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

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects

Enhanced frequency management for automatic HF radio communication systems

The work described in this thesis aims to enhance the frequency management of automatic high frequency (HF) radio communication systems. During the research programme two new frequency management tools were developed; a chirpsounder monitoring tool to provide accuracy enhancement information for propagation prediction programs and an algorithm designed to allow optimisation of signal formats, so that in-band interference is avoided and the overall system throughput rate is increased. Two new HF communication system architectures are presented, which use system design and programming methodologies derived from the fields of artificial intelligence and computer networks.The characteristics of the HF band are presented from a communicator's viewpoint, rather than the generalised, technical approach normally associated with such reviews. The methods employed by current HF communication systems to overcome the inherent time and frequency variability of HF channels are presented in the form of reviews of propagation, natural noise and co-channel interference prediction methods, embedded real-time channel evaluation algorithms and HF communications system architectures. The inadequacies of these current techniques are analysed. The eradication of their shortcomings is the main objective of the work described in the thesis.The short-term inaccuracies associated with current propagation analysis procedures can limit the performance of automatic HF communication systems. An accuracy enhancement methodology is proposed which makes use of measurements made on oblique chirpsounder transmitters. In order to provide accuracy enhancement data, a chirpsounder-based, propagation monitor was constructed. Its implementation and trials are described and methods of using its output to enhance prediction model accuracy are discussed. Ways in which its performance may be improved are detailed.The theory of a technique, termed "template correlation", which provides automatic HF communication systems with signal format adaptation data in order to enable them to avoid in-band interference, is presented. The objective of this work is to enhance the error-free capacity of a channel via adaptation of the signal. The results of computer simulations and laboratory bench trials of template correlation are presented. Enhancements of the technique in the light of the trials results are included.Two proposed design methodologies for automatic HF communication systems are described. The first uses many of the frequency management tools associated with current automatic systems and it combines the information from these using a blackboard-based expert system architecture. The second proposed design is more conceptual than the first. An inductive expert system is employed to produce rules describing the ways in which an automatic HF system should respond to certain path conditions. Examples of how such a system might function are given.The single, most important factor which has enabled the techniques described in this thesis to be feasible is the availability of cheap but powerful microprocessors. Thus the overall philosophy of the work is to improve the performance of automatic HF communication systems via the incorporation of processing power and "intelligent software" into the communication system's terminals

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

Uplink data measurement and analysis for 5G eCPRI radio unit

Abstract. The new 5G mobile network generation aims to enhance the performance of the cellular network in almost every possible aspect, offering higher data rates, lower latencies, and massive number of network connections. Arguably the most important change from LTE are the new RU-BBU split options for 5G promoted by 3GPP and other organizations. Another big conceptual shift introduced with 5G is the open RAN concept, pushed forward by organizations such as the O-RAN alliance. O-RAN aims to standardize the interfaces between different RAN elements in a way that promotes vendor interoperability and lowers the entry barrier for new equipment suppliers. Moreover, the 7-2x split option standardized by O-RAN has risen as the most important option within the different low layer split options. As the fronthaul interface, O-RAN has selected the packet-based eCPRI protocol, which has been designed to be more flexible and dynamic in terms of transport network and data-rates compared to its predecessor CPRI. Due to being a new interface, tools to analyse data from this interface are lacking. In this thesis, a new, Python-based data analysis tool for UL eCPRI data was created for data quality validation purposes from any O-RAN 7-2x functional split based 5G eCPRI radio unit. The main goal for this was to provide concrete KPIs from captured data, including timing offset, signal power level and error vector magnitude. The tool produces visual and text-based outputs that can be used in both manual and automated testing. The tool has enhanced eCPRI UL datapath testing in radio unit integration teams by providing actual quality metrics and enabling test automation.Uplink datamittaukset ja -analyysi 5G eCPRI radiolla. Tiivistelmä. Uusi 5G mobiiliverkkogeneraatio tuo mukanaan parannuksia lähes kaikkiin mobiiliverkon ominaisuuksiin, tarjoten nopeamman datasiirron, pienemmät viiveet ja valtavat laiteverkostot. Luultavasti tärkein muutos LTE teknologiasta ovat 3GPP:n ja muiden organisaatioiden ehdottamat uudet radion ja systeemimoduulin väliset funktionaaliset jakovaihtoehdot. Toinen huomattava muutos 5G:ssä on O-RAN:in ajama avoimen RAN:in konsepti, jonka tarkoituksena on standardisoida verkkolaitteiden väliset rajapinnat niin, että RAN voidaan rakentaa eri valmistajien laitteista, laskien uusien laitevalmistajien kynnystä astua verkkolaitemarkkinoille. O-RAN:n standardisoima 7-2x funktionaalinen jako on noussut tärkeimmäksi alemman tason jakovaihtoehdoista. Fronthaul rajapinnan protokollaksi O-RAN on valinnut pakettitiedonsiirtoon perustuvan eCPRI:n, joka on suunniteltu dynaamisemmaksi ja joustavammaksi datanopeuksien ja lähetysverkon suhteen kuin edeltävä CPRI protokolla. Uutena protokollana, eCPRI rajapinnalle soveltuvia data-analyysityökaluja ei ole juurikaan saatavilla. Tässä työssä luotiin uusi pythonpohjainen data-analyysityökalu UL suunnan eCPRI datalle, jotta datan laatu voidaan määrittää millä tahansa O-RAN 7-2x funktionaaliseen jakoon perustuvalla 5G eCPRI radiolla. Työkalun päätarkoitus on analysoida ja kuvata datan laatua laskemalla datan ajoitusoffsettia, tehotasoa, sekä EVM:ää. Työkalu tuottaa tulokset visuaalisena ja tekstipohjaisena, jotta analyysia voidaan tehdä niin manuaalisessa kuin automaattisessa testauksessa. Työkalun käyttöönotto on tehostanut UL suunnan dataputken testausta radio-integrointitiimeissä, tarjoten datan laatua kuvaavaa metriikkaa sekä mahdollistaen testauksen automatisoinnin

Integration of hybrid networks, AI, Ultra Massive-MIMO, THz frequency, and FBMC modulation toward 6g requirements : A Review

The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( μm -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research