LoRa is an extremely flexible low-power wide-area technology that enables each IoT node to individually adjust its transmission parameters. Consequently, the average per-node throughput of LoRa-based networks has been mathematically formulated and the optimal network-level configuration derived. For end nodes to update their transmission parameters, this centrally-computed global configuration must then be disseminated by LoRa gateways. Unfortunately, the regional limitations imposed on the usage of ISM bands –especially those related to the maximum utilization of the band– pose a potential handicap to this parameter dissemination. To solve this problem, a set of tools from the Machine Learning field have been used. Precisely, the updating process has been formulated as a Reinforcement Learning (RL) problem whose solution prescribes optimal disseminating policies. The use of these policies together with the optimal network configuration has been extensively analyzed and compared to other well-established alternatives. Results show an increase of up to 147% in the accumulated per-node throughput when our RL-based approach is employed.

Wireless Sensor Networks (WSNs) are being deployed in very diverse application scenarios, including rural and forest environments. In these particular contexts, specimen protection and conservation is a challenge, especially in natural reserves, dangerous locations or hot spots of these reserves (i.e., roads, railways, and other civil infrastructures). This paper proposes and studies a WSN based system for generic target (animal) tracking in the surrounding area of wildlife passages built to establish safe ways for animals to cross transportation infrastructures. In addition, it allows target identification through the use of video sensors connected to strategically deployed nodes. This deployment is designed on the basis of the IEEE 802.15.4 standard, but it increases the lifetime of the nodes through an appropriate scheduling. The system has been evaluated for the particular scenario of wildlife monitoring in passages across roads. For this purpose, different schemes have been simulated in order to find the most appropriate network operational parameters. Moreover, a novel prototype, provided with motion detector sensors, has also been developed and its design feasibility demonstrated. Original software modules providing new functionalities have been implemented and included in this prototype. Finally, main performance evaluation results of the whole system are presented and discussed in depth.

Indoor navigation is a challenging task for visually impaired people. Although there are guidance systems available for such purposes, they have some drawbacks that hamper their direct application in real-life situations. These systems are either too complex, inaccurate, or require very special conditions (i.e., rare in everyday life) to operate. In this regard, Ultra-Wideband (UWB) technology has been shown to be effective for indoor positioning, providing a high level of accuracy and low installation complexity. This paper presents SUGAR, an indoor navigation system for visually impaired people which uses UWB for positioning, a spatial database of the environment for pathfinding through the application of the A* algorithm, and a guidance module. The interaction with the user takes place using acoustic signals and voice commands played through headphones. The suitability of the system for indoor navigation has been verified by means of a functional and usable prototype through a field test with a blind person. In addition, other tests have been conducted in order to show the accuracy of different relevant parts of the system.