It is known that an ultrawideband (UWB)-based indoor positioning system (IPS) has superior positioning performance and can meet the requirements of location-based services (LBSs) as the Internet of Things (IoT) applications. However, there is a limitation of UWB positioning when it is conducted at the nonline-of-sight (NLOS) channels degrading the UWB ranging accuracy at indoor environments. In this article, we propose an artificial intelligence (AI) applied UWB positioning system that can enhance the positioning performance by classifying channel conditions with channel impulse response (CIR) of the received UWB signal. The proposed system mitigates the positioning degradation caused by the NLOS situations by performing extended Kalman filter (EKF) localization and long short-term memory (LSTM) training of the observed channel status. The main feature of the proposed UWB positioning method is that it can be used even at unknown locations not trained with the LSTM model learning the channel status, because of our training strategy of not the position coordinates, but the UWB ranging error between UWB devices corresponding to CIR of the received UWB signal. This article provides the experimental setup and performance evaluation results of the proposed system. The evaluation results showed that the proposed AI-applied UWB positioning method significantly improved its accuracy performance compared with the existing positioning methods.

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.

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.

LoRaWAN exhibits an essential feature, namely, the adaptive data rate (ADR), which has been recommended for the management of resources (e.g., the spreading factor and transmit power) of static end devices (EDs) based on channel conditions. Blind ADR (BADR) has been introduced for LoRaWAN mobile applications that experience frequent channel attenuation when the ED moves (e.g., pet-tracking). This channel condition leads to massive packet loss and retransmission, which significantly increases energy consumption. In this study, ADR and BADR are investigated in mobility environments, their limitations are highlighted, and a novel ADR “retransmission-assisted resource management (R-ARM)” system is proposed. The proposed R-ARM system operates concurrently on the ED and network server sides. This improves the network performance of the LoRaWAN. When compared to those of typical ADR approaches, the simulation results, in this case, show that R-ARM significantly enhances the packet success ratio and convergence period, and it lowers the energy consumption and packet loss ratio.

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 network-managed 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.

Some of the researches on indoor positioning have been conducted, but there are still many constraints on indoor positioning approaches. Among these approaches, ultra-wideband (UWB) provides a fast and precise positioning performance but requires a sufficient infrastructure and a clear line-of-sight (LOS) channel. However, inertial sensor-based pedestrian dead reckoning (PDR) operates without infrastructure, but it requires position initialization and has error drift problems. In this study, we propose a hybrid positioning system that fully combines UWB and PDR to overcome such constraints and improve the positioning performance. This hybrid positioning system uses a Kalman filter (KF) based fusion method that identifies non-line-of-sight (NLOS) environments and mitigates UWB errors through PDR. We also evaluated the proposed system implemented using practical testbed devices at indoor environments classified as LOS, weak NLOS, and hard NLOS. The evaluation results showed that the proposed system significantly improves the positioning performance and alleviates the positioning constraints, as compared to the single positioning system. Our system has been designed to be lightweight compared to the existing extended KF-based convergence system, but is more robust to both weak and hard NLOS environments. Eventually, it improved positioning performance by 35.5% than existing hybrid systems in the hard NLOS environments.