• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.147-161
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• 2012

In recent years, machine-to-machine (M2M) communications are under rapid development to meet the fast-increasing requirements of multi-type wireless services and applications. In order to satisfy M2M communications requirements, heterogeneous networks convergence appears in many areas, i.e., mobile cellular networks (MCNs) and wireless sensor networks (WSNs) are evolving from heterogeneous to converged. In this paper, we introduce the system architecture and application requirement for converged MCN and WSN, where mobile terminals in MCN are acting as both sensor nodes and gateways for WSN. And then, we discuss the joint optimization of converged networks for M2M communications. Finally, we discuss the technical challenges in the converged process of MCN and WSN.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.67-74
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• 2021

As IoT(Internet Of Things) devices like a smart sensor have constrained power sources, a power strategy is critical in WSN(Wireless Sensor Networks). Therefore, it is necessary to figure out the residual power of each sensor node for managing power strategies in WSN, which, however, requires additional data transmission, leading to more power consumption. In this paper, a residual power estimation method was proposed, which uses ignorantly small amount of power consumption in the resource-constrained wireless networks including WSN. A residual power prediction is possible with the least data transmission by using Machine Learning method with some training data in this proposal. The performance of the proposed scheme was evaluated by machine learning method, simulation, and analysis.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.833-852
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• 2019

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

• 전자통신동향분석
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• 제34권3호
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• pp.93-105
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• 2019

This study emphasizes the importance of the newly added Ultra-Reliable and Low-Latency Communications (URLLC) service as an important evolutionary step for 5G mobile communication, and proposes a remedial application. We analyze the requirements for the application of 5G mobile communication technology in high-precision vertical industries and applications, introduce the 5G URLLC design principles and standards of 3GPP, and summarize the current state of applied artificial intelligence technology in wireless communication. Additionally, we summarize the current state of research on ultra-reliable and low-latency machine learning-based wireless communication technology for application in ultra-high-precision vertical industries and applications. Furthermore, we discuss the technological direction of artificial intelligence technology for URLLC wireless communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.162-177
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• 2012

In this paper we present a novel method of applying image scrambling technology which belongs to the information hiding field in the error control coding to introduce confidentiality in wireless machine to machine communication. The interleaver in serial concatenated convolutional codes, which is the key module in overcoming burst errors, is deliberately designed with the scrambling function to provide a low error rate for those authorized transceivers. By contrast, the unauthorized transceivers without keys would get so high an error rate that decoding bits could bring little value, thus realizing both the confidentiality and reliability in wireless machine to machine communication.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.451-453
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• 2019

최근 무선 통신기기의 사용이 증가함에 따라 무선 네트워크 사용량이 증가하여 유선 네트워크와 무선 네트워크가 혼합되어 네트워크가 형성 되었다. 기존 TCP 알고리즘들은 유선 네트워크에 적합하게 설계 되었다. 따라서 현대의 네트워크 환경에서 패킷 손실을 정확히 구별하지 못하고 부적절한 혼잡제어를 수행하여 TCP의 성능 저하를 초래한다. 본 논문에서는 TCP 성능을 개선하기 위하여 패킷 손실이 발생한 환경에 따라 정확히 구분할 수 있는 SLDA(Support vector machine based Loss Discrimination Al gorithm)를 제안하고 그 성능을 평가한다.

• 한국통신학회논문지
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• 제36권7B호
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• pp.771-779
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• 2011

It is expected that many heterogeneous wireless systems, such as 3GPP LTE systems, WiMAX systems and WLAN systems, will coexist in the next generation wireless communication environments. Integrated radio resource management and seamless vertical handoff (VHO) should be supported to provide integrated communication services over multi-radio access networks. A new class of adaptive VHO system that views the handoff problem as a pattern recognition problem is proposed. In this paper, we propose a unified radio resource management (URRM) architecture and Support Vector Machine (SVM) based vertical handoff decision system. Extensive simulation studies show the proposed VHO algorithm outperforms RSS based VHO algorithms in terms of throughput and service cost.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2255-2281
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• 2021

Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.

• 한국통신학회논문지
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• 제29권4C호
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• pp.581-593
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• 2004

무선랜 시스템이 초고속 무선인터넷의 인프라로 자리 잡으면서 무선랜 보안에 관한 관심이 급속히 커가고 있다. 기존의 IEEE 802.11 기반의 무선랜 보안 요소라 할 수 있는 WEP 알고리즘의 취약점을 극복하기 위한 노력의 일환으로 Wi-Fi에서는 WPA 보안규격을 발표하였다. WEP 알고리즘을 사용하는 단말기와 WPA 지원 단말기가 동시에 존재하는 혼합모드 무선랜 환경에서는 각 단말기별 unicast용 pairwise 키 관리와 전체 단말기에 대한 broadcast용 group 키 관리가 훨씬 복잡하다. 본 논문에서는 pairwise 키와 group 키 관리를 위한 WPA authenticator 키 관리 상태머신의 취약점을 분석하고, 분석된 각각의 취약점을 극복할 수 있는 대응방안을 제시한다. 또한, 제시된 해결방안이 적용된 WPA authenticator 키 관리 상태머신의 재구성된 형태를 보인다. 본 논문에서 재구성한 키 관리 방식은 혼합모드 무선랜 환경에서 다양한 접속 방식의 단말기들에 대해서 group 키 교환과 group 키 업데이트 수행을 효과적으로 처리할 수 있는 토대를 제공한다.