International Conference on Computer and Knowledge Engineering

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Improving LoRaWAN Scalability for IoT Applications using Context Information

Authors :

Hamed Mahmoudi¹ Behrouz ShahgholiGhahfarokhi²

1- Department of Information Technology Engineering, Faculty of Computer Engineering, University o 2- Department of Information Technology Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

Keywords :

IoT, LoRaWAN, Scalability, Context awareness

Abstract :

Next generation of wireless network technologies are expected to have extensive communications, including hundreds of billions of low-power devices (battery-powered), such as sensors, in a wide range of applications such as smart agriculture, smart cities, smart environment, smart healthcare, smart homes and buildings, smart industrial control, smart metering, smart supply chain and smart logistics and lighting. Some protocols are designed for low-power networks (LPWANs). In terms of wireless networks and energy consumption, which is the main basis of Internet of Things (IoT) devices, LPWANs are considered as a suitable solution for IoT applications. The most important LPWAN protocols are SigFox, NB-IoT(NarrowBand-IoT), and LoRa. LoRa is more popular than others because of industry supports such as LoRa Alliance, IBM, Cisco, etc. The large number of devices that communicate with each other in IoT applications has raised concerns about resources availability and the suitable technologies for managing these resources in LoRaWAN networks. There are many applications in the IoT in which the time to receive information from devices (sensors) can be adjusted based on the context information and the internal state of the system in a way that reduces collisions. Therefore, the time of sending information by sensors can be adjusted to some extent, so that sensors have less collisions during transmissions, and thus the problem of scalability of the system is almost solved. In this research, we have presented a framework to improve the performance of LoRaWAN network, in which the devices are scheduled according to their QoS requirements, the density of the network, and the context information. The performance of the proposed model is evaluated by simulations in which the sensors send packets to the server based on the proposed scheduling. Evaluations show that the proposed method has reduced congestion by 51% and energy consumption by 52% on average compared to the baseline solution.

Papers List

List of archived papers

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An Adaptive Budget and Deadline-aware Algorithm for Scheduling Workflows Ensemble in IaaS Clouds

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Reliability Evaluation of 4:2 Compressors Based on Hammock Networks

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Adaptive Active Queue Management for Time Slot Channel Hopping in Industrial Internet of Things

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Towards Efficient Capsule Networks through Approximate Squash Function and Layer-wise Quantization

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LightFedSelect: A Lightweight Framework for Byzantine-Robust Federated Learning

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Automatic Infrared-Based Volume and Mass Estimation System for Agricultural Products

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Weakly Supervised Learning in a Group of Learners with Communication

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