LoRa-MAB: A Flexible Simulator for Decentralized Learning Resource Allocation in IoT Networks

Abstract

LoRaWAN is a media access control (MAC) protocol for wide area networks. It is designed to allow low-powered devices to communicate with Internet-connected applications over long-range wireless connections. The targeted dense deployment will inevitably cause a shortage in radio resources. Hence, autonomous and lightweight radio resource management is crucial to offer ultra-long battery lifetime for LoRa devices. One of the most promising solutions to such a challenge is the use of artificial intelligence. This will enable LoRa devices to use innovative and inherently distributed learning techniques, thus freeing them from draining their limited energy by constantly communicating with a centralized controller.Before proceeding with the deployment of self-managing solutions on top of a LoRaWAN application, it is sensible to conduct simulation-based studies to optimize the design of learningbased algorithms as well as the application under consideration. Unfortunately, a network simulator for such a context is not fully considered or lacks real deployment parameters. In order to address this shortcoming, we have developed a LoRaWAN simulator which aims for resources allocation problem in LoRaWAN network. The Multi-Armed Bandit and its reinforcement learning based algorithm are used to formulate and finding a resource allocation solution. To demonstrate the usefulness of our simulator, extensive simulations were run in a realistic environment taking into account physical phenomenon in LoRaWAN such as the capture effect and inter-spreading factor interference. The simulation results show that the proposed simulator provides a flexible and efficient environment to evaluate various network design parameters and self-management solutions as well as verify the effectiveness of the distributed learning algorithms for resource allocation problems in LoRaWAN.

Adaptive Wireless Network Management with Multi-Agent Reinforcement Learning

“…They evaluated their proposed approach with a network containing 100 nodes. A significant improvement in PDR performance was demonstrated compared with the approach implemented in the LoRa-MAB simulation framework [ 20 ] when node mobility was introduced.…”

Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance

“…Reference [23] evaluates the performance of LoRaWAN by using the EXP3 adversarial MAB optimization algorithm in the LoRa network configured with 100 nodes to demonstrate the usefulness of physical phenomena in LoRaWAN such as the capture effect on the inter-spreading factor interference.…”

Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks

“…Other simulation tool options can also be found in the literature. A LoRa-MAB simulator is analysed in [17]. The performance of LoRaWAN class A during uplink was evaluated with 100 nodes, with the possibility of selecting an SF parameter from 7 to 12.…”