LPWANs are a promising solution for wireless sensor networks. To compete with such widespread technologies as LoRaWAN and Sigfox, recently a new LPWAN technology called NB-Fi has been developed. In a short time, many NB-Fi networks have been deployed in various countries. Although NB-Fi, Sigfox, and LoRaWAN have been designed for similar applications, they implement different approaches. However, no detailed comparisons of them are present in academic literature. This paper aims to fill this gap by analyzing and comparing NB-Fi, Sigfox, and LoRaWAN focusing on performance evaluation results rather than just nominal parameters declared by the developers. Specifically, the paper evaluates the packet loss rate, packet error rate, and average delay in these networks in different scenarios. The results are used to provide guidelines to decide which technology to use under which conditions. Specifically, Sigfox performs best in scenarios when devices transmit small pieces of data without repetitions and acknowledgments, and LoRaWAN is the most reliable for transmitting bigger pieces of data, while NB-Fi is best suited for acknowledged transmissions of small pieces of data.

LoRaWAN infrastructure has become widely deployed to provide wireless communications for various sensor applications. These applications generate different traffic volumes and require different quality of service (QoS). The paper presents an accurate mathematical model of low-power data transmission in a LoRaWAN sensor network, which allows accurate validation of key QoS indices, such as network capacity and packet loss ratio. Since LoRaWAN networks operate in the unlicensed spectrum, the model takes into account transmission attempt failures caused by random noise in the channel. Given QoS requirements, we can use the model to study how the performance of a LoRaWAN network depends on the traffic load and other scenario parameters. Since in LoRaWAN networks the transmissions at different modulation and coding schemes (MCSs) typically do not collide, we use the model to assign MCSs to the devices to satisfy their QoS requirements.

Wi-Fi is evolving to support new areas such as extended reality, Industrial Internet of Things, and environments with numerous devices. Unlike previous generations that aimed to maximize peak and user-perceived throughput, Wi-Fi 8 (also known as IEEE 802.11bn Ultra High Reliability) intends to make Wi-Fi operation more reliable, i.e., to improve performance across the full range of scenarios, particularly in worst-case conditions. Specifically, 802.11bn is striving to increase throughput at various signal to interference and noise ratio levels, diminish the 95th percentile of latency, decrease packet losses, and reduce power consumption. To attain these ambitions, Wi-Fi 8 is going to introduce a series of groundbreaking mechanisms, including distributed-tone resource units, unequal modulations, prioritized and non-primary channel access, advanced roaming features, multi-AP coordination, and so on. We present the current status of Wi-Fi 8 development with an emphasis on the targets and enabling technologies. This tutorial also discusses open research directions that require the development of new intelligent methods and algorithms.

The recent Wi-Fi HaLow technology focuses on adopting Wi-Fi for the needs of the Internet of Things. A key feature of Wi-Fi HaLow is the Restricted Access Window (RAW) mechanism that allows an access point to divide the sensors into groups and to assign each group to an exclusively reserved time interval where only the stations of a particular group can transmit. In this work, we study how to optimally configure RAW in a scenario with a high number of energy harvesting sensor devices. For such a scenario, we consider a problem of device grouping and develop a model of data transmission, which takes into account the peculiarities of channel access and the fact that the devices can run out of energy within the allocated intervals. We show how to use the developed model in order to determine the optimal duration of RAW intervals and the optimal number of groups that provide the required probability of data delivery and minimize the amount of consumed channel resources. The numerical results show that the optimal RAW configuration can reduce the amount of consumed channel resources by almost 50%.

This paper studies the usage of orthogonal frequency division multiple access (OFDMA) for uplink transmissions in IEEE 802.11ax networks. OFDMA enables simultaneous multi-user transmissions in Wi-Fi, but its usage requires efficient resource allocation algorithms. These algorithms should be able to adapt to the changing channel conditions, including the frequency-selective fading. This paper presents an OFDMA resource allocation algorithm for channels with frequency-selective fading and proposes an approach to adapt the user transmission power and modulation and coding schemes to the varying channel conditions, which is efficient even in the case when the access point has outdated channel state information. The proposed scheduling algorithm and power allocation approach can double the goodput and halve the data transmission time in Wi-Fi networks even in dense deployments of access points.

Energy efficiency is a significant challenge for modern wireless networking devices. It is crucial for the Internet of Things devices, is required for battery-supplied user devices such as smartphones, and is advisable for high-performance devices such as wireless VR headsets. This paper examines the ability of modern Wi-Fi devices to achieve extremely low power consumption when they rarely send and receive data. Two recently developed mechanisms, namely Target Wake Time (TWT) and Wake-Up Radio (WUR), are studied. The first one allows stations to schedule the frame exchanges in advance, while the second one introduces a low-power radio for control information exchange. Although TWT and WUR differ significantly, they both suffer from the clock drift effect that significantly degrades their performance in the case of rare traffic. The paper describes these mechanisms, focusing on their revolutionary features, and presents mathematical models to evaluate the impact of this effect on TWT and WUR mechanism efficiency in terms of energy and channel time consumption. The paper also proposes and thoroughly examines various approaches to the joint and separate usage of TWT and WUR in Wi-Fi networks.