基於機器學習的Beacon室內定位應用於智慧零售系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：111

、訪客IP：18.119.253.184

姓名

林琮皓(Tsung-Hao Lin) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

基於機器學習的Beacon室內定位應用於智慧零售系統
(ML-based Beacon Indoor positioning for Smart Retail System)

相關論文

★ 整合GRAFCET虛擬機器的智慧型控制器開發平台	★ 分散式工業電子看板網路系統設計與實作
★ 設計與實作一個基於雙攝影機視覺系統的雙點觸控螢幕	★ 智慧型機器人的嵌入式計算平台
★ 一個即時移動物偵測與追蹤的嵌入式系統	★ 一個固態硬碟的多處理器架構與分散式控制演算法
★ 基於立體視覺手勢辨識的人機互動系統	★ 整合仿生智慧行為控制的機器人系統晶片設計
★ 嵌入式無線影像感測網路的設計與實作	★ 以雙核心處理器為基礎之車牌辨識系統
★ 基於立體視覺的連續三維手勢辨識	★ 微型、超低功耗無線感測網路控制器設計與硬體實作
★ 串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計	★ 一個快速立體視覺系統的嵌入式硬體設計
★ 即時連續影像接合系統設計與實作	★ 基於雙核心平台的嵌入式步態辨識系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-7-13以後開放)

摘要(中)

零售業導入智慧零售的需求日益增長，但導入無人商店的智慧零售缺乏了互動性，也因此阻礙了智慧零售的發展。為了讓顧客不僅是體驗智慧零售而來，提升購物體驗則成了最大的課題，這也因此影響了零售業導入智慧零售的意願。本論文中提出一套基於機器學習的Beacon室內定位智慧零售互動平台，利用藍芽Beacon訊號進行特徵擷取，再透過遞迴式機率神經網路進行位置的辨識，搭配樹莓派作為電子紙互動平台閘道器，並將辨識結果傳至樹莓派控制電子紙並與顧客進行互動。透過MQTT作為樹莓派與PC之間的通訊方式，並使用PSO結合RPNN進行Beacon訊號的辨識，其平均辨識準確度可達到97.56%，與LSTM相比其效果提升6%。本論文規劃一套完整的智慧零售系統，使用藍芽Beacon結合機器學習提升定位準確度，並利用電子紙平台與顧客進行互動，以藍芽及樹莓派的應用降低智慧零售導入的成本，實現零售業的數位轉型。

摘要(英)

There is a growing demand for smart retail in the retail industry, but the lack of interactivity in unmanned stores has hindered the development of smart retail. In order to let customers not only experience smart retailing, improving the shopping experience is the biggest issue, which affects the willingness of the retail industry to implement smart retailing. In this paper, we propose a machine-learning-based Beacon indoor location-based smart retailing interactive platform, which uses Bluetooth Beacon signals for feature acquisition, and then uses a recursive probabilistic neural network for location identification, and Raspberry Pi as the gateway to the e-paper interactive platform then transmits the identification results to Raspberry Pi to control the e-paper and interact with customers. By using MQTT as the communication method between Raspberry Pi and PC, then using PSO combined with RPNN for Beacon signal recognition, the average recognition accuracy can reach 97.56%, which is 6% better than LSTM. In this paper, we plan a complete smart retail system, using Bluetooth Beacon combined with machine learning to improve the positioning accuracy, and using the e-paper platform to interact with customers, using Bluetooth and Raspberry Pi to reduce the cost of smart retail implementation and realize the digital transformation of the retail industry.

關鍵字(中)

★ 智慧零售
★ 遞迴式機率神經網路
★ 藍芽Beacon
★ 室內定位

關鍵字(英)

★ Smart Retail
★ Recursive Probabilistic Neural Network
★ Bluetooth Beacon
★ Indoor Positioning

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 3
第二章、技術回顧 4
2.1. 室內定位技術 4
2.1.1. 到達時間定位法(TOA) 4
2.1.2. 到達時間差定位法(TDOA) 5
2.1.3. 到達角度定位法(AOA) 6
2.1.4. 接收訊號強度指示(RSSI) 6
2.2. RSSI訊號過濾 8
2.2.1. 簡單移動平均線 8
2.2.2. 均值及中值濾波器 8
2.2.3. 卡爾曼濾波器(Kalman Filter，KF) 8
2.3. 機器學習的室內定位技術 10
2.3.1. 機率神經網路 10
2.3.2. 遞迴神經網路 12
2.3.3. 遞迴式機率神經網路 15
第三章、系統架計 17
3.1. 系統設計方法論 17
3.2. 智慧零售互動系統架構 19
3.2.1. 藍芽通訊子系統 21
3.2.2. 位置特徵擷取子系統 22
3.2.3. RPNN位置辨識子系統 24
3.2.4. 電子紙互動子系統 25
3.3. 系統程式合成 27
第四章、系統整合與實驗 30
4.1. 實驗平台 30
4.1.1. Raspberry pi 4B閘道器 30
4.1.2. Beacon 信標 31
4.1.3. 電子紙模組 33
4.1.4. 實驗環境及位置分割 34
4.1.5. MQTT通訊協定 35
4.2. 資料前處理 36
4.3. 位置特徵資料庫建立 39
4.4. 位置辨識模型實驗 41
4.4.1. 實驗模型評估指標 41
4.4.2. 模型設計與應用 43
4.4.3. 實驗結果 45
4.5. 智慧零售互動實驗 48
第五章、結論與未來展望 50
5.1. 結論 50
5.2. 未來展望 51
參考文獻 52

參考文獻

[1] C. Xiao, D. Yang, Z. Chen, and G. Tan, "3-D BLE indoor localization based on denoising autoencoder," IEEE Access, vol. 5, pp. 12751-12760, 2017.
[2] Y. Zheng and Y. Li, "Unmanned retail’s distribution strategy based on sales forecasting," 2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS), pp. 1-5, 2018.
[3] B. Guo, Z. Wang, P. Wang, T. Xin, D. Zhang, and Z. Yu, "DeepStore: Understanding customer behaviors in unmanned stores," IT Professional, vol. 22, no. 3, pp. 55-63, 2020.
[4] H. Liu, H. Darabi, P. Banerjee, and J. Liu, "Survey of wireless indoor positioning techniques and systems," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 6, pp. 1067-1080, 2007.
[5] M. Azizyan, I. Constandache, and R. R. Choudhury, "Surroundsense: Mobile phone localization via ambience fingerprinting," Proceedings of the 15th annual international conference on Mobile computing and networking, Beijing, China, pp. 261–272, 2009.
[6] J. Wei, X. Zhou, F. Zhao, H. Luo, and L. Ye, "Zero-cost and map-free shop-level localization algorithm based on crowdsourcing fingerprints," 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), pp. 1-10, 2018.
[7] R. Mautz and S. Tilch, "Survey of optical indoor positioning systems," 2011 International Conference on Indoor Positioning and Indoor Navigation, pp. 1-7, 2011.
[8] P. Bahl and V. N. Padmanabhan, "RADAR: an in-building RF-based user location and tracking system," Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), vol. 2, pp. 775-784 vol.2, 2000.
[9] S. S. Saab and Z. S. Nakad, "A standalone RFID indoor positioning system using passive tags," IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 1961-1970, 2011.
[10] Z. Sheng and J. K. Pollard, "Position measurement using Bluetooth," IEEE Transactions on Consumer Electronics, vol. 52, no. 2, pp. 555-558, 2006.
[11] A. Mackey, P. Spachos, L. Song, and K. N. Plataniotis, "Improving BLE beacon proximity estimation accuracy through bayesian filtering," IEEE Internet of Things Journal, vol. 7, no. 4, pp. 3160-3169, 2020.
[12] S. Wu, W. Huang, M. Li, and K. Xu, "A novel RSSI fingerprint positioning method based on virtual AP and convolutional neural network," IEEE Sensors Journal, vol. 22, no. 7, pp. 6898-6909, 2022.
[13] H. K. Fard, Y. Chen, and K. K. Son, "Indoor positioning of mobile devices with agile iBeacon deployment," 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 275-279, 2015.
[14] L. Bai, F. Ciravegna, R. Bond, and M. Mulvenna, "A low cost indoor positioning system using Bluetooth low energy," IEEE Access, vol. 8, pp. 136858-136871, 2020.
[15] S. Sophia, B. M. Shankar, K. Akshya, A. C. Arunachalam, V. T. Y. Avanthika, and S. Deepak, "Bluetooth Low Energy based Indoor Positioning System using ESP32," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1698-1702, 2021.
[16] S. R. Misal, S. R. Prajwal, H. M. Niveditha, H. M. Vinayaka, and S. Veena, "Indoor Positioning System (IPS) using ESP32, MQTT and Bluetooth," 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 79-82, 2020.
[17] F. Zafari, A. Gkelias, and K. K. Leung, "A survey of indoor localization systems and technologies," IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568-2599, 2019.
[18] A. Mackey, P. Spachos, and K. N. Plataniotis, "Smart parking system based on Bluetooth low energy beacons with particle filtering," IEEE Systems Journal, vol. 14, no. 3, pp. 3371-3382, 2020.
[19] P. Dickinson, G. Cielniak, O. Szymanezyk, and M. Mannion, "Indoor positioning of shoppers using a network of Bluetooth low energy beacons," 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-8, 2016.
[20] K. E. Jeon, J. She, P. Soonsawad, and P. C. Ng, "BLE beacons for Internet of Things applications: Survey, challenges, and opportunities," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 811-828, 2018.
[21] S. S. Chawathe, "Beacon placement for Indoor localization using Bluetooth," 2008 11th International IEEE Conference on Intelligent Transportation Systems, pp. 980-985, 2008.
[22] F. Palumbo, P. Barsocchi, S. Chessa, and J. C. Augusto, "A stigmergic approach to indoor localization using Bluetooth low energy beacons," 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-6, 2015.
[23] R. Faragher and R. Harle, "Location fingerprinting with Bluetooth low energy beacons," IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, pp. 2418-2428, 2015.
[24] J. Röbesaat, P. Zhang, M. Abdelaal, and O. Theel, "An improved BLE indoor localization with Kalman-based fusion: An experimental study," Sensors, vol. 17, no. 5, p. 951, 2017.
[25] R. K. Yadav, B. Bhattarai, H. Gang, and J. Pyun, "Trusted K nearest bayesian estimation for indoor positioning system," IEEE Access, vol. 7, pp. 51484-51498, 2019.
[26] D. D. Nguyen and M. T. Le, "Enhanced indoor localization based BLE using gaussian process regression and improved weighted kNN," IEEE Access, vol. 9, pp. 143795-143806, 2021.
[27] Z. Su, K. Pahlavan, and E. Agu, "Performance evaluation of COVID-19 proximity detection using Bluetooth LE signal," IEEE Access, vol. 9, pp. 38891-38906, 2021.
[28] Y. Wang, Q. Yang, G. Zhang, and P. Zhang, "Indoor positioning system using euclidean distance correction algorithm with bluetooth low energy beacon," 2016 International Conference on Internet of Things and Applications (IOTA), pp. 243-247, 2016.
[29] P. C. Ng, P. Spachos, and K. N. Plataniotis, "COVID-19 and your smartphone: BLE-based smart contact tracing," IEEE Systems Journal, vol. 15, no. 4, pp. 5367-5378, 2021.
[30] K. Echizenya and K. Kondo, "The effect of density and placement of BLE beacons on indoor location and motion direction estimation accuracy," 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 2023-2027, 2021.
[31] D. Sun, E. Wei, L. Yang, and S. Xu, "Improving fingerprint indoor localization using convolutional neural networks," IEEE Access, vol. 8, pp. 193396-193411, 2020.
[32] M. T. Hoang, B. Yuen, X. Dong, T. Lu, R. Westendorp, and K. Reddy, "Recurrent neural networks for accurate RSSI indoor localization," IEEE Internet of Things Journal, vol. 6, no. 6, pp. 10639-10651, 2019.
[33] N. Singh, S. Choe, and R. Punmiya, "Machine learning based indoor localization using Wi-Fi RSSI fngerprints: An overview," IEEE Access, vol. 9, pp. 127150-127174, 2021.
[34] A. Nessa, B. Adhikari, F. Hussain, and X. N. Fernando, "A survey of machine learning for indoor positioning," IEEE Access, vol. 8, pp. 214945-214965, 2020.
[35] V. F. Miramá, L. E. Díez, A. Bahillo, and V. Quintero, "A survey of machine learning in pedestrian localization systems: Applications, open issues and challenges," IEEE Access, vol. 9, pp. 120138-120157, 2021.
[36] B. T. Fang, "Simple solutions for hyperbolic and related position fixes," IEEE Transactions on Aerospace and Electronic Systems, vol. 26, no. 5, pp. 748-753, 1990.
[37] C. Drane, M. Macnaughtan, and C. Scott, "Positioning GSM telephones," IEEE Communications Magazine, vol. 36, no. 4, pp. 46-54, 1998.
[38] D. Niculescu and N. Badri, "Ad hoc positioning system (APS) using AOA," IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), vol. 3, pp. 1734-1743 vol.3, 2003.
[39] S. Mazuelas, A. Bahillo, R. M. Lorenzo, P. Fernandez, F. A. Lago, E. Garcia, J. Blas, and E. J. Abril, "Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks," IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 5, pp. 821-831, 2009.
[40] G. Welch and G. Bishop, "An introduction to the Kalman filter," pp. 127-132, 1995.
[41] C.-H. Chen, C.-C. Wang, and Y.-Z. Chen, "Intelligent brushing monitoring using a smart toothbrush with recurrent probabilistic neural network," Sensors, vol. 21, no. 4, p. 1238, 2021.
[42] D. F. Specht, "Probabilistic neural networks and the polynomial adaline as complementary techniques for classification," IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 111-121, 1990.
[43] S. Hochreiter, "The vanishing gradient problem during learning recurrent neural nets and problem solutions," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 06, no. 02, pp. 107-116, 1998.
[44] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[45] T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, vol. 270, no. 2, pp. 654-669, 2018.
[46] C.-H. Chen, P.-W. Chen, P.-J. Chen, and T.-H. Liu, "Indoor positioning using magnetic fingerprint map captured by magnetic sensor array," Sensors, vol. 21, no. 17, p. 5707, 2021.
[47] C.-H. Chen, M.-Y. Lin, and X.-C. Guo, "High-level modeling and synthesis of smart sensor networks for industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, 2017.
[48] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, "Internet of Things: A survey on enabling technologies, protocols, and applications," IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347-2376, 2015.
[49] S. Ioffe and C. Szegedy, "Batch normalization: accelerating deep network training by reducing internal covariate shift," International conference on machine learning, pp. 448-456, 2015.

指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2022-7-25

推文