The long-range wide area network (LoRaWAN) has recently emerged as one of the most potential technologies for the mobile Internet of Things (IoT) applications. In a LoRaWAN, the networkmanaged adaptive data rate (ADR) method is responsible for managing resource allocation (i.e., spreading factor (SF) and transmit power (TP)) to end-devices (EDs) through ADR commands. When an ED is mobile and receives a new configuration (i.e., SF and TP) from the network server, the propagation scenario could have been dramatically changed. Hence, the SF and the link budget will no longer be valid, which results in packet loss and massive retransmissions. Therefore, we propose a mobility-aware resource (SF) assignment scheme (M-ASFA), which aims to allocate the best SF to IoT-enabled mobile EDs at each uplink transmission by considering the strength of the signals that a gateway receives from the EDs. The simulation results demonstrate that, in our proposed M-ASFA solution, the SF is assigned to mobile EDs by proactively responding to the mobility of the EDs. This proactive behavior of the proposed scheme enhances the packet success ratio by significantly reducing the impact of interference, retransmissions, and packet loss when compared with the LoRaWAN-based ADR.
A long-range wide area network (LoRaWAN) adapts the ALOHA network concept for channel access, resulting in packet collisions caused by intra- and inter-spreading factor (SF) interference. This leads to a high packet loss ratio. In LoRaWAN, each end device (ED) increments the SF after every two consecutive failed retransmissions, thus forcing the EDs to use a high SF. When numerous EDs switch to the highest SF, the network loses its advantage of orthogonality. Thus, the collision probability of the ED packets increases drastically. In this study, we propose two SF allocation schemes to enhance the packet success ratio by lowering the impact of interference. The first scheme, called the channel-adaptive SF recovery algorithm, increments or decrements the SF based on the retransmission of the ED packets, indicating the channel status in the network. The second approach allocates SF to EDs based on ED sensitivity during the initial deployment. These schemes are validated through extensive simulations by considering the channel interference in both confirmed and unconfirmed modes of LoRaWAN. Through simulation results, we show that the SFs have been adaptively applied to each ED, and the proposed schemes enhance the packet success delivery ratio as compared to the typical SF allocation schemes.
A long-range wide area network (LoRaWAN) is one of the leading communication technologies for Internet of Things (IoT) applications. In order to fulfill the IoT-enabled application requirements, LoRaWAN employs an adaptive data rate (ADR) mechanism at both the end device (ED) and the network server (NS). NS-managed ADR aims to offer a reliable and battery-efficient resource to EDs by managing the spreading factor (SF) and transmit power (TP). However, such management is severely affected by the lack of agility in adapting to the variable channel conditions. Thus, several hours or even days may be required to converge at a level of stable and energy-efficient communication. Therefore, we propose two NS-managed ADRs, a Gaussian filter-based ADR (G-ADR) and an exponential moving average-based ADR (EMA-ADR). Both of the proposed schemes operate as a low-pass filter to resist rapid changes in the signal-to-noise ratio of received packets at the NS. The proposed methods aim to allocate the best SF and TP to both static and mobile EDs by seeking to reduce the convergence period in the confirmed mode of LoRaWAN. Based on the simulation results, we show that the G-ADR and EMA-ADR schemes reduce the convergence period in a static scenario by 16% and 68%, and in a mobility scenario by 17% and 81%, respectively, as compared to typical ADR. Moreover, we show that the proposed schemes are successful in reducing the energy consumption and enhancing the packet success ratio.
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