Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks

Author:

Asteriou Vassilis1ORCID,Kantelis Konstantinos1,Beletsioti Georgia A.1,Valkanis Anastasios1,Nicopolitidis Petros1ORCID,Papadimitriou Georgios1

Affiliation:

1. Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Abstract

Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with a random access-based medium access algorithm, this can lead to a cascade of frame collisions, temporarily resulting in degraded performance and diminished system operational capacity, despite LoRaWANs’ physical layer interference and collision reduction techniques. In this paper, a novel scheduling algorithm is proposed that can increase system reliability in the case of such events. The new adaptive spatial scheduling algorithm is based on learning automata, as well as previous developments in scheduling over LoRaWANs, and it leverages network feedback information and traffic spatial correlation to increase network performance while maintaining high reliability. The proposed algorithm is investigated via an extensive simulation under a variety of network conditions and compared with a previously proposed scheduler for event-triggered traffic. The results show a decrease of up to 30% in average frame delay compared to the previous approach and an order of magnitude lower delay compared to the baseline algorithm. These findings highlight the importance of using spatial information in adaptive schemes for improving network performance, especially in location-sensitive applications.

Publisher

MDPI AG

Reference42 articles.

1. Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs;Qadir;IEEE Access,2018

2. Laner, M., Svoboda, P., Nikaein, N., and Rupp, M. (2013, January 27–30). Traffic Models for Machine Type Communications. Proceedings of the Tenth International Symposium on Wireless Communication Systems (ISWCS 2013), Ilmenau, Germany.

3. Technical Specification Group Radio Access Network (2011). Study on RAN Improvements for Machine-Type Communications, 3rd Generation Partnership Project. Technical Report 37.868 V11.0.0.

4. Gupta, V., Devar, S.K., Kumar, N.H., and Bagadi, K.P. (2017, January 4–8). Modelling of IoT Traffic and Its Impact on LoRaWAN. Proceedings of the GLOBECOM 2017—2017 IEEE Global Communications Conference, Singapore.

5. Autonomous Decentralized Traffic Control Using Q-Learning in LPWAN;Kaburaki;IEEE Access,2021

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