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

Architecture and communication protocol to monitor and control water quality and irrigation in agricultural environments

[ES] La introducción de soluciones tecnológicas en la agricultura permite reducir el uso de recursos y aumentar la producción de los cultivos. Además, la calidad del agua de regadío se puede monitorizar para asegurar la seguridad de los productos para el consumo humano. Sin embargo, la localización remota de la mayoría de los campos presenta un problema para proveer de cobertura inalámbrica a los nodos sensores y actuadores desplegados en los campos y los canales de agua para regadío. El trabajo presentado en esta tesis aborda el problema de habilitar la comunicación inalámbrica entre los dispositivos electrónicos desplegados para la monitorización de la calidad del agua y el campo a través de un protocolo de comunicación y arquitectura heterogéneos. La primera parte de esta tesis introduce los sistemas de agricultura de precisión (PA) y la importancia de la monitorización de la calidad del agua y el campo. Asimismo, las tecnologías que permiten la comunicación inalámbrica en sistemas PA y el uso de soluciones alternativas como el internet de las cosas bajo tierra (IoUT) y los vehículos aéreos no tripulados (UAV) se introducen también. Después, se realiza un análisis en profundidad del estado del arte respecto a los sensores para la monitorización del agua, el campo y las condiciones meteorológicas, así como sobre las tecnologías inalámbricas más empleadas en PA. Además, las tendencias actuales y los desafíos de los sistemas de internet de las cosas (IoT) para regadío, incluyendo las soluciones alternativas introducidas anteriormente, han sido abordados en detalle. A continuación, se presenta la arquitectura propuesta para el sistema, la cual incluye las áreas de interés para las actividades monitorización que incluye las áreas de los canales y el campo. A su vez, la descripción y los algoritmos de operación de los nodos sensores contemplados para cada área son proporcionados. El siguiente capítulo detalla el protocolo de comunicación heterogéneo propuesto, incluyendo los mensajes y alertas del sistema. Adicionalmente, se presenta una nueva topología de árbol para redes híbridas LoRa/WiFi multisalto. Las funcionalidades específicas adicionales concebidas para la arquitectura propuesta están descritas en el siguiente capítulo. Éstas incluyen algoritmos de agregación de datos para la topología propuesta, un esquema de las amenazas de seguridad para los sistemas PA, algoritmos de ahorro de energía y tolerancia a fallos, comunicación bajo tierra para IoUT y el uso de drones para adquisición de datos. Después, los resultados de las simulaciones para las soluciones propuestas anteriormente son presentados. Finalmente, se tratan las pruebas realizadas en entornos reales para el protocolo heterogéneo presentado, las diferentes estrategias de despliegue de los nodos empleados, el consumo energético y la función de cuantificación de fruta. Estas pruebas demuestran la validez de la arquitectura y protocolo de comunicación heterogéneos que se han propuesto.[CA] La introducció de solucions tecnològiques en l'agricultura permet reduir l'ús de recursos i augmentar la producció dels cultius. A més, la qualitat de l'aigua de regadiu es pot monitoritzar per assegurar la qualitat dels productes per al consum humà. No obstant això, la localització remota de la majoria dels camps presenta un problema per a proveir de cobertura sense fils als nodes sensors i actuadors desplegats als camps i els canals d'aigua per a regadiu. El treball presentat en aquesta tesi tracta el problema d'habilitar la comunicació sense fils entre els dispositius electrònics desplegats per a la monitorització de la qualitat de l'aigua i el camp a través d'un protocol de comunicació i arquitectura heterogenis. La primera part d'aquesta tesi introdueix els sistemes d'agricultura de precisió (PA) i la importància de la monitorització de la qualitat de l'aigua i el camp. Així mateix, també s'introdueixen les tecnologies que permeten la comunicació sense fils en sistemes PA i l'ús de solucions alternatives com l'Internet de les coses sota terra (IoUT) i els vehicles aeris no tripulats (UAV). Després, es realitza una anàlisi en profunditat de l'estat de l'art respecte als sensors per a la monitorització de l'aigua, el camp i les condicions meteorològiques, així com sobre les tecnologies sense fils més emprades en PA. S'aborden les tendències actuals i els reptes dels sistemes d'internet de les coses (IoT) per a regadiu, incloent les solucions alternatives introduïdes anteriorment. A continuació, es presenta l'arquitectura proposada per al sistema, on s'inclouen les àrees d'interès per a les activitats monitorització en els canals i el camp. Finalment, es proporciona la descripció i els algoritmes d'operació dels nodes sensors contemplats per a cada àrea. El següent capítol detalla el protocol de comunicació heterogeni proposat, així como el disseny del missatges i alertes que el sistema proposa. A més, es presenta una nova topologia d'arbre per a xarxes híbrides Lora/WiFi multi-salt. Les funcionalitats específiques addicionals concebudes per l'arquitectura proposada estan descrites en el següent capítol. Aquestes inclouen algoritmes d'agregació de dades per a la topologia proposta, un esquema de les alertes de seguretat per als sistemes PA, algoritmes d'estalvi d'energia i tolerància a fallades, comunicació per a IoUT i l'ús de drons per a adquisició de dades. Després, es presenten els resultats de les simulacions per a les solucions proposades. Finalment, es duen a terme les proves en entorns reals per al protocol heterogeni dissenyat. A més s'expliquen les diferents estratègies de desplegament dels nodes empleats, el consum energètic, així com, la funció de quantificació de fruita. Els resultats d'aquetes proves demostren la validesa de l'arquitectura i protocol de comunicació heterogenis propost en aquesta tesi.[EN] The introduction of technological solutions in agriculture allows reducing the use of resources and increasing the production of the crops. Furthermore, the quality of the water for irrigation can be monitored to ensure the safety of the produce for human consumption. However, the remote location of most fields presents a problem for providing wireless coverage to the sensing nodes and actuators deployed on the fields and the irrigation water canals. The work presented in this thesis addresses the problem of enabling wireless communication among the electronic devices deployed for water quality and field monitoring through a heterogeneous communication protocol and architecture. The first part of the dissertation introduces Precision Agriculture (PA) systems and the importance of water quality and field monitoring. In addition, the technologies that enable wireless communication in PA systems and the use of alternative solutions such as Internet of Underground Things (IoUT) and Unmanned Aerial Vehicles (UAV) are introduced as well. Then, an in-depth analysis on the state of the art regarding the sensors for water, field and meteorology monitoring and the most utilized wireless technologies in PA is performed. Furthermore, the current trends and challenges for Internet of Things (IoT) irrigation systems, including the alternate solutions previously introduced, have been discussed in detail. Then, the architecture for the proposed system is presented, which includes the areas of interest for the monitoring activities comprised of the canal and field areas. Moreover, the description and operation algorithms of the sensor nodes contemplated for each area is provided. The next chapter details the proposed heterogeneous communication protocol including the messages and alerts of the system. Additionally, a new tree topology for hybrid LoRa/WiFi multi-hop networks is presented. The specific additional functionalities intended for the proposed architecture are described in the following chapter. It includes data aggregation algorithms for the proposed topology, an overview on the security threats of PA systems, energy-saving and fault-tolerance algorithms, underground communication for IoUT, and the use of drones for data acquisition. Then, the simulation results for the solutions previously proposed are presented. Finally, the tests performed in real environments for the presented heterogeneous protocol, the different deployment strategies for the utilized nodes, the energy consumption, and a functionality for fruit quantification are discussed. These tests demonstrate the validity of the proposed heterogeneous architecture and communication protocol.García García, L. (2021). Architecture and communication protocol to monitor and control water quality and irrigation in agricultural environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17422

Congestion Prediction in Internet of Things Network using Temporal Convolutional Network A Centralized Approach

The unprecedented ballooning of network traffic flow, specifically, Internet of Things (IoT) network traffic, has big stressed of congestion on todays Internet. Non-recurring network traffic flow may be caused by temporary disruptions, such as packet drop, poor quality of services, delay, etc. Hence, the network traffic flow estimation is important in IoT networks to predict congestion. As the data in IoT networks is collected from a large number of diversified devices which have unlike format of data and also manifest complex correlations, so the generated data is heterogeneous and nonlinear in nature. Conventional machine learning approaches unable to deal with nonlinear datasets and suffer from misclassification of real network traffic due to overfitting. Therefore, it also becomes really hard for conventional machine learning tools like shallow neural networks to predict the congestion accurately. Accuracy of congestion prediction algorithms play an important role to control the congestion by regulating the send rate of the source. Various deeplearning methods (LSTM, CNN, GRU, etc.) are considered in designing network traffic flow predictors, which have shown promising results. In this work, we propose a novel congestion predictor for IoT, that uses Temporal Convolutional Network (TCN). Furthermore, we use Taguchi method to optimize the TCN model that reduces the number of runs of the experiments. We compare TCN with other four deep learning-based models concerning Mean Absolute Error (MAE) and Mean Relative Error (MRE). The experimental results show that TCN based deep learning framework achieves improved performance with 95.52% accuracy in predicting network congestion. Further, we design the Home IoT network testbed to capture the real network traffic flows as no standard dataset is available

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

none5noNatural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.openEsposito M.; Palma L.; Belli A.; Sabbatini L.; Pierleoni P.Esposito, M.; Palma, L.; Belli, A.; Sabbatini, L.; Pierleoni, P

Digital-Based Analog Processing in Nanoscale CMOS ICs for IoT Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Redes de sensores WiFi de bajo consumo

Las redes de sensores inalámbricas o WSN, por sus siglas en inglés, demandan metodologías de diseño de baja potencia que puedan manejar efectivamente la cantidad de energía provista para prologar la longevidad de los sistemas tanto como sea posible. Nodos alimentados por baterías son poco adecuadas en algunos casos, puesto que el mantenimiento, reemplazo y otros factores podrían hacerlo inviable, lo cual presenta la oportunidad del uso de energy harvesting como una opción factible. Las comunicaciones se presentan como un área de diseño crucial en las WSN, ya que estas se presentan como el bloque más demandante energéticamente. Es común encontrar diseños que adoptan estándares basados en el 802.15.4, como lo es ZigBee, los cuales fueron desarrollados con el afán de satisfacer la demanda de dispositivos electrónicos de bajo consumo. Debido a esto, es difícil pensar que un protocolo como WiFi, que tiene una demanda alta de energía, pueda ser una implementación viable para un sistema de este tipo. Sin embargo, con los últimos progresos en los estándares de este protocolo, así como estrategias y dispositivos de bajo consumo, es posible lograr diseños que permiten utilizar la ya tan conocida y propagada red de WiFi para WSN. En el presente trabajo se realizará un análisis a fondo sobre las particularidades de WiFi, enfocándose en el consumo energético y los escenarios en los que presenta beneficios a las WSN. Para esto se concretará un modelo de nodo sensor, donde se definirán los procesos necesarios para un sensado y transmisión de datos, tomando en cuenta los tiempos requeridos para dichas actividades. Posteriormente se describirá el hardware utilizado para la validación, medición y comparación de los protocolos WiFi y ZigBee, éste último siendo punto de referencia/comparación para redes de sensores inalámbricos. Ambos protocolos serán estudiados para conocer sus límites de implementación y en el caso de WiFi, como optimizarlo para prolongar su longevidad. Adicionalmente se realizará una simulación de un circuito de energy harvesting termoeléctrico el cual alimentará el nodo sensor con el afán de definir la viabilidad de este tipo de fuentes de alimentación en WSN, específicamente con comunicaciones WiFi

Internet of Robotic Things Intelligent Connectivity and Platforms

The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.publishedVersio

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

Energy harvesting system design and optimization for wireless sensor networks

Wireless sensor networks (WSN) are becoming widely adopted for many applications including complicated tasks like building energy management. However, one major concern for WSN technologies is the short lifetime and high maintenance cost due to the limited battery energy. One of the solutions is to scavenge ambient energy, which is then rectified to power the WSN. The objective of this thesis was to investigate the feasibility of an ultra-low energy consumption power management system suitable for harvesting sub-mW photovoltaic and thermoelectric energy to power WSNs. To achieve this goal, energy harvesting system architectures have been analyzed. Detailed analysis of energy storage units (ESU) have led to an innovative ESU solution for the target applications. Battery-less, long-lifetime ESU and its associated power management circuitry, including fast-charge circuit, self-start circuit, output voltage regulation circuit and hybrid ESU, using a combination of super-capacitor and thin film battery, were developed to achieve continuous operation of energy harvester. Low start-up voltage DC/DC converters have been developed for 1mW level thermoelectric energy harvesting. The novel method of altering thermoelectric generator (TEG) configuration in order to match impedance has been verified in this work. Novel maximum power point tracking (MPPT) circuits, exploring the fractional open circuit voltage method, were particularly developed to suit the sub-1mW photovoltaic energy harvesting applications. The MPPT energy model has been developed and verified against both SPICE simulation and implemented prototypes. Both indoor light and thermoelectric energy harvesting methods proposed in this thesis have been implemented into prototype devices. The improved indoor light energy harvester prototype demonstrates 81% MPPT conversion efficiency with 0.5mW input power. This important improvement makes light energy harvesting from small energy sources (i.e. credit card size solar panel in 500lux indoor lighting conditions) a feasible approach. The 50mm × 54mm thermoelectric energy harvester prototype generates 0.95mW when placed on a 60oC heat source with 28% conversion efficiency. Both prototypes can be used to continuously power WSN for building energy management applications in typical office building environment. In addition to the hardware development, a comprehensive system energy model has been developed. This system energy model not only can be used to predict the available and consumed energy based on real-world ambient conditions, but also can be employed to optimize the system design and configuration. This energy model has been verified by indoor photovoltaic energy harvesting system prototypes in long-term deployed experiments