, capaz de sustentar su demanda de potencia con una etapa de energy harvesting termoeléctrico. Se utilizó este enfoque para poder ofrecer una solución intuitiva con poca interacción por parte de los usuarios. Con respecto al segundo, se pretende proveer una alternativa a los sistemas de monitorización de líneas costeras, donde se busca realizar análisis de corrientes marinas superficiales y variables físicas del entorno. Para este desarrollo fue necesario que el sistema pudiese ser desplegado de la manera más sencilla posible, minimizando el impacto en el entorno dada su clasificación como parque nacional protegido. Por estos motivos se diseñó, desarrolló e implementó una red de boyas de deriva asistida por dron, donde las primeras actuaban como nodos sensores y el dron ejercía como recolector de datos remoto, utilizando un protocolo de comunicaciones inalámbrico basado en la modulación LoRa.En tema de aportaciones metodológicas, se realizó una recopilación literaria de métricas para el análisis, selección y diseño de una WSN, con el afán de definir el impacto que estas presentan en dicha labor. Esto a su vez propició el desarrollo de una propuesta de metodología aplicable a nuevas implementaciones o sistemas activos con posibles mejoras. La metodología se realizó con el objetivo de proveer una serie de directrices claras al momento de diseñar una WSN, buscando también cubrir los aspectos más relevantes de estas mismas, es decir, la parte de hardware, red y requerimientos de una aplicación. Aunado a lo anterior, se ejemplifica el uso de dicha metodología, aplicada a tres escenarios tecnológicos distintos, para demostrar la relevancia de un diseño apropiado de una WSN.<br /

NeBula: TEAM CoSTAR’s robotic autonomy solution that won phase II of DARPA subterranean challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR’s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version

Command and Control Systems for Search and Rescue Robots

The novel application of unmanned systems in the domain of humanitarian Search and Rescue (SAR) operations has created a need to develop specific multi-Robot Command and Control (RC2) systems. This societal application of robotics requires human-robot interfaces for controlling a large fleet of heterogeneous robots deployed in multiple domains of operation (ground, aerial and marine). This chapter provides an overview of the Command, Control and Intelligence (C2I) system developed within the scope of Integrated Components for Assisted Rescue and Unmanned Search operations (ICARUS). The life cycle of the system begins with a description of use cases and the deployment scenarios in collaboration with SAR teams as end-users. This is followed by an illustration of the system design and architecture, core technologies used in implementing the C2I, iterative integration phases with field deployments for evaluating and improving the system. The main subcomponents consist of a central Mission Planning and Coordination System (MPCS), field Robot Command and Control (RC2) subsystems with a portable force-feedback exoskeleton interface for robot arm tele-manipulation and field mobile devices. The distribution of these C2I subsystems with their communication links for unmanned SAR operations is described in detail. Field demonstrations of the C2I system with SAR personnel assisted by unmanned systems provide an outlook for implementing such systems into mainstream SAR operations in the future

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)