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Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme

Year 2017, Volume: 10 Issue: 2, 64 - 76, 26.12.2017

Mehmet Akif Çifçi , Atilla Elçi

Abstract

Kablosuz sistemlerdeki ilerlemeler
düşük maliyetli, tasarruflu, çok işlevli, minyatür algılama aygıtlarının
üretilmesine imkân sağlamıştır. Bu aygıtlardan yüzlercesi, hatta binlercesi
yardımıyla kablosuz algılayıcı ağları oluşturulmaktadır. Kablosuz algılayıcı
ağlarda algılanan verilerin toplanması, analiz edilmesi ve bir baz istasyonuna
gönderilmesi gibi aşamalar beraberinde bazı sorunları getirmektedir. Bu
sorunlardan bazıları, algılayıcıların kısıtları, verinin doğru toplanması,
gereksiz ve benzer veri sorunu, verinin güvenliği ile kablosuz algılayıcı ağ
topolojisinde meydana gelen sorunlardır. Bunların en önemlisi ise
algılayıcıların kısıtlı enerji sorunudur. Geleneksel yöntemler böylesi
sorunlarla başa çıkamadığından, bu durum göz önünde bulundurularak, kablosuz
algılayıcı ağların Yapay Zekâ ile daha işlevsel hale getirilmesinin gerekliliği
üzerinde çalışıldı. Bu çalışmada, kablosuz ağların işlevselliği ve hayatta
kalma özelliklerinin iyileştirilmesi için yapay zekâ yoluyla ağdaki
"akıllı hesaplama", "kendi kendine öğrenme" ve “sürü
öğrenme” yeteneğinin geliştirilmesi önerilmektedir. Yeni bir sistem önerisi ile
algılayıcı düğümün yapısında bazı donanımsal değişiklikler yapılması
tartışılmıştır. Bunun yanı sıra, düğümler üzerinde işlevselliğin artırılması,
yeni sistemde aktivasyonunun sağlanması ve algılayıcı ağlarda Sürü Zekâsı vb.
öğrenme tekniklerinin uygulanması hedeflenmiştir. Bu çalışmalar ve düğümdeki
donanımsal değişiklikler sonucunda düğümler üzerinde işlenen kodların
yoğunluğundan kaynaklanan pil (batarya) zafiyeti hafifletilmiştir.

Keywords

Kablosuz algılayıcı ağı yapay zekâ bulanık mantık algılayıcı düğüm sürü zekâsı

References

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  • [7] Mihnea, A. and M. Cardei, Efficient Wireless Communication in Grid Networks. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 2015. 7(3): p. 57-79.
  • [8] Singh, N.K., A. Kasana, and V.K. Sachan, Enhancement in lifetime of sensor node using Data Reduction Technique in Wireless Sensor Network. International Journal of Computer Applications, 2016. 145(11): p. 1-5.
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  • [14] Lei, J., et al., An in-network data cleaning approach for wireless sensor networks. Intelligent Automation & Soft Computing, 2016. 22(4): p. 599-604.
  • [15] Yang, P.-T. and S. Lee. Spanning tree of residual energy based on data aggregation for maximizing the lifetime of wireless multimedia sensor networks. in Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, Jan. 2014.
  • [16] Alsheikh, M.A., et al., Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials, 2014. 16(4): p. 1996-2018.
  • [17] Saravanan, M. and M. Madheswaran, A Spanning Tree for Enhanced Cluster Based Routing in Wireless Sensor Network. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2016. 9(9): p. 2102-2111.
  • [18] Liu, W., et al., A survey of deep neural network architectures and their applications. Neurocomputing, 2017. 234: p. 11-26.
  • [19] Prieto, A., et al., Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing, 2016. 214: p. 242-268.
  • [20] Lai, K.P., A deep learning model for automatic image texture classification: Application to vision-based automatic aircraft landing. 2016, Queensland University of Technology.
  • [21] Zhang, Z., et al., On swarm intelligence inspired self-organized networking: its bionic mechanisms, designing principles and optimization approaches. IEEE Communications Surveys & Tutorials, 2014. 16(1): p. 513-537.
  • [22] Shahid, N., I.H. Naqvi, and S.B. Qaisar, One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments. Artificial Intelligence Review, 2015. 43(4): p. 515-563.
  • [23] Guo, W. and W. Zhang, A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 2014. 38: p. 185-201.
  • [24] Abbas, N.I., M. Ilkan, and E. Ozen, Fuzzy approach to improving route stability of the AODV routing protocol. EURASIP Journal on Wireless Communications and Networking, 2015. 2015(1): p. 235.
  • [25] Toral-Cruz, H., et al., A Survey on Wireless Sensor Networks, in Next Generation Wireless Network Security and Privacy. 2015, IGI Global. p. 171-210.
  • [26] Younis, M., et al., Topology management techniques for tolerating node failures in wireless sensor networks: A survey. Computer Networks, 2014. 58: p. 254-283.
  • [27] Kumar, J., S. Tripathi, and R.K. Tiwari, A survey on routing protocols for wireless sensor networks using swarm intelligence. International Journal of Internet Technology and Secured Transactions, 2016. 6(2): p. 79-102.
  • [28] Rault, T., A. Bouabdallah, and Y. Challal, Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 2014. 67: p. 104-122.
  • [29] Dias, G.M., B. Bellalta, and S. Oechsner, A survey about prediction-based data reduction in wireless sensor networks. ACM Computing Surveys (CSUR), 2016. 49(3): p. 58.

Year 2017, Volume: 10 Issue: 2, 64 - 76, 26.12.2017

Mehmet Akif Çifçi , Atilla Elçi

Abstract

References

  • [1] Sadowski, B., O. Nomaler, and J. Whalley, Technological Diversification of ICT companies into the Internet of things (IoT): A Patent-based Analysis. 2016.
  • [2] Santos, L.R. and A.G. Rosati, The evolutionary roots of human decision making. Annual review of psychology, 2015. 66: p. 321-347.
  • [3] Hendler, J. and A. Mulvehill, Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity. 2016: Apress.
  • [4] Ryan, M., The Digital Mind: An Exploration of artificial intelligence. 2014: Michael Ryan.
  • [5] Ilyas, M. and I. Mahgoub, Smart Dust: Sensor network applications, architecture and design. 2016: CRC press.
  • [6] Amjad, A., et al., Characterization of Field-of-View for Energy Efficient Application-Aware Visual Sensor Networks. IEEE Sensors Journal, 2016. 16(9): p. 3109-3122.
  • [7] Mihnea, A. and M. Cardei, Efficient Wireless Communication in Grid Networks. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 2015. 7(3): p. 57-79.
  • [8] Singh, N.K., A. Kasana, and V.K. Sachan, Enhancement in lifetime of sensor node using Data Reduction Technique in Wireless Sensor Network. International Journal of Computer Applications, 2016. 145(11): p. 1-5.
  • [9] Rawat, P., et al., Wireless sensor networks: a survey on recent developments and potential synergies. The Journal of supercomputing, 2014. 68(1): p. 1-48.
  • [10] Jan, M.A., Energy-efficient routing and secure communication in wireless sensor networks. 2016.
  • [11] Chawla, S. and S. Singh, Computational Intelligence Techniques for Wireless Sensor Network: Review. International Journal of Computer Applications, 2015. 118(14).
  • [12] Witten, I.H., et al., Data Mining: Practical machine learning tools and techniques. 2016: Morgan Kaufmann.
  • [13] Bevilacqua, F., et al., 2007: Wireless Sensor Interface and Gesture-Follower for Music Pedagogy, in A NIME Reader. 2017, Springer. p. 267-284.
  • [14] Lei, J., et al., An in-network data cleaning approach for wireless sensor networks. Intelligent Automation & Soft Computing, 2016. 22(4): p. 599-604.
  • [15] Yang, P.-T. and S. Lee. Spanning tree of residual energy based on data aggregation for maximizing the lifetime of wireless multimedia sensor networks. in Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, Jan. 2014.
  • [16] Alsheikh, M.A., et al., Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials, 2014. 16(4): p. 1996-2018.
  • [17] Saravanan, M. and M. Madheswaran, A Spanning Tree for Enhanced Cluster Based Routing in Wireless Sensor Network. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2016. 9(9): p. 2102-2111.
  • [18] Liu, W., et al., A survey of deep neural network architectures and their applications. Neurocomputing, 2017. 234: p. 11-26.
  • [19] Prieto, A., et al., Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing, 2016. 214: p. 242-268.
  • [20] Lai, K.P., A deep learning model for automatic image texture classification: Application to vision-based automatic aircraft landing. 2016, Queensland University of Technology.
  • [21] Zhang, Z., et al., On swarm intelligence inspired self-organized networking: its bionic mechanisms, designing principles and optimization approaches. IEEE Communications Surveys & Tutorials, 2014. 16(1): p. 513-537.
  • [22] Shahid, N., I.H. Naqvi, and S.B. Qaisar, One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments. Artificial Intelligence Review, 2015. 43(4): p. 515-563.
  • [23] Guo, W. and W. Zhang, A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 2014. 38: p. 185-201.
  • [24] Abbas, N.I., M. Ilkan, and E. Ozen, Fuzzy approach to improving route stability of the AODV routing protocol. EURASIP Journal on Wireless Communications and Networking, 2015. 2015(1): p. 235.
  • [25] Toral-Cruz, H., et al., A Survey on Wireless Sensor Networks, in Next Generation Wireless Network Security and Privacy. 2015, IGI Global. p. 171-210.
  • [26] Younis, M., et al., Topology management techniques for tolerating node failures in wireless sensor networks: A survey. Computer Networks, 2014. 58: p. 254-283.
  • [27] Kumar, J., S. Tripathi, and R.K. Tiwari, A survey on routing protocols for wireless sensor networks using swarm intelligence. International Journal of Internet Technology and Secured Transactions, 2016. 6(2): p. 79-102.
  • [28] Rault, T., A. Bouabdallah, and Y. Challal, Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 2014. 67: p. 104-122.
  • [29] Dias, G.M., B. Bellalta, and S. Oechsner, A survey about prediction-based data reduction in wireless sensor networks. ACM Computing Surveys (CSUR), 2016. 49(3): p. 58.
There are 29 citations in total.

Details

Subjects Engineering
Journal Section Makaleler(Araştırma)
Authors

Mehmet Akif Çifçi

Atilla Elçi

Publication Date December 26, 2017
Published in Issue Year 2017 Volume: 10 Issue: 2

Cite

APA Çifçi, M. A., & Elçi, A. (2017). Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 10(2), 64-76.
AMA Çifçi MA, Elçi A. Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme. TBV-BBMD. December 2017;10(2):64-76.
Chicago Çifçi, Mehmet Akif, and Atilla Elçi. “Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 10, no. 2 (December 2017): 64-76.
EndNote Çifçi MA, Elçi A (December 1, 2017) Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 10 2 64–76.
IEEE M. A. Çifçi and A. Elçi, “Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme”, TBV-BBMD, vol. 10, no. 2, pp. 64–76, 2017.
ISNAD Çifçi, Mehmet Akif - Elçi, Atilla. “Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 10/2 (December 2017), 64-76.
JAMA Çifçi MA, Elçi A. Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme. TBV-BBMD. 2017;10:64–76.
MLA Çifçi, Mehmet Akif and Atilla Elçi. “Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, vol. 10, no. 2, 2017, pp. 64-76.
Vancouver Çifçi MA, Elçi A. Yapay Zekâ İle Kablosuz Algılayıcı Ağları Eniyileme. TBV-BBMD. 2017;10(2):64-76.
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