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.

Low-Power Wide-Area Network (LPWAN) technologies enable IoT devices to efficiently and robustly communicate over long distances, thus making them especially suited for industrial environments. However, the stringent regulations on the usage of certain ISM (Industrial, Scientific and Medical) bands in many countries in which LPWAN operate limit the amount of time IoT motes can occupy the shared bands. This is particularly challenging in industrial scenarios, where not being able to report some detected events might result in the failure of critical assets. To alleviate this, and by mathematically modeling LPWAN-based IoT motes, we have derived optimal transmission policies that maximize the number of reported events (prioritized by their importance) while still complying with current regulations. The proposed solution has been customized for two widely known LPWAN technologies: LoRa and Sigfox. Analytical results reveal that our solution is feasible and performs remarkably close to the Theoretical Limit for a wide range of network activity patterns.

IoT is rapidly expanding to all productive sectors; however, as thousands of devices are envisioned to be deployed, a careful study of the radio spectrum (and its interferences) must be considered to predict and improve the network performance. In this sense, radio-channel characterization is essential; in particular, path-loss characterization allows to mathematically predict the propagation behavior and interferences of IoT devices with precision. In this work, we show that by means of a set of mathematical corrections and measuring procedures, we can soundly employ the RSSI indicator, available in most IoT motes, to derive accurate path-loss models of industrial infrastructures. Experimental results reveal that the proposed scheme substantially improves the characterization accuracy of a real Smart Grid environment. This, in turn, is proven to have a measurable positive impact on the planning stage of an IoT network, whose life-of-time and cost analysis can be better anticipated.

The application of long-range communication technologies such as LoRa to certain environments like smart cities/industries is a natural step forward towards the future IoT. However, the strict limitations imposed in some countries on the duty cycle of ISM bands can jeopardize this technological progress. To overcome this and, at the same time, improve IoT nodes’ transmission and power efficiency, a novel mathematical model has been derived. Its numerical analysis leads to a closed-form formula that characterizes node performance in terms of its transmission policy. The maximization of this formula—required to increase node performance—is then posed as an optimization problem whose solution yields smart transmission policies that maximize throughput while limiting energy consumption. The results achieved under this approach are compared to those obtained using the ADR algorithm (the most extended mechanism for choosing transmission parameters in LoRaWAN networks) and to random/conservative policies to further highlight the benefits of the proposal. Results reveal that the proposed method outperforms ADR by 33.20%, the conservative policy by 91.81%, and the random policy by 238.8%.

LoRa-based networks exhibit good flexibility in terms of configurable parameters and adjustable modulation properties. Thanks to this, wireless nodes can be tuned to improve their communication behavior. In fact, optimal network-level transmission configurations (C opt ) can be derived in such a way that the global network performance is maximized. To derive this C opt , one must know the radio-propagation behavior of each node beforehand. Traditionally, this has been pursued by using general, low-precision, propagation models due to the infeasibility (in terms of time and energy) of deriving each individual node propagation behavior. In this work we propose a straightforward bounding technique that reduces up to 73% the energy and time required to obtain the radio-propagation behavior of each individual node in the network, enabling the derivation of network-level optimal transmission configurations. Also, we provide mechanisms to keep this knowledge updated, swiftly reacting to changes in the environment and leading to network performance improvements of 15% when compared to traditional alternatives like LoRaWAN ADR. Furthermore, by means of a testbed we demonstrate that this mechanism can also provide resistance to Denial-of-service attacks. Finally, we incorporate the power consumption into the proposed Copt formulation and provide a generalizable power-consumption determination methodology. This way we can limit the set of eligible transmission configurations to help extending LoRa network lifetimes more than 40%.

Adenosine triphosphate (ATP) released from injured or dying cells is a potent pro-inflammatory “danger” signal. Alkaline phosphatase (AP), an endogenous enzyme that de-phosphorylates extracellular ATP, likely plays an anti-inflammatory role in immune responses. We hypothesized that ilofotase alfa, a human recombinant AP, protects kidneys from ischemia-reperfusion injury (IRI), a model of acute kidney injury (AKI), by metabolizing extracellular ATP to adenosine, which is known to activate adenosine receptors. Ilofotase alfa (iv) with or without ZM241,385 (sc), a selective adenosine A_2A receptor (A_2AR) antagonist, was administered 1 h before bilateral IRI in WT, A_2AR KO (Adora2a^–/–) or CD73^–/– mice. In additional studies recombinant alkaline phosphatase was given after IRI. In an AKI-on-chronic kidney disease (CKD) ischemic rat model, ilofotase alfa was given after the three instances of IRI and rats were followed for 56 days. Ilofotase alfa in a dose dependent manner decreased IRI in WT mice, an effect prevented by ZM241,385 and partially prevented in Adora2a^–/– mice. Enzymatically inactive ilofotase alfa was not protective. Ilofotase alfa rescued CD73^–/– mice, which lack a 5′-ectonucleotidase that dephosphorylates AMP to adenosine; ZM241,385 inhibited that protection. In both rats and mice ilofotase alfa ameliorated IRI when administered after injury, thus providing relevance for therapeutic dosing of ilofotase alfa following established AKI. In an AKI-on-CKD ischemic rat model, ilofotase alfa given after the third instance of IRI reduced injury. These results suggest that ilofotase alfa promotes production of adenosine from liberated ATP in injured kidney tissue, thereby amplifying endogenous mechanisms that can reverse tissue injury, in part through A_2AR-and non-A_2AR-dependent signaling pathways.