A Block-based Linear Index Supporting NN Search for Massive Clients

doi:10.22716/sckt.2025.13.3.010

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2025 Vol.13, Issue 3 Preview Page Next Page

Research Article

A Block-based Linear Index Supporting NN Search for Massive Clients 대규모 클라이언트를 지원하는 NN 탐색 블록 기반 선형 인덱스: Seokjin Im¹
임석진¹; ¹Professor, School of Liberal Arts, Hansei University

¹한세대학교 교양학부 교수

30 September 2025. pp. 103-111

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Abstract

인공지능(AI), 사물인터넷(IoT), 빅데이터, 클라우드 컴퓨팅, 블록체인, 5G와 같은 4차 산업혁명 기술은 방대한 수의 클라이언트를 동시에 지원할 수 있는 효율적인 데이터 전송 메커니즘에 대한 수요를 급격히 증가시켰다. 특히 IoT 환경에서 무선 데이터 방송은 이러한 요구를 충족시킬 수 있는 경쟁력있는 대안으로 주목받고 있다. 본 논문에서는 무선 방송 채널에서 최근접 이웃(Nearest Neighbor, NN) 검색을 효율적으로 수행하기 위한 블록 기반 선형 인덱스(Block-based Linear Index, BLI)를 제안한다. 제안된 방법은 공간 데이터를 클러스터링을 통해 불규칙한 형태의 블록으로 분할하고, 블록 분포 정보를 선형 테이블 구조에 저장한다. 이를 통해 NN 탐색 공간을 줄여서 기존의 HCI, DSI, NSPI와 같은 인덱싱 기법에 비해 접근 시간과 튜닝 시간을 단축한다. 시뮬레이션 결과, BLI는 NN 검색 시간과 에너지 효율성을 향상시켜, 대규모 실시간 데이터 접근이 요구되는 현대 무선 컴퓨팅 환경에 적합한 인덱싱 기법임을 보였다.

The convergence of Fourth Industrial Revolution technologies—such as artificial intelligence (AI), the Internet of Things (IoT), big data, cloud computing, blockchain, and 5G—has created an urgent demand for efficient data delivery mechanisms capable of serving massive numbers of clients simultaneously. Wireless data broadcasting has emerged as a promising solution for such environments, particularly in IoT applications. In this paper, we propose a block-based linear index (BLI) to enable efficient nearest neighbor (NN) search in wireless broadcast channels. The proposed method partitions spatial data into irregularly shaped blocks using clustering, and stores block distribution information in a linear table structure. This approach reduces the NN search space, resulting in lower access time and tuning time compared to conventional indexing techniques such as HCI, DSI, and NSPI. Simulation results demonstrate that BLI achieves faster NN retrieval and improved energy efficiency, making it suitable for large-scale, real-time data access in modern wireless computing environments.

Keywords

Data Block

Nearest Neighbor

Wireless Data Broadcasting

References

D. J. Shin, S. Y. Hwang, and J. J. Kim, “Development of system for drunk driving prevention using dig data in IoT environment,” The Journal of The Institute of Internet, Broadcasting and Communication (IIBC), Vol. 22, No. 6, pp. 69-74, December, 2022.
S. Minzheong, “Business model types of Web3.0 social token shaped by tokenomics,” International Journal of Advanced Smart Convergence (IJASC), Vol. 13, No. 3, pp. 156-169, September, 2024.
Y. Hwang, and Y. Wu, “Methodology for visual communication design based on generative AI,” International Journal of Advanced Smart Convergence (IJASC), Vol. 13, No. 3, pp. 170-175, September, 2024.
S. Im, “NN Search based on non-uniform space partition on air,” Turkish Journal of Computer and Mathematics Education, Vol. 12, No. 5, pp. 317-326, 2021.
10.17762/turcomat.v12i5.953
A. B. Waluyo, F. Zhu, D. Taniar, and B. Srinivasan, “Design and implementation of a mobile broadcast system,” IEEE International Conference on Advanced Information Networking and Applications, Victoria, Canada, 2014.
10.1109/AINA.2014.56
S. Im, H. Youn, J. Choi, and J. Ouyang, “A novel air indexing scheme for window query in non-flat wireless spatial data broadcast,” Journal of Communication and Networks, Vol. 13, No. 4, pp. 400-407.
10.1109/JCN.2011.6157460
B. Zheng, W. C. Lee, and D. L. Lee, “Spatial queries in wireless broadcast systems,” Wireless Network, Vol. 10, No. 6, 723-736, pp. 723-236, 2004.
10.1023/B:WINE.0000044031.03597.97
B. Zheng, W. C. Lee, K. C. K, D. L. Lee, and M. Shao, “A distributed spatial index for error-prone wireless data broadcast,” VLDB Journal, Vol. 18, No. 4, pp. 959-986, 2009.
10.1007/s00778-009-0137-2

Information

Publisher :The Society of Convergence Knowledge
Publisher(Ko) :융복합지식학회
Journal Title :The Society of Convergence Knowledge Transactions
Journal Title(Ko) :융복합지식학회논문지
Volume : 13
No :3
Pages :103-111
DOI :https://doi.org/10.22716/sckt.2025.13.3.010

[1] D. J. Shin, S. Y. Hwang, and J. J. Kim, “Development of system for drunk driving prevention using dig data in IoT environment,” The Journal of The Institute of Internet, Broadcasting and Communication (IIBC), Vol. 22, No. 6, pp. 69-74, December, 2022.

[2] S. Minzheong, “Business model types of Web3.0 social token shaped by tokenomics,” International Journal of Advanced Smart Convergence (IJASC), Vol. 13, No. 3, pp. 156-169, September, 2024.

[3] Y. Hwang, and Y. Wu, “Methodology for visual communication design based on generative AI,” International Journal of Advanced Smart Convergence (IJASC), Vol. 13, No. 3, pp. 170-175, September, 2024.

[4] S. Im, “NN Search based on non-uniform space partition on air,” Turkish Journal of Computer and Mathematics Education, Vol. 12, No. 5, pp. 317-326, 2021.
10.17762/turcomat.v12i5.953

[5] A. B. Waluyo, F. Zhu, D. Taniar, and B. Srinivasan, “Design and implementation of a mobile broadcast system,” IEEE International Conference on Advanced Information Networking and Applications, Victoria, Canada, 2014.
10.1109/AINA.2014.56

[6] S. Im, H. Youn, J. Choi, and J. Ouyang, “A novel air indexing scheme for window query in non-flat wireless spatial data broadcast,” Journal of Communication and Networks, Vol. 13, No. 4, pp. 400-407.
10.1109/JCN.2011.6157460

[7] B. Zheng, W. C. Lee, and D. L. Lee, “Spatial queries in wireless broadcast systems,” Wireless Network, Vol. 10, No. 6, 723-736, pp. 723-236, 2004.
10.1023/B:WINE.0000044031.03597.97

[8] B. Zheng, W. C. Lee, K. C. K, D. L. Lee, and M. Shao, “A distributed spatial index for error-prone wireless data broadcast,” VLDB Journal, Vol. 18, No. 4, pp. 959-986, 2009.
10.1007/s00778-009-0137-2

The Society of Convergence Knowledge Transactions ISSN:2287-8920(Print) 융복합지식학회논문지

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