Abstract
Bu makale çalışmasında, yeraltı kablosuz
algılayıcı ağlarında kayıpsız veri iletimi yapabilmesi için bulanık mantık
tabanlı toplayıcı istasyon karar yaklaşımı önerilmiştir. Algılayıcı düğümlerin
enerji tasarruflu kayıpsız veri iletimi yapabilmesi için, toplayıcı istasyon
karar işlemleri bulanık mantık yardımıyla gerçekleştirilmiştir. Önerilen
yeraltı kablosuz algılayıcı ağ yapısının benzetim modeli Riverbed yazılımı
kullanılarak gerçekleştirilmiştir. Matlab yazılımı kullanılarak anlık olarak
bulanık mantık tabanlı karar işlemi yapılmıştır. Bulanık mantık sisteminde;
enerji, derinlik ve kullanım giriş parametreleri değerlendirilerek toplayıcı
istasyon kararı çıkış değeri elde edilmektedir. Kablosuz algılayıcı ağlarda
sıklıkla kullanılan iş çıkarma başarımı ve enerji tüketimi parametreleri
incelenerek, önerilen yeraltı kablosuz algılayıcı ağ performansı
değerlendirilmiştir. Önerilen algılayıcı ağ performansını değerlendirmek için
sonuçlara bakıldığında, maksimum iş çıkarma başarım oranı ve ortalama enerji
tüketimi ile yeraltı kablosuz algılayıcı ağlarda kayıpsız veri iletimi
yapıldığı gözler önüne serilmiştir. Önerilen bulanık mantık sistemi sayesinde; kablosuz
algılayıcı ağlar için en uygun toplayıcı istasyon seçimi yapılmakta ve enerji
tüketimi mümkün olan en düşük seviyede tutulmaktadır.
References
- Ahmad A H, Jaafar J, Mahmood A K, Agent-based personal monitoring system simulation using type-2 fuzzy, National Postgraduate Conference, 2011, Kuala Lumpur, pp. 1-5.
- Alzoubi M A, Zueter A, Nie-Rouquette A, Sasmito A P, Freezing on demand: A new concept for mine safety and energy savings in wet underground mines. Int J Min Sci Technol 2019; 29(4): 621-627.
- Fischer D, Szabados B, Poehlman S, Combining neural networks, fuzzy logic, and Kalman filtering in an oil leak detector for underground electric power cables, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510), 2004, Como, pp. 2099-2104.
- Gauss V A, Bay J S, A fuzzy logic solution for navigation of an autonomous subsurface planetary exploration robot, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell, 1998, Gaithersburg, MD, USA, pp. 559-564.
- Gupta S, Kumar P, Karmakar N C, Palei S K, Quantification of human error rate in underground coal mines - A fuzzy mapping and rough set based approach, IEEE International Conference on Industrial Engineering and Engineering Management, 2013, Bangkok, pp. 140-144.
- Huang Y R, Chen Z P, Han T, Liu X T, One energy-efficient random-walk topology evolution method for underground wireless sensor networks. Int J Distrib Sens N 2018; 19(9): 1-9.
- Ichihashi H, Katada T, Fujiyoshi M, Notsu A, Honda K, Improvement in the performance of camera based vehicle detector for parking lot, International Conference on Fuzzy Systems, 2010, Barcelona, pp. 1-7.
- Ichihashi H, Notsu A, Honda K, Katada T, Fujiyoshi M, Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier, IEEE International Conference on Fuzzy Systems, 2009, Jeju Island, pp. 127-134.
- Jaryani S, Broumandnia A, Oghani M A, Improving routing in wireless sensor networks having mobile sinks through fuzzy algorithm, 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2015, Tehran, pp. 497-503.
- Lokshina I V, Insinga R C, Decision support system of ventilation operator based on fuzzy methods applied to interpretation and processing of gas-dynamic images, SympoTIC'03, Joint 1st Workshop on Mobile Future and Symposium on Trends in Communications, 2003, Bratislava, Slovakia, pp. 84-89.
- Ma F, Sensor networks-based monitoring and fuzzy information fusion System for underground Gas disaster, 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012, Sichuan, pp. 596-600.
- Muduli L, Jana P K, Mishra D P, Wireless sensor network based fire monitoring in underground coal mines: A fuzzy logic approach. Process Saf Environ 2018; 113: 435-447.
- Shahooei S, Mattingly S P, Shahandashti M, Ardekani S, Propulsion system design and energy optimization for autonomous underground freight transportation systems. Tunn Undergr Sp Tech 2019; 89: 125-132.
- Sinha S K, Karray F, Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm. IEEE Trans Neural Netw 2002; 13(2): 393-401.
- Sinha S K, Karray F, Fieguth P W, Underground pipe cracks classification using image analysis and neuro-fuzzy algorithm, Proceedings of the IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014), 1999, Cambridge, MA, USA, pp. 399-404.
- Xianmin M, Lan L, Monitoring System of Coal Electrical Haulage Shearer Based on Data Fusion Theory, International Symposium on Computer, Consumer and Control, 2014, Taichung, pp. 231-234.
- Zyada Z, Kawai Y, Matsuno T, Fukuda T, Sensor Fusion Based Fuzzy Rules Learning for Humanitarian Mine Detection, SICE-ICASE International Joint Conference, 2006, Busan, pp. 1860-1865.
Abstract
In this paper, fuzzy logic based collector station
decision approach is proposed in order to provide lossless data transmission in
underground wireless sensor networks. With the aim of allowing the sensor nodes
to transmit energy-efficient lossless data, the decision of the collector
station is performed with the help of fuzzy logic. The simulation model of the
proposed underground wireless sensor network was performed using Riverbed
software. Fuzzy logic-based decision processing was performed by using Matlab
software. In the fuzzy logic system; collector station decision output value is
obtained by evaluating energy, depth and usage input parameters. The proposed
underground wireless sensor network performance is evaluated by examining the throughput
and energy consumption parameters which are commonly used in wireless sensor
networks. When the results are examined to evaluate the proposed sensor network
performance, it is revealed that lossless data transmission is performed in
underground wireless sensor networks with maximum throughput performance and
average energy consumption. Thanks to the proposed fuzzy logic system; the most
suitable collector station is selected for wireless sensor networks and energy
consumption is kept at the lowest possible level.
References
- Ahmad A H, Jaafar J, Mahmood A K, Agent-based personal monitoring system simulation using type-2 fuzzy, National Postgraduate Conference, 2011, Kuala Lumpur, pp. 1-5.
- Alzoubi M A, Zueter A, Nie-Rouquette A, Sasmito A P, Freezing on demand: A new concept for mine safety and energy savings in wet underground mines. Int J Min Sci Technol 2019; 29(4): 621-627.
- Fischer D, Szabados B, Poehlman S, Combining neural networks, fuzzy logic, and Kalman filtering in an oil leak detector for underground electric power cables, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510), 2004, Como, pp. 2099-2104.
- Gauss V A, Bay J S, A fuzzy logic solution for navigation of an autonomous subsurface planetary exploration robot, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell, 1998, Gaithersburg, MD, USA, pp. 559-564.
- Gupta S, Kumar P, Karmakar N C, Palei S K, Quantification of human error rate in underground coal mines - A fuzzy mapping and rough set based approach, IEEE International Conference on Industrial Engineering and Engineering Management, 2013, Bangkok, pp. 140-144.
- Huang Y R, Chen Z P, Han T, Liu X T, One energy-efficient random-walk topology evolution method for underground wireless sensor networks. Int J Distrib Sens N 2018; 19(9): 1-9.
- Ichihashi H, Katada T, Fujiyoshi M, Notsu A, Honda K, Improvement in the performance of camera based vehicle detector for parking lot, International Conference on Fuzzy Systems, 2010, Barcelona, pp. 1-7.
- Ichihashi H, Notsu A, Honda K, Katada T, Fujiyoshi M, Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier, IEEE International Conference on Fuzzy Systems, 2009, Jeju Island, pp. 127-134.
- Jaryani S, Broumandnia A, Oghani M A, Improving routing in wireless sensor networks having mobile sinks through fuzzy algorithm, 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2015, Tehran, pp. 497-503.
- Lokshina I V, Insinga R C, Decision support system of ventilation operator based on fuzzy methods applied to interpretation and processing of gas-dynamic images, SympoTIC'03, Joint 1st Workshop on Mobile Future and Symposium on Trends in Communications, 2003, Bratislava, Slovakia, pp. 84-89.
- Ma F, Sensor networks-based monitoring and fuzzy information fusion System for underground Gas disaster, 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012, Sichuan, pp. 596-600.
- Muduli L, Jana P K, Mishra D P, Wireless sensor network based fire monitoring in underground coal mines: A fuzzy logic approach. Process Saf Environ 2018; 113: 435-447.
- Shahooei S, Mattingly S P, Shahandashti M, Ardekani S, Propulsion system design and energy optimization for autonomous underground freight transportation systems. Tunn Undergr Sp Tech 2019; 89: 125-132.
- Sinha S K, Karray F, Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm. IEEE Trans Neural Netw 2002; 13(2): 393-401.
- Sinha S K, Karray F, Fieguth P W, Underground pipe cracks classification using image analysis and neuro-fuzzy algorithm, Proceedings of the IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014), 1999, Cambridge, MA, USA, pp. 399-404.
- Xianmin M, Lan L, Monitoring System of Coal Electrical Haulage Shearer Based on Data Fusion Theory, International Symposium on Computer, Consumer and Control, 2014, Taichung, pp. 231-234.
- Zyada Z, Kawai Y, Matsuno T, Fukuda T, Sensor Fusion Based Fuzzy Rules Learning for Humanitarian Mine Detection, SICE-ICASE International Joint Conference, 2006, Busan, pp. 1860-1865.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | October 15, 2019 |
| Submission Date | April 2, 2019 |
| Acceptance Date | September 9, 2019 |
| Published in Issue | Year 2019 Volume: 9 Issue: 4 |
