Magnon topology and thermal Hall effect in trimerized triangular lattice antiferromagnet

The non-trivial magnon band topology and its consequent responses have been extensively studied in two-dimensional magnetisms. However, the triangular lattice antiferromagnet (TLAF), the best-known frustrated two-dimensional magnet, has received less attention than the closely related Kagome system, because of the spin-chirality cancellation in the umbrella ground state of the undistorted TLAF. In this work, we study the band topology and the thermal Hall effect (THE) of the TLAF with (anti-)trimerization distortion under the external perpendicular magnetic field using the linearized spin wave theory. We show that the spin-chirality cancellation is removed in such case, giving rise to the non-trivial magnon band topology and the finite THE. Moreover, the magnon bands exhibit band topology transitions tuned by the magnetic field. We demonstrate that such transitions are accompanied by the logarithmic divergence of the first derivative of the thermal Hall conductivity. Finally, we examine the above consequences by calculating the THE in the hexagonal manganite YMnO$_3$, well known to have anti-trimerization.Comment: 6 + 7 pages, 3 + 5 figures, 0 + 1 table; Journal reference adde

Adaptive Scheduling and Power Control for Multi-Objective Optimization in IEEE 802.15.6 Based Personalized Wireless Body Area Networks

Multi-objective optimization (MOO) has been a topic of intense interest in providing flexible trade-offs between conflicting optimization criteria in wireless body area networks (WBANs). To solve diverse multi-objective optimization problems (MOPs), conventional resource management schemes have dealt with the classic issues of WBANs, such as traffic heterogeneity, emergency response, and body shadowing. However, existing approaches have difficulty achieving MOO because, despite the personalization of WBANs, they still miss the new constraints or considerations derived from user-specific characteristics. To address this problem, in this paper, we propose an adaptive scheduling and power control scheme for MOO in personalized WBANs. Specifically, we investigate the existing scheduling and power control schemes for solving MOPs in WBANs, clarify their limitations, and present two feasible solutions: priority-based adaptive scheduling and deep reinforcement learning (DRL) power control. By integrating these two mechanisms in compliance with the IEEE 802.15.6 standard, we can jointly improve the optimization criteria, that is, differentiated quality of service (QoS), transmission reliability, and energy efficiency. Through comprehensive simulations, we captured the performance variations under realistic WBAN deployment scenarios and verified that the proposed scheme can achieve a higher throughput and packet delivery ratio, lower power consumption ratio, and shorter delay compared with a conventional approach

Adaptive scheduling for multi-objective resource allocation through multi-criteria decision-making and deep Q-network in wireless body area networks

To provide compelling trade-offs among conflicting optimization criteria, various scheduling techniques employing multi-objective optimization (MOO) algorithms have been proposed in wireless body area networks (WBANs). However, existing MOO algorithms have difficulty solving diverse multi-objective optimization problems (MOPs) in dynamic and heterogeneous WBANs because they require a prior preference of the decision makers or they are unable to solve non-discrete optimization problems, such as time slot scheduling. To overcome this limitation, in this paper, we propose a new adaptive scheduling algorithm that complements existing MOO algorithms. The proposed algorithm consists of two parts: scheduling order optimization and the auto-scaling of relative importance. With the former, we logically integrate the decision criteria using a multi-criteria decision-making (MCDM) method and then optimize the scheduling order. For the latter, we adaptively adjust the scales of the relative importance among the decision criteria based on the network conditions using a deep Q-network (DQN). By tightly integrating these two mechanisms, we can eliminate the intervention of decision makers and optimize non-discrete tasks simultaneously. The simulation results prove that the proposed scheme can provide a flexible trade-off among conflicting optimization criteria, that is, a differentiated QoS, reliability, and energy efficiency/balance compared with a conventional approach

An Enhanced Temperature Aware Routing Protocol in Wireless Body Area Networks

© 2018 IEEE. In this paper, we propose a new enhanced temperature aware routing protocol to assign the temperature of node by considering current temperature and expected rise caused by the packets in the buffer. Also, two hops ahead algorithm is employed to ensure further packet forwarding to the sink. The simulation results are shown to prove that the proposed scheme is able to increase packet delivery ratio and network lifetime

BANSIM: A new discrete-event simulator for wireless body area networks with deep reinforcement learning in Python

Many studies have investigated machine learning algorithms to improve the performance of wireless body area networks (WBANs). However, it was difficult to evaluate algorithms in a network simulator because of missing interfaces between the simulators and machine learning libraries. To solve the problem of compatibility, some researchers have attempted to interconnect existing network simulators and artificial intelligence (AI) frameworks. For example, ns3-gym is a simple interface between ns-3 (in C++) and the AI model (in Python) based on message queues and sockets. However, the most essential part is the implementation of an integrated event scheduler, which is left to the user. In this study, we aim to develop a new integrated event scheduler. We present BANSIM, a discrete-event network simulator for WBAN in standard Python that supports deep reinforcement learning (DRL). BANSIM provides an intuitive and simple DRL development environment with basic packet communication and BAN-specific components, such as the human mobility model and on-body channel model. Using BANSIM, users can easily build a WBAN environment, design a DRL-based protocol, and evaluate its performance. We experimentally demonstrated that BANSIM captured a wide range of interactions that occurred in the network. Finally, we verified the completeness and applicability of BANSIM by comparing it with an existing network simulator

Reuse of imputed data in microarray analysis increases imputation efficiency

BACKGROUND: The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. RESULTS: We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. CONCLUSIONS: Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data

A survey on analytical models for dynamic resource management in wireless body area networks

Compared with typical wireless sensor networks, wireless body area networks (WBANs) have distinct features: on-body communication, a large amount of interference, and dynamic topology changes caused by gestures. Accordingly, the resource management algorithm in the medium access control (MAC) protocol should be dynamic, adaptive, and energy-efficient. Hence, recent studies tend to optimize the available resources by applying several types of analytical models. Although these models have been categorized in terms of their objectives, the major differences between their methodologies have not been emphasized and discussed. In this study, we classify the analytical models applicable to dynamic resource management, and clarify their characteristics and use cases. We present the basic principles, approach classification, comparison, and guidance for dynamic resource management, and investigate state-of-the-art resource management techniques according to the corresponding analytical models. Furthermore, research challenges on dynamic resource management in WBAN are identified to facilitate future research in this area