Spreading Factor Allocation in LoRa Networks through a Game Theoretic Approach

Abstract

LoRa is a low-power wide-area network solution that is recently gaining popularity in the context of the Internet of Things due to its ability to handle massive number of devices. One of the main challenges faced by LoRa implementations is the allocation of Spreading Factors to the devices. While the assignment of these parameters is virtually simple to execute, scalability and complexity issues hint at its implementation through a game theoretic approach. This would offer the advantage of being readily implementable in vast networks of devices with limited hardware capabilities. Hence, we formulate the SF allocation problem as a Bayesian game, of which we compute the Bayesian Nash equilibria. We also implement the procedure in the ns-3 network simulator and evaluate the resulting performance, showing that our approach is scalable and robust, and also offers room for improvement with respect to existing approaches.

Learning How to Configure LoRa Networks With No Regret: A Distributed Approach

“…Distributed assignment. Distributed approaches have primarily leveraged game theory [14,20] for nodes to autonomously choose parameters, with appropriate feedback from the gateways or the network server. For instance, Tolio et al [14] present a two-player game to allocate SFs based on complete information.…”

“…Distributed approaches have primarily leveraged game theory [14,20] for nodes to autonomously choose parameters, with appropriate feedback from the gateways or the network server. For instance, Tolio et al [14] present a two-player game to allocate SFs based on complete information. However, that work requires each node to have complete information about the SFs used by other nodes and assumes that all nodes transmit with the highest TP.…”

“…Thus, it runs at the central network server [19]. Tolio is the two-player game proposed in [14] to assign SFs to LoRa nodes, played between each node and the rest of the network. Each node is aware of the aggregate distribution of SFs in the network and chooses between keeping the current SF or increasing it by one.…”

“…To maintain an acceptable network performance under varying channel conditions, centralized optimization problems [7,8] need to be re-run periodically, resulting in an increased downlink traffic to communicate parameters, which further reduces network performance [11]. A few adaptive and distributed approaches have also been proposed [12][13][14], wherein nodes can choose their SFs and TPs with some feedback from the gateway or the network server. However, some of these solutions require all uplink packets to be acknowledged, which increases the overall traffic in the network, while others target scenarios where network conditions do not frequently vary [14].…”

“…A few adaptive and distributed approaches have also been proposed [12][13][14], wherein nodes can choose their SFs and TPs with some feedback from the gateway or the network server. However, some of these solutions require all uplink packets to be acknowledged, which increases the overall traffic in the network, while others target scenarios where network conditions do not frequently vary [14].…”