Time Slotted Channel Hopping (TSCH) is widely used in the industrial wireless sensor networks due to its high reliability and energy efficiency. Various timeslot and channel scheduling schemes have been proposed for achieving high reliability and energy efficiency for TSCH networks. Recently proposed autonomous scheduling schemes provide flexible timeslot scheduling based on the routing topology, but do not take into account the network traffic and packet forwarding delays. In this paper, we propose an autonomous scheduling scheme for convergecast in TSCH networks with RPL as a routing protocol, named Escalator. Escalator generates a consecutive timeslot schedule along the packet forwarding path to minimize the packet transmission delay. The schedule is generated autonomously by utilizing only the local routing topology information without any additional signaling with other nodes. The generated schedule is guaranteed to be conflict-free, in that all nodes in the network could transmit packets to the sink in every slotframe cycle. We implement Escalator and evaluate its performance with existing autonomous scheduling schemes through a testbed and simulation. Experimental results show that the proposed Escalator has lower end-to-end delay and higher packet delivery ratio compared to the existing schemes regardless of the network topology.

The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. This paper proposes an algorithm to support node mobility in RPL in an energy-efficient manner and describes its operating principle based on different scenarios. The proposed algorithm supports the mobility of nodes by dynamically adjusting the transmission interval of the messages that request the route based on the speed and direction of the motion of mobile nodes, as well as the costs between neighboring nodes. The performance of the proposed algorithm and previous algorithms for supporting node mobility were examined experimentally. From the experiment, it was observed that the proposed algorithm requires fewer messages per unit time for selecting a new parent node following the movement of a mobile node. Since fewer messages are used to select a parent node, the energy consumption is also less than that of previous algorithms.

With the discovery of cell-free fetal DNA in maternal blood, the demand for non-invasive prenatal testing (NIPT) has been increasing. To obtain reliable NIPT results, it is important to accurately estimate the fetal fraction. In this study, we propose an accurate and cost-effective method for measuring fetal fractions using single-nucleotide polymorphisms (SNPs). A total of 84 samples were sequenced via semiconductor sequencing using a 0.3× sequencing coverage. SNPs were genotyped to estimate the fetal fraction. Approximately 900 000 SNPs were genotyped, and 250 000 of these SNPs matched the semiconductor sequencing results. We performed SNP imputation (1000Genome phase3 and HRC v1.1 reference panel) to increase the number of SNPs. The correlation coefficients (R²) of the fetal fraction estimated using the ratio of non-maternal alleles when coverage was reduced to 0.01 following SNP imputation were 0.93 (HRC v1.1 reference panel) and 0.90 (1000GP3 reference panel). An R² of 0.72 was found at 0.01× sequencing coverage with no imputation performed. We developed an accurate method to measure fetal fraction using SNP imputation, showing cost-effectiveness by using different commercially available SNP chips and lowering the coverage. We also showed that semiconductor sequencing, which is an inexpensive option, was useful for measuring fetal fraction. python source code and guidelines can be found at https://github.com/KMJ403/fetalfraction-SNPimpute. kangskim@ajou.ac.kr or sunshinkim3@gmail.com Supplementary data are available at Bioinformatics online.