The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence.

Ultra-wideband (UWB) is a popular technology for indoor positioning systems (IPSs) due to its robust signaling in harsh environments, through-wall propagation, and high-resolution ranging. However, the propagation of wireless signals in indoor environments is affected by nonline-of-sight (NLOS) conditions, which can lead to positively biased distance estimates. Several research works were carried out in this area, but the NLOS effect caused by the human body shadowing on the ranging performance is not properly addressed in the literature. In this paper, a commercial UWB transceiver is used to assess the impact of the human body shadowing on the ranging accuracy. A set of real-time features is combined with different machine learning algorithms for NLOS identification and mitigation. With a subset of four features, a 0.97 F1-score was obtained for NLOS identification. For NLOS mitigation, with a subset of three features, the residual error follows a Gaussian distribution, has a mean error close to zero, and an STD of 0.67 m.

Sudden Infant Death Syndrome (SIDS) is one of the major causes of death among infants during their sleep. To increase the safety of the infants, we matched different emergent research fields for the development of Baby Night Watch. This Smart Wearable System (SWS), developed under the context of the European Texas Instruments Innovation Challenge (TIIC) 2015, is composed by the following elements: a Wearable IoT Device, a Gateway and the H Medical Interface. The Wearable IoT Device is a wireless sensor node integrated in a Chest Belt, and it has the capacity to monitor the following parameters: body temperature, heart and breathing rates and body position. After a minimal data processing, this set of information is sent to the Gateway, via ZigBee technology, and it is accessible to the user through the H Medical Interface. If a critical event occurs, the device will trigger an alarm, visible and audible in the proximity, and sends a distress message to a mobile application. The Baby Night Watch is an important tool for medical studies, since it allows the visualization of previous physiological data and export it to different types of datasets. Experimental tests have proven that the SWS has the potential to identify situations that could be potentially life-threatening for an infant.

Globally, billions of flying animals undergo seasonal migrations, many of which occur at night. The temporal and spatial scales at which migrations occur and our inability to directly observe these nocturnal movements makes monitoring and characterizing this critical period in migratory animals’ life cycles difficult. Remote sensing, therefore, has played an important role in our understanding of large-scale nocturnal bird migrations. Weather surveillance radar networks in Europe and North America have great potential for long-term low-cost monitoring of bird migration at scales that have previously been impossible to achieve. Such long-term monitoring, however, poses a number of challenges for the ornithological and ecological communities: how does one take advantage of this vast data resource, integrate information across multiple sensors and large spatial and temporal scales, and visually represent the data for interpretation and dissemination, considering the dynamic nature of migration? We assembled an interdisciplinary team of ecologists, meteorologists, computer scientists, and graphic designers to develop two different flow visualizations, which are interactive and open source, in order to create novel representations of broad-front nocturnal bird migration to address a primary impediment to long-term, large-scale nocturnal migration monitoring. We have applied these visualization techniques to mass bird migration events recorded by two different weather surveillance radar networks covering regions in Europe and North America. These applications show the flexibility and portability of such an approach. The visualizations provide an intuitive representation of the scale and dynamics of these complex systems, are easily accessible for a broad interest group, and are biologically insightful. Additionally, they facilitate fundamental ecological research, conservation, mitigation of human–wildlife conflicts, improvement of meteorological products, and public outreach, education, and engagement.

In Smart Wearable Systems (SWS), the wearable devices are powered by batteries with very limited energy available. These emergent systems have strong Quality of Service (QoS) requirements, with focus on reliable communication and low power consumption. This is the scope of the Baby Night Watch, a project developed in the context of the European Texas Instruments Innovation Challenge (TIIC) 2015. This Project consists of a monitoring tool for infants, which matches different emergent research fields. SWSs require energy saving mechanism to reduce the energy wasting during wireless communications. A Transmission Power Control (TPC) mechanism that changes its characteristics according to the scenario of operation, is proposed. It uses sensors to determine the position of the infant and, based on that, predicts the current state of the channel. Other TPC algorithms are implemented and their performance are compared with our novel mechanism. The proposed TPC mechanism outperforms the existing ones in terms of the energy saving.

Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.