Deriving and Updating Optimal Transmission Configurations for Lora Networks

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 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 C opt 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%.

A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things

“…These methods have provided acceptable performance of packet reception probability with a reduced energy consumption amount. Authors in [ 42 , 43 ] have addressed the energy consumption issue in LoRa EDs by proposing a delimitation technique that evaluates the radio propagation behavior of the EDs in the network. The work [ 44 ] investigates the optimal SF parameter and transmission power ( allocated to EDs in LoRa networks.…”

Improvement of LoRa Communication Scalability using Machine Learning Based Adaptiveness

“…In recent years, there has been an intensive research in this field [8], [9], [10], [11]. Much research has been conducted in the area of computing the optimal communication-channel parameters used by IoT end nodes and the network server [12], [13], [14], [15]. The work has been especially focused on optimizing energy requirements of end nodes [16], [17], [18], [19], [20], selecting a communication channel [21], [22], [23], reducing the number of collisions [24], [25], [26], predicting collisions [27], or making the communication generally more efficient [28], [29], [10].…”

“…The authors of [32], [15] have also considered a dissemination of acquired information from the network server to the end nodes. As already mentioned in previous section, all LoRa nodes communication is limited by the duty-cycle restriction.…”

“…To conclude, a network-wise approach instead of a nodewise approach has been used in [32], [15], both in terms of a proper communication-parameters selection and a message importance. A message importance can be considered a form of QoS, as in [21], [32].…”

“…To the authors' best knowledge, the analyzed works did not really take into account an overhead for LoRa gateways to propagate the communication parameters to the end devices, which significantly influences their duty cycle. Besides [32], [15], other works have not considered the duty-cycle restrictions at all. It is important to find an efficient form to disseminate a control-plane data from the network server to the end devices.…”