بهبود کارایی و قابلیت اطمینان در سیستم مانیتورینگ داده‌های لرزه نگاری مبتنی بر اینترنت اشیاء با اعمال افزونگی در سنسورها و کنترلرها
محورهای موضوعی : اینترنت اشیا
ایمان زنگنه
1
,
امیر مسعود بیدگلی
2
,
اردشیر دولتی
3
1 - گروه مهندسی کامپیوتر، واحد تهران‌شمال، دانشگاه آزاد اسلامی، تهران، ایران
2 - گروه مهندسی کامپیوتر، واحد تهران‌شمال، دانشگاه آزاد اسلامی، تهران، ایران
3 - گروه علوم کامپیوتر، دانشکده علوم پایه، دانشگاه شاهد، تهران، ایران
کلید واژه: زلزله, اینترنت اشیا, مصرف انرژی, نرخ تحویل بسته, خطای بیتی,
چکیده مقاله :
زلزله معمولا خسارات همراه است. لذا هر اقدامی در جهت پیش‌بینی آن ضروری است. در سیستم‌های مانیتورینگ داده, بلادرنگ بودن و صحت و دقت داده‌ها, نقشی کلیدی دارد. در این مقاله, یک سیستم مانیتورینگ مبتنی بر اینترنت اشیا, برای پیام‌رسانی داده‌های مربوط به لرزه‌نگاری پیشنهاد شد. در راهکار اول, پروتکل سبک وزن انتقال تله‌متری صف پیام (MQTT) برای پیام‌رسانی انتخاب و بررسی شد. در راهکار دوم, با استفاده از الگوریتم گرگ خاکستری, افزونگی در لایه حسگر اعمال شد و در راهکار سوم, افزونگی در لایه کنترلر نیز اعمال شد. نتایج شبیه‌سازی نشان داد که افزونگی در لایه حسگر و کنترلر تا بیش از سی درصد در مصرف انرژی, صرفه جویی ایجاد کرد. همچنین میانگین تاخیر انتها به انتها در راهکار دوم و سوم بصورت معناداری کاهش یافت. نهایتا در راهکار اول, نرخ تحویل موفق بسته‌ها برای تعداد مختلف بسته‌ها, مقدار ثابت 98/78 درصد بود. اما با اعمال افزونگی در حسگر و کنترلر, نرخ تحویل بسته‌ها به بالای 92 درصد افزایش یافت که این میتواند نتیجه افزایش تعداد حسگرها و کنترلرها و جایگذاری مناسب آنها باشد.
Earthquakes are usually associated with damage. Therefore, any action to predict it is necessary. In data monitoring systems, being real-time and accuracy of data play a key role. In this article, a monitoring system based on the Internet of Things was proposed for the messaging of seismic data. In the first solution, the lightweight protocol Message Queuing Telemetry Transfer (MQTT) was chosen for messaging. In the second solution, redundancy was applied in the sensor layer using the gray wolf algorithm, and in the third solution، redundancy was applied in the controller layer. The simulation results showed that the redundancy in the sensor and controller layer saved energy consumption by more than thirty percent. Also, the average end-to-end delay was significantly reduced in the second and third solutions. Finally، in the first solution, the rate of successful package delivery for different numbers of packages was a constant value of 78.98%. But by applying redundancy in the sensor and controller, the package delivery rate increased to over 92%, which can be the result of increasing the number of sensors and controllers and their proper placement.
بهبود کارایی سیستم مانیتورینگ لرزه نگاری با اعمال افزونگی حسگرهای لایه حسگرهای مبتنی بر اینترنت اشیاء
اعمال افزونگی در لایه کنترلر سیستم لرزه نگاری مبتنی بر اینترنت اشیاء
بهبود تحمل پذیری خطا در لایه ارتباطاتی اینترنت اشیاء با اصلاح مکانیزمهای انتقال اطلاعات از کنترلر به زیر ساخت
[1] R. Dugga, N. Gupta, A. Pandya, P. Mahajan, K. Sharma, T. kaundal and P. Angra, “Building structural analysis based Internet of Things network assisted earthquake detection,” Internet of Things، vol. 19, p. 100561, August 2022, doi: 10.1016/j.iot.2022.100561.
[2] K. Saini, S. Kalra and S. K. Sood, “An Integrated Framework for Smart Earthquake Prediction: IoT، Fog and Cloud Computing,” Journal of Grid Computing , vol. 20, Article number: 17, May 2022, doi:10.1007/s10723-022-09600-7.
[3] S. K. McBride, D. F. Sumy, A. L. Llenos, G. A. Parker, J. McGuire, J. K. Saunders, M.-A. Meier, P. Schuback, D. Given, R. De-Groot, “Latency and geofence testing of wireless emergency alerts intended for the ShakeAlert earthquake early warning system for the West Coast of the United States of America,” Safety Science, vol. 157, p. 105898, January 2023, doi: 10.1016/j.ssci.2022.105898.
[4] R. Wanare, K. K. R. Iyer and P. Jayanthi, “Recent Advances in Early Warning Systems for Landslide Forecasting,” Geohazard Mitigation, pp. 249–260, doi:10.1007/978-981-16-6140-2_20.
[5] V. Babu and V. Rajan, "Flood and Earthquake Detection and Rescue Using IoT Technology," 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2019, pp. 1256-1260, doi: 10.1109/ICCES45898.2019.9002406.
[6] A. Wu, J. Lee, I. Khan and Y. -W. Kwon, "CrowdQuake+: Data-driven Earthquake Early Warning via IoT and Deep Learning," IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 2068-2075, doi: 10.1109/BigData52589.2021.9671971.
[7] S. Kim, I. Khan, S. Choi and Y. -W. Kwon, "Earthquake Alert Device Using a Low-Cost Accelerometer and its Services," in IEEE Access, vol. 9, pp. 121964-121974, 2021, doi: 10.1109/ACCESS.2021.3103505.
[8] I. Khan, M. Pandey and Y. -W. Kwon, "An earthquake alert system based on a collaborative approach using smart devices," IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft), Madrid, Spain, 2021, pp. 61-64, doi: 10.1109/MobileSoft52590.2021.00014.
[9] A. Alphonsa and G. Ravi, “Earthquake early warning system by iot using wireless sensor networks,” International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016, pp. 1201-1205, doi: 10.1109/WiSPNET.2016.7566327.
[10] R. Pirmagomedov, M. Blinnikov, A. Amelyanovich, R. Glushakov, S. Loskutov, A. Koucheryavy, R. Kirichek and E. Bobrikov, “IoT Based Earthquake Prediction Technology,” International Conference on Next Generation Wired/Wireless Networking Conference on Internet of Things and Smart Spaces, Internet of Things, Smart Spaces, and Next Generation Networks and Systems, pp 535–546, September 2018, doi: 10.1007/978-3-030-01168-0_48.
[11] P. Pierleoni, A. Belli, M. Esposito, R. Concetti and L. Palma, “Earthquake Early Warning Services Based on Very Low-Cost Internet of Things Devices,” 2022 61st FITCE International Congress Future Telecommunications: Infrastructure and Sustainability (FITCE), Article ID: 253424887, November 2022, doi: 10.23919/FITCE56290.2022.9934792.
[12] S. K. McBride, A. Bostrom, J. Sutton, R. M. De-Groot, A. S. Baltay, B. Terbush, P. Bodin, M. Dixon, E. Holland, R. Arba, P. Laustsen, S. Liu and M. Vinci, “Developing post-alert messaging for ShakeAlert, the earthquake early warning system for the West Coast of the United States of America,” International Journal of Disaster Risk Reduction, vol. 50, p. 101713, November 2020, doi: 10.1016/j.ijdrr.2020.101713.
[13] A.-M. Zambrano, I. Pérez, C. E. Palau and Manuel Esteve, “Sensor Web Enablement Applied to an Earthquake Early Warning System,” International Conference on Internet and Distributed Computing Systems, 2015, pp. 51–62, doi: 10.1007/978-3-319-23237-9_6.
[14] Y. Chavez-Rivera, B. Espinoza-Garcia and P. R. Yanyachi, "Low Cost Embedded IoT System to Record Meteorological, and Inertial Data in Remote Places," IEEE URUCON, Montevideo, Uruguay, 2021, pp. 273-277, doi: 10.1109/URUCON53396.2021.9647077.
[15] P. Boccadoro, B. Montaruli and L. A. Grieco, "QuakeSense, a LoRa-compliant Earthquake Monitoring Open System," IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), Cosenza, Italy, 2019, pp. 1-8, doi: 10.1109/DS-RT47707.2019.8958675.
[16] N. Moussa, E. Nurellari and A. E. El-Alaoui، “A Novel Energy-Efficient and Reliable ACO-Based Routing Protocol for WSN-Enabled Forest Fires Detection,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 9, February 2022, doi: 10.1007/s12652-022-03727-x.
[17] S. Kumari, R. Kumar, S. Kadry, S. Namasudra and D. Taniar, “Maintainable stochastic communication network reliability within tolerable packet error rate,” Computer Communications, vol. 178, October 2021, pp. 161-168, October 2021, doi: 10.1016/j.comcom.2021.07.023.
[18] Y. Zhang, W. Zhao, P. Dong, X. Du, W. Qiao and M. Guizani, “Improve the reliability of 6G vehicular communication through skip network coding,” Vehicular Communications, vol. 33, p. 100400, January 2022, doi: 10.1016/j.vehcom.2021.100400.
[19] J. Yongguo, L. Qiang, Q. Changshuai, S. Jian and L. Qianqian, "Message-oriented Middleware: A Review," 5th International Conference on Big Data Computing and Communications (BIGCOM), QingDao, China, 2019, pp. 88-97, doi: 10.1109/BIGCOM.2019.00023.
[20] N. Naik, "Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP," IEEE International Systems Engineering Symposium (ISSE), Vienna, Austria, 2017, pp. 1-7, doi: 10.1109/SysEng.2017.8088251.
[21] A. Rizzardi, S. Sicari and A. Coen-Porisin, “Analysis on functionalities and security features of Internet of Things related protocols,” Wireless Networks, vol. 28, pp. 2857–2887, June 2022, doi: 10.1007/s11276-022-02999-7.
[22] A. Yamawaki, M.Yamanaka and S. Serikawa, “A sensor node architecture with zero standby power on wireless sensor network,” Artificial Life and Robotics, vol. 20, pp. 210-216, July 2015, doi: 10.1007/s10015-015-0218-9.
[23] N. Oukas and M. Boulif, “Sensor Performance Evaluation for Long-Lasting EH-WSNs by GSPN Formulation, Considering Seasonal Sunshine Levels and Dual Standby Strategy,” Arabian Journal for Science and Engineering, vol. 48, no. 3, June 2022, doi: 10.1007/s13369-022-06970-8.
[24] B. Guruprakash, C. Balasubramanian and R. Sukumar, “An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN,” Peer-to-Peer Networking and Applications, vol. 13, pp. 304–319, 2020, doi: 10.1007/s12083-019-00779-3.
[25] S. Roshni, J. Senthilkumar, Y. Suresh and V. Mohanraj, “Advertisement valid time triggered firefly and fruit-fly inspired approach for efficient cluster formation and standby CH selection in hierarchical wireless sensor network,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 4697–4713, 2021, doi: 10.1007/s12652-020-01873-8.
[26] W. Barkhoda and H. Sheikhi, “Immigrant imperialist competitive algorithm to solve the multi-constraint node placement problem in target-based wireless sensor networks,” Ad Hoc Networks, vol. 106, p. 102183, September 2020, doi: 10.1016/j.adhoc.2020.102183.
[27] S. K. Gupta, P. Kuila and P. K. Jana, “Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks,” Computers & Electrical Engineering, vol. 56, pp. 544-556, November 2016, doi: 10.1016/j.compeleceng.2015.11.009.
[28] M. Banaie-Dezfouli, M. H. Nadimi-Shahraki and Z. Beheshti, “R-GWO: Representative-based grey wolf optimizer for solving engineering problems,” Applied Soft Computing, vol. 106, p. 107328, July 2021, doi: 10.1016/j.asoc.2021.107328.
[29] H. Tang, W. Sun, A. Lin, M. Xue and X. Zhang, “A GWO-based multi-robot cooperation method for target searching in unknown environments,” Expert Systems with Applications, vol. 186, p. 115795, December 2021, doi: 10.1016/j.eswa.2021.115795.
[30] S. A. Mirjalili, S. M.Mirjalili and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software , vol. 69, March 2014, pp. 46-61, doi:10.1016/j.advengsoft.2013.12.007.
[31] S. Mirjalili, “How effective is the Grey Wolf optimizer in training multi-layer perceptrons,” Appliled Intelligence, vol. 43, pp. 150-161, 2015, doi:10.1007/s10489-014-0645-7.
[32] S. K. Sankaralingam, N. S. Nagarajan and A. S. Narmadh, “Energy aware decision stump linear programming boosting node classification based data aggregation in WSN,” Computer Communications, vol. 155, pp. 133-142, April 2020, doi: 10.1016/j.comcom.2020.02.062.
[33] Y. Pal, S. Nagendram, M. S. Al-Ansari, K. Singh, L.A. Anto-Gracious and P. Pa, “IoT based Weather, Soil, Earthquake, and Air Pollution Monitoring System”, 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, April 2023, pp. 1212-1217, doi: 10.1109/ICCMC56507.2023.10083932.
[34] M. Bhatia, T. A. Ahanger and A. Manocha, “Artificial intelligence based real-time earthquake prediction”, Engineering Applications of Artificial Intelligence, vol. 120, p. 105856, April 2023, doi: 10.1016/j.engappai.2023.105856.