Heuristic Algorithm for Virtual Link Configuration in AFDX Networks

REACTION 2014. 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications. Rome, Italy. December 2nd, 2014.As the AFDX networks have been increasingly employed for airborne networks, much research works have been conducted to support real-time service in a deterministic way. However, since they assumed the preconfigured networks where all involved parameters were already determined, the impact of configuration algorithm is not well explored. To solve this problem, in this paper, we focus on how to reduce the required bandwidth by configuring virtual link which logically consists of at least one or more application flows. To achieve this, new heuristic algorithms have been proposed by applying well-known greedy approach while taking essential constraints of AFDX networks into account. To evaulate the performance of proposed scheme, diverse case studies for airborne application flows are concerned and their number of virtual links as well as required bandwidth are compared.This work was supported by Basic Science Research Program (NRF-2013R1A1A2A10004587) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education and the MSIP(Ministry of Science, ICT & Future Planning), Korea, under the ”SW master’s course of a hiring contract” support program (NIPA-2014-HB301-14- 1014) supervised by the NIPA(National IT Industry Promotion Agency).Publicad

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use. To overcome these limitations, we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use. Experimental results indicate that this architecture could achieve the highest average of recall and accuracy (83.43%) compared to benchmarkmodels. To verify the effectiveness of the architecture, another experimental result shows that energy storage system (ESS) scheme in accordance with the forecast results of the proposed model (LSTM-MATO) in the architecture could provide peak load cost savings of 17,535,700KRWeach year comparing with original peak load costs without the method. Therefore, the proposed architecture could be utilized for practical applications such as peak load reduction in the grid

VEGETATION BEHAVIOR AND ITS HABITAT REGION AGAINST FLOOD FLOW IN URBAN STREAMS

Hydraulic effects on the vegetation behavior and on its habitat region against flood flow in the urban streams were analysed in this paper. Vegetation behavior was classified into stable, recovered, damaged and swept away stages. Criteria between recovered and damaged status were determined by the bending angle of the aquatic plants. Aquatic plants whose bending angle is lower than 30~50 degree is recovered, but they were damaged and cannot be recovered when the bending angle is higher than 30~50 degree. Phragmites japonica was inhabited in the hydraulic condition of high Froude number which shows that it was inhabited in the upstream reaches. Phragmites communis was inhabited in the relatively low Froude number compared with Phragmites japonica. This shows that it was inhabited in the downstream reaches. Persicaria blumei was found in the relatively wide range of flow velocity and flow depth, which shows that it was inhabited in the middle and downstream reaches. Criterion on the vegetation behavior of Persicaria thunbergii was not clear, which implies that it may be affected by the flow turbulence rather than flow velocity and flow depth

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