RPL is the IPv6 routing protocol for low-power and lossy networks, standardized by IETF in 2012 as RFC6550. Specifically, RPL is designed to be a simple and inter-operable networking protocol for resource-constrained devices in industrial, home, and urban environments, intended to support the vision of the Internet of Things with thousands of devices interconnected through multihop mesh networks. More than four-years have passed since the standardization of RPL, and we believe that it is time to examine and understand its current state. In this paper, we review the history of research efforts in RPL; what aspects have been (and have not been) investigated and evaluated, how they have been studied, what was (and was not) implemented, and what remains for future investigation. We reviewed over 97 RPL-related academic research papers published by major academic publishers and present a topic-oriented survey for these research efforts. Our survey shows that only 40.2% of the papers evaluate RPL through experiments using implementations on real embedded devices, ContikiOS and TinyOS are the two most popular implementations (92.3%), and TelosB was the most frequently used hardware platform (69%) on testbeds that have average and median size of 49.4 and 30.5 nodes, respectively. Furthermore, unfortunately, despite it being approximately four years since its initial standardization, we are yet to see wide adoption of RPL as part of real-world systems and applications. We present our observations on the reasons behind this and suggest directions on which RPL should evolve.

RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, industrial and environmental monitoring, and wireless sensor networks. RPL allows bidirectional end-to-end IPv6 communication on resource constrained LLN devices, leading to the concept of the Internet of Things (IoT) with thousands and millions of devices interconnected through multihop mesh networks. In this article, we investigate the load balancing and congestion problem of RPL. Specifically, we show that most of the packet losses under heavy traffic are due to congestion, and a serious load balancing problem appears in RPL in terms of routing parent selection. To overcome this problem, this article proposes a simple yet effective queue utilization based RPL (QU-RPL) that achieves load balancing and significantly improves the end-to-end packet delivery performance compared to the standard RPL. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Owing to its load balancing capability, QURPL is very effective in lowering queue losses and increasing the packet delivery ratio. We implement QU-RPL on a low-power embedded platform, and verify all of our findings through experimental measurements on a real testbed of a multihop LLN over IEEE 802.15.4. We present the impact of each design element of QU-RPL on performance in detail, and also show that QU-RPL reduces the queue loss by up to 84 percent and improves the packet delivery ratio by up to 147 percent compared to the standard RPL.

RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, industrial and environmental monitoring, and wireless sensor networks. RPL allows bi-directional end-to-end IPv6 communication on resource constrained LLN devices, leading to the concept of the Internet of Things (IoT) with thousands and millions of devices interconnected through multihop mesh networks. In this paper, we investigate the load balancing and congestion problem of RPL. Specifically, we show that most of packet losses under heavy traffic are due to congestion, and a serious load balancing problem exists in RPL in terms of routing parent selection. To overcome this problem, this paper proposes a simple yet effective queue utilization based RPL (QU-RPL) that significantly improves end-to-end packet delivery performance compared to the standard RPL. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Owing to its load balancing capability, QU-RPL is very effective in lowering the queue losses and increasing the packet delivery ratio. We verify all our findings through experimental measurements on a real testbed of a multihop LLN over IEEE 802.15.4.

Time Slotted Channel Hopping (TSCH) is a link layer protocol defined in the IEEE 802.15.4 standard. Although it is designed to provide highly reliable and efficient service targeting industrial automation systems, scheduling TSCH transmissions in the time and frequency dimensions is left to the implementers. We evaluate the performance of existing autonomous scheduling approaches for TSCH on various traffic patterns and network configurations. We thoroughly investigate the pros and cons of each scheme; moreover, we propose the use of node based channel allocation to improve the performance of the best scheme, and demonstrate its practicality and reliability, with up to 6 percentage points better packet delivery ratio than the second best option while retaining a similar radio duty cycle. Finally, based on our extensive performance evaluation, we provide some guidelines on how to select a scheduler for a given network.

Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the ‘black-box’ nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human ‘visual scoring’. Tested on KISS–a PSG image dataset from 7745 patients across four hospitals–SleepXViT achieved a Macro F1 score of 81.94%, outperforming baseline models and showing robust performances on public datasets SHHS1 and SHHS2. Furthermore, SleepXViT offers well-calibrated confidence scores, enabling expert review for low-confidence predictions, alongside high-resolution heatmaps highlighting essential features and relevance scores for adjacent epochs’ influence on sleep stage predictions. Together, these explanations reinforce the scoring consistency of SleepXViT, making it both reliable and interpretable, thereby facilitating the synergy between the AI model and human scorers in clinical settings.