Research output: Contribution to journal › Article › peer-review
Clustering optimization of LoRa networks for perturbed ultra-dense IoT networks. / Muthanna, Mohammed Saleh Ali; Wang, Ping; Wei, Min; Rafiq, Ahsan; Josbert, Nteziriza Nkerabahizi.
In: Information (Switzerland), Vol. 12, No. 2, 76, 10.02.2021.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Clustering optimization of LoRa networks for perturbed ultra-dense IoT networks
AU - Muthanna, Mohammed Saleh Ali
AU - Wang, Ping
AU - Wei, Min
AU - Rafiq, Ahsan
AU - Josbert, Nteziriza Nkerabahizi
N1 - Muthanna, M.S.A.; Wang, P.; Wei, M.; Rafiq, A.; Josbert, N.N. Clustering Optimization of LoRa Networks for Perturbed Ultra-Dense IoT Networks. Information 2021, 12, 76. https://doi.org/10.3390/info12020076
PY - 2021/2/10
Y1 - 2021/2/10
N2 - Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network's operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution.
AB - Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network's operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution.
KW - Capacity
KW - Clustering
KW - Dense networks
KW - Internet of things
KW - LoRa
KW - LPWAN
KW - QoS
KW - Throughput
KW - Internet of Things
KW - capacity
KW - dense networks
KW - clustering
KW - throughput
UR - http://www.scopus.com/inward/record.url?scp=85100939590&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/2c14d32a-6f40-3c54-a860-2e609ae44ec0/
U2 - 10.3390/info12020076
DO - 10.3390/info12020076
M3 - Article
AN - SCOPUS:85100939590
VL - 12
JO - Information (Switzerland)
JF - Information (Switzerland)
SN - 2078-2489
IS - 2
M1 - 76
ER -
ID: 87324100