Coastal marine ecosystems face mounting pressures from anthropogenic activities and climate change, necessitating advanced monitoring and modeling approaches for effective management. This paper pioneers the development of a Marine Digital Twin Platform aimed at modeling the Mar Menor Coastal Lagoon Ecosystem in the Region of Murcia. The platform leverages Artificial Intelligence to emulate complex hydrological and ecological models, facilitating the simulation of what-if scenarios to predict ecosystem responses to various stressors. We integrate diverse datasets from public sources to construct a comprehensive digital representation of the lagoon’s dynamics. The platform’s modular design enables real-time stakeholder engagement and informed decision-making in marine management. Our work contributes to the ongoing discourse on advancing marine science through innovative digital twin technologies.

Efficient water management in agriculture is a critical challenge to ensure sustainability, especially in water-constrained regions. Advanced technologies, such as FloraPulse microtensiometers, offer an innovative solution by enabling accurate, real-time monitoring of plant water status by measuring Trunk Water Potential (TWP). This information is essential for assessing water stress and optimising irrigation decisions. This work is the first stage of an overall objective to predict the optimal timing and amount of irrigation. It is important to note that the current models were trained and tested using data from the 2023 season, when all trees were irrigated to 100% of E T c . In this first phase, predictive models are developed that uses advanced Machine Learning (ML) and Deep Learning (DL) techniques to estimate TWP, providing a key tool to anticipate plant water stress as a function of climatic and edaphic variables. According to the initial comparison where ML models (RF, SVR and KNN) and DL model (LSTM) were implemented without using the previous TWP values as input to make the next hour prediction, the LSTM model outperformed the ML models, achieving average R 2 =0.75, MAE=0.19 MPa and CVRMSE=19.22%. Using the lagged TWP values, the LSTM model showed a significant improvement, with average R 2 =0.98, MAE=0.036MPa, and CVRMSE=4%. In addition, LSTM models with 6, 12 and 24 h prediction horizons were developed, all with R 2 greater than 0.89, MAE less than 0.12 MPa and CVRMSE less than 17%. These results allow the precise prediction of future TWP values and thus offer the design of more accurate and efficient irrigation systems. • Trunk Water Potential (TWP) data were collected using the FloraPulse microtensiometer sensor. • Machine Learning (ML) and Deep Learning (DL) models were used to predict the TWP values. • Comparisons were made between ML (RF, SVR, and KNN) and DL (LSTM) models. • LSTM models of 1, 6, 12, and 24 h prediction horizons were implemented. • Results reveal great potential for TWP prediction and irrigation scheduling.

This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.

Internet of Things (IoT) is a vision towards Future Internet where “things” are provided with enough intelligence to communicate with each other without the human intervention. In the near future, the number of IoT-enabled nodes is expected to grow substantially, therefore the heterogeneous nature of implementations demands effective IoT deployments that ensure proper interoperability and reliability of network infrastructures. In this line, we present a holistic IoT-based platform to gather and analyze information, and propose concrete actions according to different goals in smart environments. This platform bases on the optimal integration and use of data collected from a plethora of different sources. It has been deployed in a real IoT context framed in the smart buildings field. For its validation and evaluation, a reference scenario for indoor energy efficiency and comfort has been considered, where a set of tests have been carried out to validate the building management techniques implemented to maintain user comfort while assuring a good energy balance.

Continuous and wireless transmission of vital signs for personalized healthcare is gaining a great deal of interest from Ambient Assisted Living solutions. Personalized healthcare capabilities are limited to the patient data available, which is usually dynamic and incomplete. For that reason, regular monitoring of patients with the aim of offering a suitable analysis of patient evolution is required. Continuous monitoring requires the integration of wireless communication technologies and embedded systems into wearable and portable monitoring systems. In addition, a user interface intuitive is also required, which is easy to use and understand by the patients and caregivers. This work proposes a solution such as Near Field Communication (NFC) for personalized healthcare based on the Internet of Things. NFC is a technology integrated in smart phones that provides capabilities for identification of devices/sensors, and presents ubiquitous communication capabilities between the sensor and the device. NFC also presents challenges in terms of the performance and efficiency of data transmission, due to the constrained resources and capabilities of ubiquitous devices and the latency introduced by the NFC technology. These challenges are intrinsic to NFC since it was originally considered only for simple identification and not for continuous data transmission. In this context, this paper presents a novel monitoring system for continuous data transmission from a set of clinical devices based on NFC which has been optimized in order to make communications feasible. This novel monitoring system is also composed by a set of participatory sensing applications to support caregivers and for patients to self-monitor and self-manage their health status wirelessly. A technical evaluation based on latencies associated with use of NFC for continuous monitoring and an evaluation of usability by a group of elderly users and their caregivers, which studies the interactions between the users and the system, are demonstrated.

Mobility management is a desired feature for the emerging Internet of Things (IoT). Mobility aware solutions increase the connectivity and enhance adaptability to changes of the location and infrastructure. IoT is enabling a new generation of dynamic ecosystems in environments such as smart cities and hospitals. Dynamic ecosystems require ubiquitous access to Internet, seamless handover, flexible roaming policies, and an interoperable mobility protocol with existing Internet infrastructure. These features are challenges for IoT devices, which are usually constrained devices with low memory, processing, communication and energy capabilities. This work has, on the one hand, analysed suitability of Mobile IPv6 and IPSec for constrained devices, and on the other hand, analysed, designed, developed and evaluated a lightweight version of Mobile IPv6 and IPSec. The proposed solution of lightweight Mobile IPv6 with IPSec is aware of the requirements of the IoT and presents the best solution for dynamic ecosystems in terms of efficiency and security adapted to IoT-devices capabilities. This presents concerns in terms of higher overhead and memory requirements. But, it is proofed and concluded that even when higher memory is required and major overhead is presented, the integration of Mobile IPv6 and IPSec for constrained devices is feasible.

Communication and information access defines the basis to reach a personalized health end-to-end framework. Personalized health capability is limited to the available data from the patient. The data is usually dynamic and incomplete. Therefore, it presents a critical issue for mining, analysis and trending. For that reason, this work presents an interconnection framework for mobile Health (mHealth) based on the Internet of Things. It makes continuous and remote vital sign monitoring feasible and introduces technological innovations for empowering health monitors and patient devices with Internet capabilities. It also allows patient monitoring and supervision by remote centers, and personal platforms such as tablets. In terms of hardware it offers a gateway and a personal clinical device used for the wireless transmission of continuous vital signs through 6LoWPAN, and patient identification through RFID. In terms of software, this interconnection framework presents a novel protocol, called YOAPY, for an efficient, secure, and scalable integration of the sensors deployed in the patient's personal environment. This paper presents the architecture and evaluates its capability to provide continuous monitoring, ubiquitous connectivity, extended device integration, reliability, and security and privacy support. The proposed interconnection framework and the proposed protocol for the sensors have been exhaustively evaluated in the framework of the AIRE project, which is focused on patients with breathing problem. This evaluates for the proposed protocol the data aggregation mechanism level, Round-Trip delay Time, impact of the distance, and the impact of the security. It has been concluded that secure continuous monitoring is feasible with the use of the proposed {YOAPY}} aggregation mechanisms and the capabilities from the proposed interconnection framework.

Personalized health capability is limited to the available data from the patient, which is usually dynamic and incomplete. Therefore, it is presenting a critical issue for knowledge mining, analysis and trending. For that reason, this work presents a knowledge acquisition and management platform based on the Internet of Things (IoT). IoT is presenting the capability to connect any object to Internet. These Smart Objects are providing an enormous quantity of data. This work is focused on the personal and mobile health areas, where the integration of clinical devices in the patient's environment, this will enable new services with capabilities to predict health anomalies in real time, send alerts, reminders and offer an enriched feedback to the patient. This feedback will help and motivate to the patients with the treatments adherence and compliance and to follow a healthy lifestyle. In order to reach these services and feedback capabilities, it is required a full integration of the clinical devices and efficient processing of the collected data. The presented knowledge acquisition and management architecture is composed of, on the one hand, a gateway and a personal clinical device used for the integration of clinical devices, wireless transmission of continuous vital signs through 6LoWPAN, and patient identification through RFID. On the other hand, it is complemented with a data model and pre-processing module called YOAPY, which analyses the data from the sensors at the personal devices level. This offers an enriched data to the knowledge-based systems, and this also aggregate the data acquired in order to improve the performance of the communications to the constrains from the Smart Objects in aspects such as low bandwidth, frame size and power consumption. This architecture is being evaluated with an extended set of sensors required for patients with breathing problem in the framework of the AIRE project, it has evaluated the capabilities to provide knowledge acquisition and management from continuous monitored vital signs, the capabilities for diagnosis and detection of anomalies, and finally the support for security and privacy.

Recently the problem of providing effective and appropriate healthcare to elderly and disable people is an important field in relative to the aging of population problems. The objective of information and communication technologies (ICT) is to focus on the new technologies the medical environments, so that it can provide management to accelerate and improve the clinical process. Our contribution is to introduce an approach based on Internet of things (IoT) in medical environments to achieve a global connectivity with the patient, sensors and everything around it. The main goal of this globality feature is to provide a context awareness to make the patient's life easier and the clinical process more effective. To achieve this approach, firstly has been developed an architecture which has been designed to offer great potential and flexibility of communications, monitoring and control. This architecture includes several advanced communication technologies; among them are 6LoWPAN and RFID/NFC, which are the basis of the IoT. Moreover the research deal with the problems related to the mobility and security that happens when IoT is applied in medical environments. The mobility issue requires developing a protocol over 6LoWPAN network to be carried out in sensor networks with high specification related with low power consumption and capacity. While in the RFID/NFC technologies need to support secure communications, our proposal is to introduce a set of security techniques and cryptographic SIM card to authenticate, encrypt and sign the communications with medical devices. The preliminary results showed a reduction of time in the handover process with the protocol for mobility defined, by omitting the stages of addressing and simplifying the MIPv6 protocol. In addition to increase the security in the communications carried out by NFC devices enhanced with the inclusion of cryptographic SIM card.

The prediction of lane changes has been proven to be useful for collision avoidance support in road vehicles. This paper proposes an interactive multiple model (IMM)-based method for predicting lane changes in highways. The sensor unit consists of a set of low-cost Global Positioning System/inertial measurement unit (GPS/IMU) sensors and an odometry captor for collecting velocity measurements. Extended Kalman filters (EKFs) running in parallel and integrated by an IMM-based algorithm provide positioning and maneuver predictions to the user. The maneuver states Change Lane (CL) and Keep Lane (KL) are defined by two models that describe different dynamics. Different model sets have been studied to meet the needs of the IMM-based algorithm. Real trials in highway scenarios show the capability of the system to predict lane changes in straight and curved road stretches with very short latency times.

User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.

In this paper we describe the navigation and planning of the i-Fork system, a flexible AGV intended to operate in partially structured warehouses where frequent floor plant layout modifications occur. This is achieved by using a combination of topological and grid maps in such a way that the operator work in layout modification procedures is greatly reduced. The system is currently working in an agricultural company with great success.

This paper outlines the problem of a mobile robot navigating in an unknown environment. This is solved in a two step process by the application of two navigation techniques. In the first step a fuzzy grid map is applied for the map-building problem, and an A* search method is applied for the path-planning problem. In the second step a fuzzy segments map is applied for the map-building and localisation problems. Some results in both simulator and real robot are presented.

Map building and position estimation are basic tasks in mobile robot navigation with path planning. A method to generate a global map of the vehicle work environment using ultrasonic sensors is developed in this paper. Depending on the physical properties of the walls that form the room where the robot is navigating, sonar sensors show different behaviours. A neural network is utilized to interpret the range readings of ultrasonic sensors in the different environments. A local map composed of squared cells is formed through the neural network that gives the occupancy probabilities for each cell. Finally, a global map is built achieving integration of different views of the environment using Bayes' rule. Results of the method implementation in the construction in a specular environment as well as in rough wall environments are shown in this paper.