Abstract
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 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> ) can be derived in such a way that the global network performance is maximized. To derive this C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> , 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%.
Highlights
LoRa networks are conceived to consist of hundreds, or even thousands of devices
Note that we intentionally leave out the bandwidth –BW, which is configurable, because all LoRa nodes that belong to the same LoRaWAN network must use the same bandwidth
As mentioned in previous paragraphs, most works resorted to: (i) assuming rather simple node distributions when modeling a LoRa-based network, (ii) implementing generic radio propagation models that do not consider the particularities of each environment like the log-normal shadowing path-loss models [16], or (iii) employing environment-agnostic Packet Reception Ratio (PRR) vs Signal-to-Noise ratio (SNR) models to determine the likelihood of receiving a particular packet
Summary
LoRa networks are conceived to consist of hundreds, or even thousands of devices. This large number of IoT nodes would unavoidably increase the level of interference and congestion present in the wireless band. As mentioned in previous paragraphs, most works resorted to: (i) assuming rather simple node distributions when modeling a LoRa-based network (e.g. uniform density, Gaussian distribution, etc.), (ii) implementing generic radio propagation models that do not consider the particularities of each environment like the log-normal shadowing path-loss models [16], or (iii) employing environment-agnostic PRR vs SNR models to determine the likelihood of receiving a particular packet (that is, determining the PRR by means of the perceived SNR, which effectively ignores packet collisions and other complex phenomena) In contrast to these alternatives, we propose to incorporate in the performance-maximization process a previous step in which the specific propagation environment is characterized. This has the additional benefit of increasing the importance of more recent samples; responding faster to changes in λi (e.g. when an external node dramatically changes its packet-generation rate)
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