Distributed Detour Routing Scheme for Link Failure with Minimized Overhead in LEO Satellite Networks.

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach.

CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC.

With the growing interest in the Internet of Things (IoT), research on massive machine-type communication (mMTC) services is being actively promoted. Because mMTC services are required to serve a large number of devices simultaneously, a lack of resources during initial access can be a significant problem when providing mMTC services in cellular networks. Various studies on efficient preamble transmission have been conducted to solve the random access problem of mMTC services. However, supporting a large number of devices simultaneously with limited resources is a challenging problem. In this study, we investigate code-expanded random access (CeRA), which extends the limited preamble resources to the code domain to decrease the high collision rate. To solve the existing CeRA phantom codeword and physical uplink shared channel (PUSCH) resource shortage problems, we propose an optimal preamble codeword set selection algorithm based on mathematical analysis. The simulation results indicate that the proposed code-expanded random access scheme to enhance success probability (CeRA-eSP) achieves a higher random access success rate with a lower access delay compared to the existing random access schemes.

Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?

PURPOSE: We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers. METHODS: Data were derived from the electronic medical records of Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (estimated glomerular filtration rate [eGFR] < 60 mL·min-1·1.73 m-2 for ≥ 3 months) and followed up for at least 6 months. The study population was randomly divided into training and test sets. RESULTS: We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after excluding patients with missing data, the training and test sets included 1,625 and 1,618 patients, respectively. The integral mean was the most powerful explanatory (R2 = 0.404) variable among the 8 modified values. Ten variables (age, sex, diabetes mellitus[DM], polycystic kidney disease[PKD], serum albumin, serum hemoglobin, serum phosphorus, serum potassium, eGFR (calculated by Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]), and urinary protein) were included in the final risk prediction model for CKD stage 3 (R2 = 0.330). Ten variables (age, sex, DM, GN, PKD, serum hemoglobin, serum blood urea nitrogen[BUN], serum calcium, eGFR(calculated by Modification of Diet in Renal Disease[MDRD]), and urinary protein) were included in the final risk prediction model for CKD stage 4 (R2 = 0.386). Four variables (serum hemoglobin, serum BUN, eGFR(calculated by MDRD) and urinary protein) were included in the final risk prediction model for CKD stage 5 (R2 = 0.321). CONCLUSION: We created a prediction model according to CKD stages by using integral means. Based on the results of the Brier score (BS) and Harrel's C statistics, we consider that our model has significant explanatory power to predict the probability and interval time to the initiation of RRT.