The Routing Protocol for Low-power and Lossy Networks (RPL) is a popular routing layer protocol for multi-hop Wireless Sensor Networks (WSNs). However, typical RPL configurations are based on decade-old assumptions, leading to a mismatch with: (1) advances in wireless hardware; and (2) growing wireless contention. To soften the impact of external stressors (i.e., jamming and interference), we extended RPL to exploit the capabilities of modern multi-interfaced wireless devices. More specifically, our main contribution is the design, development, and evaluation of a novel RPL Objective Function (OF) which, through simulations, is compared to traditional single-interface approaches and a state-of-the-art multi-interface approach. We examine two scenarios, with and without the injection of jamming, respectively. Our proposed OF is shown to outperform, or otherwise perform similar to, all alternatives considered. In normal conditions, it auto-selects the best interface whilst incurring negligible protocol overhead. In our jamming simulations, it provides stable end-to-end delivery ratios exceeding 90%, whereas the closest alternative averages 65% and is considerably less stable. Given we have open-sourced our development codebase, our solution is an ideal candidate for adoption by RPL deployments that expect to suffer interference from competing technologies or are unable to select the best radio technology a priori.

To provide wireless coverage in challenging industrial environments, IEEE802.15.4 Time-Slotted Channel Hopping (TSCH) presents a robust medium access protocol. Using multiple Physical Layers (PHYs) could improve TSCH even more in these heterogeneous environments. However, TSCH only defines one fixed-duration timeslot structure allowing one packet transmission. Using multiple PHYs with various data rates therefore does not yield any improvements because of this single-packet limitation combined with a fixed slot duration. We therefore defined two alternative timeslot structures allowing multiple packets transmissions to increase the throughput for higher data rate PHYs while meeting a fixed slot duration. In addition, we developed a flexible Link Quality Estimation (LQE) technique to dynamically switch between PHYs depending on the current environment. This paper covers a theoretical evaluation of the proposed slot structures in terms of throughput, energy consumption and memory constraints backed with an experimental validation, using a proof-of-concept implementation, which includes topology and PHY switching. Our results show that a 153 % higher net throughput can be obtained with 84 % of the original energy consumption and confirm our theoretical evaluation with a 99 % accuracy. Additionally, we showed that in a real-life testbed of 33 nodes, spanning three floors and covering 2550 m 2 , a compact multi-PHY TSCH network can be formed. By distinguishing between reliable and high throughput PHYs, a maximum hop count of three was achieved with a maximum throughput of 219 kbps. Consequently, using multiple (dynamic) PHYs in a single TSCH network is possible while still being backwards compatible to the original fixed slot duration TSCH standard.

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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

While the ongoing fourth industrial revolution continues to be a major driver behind wireless communication technologies, some environments are so prohibitive that even state-of-the-art solutions can barely achieve ubiquitous wireless connectivity (if at all). For example, in industrial sites with large metal constructions (such as petrochemical plants), highly localized and time-varying changes in wireless link quality are quite common. Oddly enough, much of the capabilities needed to deal with such effects are already present at the physical layer (PHY), but remain largely unexploited by higher protocol layers. In fact, little Industrial Internet of Things (IoT) (IIoT) research has considered harnessing the full multi-modal capabilities of modern multi-PHY/multi-band IoT hardware in general. As such, in this vision paper, we: (1) analyze recent advances towards enabling multi-modal IIoT through link- and routing layer operations; and (2) describe challenges and opportunities for future IIoT deployments, based on the design choices that emerged from said analysis. In summary, we identify a combination of a modified/extended Time-Slotted Channel Hopping (TSCH) link layer, using either fixed or variable duration timeslots, together with a Parent-Oriented (PO) Routing Protocol for Low-Power and Lossy Networks (LLNs) (RPL) approach to be the most promising way forward.