Abstract
The Biot-Granier (Gbt) is a new thermal dissipation-based sap flow measurement methodology, comprising sensors, data management and automatic data processing. It relies on the conventional Granier (Gcv) methodology upgraded with a modified Granier sensor set, as well as on an algorithm to measure the absolute temperatures in the two observation points and perform the Biot number approach. The work described herein addresses the construction details of the Gbt sensors and the characterization of the overall performance of the Gbt method after comparison with a commercial sap flow sensor and independent data (i.e., volumetric water content, vapor pressure deficit and eddy covariance technique). Its performance was evaluated in three trials: potted olive trees in a greenhouse and two vineyards. The trial with olive trees in a greenhouse showed that the transpiration measures provided by the Gbt sensors showed better agreement with the gravimetric approach, compared to those provided by the Gcv sensors. These tended to overestimate sap flow rates as much as 4 times, while Gbt sensors overestimated gravimetric values 1.5 times. The adjustments based on the Biot equations obtained with Gbt sensors contribute to reduce the overestimates yielded by the conventional approach. On the other hand, the heating capacity of the Gbt sensor provided a minimum of around 7 degrees C and maximum about 9 degrees C, contrasting with a minimum around 6 degrees C and a maximum of 12 degrees C given by the Gcv sensors. The positioning of the temperature sensor on the tip of the sap flow needle proposed in the Gbt sensors, closer to the sap measurement spot, allow to capture sap induced temperature variations more accurately. This explains the higher resolution and sensitivity of the Gbt sensor. Overall, the alternative Biot approach showed a significant improvement in sap flow estimations, contributing to adjust the Granier sap flow index, a vulnerability of that methodology.

Abstract
The present work aims at developing a smart dental implant meant to restore the proprioceptive control of the masticatory muscle activity, in consequence of the loss of natural teeth. When periodontal afferent information is not available, the control of the occlusal forces is impaired and the capacity of regulating the masticatory force on a certain tooth or teeth is affected. The active implant being proposed detects the force exerted on teeth and proportionally generates stimuli to send that information to the brain in order to restore the neurobiological mechanisms associated to the masticatory sensory-motor function. After the description of the physiological and biomechanical aspects related to the loss of teeth and masticatory function, details are provided on the force sensing, processing and stimuli generation circuits included in the active implant being proposed. Preliminary simulation results that illustrate the implant functionality are presented.

Abstract
Natural teeth eventually fall out as one becomes older, making it more difficult chewing, speaking and get a reference plane for the body postural equilibrium. To minimize the problem, the missing teeth are eventually replaced by implants that restore the referred functions but miss the sensing of the applied force. As a consequence, the masticatory forces become erratic as the brain receives no feedback (or inaccurate) sensing information. The present work aims at developing a preliminary prototype of a smart dental implant meant to restore the proprioceptive control of the masticatory and chewing muscle activity. After the description of the physiological and biomechanical aspects related to tooth loss, details are provided on the force sensing and electrical stimulation provided by the active implant being proposed. Simulation results obtained with the development tool of the GreenPAK programmable chip being used are included.

Abstract
The aim of this chapter focuses on featuring firmed IoT architecture paradigms and advocating, knowingly in concrete use cases, the combined use of such architecture categories. It is common knowledge that the growing demand for embedded processing, interconnection, and integration facilities in everyday objects is being driven by a multitude of IoT projects. The smart cities, smart agriculture, manufacturing, and industrial automation areas are some of the most important application grounds. Equally important is the medical sector where specially framed in this publication, the personal home healthcare scenarios gain enormous relevance due to the potential of IoT technology application. It is also becoming clear that the IoT-trending efforts are compelling researchers into the concurrent combination of multiple IoT-computing architecture types or paradigms, to know: wide-range cloud-computing architectures, local-spread fog-computing architectures, and spottily scattered edge-computing architectures. This chapter focuses on identifying the major goals and benefits of each of these architectures classes; describing the relevant state of the art projects, which apply such architecture categories in home healthcare settings; and finally, pinpointing our own experience with home e-health demonstrative use case scenarios, where the benefits of using each of these architecture types become evident, and the concurrent combination of such IoT architectures inevitable. © 2021 Elsevier Inc.

Abstract
This paper presents an efficient, battery-powered, low-cost, and context-aware IoT edge computing solution tailored for monitoring a real enterprise cooking oil collecting infrastructure. The presented IoT solution allows the collecting enterprise to monitor the amount of oil deposited in specific barrels, deployed country-wide around several partner restaurants. The paper focuses on the specification, implementation, deployment and testing of ESP32/ESP8266-based end-node components deployed as an edge computing monitoring infrastructure. The achieved low-cost solution guarantees more than a year of battery life, reliable data communication, and enables automatic over-the-air end-node updates. The open-source software libraries developed for this project are shared with the community and may be applied in scenarios with similar requirements. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
Cloud computing is omnipresent and plays an important role in today’s world of Internet of Things (IoT). Several IoT devices and their applications already run and communicate through the cloud, easing the configuration burden for their users. With the expected exponential growth on the number of connected IoT devices this centralized approach raises latency, privacy and scalability concerns. This paper proposes the use of fog computing to overcome those concerns. It presents an architecture intended to distribute the communication, computation and storage loads to small gateways, close to the edge of the network, in charge of a group of IoT devices. This approach saves battery on end devices, enables local sensor fusion and fast response to urgent situations while improving user privacy. This architecture was implemented and tested on a project to monitor the level of used cooking oil, stored in barrels, in some restaurants where low cost, battery powered end devices are periodically reporting sensor data. Results show a 93% improvement in end device battery life (by reducing their communication time) and a 75% saving on cloud storage (by processing raw data on the fog device). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.