Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment.

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A number of industrial wireless technologies have emerged over the last decade, promising to replace the need for wires in a variety of use cases. Except for customized Time Division Multiple Access (TDMA) based wireless technologies that can achieve ultra-low latency over a very limited area, wireless communication generally has reliability and latency issues when it comes to industrial applications. Closed loop communication requires high reliability (over 99%), limited jitter and latency, which poses a challenge especially over a wide area measuring in hundreds of meters. Extended coverage is promised with the advent of sub-GHz technologies, one of them being IEEE 802.11ah which is the only one that offers sufficient data rate for frequent bidirectional communication. Thus, we evaluated IEEE 802.11ah for low-latency time-critical control loops. We propose the network setup for adjusting the network dynamics to that of control loops, enabling limited jitter and high reliability. We explore the scalability of IEEE 802.11ah network hosting both control loops and monitoring sensors that periodically transmit measurements. Assigning the control loop end-nodes to dedicated Restricted Access Window (RAW) slot results in over 99.99% successful deliveries. Furthermore, inter-packet delay is concentrated around the cycle-time in the following or preceding beacon interval in case the beacon interval is at least half the value of the shortest cycle-time. Adjusting the beacon interval to the fastest control loop in the network ensures latency requirements at the cost of maximum achievable throughput and energy consumption.

Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair coexistence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA's Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware. Index Terms-Sub-GHz, deep learning, Software-defined radio, low-cost devices I. INTRODUCTION The Internet of things (IoT) paradigm has grown exponentially in the past decade and continues this trend into the foreseeable future. At the beginning of 2020, IoT Analytics estimated that 9.5 billion devices are connected to the Internet and forecasts a growth of 28 billion devices by 2025 [1]. This is due to the proliferation of various IoT application areas such as security, tracking, agriculture, smart metering, smart cities and smart homes. To accommodate commercial deployment of such large number of devices, recently a number of IoT technologies were developed which are called low power wide area networks (LPWANs). These technologies offer very long communication ranges allowing to connect a large number of devices using limited infrastructure cost (e.g. by installing a small number of gateways). Example technologies include Sigfox, LoRA, IEEE 802.11ah, IEEE 802.11g, Dash7, Weightless, etc. These technologies operate in unlicensed sub-GHz band, typically 868 MHz in Europe and 915 MHz in North America. These radio frequencies offer good object penetration performance and can be used for a long range of communication, i.e., up to 15 km (for LoRa) [2]. However, due to

Industry 4.0 is being enabled by a number of new wireless technologies that emerged in the last decade, aiming to ultimately alleviate the need for wires in industrial use cases. However, wireless solutions are still neither as reliable nor as fast as their wired counterparts. Closed loop communication, a representative industrial communication scenario, requires high reliability (over 99%) and hard real-time operation, having very little tolerance for delays. Additionally, connectivity must be provided over an entire industrial side extending across hundreds of meters. IEEE 802.11ah fits this puzzle in terms of data rates and range, but it does not guarantee deterministic communication by default. Its Restricted Access Window (RAW), a new configurable medium access feature, enables flexible scheduling in dense, large-scale networks. However, the standard does not define how to configure RAW. The existing RAW configuration strategies assume uplink traffic only and are dedicated exclusively to sensors nodes. In this article, we present an integer nonlinear programming problem formulation for optimizing RAW configuration in terms of latency in closed loop communication between sensors and actuators, taking into account both uplink and downlink traffic. The model results in less than 1% of missed deadlines without any prior knowledge of the network parameters in heterogeneous time-changing networks.INDEX TERMS IEEE 802.11ah, industrial Internet of Things (IIoT), optimization, restricted access window (RAW), Wi-Fi HaLow, wireless automation.