Ultra‐dense LoRaWAN: Reviews and challenges

Alenezi, Mohammed; Chai, Kok Keong; Chen, Yue; Jimaa, Shihab

doi:10.1049/iet-com.2018.6128

Cited by 29 publications

(23 citation statements)

References 20 publications

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“…LoRaWAN is meant for serving IoT applications, where low latency is not usually critical requirement [19]. Hence, the simplicity in LoRaWAN protocol, which makes it suitable for IoT serving applications given the limited resources.…”

Section: Machine Learning In Lorawanmentioning

confidence: 99%

“…Although the LoRaWAN network with the most reliable SF7 provides the best performance in terms of data rate, transmission delay and energy consumption [1], [19], the LoRaWAN network still under-performs in certain scenarios due to high collision rate, especially when the network is dense. Hence, given the system model is dense at a limited area (up to d max around the GW), the analysis in this paper is based on using SF7.…”

Section: A System Modelmentioning

confidence: 99%

“…This is achieved by reducing the excessive packet collision rate associated with LoRaWAN due to the adaption of ALOHA protocol communication in Class A LoRaWAN [45]. Packet collisions happen when two packets are transmitted at the same time over the same frequency using the same SF [1], [19]. When a collision happens, the node keeps attempting to retransmit until an acknowledgement from the GW is received, which results in increasing the TTD and eventually the TEC.…”

Section: A System Modelmentioning

confidence: 99%

“…Using these parameters insures a maximum successful transmissions in LoRaWAN given a limited area [1]. More details of the BW, SF, and CR are given in [19].…”

Section: B Problem Formulationmentioning

confidence: 99%

“…; and P (z C K ) is the prior probability of z C K . In a similar approach, equation (19) is applied to obtain the posterior probability of P (T h mncon. |z C K ).…”

Section: Naive Bayes Classifier Algorithmmentioning

confidence: 99%

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Unsupervised Learning Clustering and Dynamic Transmission Scheduling for Efficient Dense LoRaWAN Networks

et al. 2020

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Long-Range (LoRa) communication technology is considered as a promising connectivity solutions for Internet of Things (IoT) dense applications. In particular, LoRa has drawn the interest due to its low power consumption and wide area coverage. Despite the benefits of LoRaWAN protocol, it still suffers from excessive random and simultaneous transmissions due to the adoption of ALOHA protocol. Therefore, resulting in severe packet collision rate as the network scales up. This leads to continuous retransmission attempts, which in return increase the transmission delay and energy consumption. Thus, this paper proposes a dynamic transmission Priority Scheduling Technique (PST) based on the unsupervised learning clustering algorithm to reduce the packet collision rate and enhance the network's transmission delay and energy consumption. Particularly, the LoRa gateway classifies the nodes into different transmission priority clusters. While the dynamic PST allows the gateway to configure the transmission intervals for the nodes according to the transmission priorities of the corresponding clusters. This work allows scaling up the network density while maintaining low packet collision rate and significantly enhances the transmission delay & the energy consumption. Simulation results show that the proposed work outperforms the typical LoRaWAN and recent clustering & scheduling schemes. Therefore, the proposed work is well suited for dense applications in LoRaWAN.

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Section: Machine Learning In Lorawanmentioning

confidence: 99%

Section: A System Modelmentioning

confidence: 99%

Section: A System Modelmentioning

confidence: 99%

“…Using these parameters insures a maximum successful transmissions in LoRaWAN given a limited area [1]. More details of the BW, SF, and CR are given in [19].…”

Section: B Problem Formulationmentioning

confidence: 99%

“…; and P (z C K ) is the prior probability of z C K . In a similar approach, equation (19) is applied to obtain the posterior probability of P (T h mncon. |z C K ).…”

Section: Naive Bayes Classifier Algorithmmentioning

confidence: 99%

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