{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T15:21:00Z","timestamp":1777908060430,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Erasmus+ Student\/Staff Mobility Exchange Program","award":["2022-1-IT02-KA171-HED-000069979"],"award-info":[{"award-number":["2022-1-IT02-KA171-HED-000069979"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1\u2013110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system\u2019s performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.<\/jats:p>","DOI":"10.3390\/s24185948","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T07:30:35Z","timestamp":1726212635000},"page":"5948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6977-2070","authenticated-orcid":false,"given":"Abdusomad","family":"Nur","sequence":"first","affiliation":[{"name":"Addis Ababa Institute of Technology, Addis Ababa University, King George VI St, Addis Ababa 1000, Ethiopia"},{"name":"Institute of Mechanical Intelligence, Scuola Superiore Sant\u2019Anna, Via G. Moruzzi 1, 56124 Pisa, Italy"}]},{"given":"Yonas","family":"Muanenda","sequence":"additional","affiliation":[{"name":"Institute of Mechanical Intelligence, Scuola Superiore Sant\u2019Anna, Via G. Moruzzi 1, 56124 Pisa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lin, W., Zhang, C., Li, L., and Liang, S. (2012, January 21\u201323). Review on development and applications of fiber-optic sensors. Proceedings of the 2012 Symposium on Photonics and Optoelectronics, Shanghai, China.","DOI":"10.1109\/SOPO.2012.6270996"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bublin, M. (2021). Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches. Sensors, 21.","DOI":"10.3390\/s21227527"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pierce, S., MacLean, A., and Culshaw, B. (2000, January 13). Optical frequency domain reflectometry for interrogation of microbend based optical fibre sensors. Proceedings of the SPIE\u2019s 7th Annual International Symposium on Smart Structures and Materials, Newport Beach, CA, USA.","DOI":"10.1117\/12.388124"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1093\/ietcom\/e91-b.4.1243","article-title":"Restorability of Rayleigh Backscatter Traces Measured by Coherent OTDR with Precisely Frequency-Controlled Light Source","volume":"E91-B","author":"Imahama","year":"2008","journal-title":"IEICE Trans. Commun."},{"key":"ref_5","first-page":"105105","article-title":"Characterization of Optical Components Using OTDR","volume":"55","author":"Chang","year":"2016","journal-title":"Opt. Eng."},{"key":"ref_6","first-page":"56","article-title":"Quality Assurance Techniques for Fiber Optic Networks","volume":"25","author":"Lee","year":"2017","journal-title":"Fiber Opt. Rev."},{"key":"ref_7","first-page":"2405","article-title":"Principles and Applications of Distributed Acoustic Sensing Using Rayleigh back-scattering","volume":"38","author":"Smith","year":"2020","journal-title":"J. Light. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Muanenda, Y., Oton, C.J., Faralli, S., and Di Pasquale, F. (2017, January 11\u201318). A \u03d5-OTDR Sensor for High-Frequency Distributed Vibration Measurements with Minimal Post-Processing. Proceedings of the 19th Italian National Conference on Photonic Technologies, Padua, Italy.","DOI":"10.1049\/cp.2017.0213"},{"key":"ref_9","first-page":"1830","article-title":"Advancements in Distributed Acoustic Sensors for Structural Health Monitoring","volume":"19","author":"Zhang","year":"2019","journal-title":"Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lu, B., Ye, Q., and Cai, H. (2020). Recent Progress in Distributed Fiber Acoustic Sensing with \u03d5-OTDR. Sensors, 20.","DOI":"10.3390\/s20226594"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pan, Z., Liang, K., Ye, Q., Cai, H., Qu, R., and Fang, Z. (2011, January 13\u201316). Phase-sensitive OTDR system based on digital coherent detection. Proceedings of the Asia Communications and Photonics Conference and Exhibition, Shanghai, China.","DOI":"10.1364\/ACP.2011.83110S"},{"key":"ref_12","first-page":"56","article-title":"Advancements in OTDR Technology: A Review","volume":"13","author":"Wang","year":"2021","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_13","unstructured":"Smith, P., and Johnson, R. (2020, January 8\u201312). Advancements in OTDR Technology for Fiber Network Maintenance. Proceedings of the Optical Fiber Communication Conference (OFC), San Diego, CA, USA. Paper Th3G.2."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3481","DOI":"10.1143\/JJAP.42.3481","article-title":"Fiber Optic Intrusion Sensor using Coherent Optical Time Domain Reflectometer","volume":"42","author":"Park","year":"2003","journal-title":"Jpn. J. Appl. Phys."},{"key":"ref_15","first-page":"95","article-title":"Distributed Acoustic Sensing in Oil and Gas Industry","volume":"76","author":"Chen","year":"2021","journal-title":"Oil Gas Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"21957","DOI":"10.3390\/s150921957","article-title":"A Long Distance Phase-Sensitive Optical Time Domain Reflectometer with Simple Structure and High Locating Accuracy","volume":"15","author":"Shi","year":"2015","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1109\/JSEN.2012.2207716","article-title":"Development of an Intrusion Sensor Based on a Polarization-OTDR System","volume":"12","author":"Linze","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1049\/el:19840022","article-title":"Fading in heterodyne OTDR","volume":"20","author":"Healey","year":"1984","journal-title":"Electron. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/JLT.2008.928957","article-title":"Fiber-Optic Distributed Strain and Temperature Sensing With Very High Measurand Resolution Over Long Range Using Coherent OTDR","volume":"27","author":"Koyamada","year":"2009","journal-title":"J. Light. Technol."},{"key":"ref_20","first-page":"203","article-title":"Signal Processing Techniques for OTDR Data Interpretation","volume":"30","author":"Patel","year":"2018","journal-title":"Signal Process. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Humane, P., and Varshapriya, J.N. (2015, January 6\u20138). Simulation of cloud infrastructure using CloudSim simulator: A practical approach for researchers. Proceedings of the 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Avadi, India.","DOI":"10.1109\/ICSTM.2015.7225415"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jeon, H., Cho, C., Shin, S., and Yoon, S. (2019, January 5\u20137). A CloudSim-Extension for Simulating Distributed Functions-as-a-Service. Proceedings of the 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Gold Coast, QLD, Australia.","DOI":"10.1109\/PDCAT46702.2019.00076"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jesit.2016.10.004","article-title":"Simulation modeling of cloud computing for smart grid using CloudSim","volume":"4","author":"Mehmi","year":"2017","journal-title":"J. Electr. Syst. Inf. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.future.2023.02.010","article-title":"A scalable simulator for cloud, fog and edge computing platforms with mobility support","volume":"144","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Samuel, J.R., Singh, J., Mehrotra, S., and Baiju, B.V. (2023, January 14\u201316). Classification and Analysis of Issues Faced by Open Source Simulation Software in the Field of Fog and Edge Computing. Proceedings of the 2023 International Conference on Next Generation Electronics (NEleX), Vellore, India.","DOI":"10.1109\/NEleX59773.2023.10421123"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5783","DOI":"10.1007\/s10586-024-04277-x","article-title":"Edge Server Placement and Allocation Optimization: A Tradeoff for Enhanced Performance","volume":"27","author":"Ghasemzadeh","year":"2024","journal-title":"Cluster Comput."},{"key":"ref_27","first-page":"409","article-title":"Fiber Optic Distributed Sensing","volume":"7","author":"Alfredo","year":"2014","journal-title":"J. Light. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3671","DOI":"10.1109\/JLT.2021.3059771","article-title":"Optical fiber distributed acoustic sensors: A review","volume":"39","author":"He","year":"2021","journal-title":"J. Light. Technol."},{"key":"ref_29","first-page":"1500","article-title":"Emerging Trends in Distributed Acoustic Sensing: Challenges and Opportunities","volume":"22","author":"Garcia","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3897873","DOI":"10.1155\/2018\/3897873","article-title":"Recent Advances in Distributed Acoustic Sensing Based on Phase-Sensitive Optical Time Domain Reflectometry","volume":"2018","author":"Muanenda","year":"2018","journal-title":"J. Sensors"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"10644","DOI":"10.1364\/OE.27.010644","article-title":"Dynamic phase extraction in high-SNR DAS based on UWFBGs without phase unwrapping using scalable homodyne demodulation in direct detection","volume":"27","author":"Muanenda","year":"2019","journal-title":"Opt. Express"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big Data for Remote Sensing: Challenges and Opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"020401","DOI":"10.1063\/1.5144123","article-title":"Big data on the horizon from a new generation of distributed optical fiber sensors","volume":"5","author":"Westbrook","year":"2020","journal-title":"APL Photonics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1757-899X\/1032\/1\/012012","article-title":"Storing data from sensors networks","volume":"Volume 1032","author":"Tsvetanov","year":"2021","journal-title":"2021 IOP Conference Series: Materials Science and Engineering"},{"key":"ref_35","first-page":"683015","article-title":"Ground target detection, classification, and sensor fusion in distributed fiber seismic sensor network","volume":"Volume 6831","author":"Xing","year":"2007","journal-title":"Advanced Sensor Systems and Applications III"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.3390\/smartcities4030064","article-title":"Cloud-Based IoT Applications and Their Roles in Smart Cities","volume":"4","author":"Alam","year":"2021","journal-title":"Smart Cities"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"128889","DOI":"10.1109\/ACCESS.2021.3112880","article-title":"A Systematic Review of Data Models for the Big Data Problem","volume":"9","author":"Mostajabi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_38","unstructured":"Elhemali, M., Gallagher, N., Tang, B., Gordon, N., Huang, H., Chen, H., Idziorek, J., Wang, M., Krog, R., and Zhu, Z. (2022, January 11\u201313). Amazon DynamoDB: A scalable, predictably performant, and fully managed NoSQL database service. Proceedings of the 2022 USENIX Annual Technical Conference, Carlsbad, CA, USA."},{"key":"ref_39","unstructured":"Amazon Web Services (2023). Amazon DynamoDB Developer Guide, Amazon Web Services, Inc."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Agrawal, G.P. (2010). Fiber-Optic Communication Systems, John Wiley & Sons. [4th ed.].","DOI":"10.1002\/9780470918524"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., and Vogels, W. (2007). Dynamo: Amazon\u2019s Highly Available Key-Value Store, Amazon.com.","DOI":"10.1145\/1294261.1294281"},{"key":"ref_42","first-page":"1493","article-title":"Optical Time-Domain Reflectometry: Principles and Applications","volume":"30","author":"Beltran","year":"2018","journal-title":"IEEE Photonics Technol. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4304\/jcp.6.3.404-411","article-title":"An Effective Economic Management of Resources in Cloud Computing","volume":"6","author":"Belalem","year":"2011","journal-title":"J. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Nur, A., Di Pasquale, F., and Muanenda, Y. (2023, January 18\u201320). Design of a real-time big data analytics scheme for continuous monitoring with a distributed acoustic sensor. Proceedings of the PIE Future Sensing Technologies 2023, Yokohama, Japan.","DOI":"10.1117\/12.2644955"},{"key":"ref_45","first-page":"1872","article-title":"OTDR-Based Characterization of Optical Components in Dense Wavelength Division Multiplexing Systems","volume":"37","author":"Gupta","year":"2019","journal-title":"J. Light. Technol."},{"key":"ref_46","first-page":"125","article-title":"CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms","volume":"40","author":"Calheiros","year":"2010","journal-title":"Softw. Pract. Exp."},{"key":"ref_47","first-page":"11","article-title":"A Simulative Study on the Performance of Load Balancing Techniques over Varying Cloud Infrastructure Using CloudSim","volume":"8","author":"Khan","year":"2020","journal-title":"Am. J. Comput. Sci. Eng. Surv."},{"key":"ref_48","unstructured":"Ahmed, A.A.N., and Firas, D. (2013). Cloud Computing: Technical Challenges and CloudSim Functionalities. Int. J. Sci. Res. (IJSR), 2."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/5948\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:55:43Z","timestamp":1760111743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/5948"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":48,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24185948"],"URL":"https:\/\/doi.org\/10.3390\/s24185948","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,13]]}}}