• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12A
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• pp.1189-1197
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• 2006

Ultra-wideband(UWB) is a killer technology for short-range wireless communications. In the past, most of the UWB research focused on physical layer but the unique characteristics of UWB make it different to design the upper layer protocols than conventional narrow band systems. Cross-layer protocols have received high attention for UWB networks. In this paper, we investigate the performance of two physical layer schemes: Time Hopping Binary Pulse Position Modulation(TH-BPPM) and Time Hopping Binary Pulse Amplitude Modulation (TH-BPAM) with proposed private MAC protocol for UWB ad-hoc networks. From pulse level to packet level simulation is done in network simulator ns-2 with realistic network environments for varying traffic load, mobility and network density. Our simulation result shows TH-BPAM outperforms TH-BPPM in high traffic load, mobility and dense network cases but in a low traffic load case identical performance is achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2936-2951
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• 2016

An emerging satellite technology, Operationally Responsive Space (ORS) is expected to provide a fast and flexible solution for emergency response, such as target tracking, dense earth observation, communicate relaying and so on. To realize large distance transmission, we propose the use of available relay satellites as relay nodes. Accordingly, we apply software defined network (SDN) technology to ORS networks. We additionally propose a satellite network architecture refered to as the SDN-based ORS-Satellite (Sat) networking scheme (SDOS). To overcome the issures of node failures and dynamic topology changes of satellite networks, we combine centralized and distributed routing mechanisms and propose a fast recovery routing algorithm (FRA) for SDOS. In this routing method, we use centralized routing as the base mode.The distributed opportunistic routing starts when node failures or congestion occur. The performance of the proposed routing method was validated through extensive computer simulations.The results demonstrate that the method is effective in terms of resoving low end-to-end delay, jitter and packet drops.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7B
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• pp.510-517
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• 2008

In wireless sensor networks, a number of nodes deployed in dense manner should be self-configured to establish a topology that provides communication and sensing coverage under stringent energy constraints. To establish an efficient topology, we propose the TRaffic-Aware Topology control (TRAT) algorithm that reduces energy dissipation by considering total amount of data flows in the network. Our algorithm controls the number of active nodes with traffic information and adjusts nodal transmission power by estimating amount of data flows. According to the result, the proposed algorithm shows about 30% better performance than the other methods in terms of energy efficiency.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.177-183
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• 2008

In this paper we suggest a hybrid MAC protocol for wireless sensor networks (WSN) to enhance network performance. The proposed MAC scheme is specifically designed for wireless sensor networks which consist of lots nodes. The contributions of this paper are: First, the proposed scheduling algorithm is independent of network topology. Even though the BS node has lots of one hop node in dense mode network, all the time slots can be assigned fully without increasing frequencies. Second, BS one hop nodes can use more than one time slots if necessary, so total network performance is increased. We compare the network performance of the proposed scheme with previous one, HyMAC [1].

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.680-682
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• 2018

기후변화에 따라 매개 질병의 발병 빈도가 증가하고 있으며 모기와 같은 매개체에 의해 전염되는 매개 질병은 인구집단에 대한 중요한 위협 요인이다. 이러한 질병 관리를 위해 지역별 모기 서식 현황을 모니터링 하는 시스템의 필요성이 강조되고 있다. 하지만 현재의 모기 모니터링은 개체 파악을 위한 분류와 동정을 사람이 직접 수행하기에 오랜 시간이 소요된다. 이 연구는 그러한 문제점을 해결하고 미래 매개곤충 서식 현황 파악 시스템의 기반을 마련하기 위해 심층 신경망(Deep Neural Networks)을 활용하여 한국 주요 매개모기 종 분류를 수행하고 결과를 분석하였다. 종 분류를 위한 모델은 잘 알려진 신경망 모델인 DenseNet(Densely Connected Networks)을 사용하였고 이를 직접 촬영한 모기 데이터와 약간의 변형을 가한 모기 데이터를 사용하여 학습시켰다. 학습 데이터를 각각 5배, 20배, 100배로 증강하여 실제 데이터의 부족을 보완하였으며, 이를 통해 최대 99.48%의 정확도를 달성하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3034-3055
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• 2014

Wireless Sensor Networks have extensively been utilized for ambient data collection from simple linear structures to dense tiered deployments. Issues related to optimal resource allocation still persist for simplistic deployments including linear and hierarchical networks. In this work, we investigate the case of dimensioning parameters for linear and tiered wireless sensor network deployments with notion of providing extended lifetime and reliable data delivery over extensive infrastructures. We provide a single consolidated reference for selection of intrinsic sensor network parameters like number of required nodes for deployment over specified area, network operational lifetime, data aggregation requirements, energy dissipation concerns and communication channel related signal reliability. The dimensioning parameters have been analyzed in a pipeline monitoring scenario using ZigBee communication platform and subsequently referred with analytical models to ensure the dimensioning process is reflected in real world deployment with minimum resource consumption and best network connectivity. Concerns over data aggregation and routing delay minimization have been discussed with possible solutions. Finally, we propose a node placement strategy based on a dynamic programming model for achieving reliable received signals and consistent application in structural health monitoring with multi hop and long distance connectivity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2029-2043
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• 2018

In this paper, we propose a novel inter-cell handover approach from a new perspective in dense Heterogeneous and Small Cell Networks (HetSNets). We first devise a cell selection mechanism to choose a proper candidate small cell for the UEs that tend to implement inter-small cell handover (ICH). By exploiting the property of a typical non-concentric circle, i.e., circle of Apollonius, we then propose a novel analytical method for modeling inter-cell handover regions and present mathematical derivation to prove that the inter-small cell handover issues fit the property of the circle of Apollonius. We design an inter-cell handover algorithm (ICHA) by means of our proposed handover model to dynamically configure hysteresis margin and properly implement handover decision in terms of UE's mobility. Simulation results demonstrate that the proposed ICHA yields lower call drop rate and radio link failure rate than the conventional methods and hence achieve high Handover Performance Indicator (HPI).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5425-5448
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• 2018

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1238-1259
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• 2019

Saving energy is a big challenge for Wireless Sensor Networks (WSNs), which becomes even more critical in large-scale WSNs. Most energy waste is communication related, such as collision, overhearing and idle listening, so the schedule-based access which can avoid these wastes is preferred for WSNs. On the other hand, clustering technique is considered as the most promising solution for topology management in WSNs. Hence, providing interference-free clustering is vital for WSNs, especially for large-scale WSNs. However, schedule management in cluster-based networks is never a trivial work, since it requires inter-cluster cooperation. In this paper, we propose a clustering method, called Interference-Free Clustering Protocol (IFCP), to partition a WSN into interference-free clusters, making timeslot management much easier to achieve. Moreover, we model the clustering problem as a multi-objective optimization issue and use non-dominated sorting genetic algorithm II to solve it. Our proposal is finally compared with two adaptive clustering methods, HEED-CSMA and HEED-BMA, demonstrating that it achieves the good performance in terms of delay, packet delivery ratio, and energy consumption.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.