np-CECADA: Enhancing Ubiquitous Connectivity of LoRa Networks

TY - GEN

T1 - np-CECADA

T2 - IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

AU - Kouvelas, Nikolaos

AU - Prasad, R Venkatesha

AU - Yazdani, Niloofar

AU - Lucani Rötter, Daniel Enrique

PY - 2021

Y1 - 2021

N2 - Long Range Wide Area Networks (LoRaWAN) offer ubiquitous communications for The Internet of Things (IoT). However, there are many challenges in rolling out LoRaWAN - mainly scalability, energy efficiency, Packet Reception Ratio (PRR), and keeping the channel access as simple as unslotted ALOHA. To this end, we design non-persistent Capture Effect Channel Activity Detection Algorithm (np-CECADA), which is a novel, distributed protocol for the MAC layer of LoRaWAN. It utilizes Channel Activity Detection (CAD), which is a built-in imperfect mechanism for channel sensing and minimal feedback from the gateways. In np-CECADA each device independently adapts backoff times based on the traffic in its vicinity and the transmission power based on the heuristically inferred probability of capturing the channel. To achieve this, first, we carried out an extensive on-field evaluation to measure the effectiveness of CAD and capture effect in LoRa. Using them we designed np CECADA and developed ns-3 modules. Packet Reception Ratio of np-CECADA is 15.74× and 5.13× higher than vanilla LoRaWAN and p-CARMA, respectively. Channel utilization is 11.24× higher compared to LMAC. Further, on a testbed of 30 LoRa devices np-CECADA outperforms LoRaWAN up to 5 times.

AB - Long Range Wide Area Networks (LoRaWAN) offer ubiquitous communications for The Internet of Things (IoT). However, there are many challenges in rolling out LoRaWAN - mainly scalability, energy efficiency, Packet Reception Ratio (PRR), and keeping the channel access as simple as unslotted ALOHA. To this end, we design non-persistent Capture Effect Channel Activity Detection Algorithm (np-CECADA), which is a novel, distributed protocol for the MAC layer of LoRaWAN. It utilizes Channel Activity Detection (CAD), which is a built-in imperfect mechanism for channel sensing and minimal feedback from the gateways. In np-CECADA each device independently adapts backoff times based on the traffic in its vicinity and the transmission power based on the heuristically inferred probability of capturing the channel. To achieve this, first, we carried out an extensive on-field evaluation to measure the effectiveness of CAD and capture effect in LoRa. Using them we designed np CECADA and developed ns-3 modules. Packet Reception Ratio of np-CECADA is 15.74× and 5.13× higher than vanilla LoRaWAN and p-CARMA, respectively. Channel utilization is 11.24× higher compared to LMAC. Further, on a testbed of 30 LoRa devices np-CECADA outperforms LoRaWAN up to 5 times.

UR - http://www.scopus.com/inward/record.url?scp=85123957852&partnerID=8YFLogxK

U2 - 10.1109/MASS52906.2021.00054

DO - 10.1109/MASS52906.2021.00054

M3 - Article in proceedings

SN - 978-1-6654-4936-6

SP - 374

EP - 382

BT - Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021

PB - IEEE

Y2 - 4 October 2021 through 7 October 2021

ER -