Nesnelerin İnternetinde (Iot) Devasa Erişim Için Makine Öğrenmesine Dayalı Bütünleşik Tahmin-Çizelgeleme Yönteminin Geliştirilmesi
Loading...
Date
2022
Authors
Cüneyt GÜZELİŞ
Volkan Rodoplu
Journal Title
Journal ISSN
Volume Title
Publisher
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
Nesnelerin İnternetinin (IoT) Devasa Erişim Sorunu çok sayıda IoT cihazının kablolu altyapıya kablosuz erişimini yüksek verimlilik ve düşük güç tüketimi ölçütlerini gözeterek sağlama sorunudur. Bu projenin ana katkısı IoT'nin Devasa Erişim Sorununu çözmeyi hedefleyen bir metodoloji olarak Bütünleşik Tahmin-Çizelgelemenin (JFS) geliştirilmesidir. Bu amaçla ilk olarak bir IoT ağ geçidinde koşturulacak olan JFS için çok ölçekli bir algoritma (multi-scale algorithm: MSA) geliştirilmiştir. Orta Erişim Kontrolü (MAC) katmanında IoT veri trafiği için rastgele varışlar olduğunu varsayan Devasa Erişim Sorununa yönelik mevcut yaklaşımların aksine MSA IoT cihazlarının yaklaşan trafiğini tahmin eder ve bu tahminlere dayalı olarak uplink kablosuz kaynaklarını önceden tahsis eder. İkinci olarak JFS'de yüksek tahmin doğruluğu elde etmek için Uyarlanabilir Öğrenme Hızıyla bir Alt Uzayda Hareket (MOSAL) adlı bir algoritma geliştirilmiştir. Algoritmamız tahmin hatalarının bir alt uzayına yakın kalırken Yapay Sinir Ağı aracılığıyla Uygulamaya Özgü Hata İşlevinin öykünmesine dayalı performans kaybını en aza indirerek bir JFS sisteminde tahmincileri eğitir. Üçüncü olarak kapsama alanındaki her cihaz sınıfında değişen sayıda IoT cihazına dinamik olarak uyum sağlayacak şekilde JFS için en iyi performans gösteren tahmin şemasını seçen Dinamik Otomatik Tahminci Seçimi (DAFS) yöntemi geliştirilmiştir. Dördüncüsü çok kanallı JFS için Minimal Kapasitede Minimum Kayıp ile Çok Kanallı Alt Küme Yineleme (MC-SIMLAC) adlı bir algoritma geliştirilmiştir. Algoritmamız tüm IoT cihaz trafiği veri bloklarının alt kümeleri üzerinde yinelenir ve toplam kullanılabilir kapasitedeki kaybın en aza indirilmesini hedefleyerek kanal-yuva çiftlerini seçer. Bu projede elde edilen sonuçlar yeni nesil kablosuz ağlarda çok sayıda IoT cihazını yönetmek için ölçeklenebilir JFS motorları oluşturmanın yolunu açmaktadır.
Description
Keywords
Bilgisayar Bilimleri- Yazılım Mühendisliği-Bilgisayar Bilimleri- Yapay Zeka
Fields of Science
Citation
Abdellah A. R. Mahmood O. A. K. Paramonov A. and Koucheryavy A. (2019). IoT traffic prediction using multi-step ahead prediction with neural network. In 2019 11th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT) pages 1–4. IEEE.Adame T. Bel A. Bellalta B. Barcelo J. and Oliver M. (2014). IEEE 802.11 ah: the WiFi approach for M2M communications. IEEE Wireless Communications 21(6):144–152.Aijaz A. and Aghvami A. H. (2015). Cognitive machine-to-machine communications for Internet- of Things: A protocol stack perspective. IEEE Internet of Things Journal 2(2):103– 112.Aijaz A. Ping S. Akhavan M. R. and Aghvami A.-H. (2014). CRB-MAC: A receiver-based MAC protocol for cognitive radio equipped smart grid sensor networks. IEEE Sensors Journal 14(12):4325–4333.Alavikia Z. and Ghasemi A. (2018). Collision-aware resource access scheme for LTE-based machine-to-machine communications. IEEE Transactions on Vehicular Technology 67(5):4683–4688.Ali S. Rajatheva N. and Saad W. (2019). Fast uplink grant for machine type communications: Challenges and opportunities. IEEE Communications Magazine 57(3):97– 103.Ansari J. Zhang X. Achtzehn A. Petrova M. and Ma ̈ho ̈nen P. (2011). A flexible MAC development framework for cognitive radio systems. In 2011 IEEE Wireless Communications and Networking Conference pages 156–161. IEEE.Astely D. Stattin M. Wikstrom G. Cheng J. Hoglund A. Frenne M. and Gunnarsson F. (2016). LTE release 14 outlook. IEEE Communications Magazine 54(6):44–49.Boutaba R. Salahuddin M. A. Limam N. Ayoubi S. Shahriar N. Estrada-Solano F. and Caicedo O. M. (2018). A comprehensive survey on machine learning for networking: evolution applications and research opportunities. Journal of Internet Services and Applications 9(1):16.Bryll R. Gutierrez-Osuna R. and Quek F. (2003). Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern recognition 36(6):1291–1302. Cai D. He X. and Han J. (2007). Spectral regression for efficient regularized subspace learning. In 2007 IEEE 11th International Conference on Computer Vision pages 1–8.Chinchali S. Hu P. Chu T. Sharma M. Bansal M. Misra R. Pavone M. and Katti S. (2018). Cellular network traffic scheduling with deep reinforcement learning. In AAAI pages 766–774.Cui Y. Li S. and Zhang W. (2020). Jointly sparse signal recovery and support recovery via deep learning with applications in mimo-based grant-free random access. IEEE Journal on Selected Areas in Communications 39(3):788– 803.De La Torre F. and Black M. J. (2003). A framework for robust subspace learning. International Journal of Computer Vision 54(1):117–142.Derhab A. Guerroumi M. Gumaei A. Maglaras L. Ferrag M. A. Mukherjee M. and Khan F. A. (2019). Blockchain and random subspace learning-based ids for sdn-enabled industrial IoT security. Sensors 19(14):3119.Doerr C. Neufeld M. Fifield J. Weingart T. Sicker D. C. and Grunwald D. (2005). Multimac-an adaptive MAC framework for dynamic radio networking. In First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks 2005. DySPAN 2005. pages 548–555. IEEE.Edalat Y. Ahn J.-S. and Obraczka K. (2016). Smart experts for network state estimation. IEEE Transactions on Network and Service Management 13(3):622–635.Eldeeb E. Shehab M. and Alves H. (2021). A learning-based fast uplink grant for massive IoT via support vector machines and long short-term memory. IEEE Internet of Things Journal.Ericsson (Nov. 2018). Ericsson Mobility Report.Farago A. Myers A. D. Syrotiuk V. R. and Zaruba G. V. (2000). A new approach to MAC protocol optimization. In Globecom’00-IEEE. Global Telecommunications Conference. Conference Record (Cat. No. 00CH37137) volume 3 pages 1742–1746. IEEE.Flick N. Garlisi D. Syrotiuk V. R. and Tinnirello I. (2016). Testbed implementation of the meta-MAC protocol. In 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pages 580–585.Gao J. Li M. Zhuang W. Shen X. and Li X. (2020). MAC for machine-type communications in industrial IoT—part II: Scheduling and numerical results. IEEE Internet of Things Journal 8(12):9958–9969.Gao J. Zhuang W. Li M. Shen X. and Li X. (2021). MAC for machine type communications in industrial IoT–part I: Protocol design and analysis. IEEE Internet of Things Journal 8(12):9945–9957.Ghavimi F. and Chen H.-H. (2015). M2M communications in 3GPP LTE/LTE-A networks: Architectures service requirements challenges and applications. IEEE Communications Surveys & Tutorials 17(2):525–549.Gu Q. Li Z. and Han J. (2011). Joint feature selection and subspace learning. In IJCAI Proceedings- International Joint Conference on Artificial Intelligence volume 22 page 1294.Hammad K. Moubayed A. Primak S. L. and Shami A. (2017). QoS-aware energy and jitter-efficient downlink predictive scheduler for heterogeneous traffic LTE networks. IEEE Transactions on Mobile Computing 17(6):1411– 1428.Hu S. Yao Y. and Yang Z. (2014). MAC protocol identification using support vector machines for cognitive radio networks. IEEE Wireless Communications 21(1):52–60.Hu S. Yao Y.-D. and Yang Z. (2012). MAC protocol identification approach for implement smart cognitive radio. In 2012 IEEE International Conference on Communications (ICC) pages 5608–5612. IEEE.Hu S. Yao Y.-D. and Yang Z. (2014). MAC protocol identification using support vector machines for cognitive radio networks. IEEE Wireless Communications 21(1):52–60.Hu W. Li X. and Yousefi’zadeh H. (2009). LA-MAC: A load adaptive MAC protocol for manets. In GLOBECOM 2009-2009 IEEE Global Telecommunications Conference pages 1– 6. IEEE.Huang K.-C. Jing X. and Raychaudhuri D. (2009). MAC protocol adaptation in cognitive radio networks: An experimental study. In 2009 Proceedings of 18th International Conference on Computer Communications and Networks pages 1–6. IEEE.IEEE standard for information technology–telecommunications and information exchange between systems local and metropolitan area networks–specific requirements- part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications amendment 2: Sub 1 GHz license exempt operation. IEEE Std 802.11ah-2016 (Amendment to IEEE Std 802.11-2016 as amended by IEEE Std 802.11ai-2016) pages 1–594. IoT Traffic Generation Pattern Dataset 2021.Jang H. S. Jin H. J ung B. C. and Quek T. Q. (2021). Resource-optimized recursive access class barring for bursty traffic in cellular IoT networks. IEEE Internet of Things Journal.Jiang N. Deng Y. Nallanathan A. and Yuan J. (2020). A decoupled learning strategy for massive access optimization in cellular IoT networks. IEEE Journal on Selected Areas in Communications 39(3):668–685.Jiang X. (2011). Linear subspace learning-based dimensionality reduction. IEEE Signal Processing Magazine 28(2):16–26.Jin H. Toor W. T. Jung B. C. and Seo J.-B. (2017). Recursive pseudo-Bayesian access class barring for M2M communications in LTE systems. IEEE Transactions on Vehicular Technology 66(9):8595–8599.Kim H. Cho T. Lee J. Shin W. and Poor H. V. (2020). Optimized shallow neural networks for sum-rate maximization in energy harvesting downlink multiuser noma systems. IEEE Journal on Selected Areas in Communications 39(4):982–997.Kim H.-Y. and Kim J.-M. (2017). A load balancing scheme based on deep-learning in IoT. Cluster Computing 20(1):873–878.Lawal I. A. Abdulkarim S. A. Hassan M. K. and Sadiq J. M. (2016). Improving HSDPA traffic forecasting using ensemble of neural networks. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) pages 308–313. IEEE.Li H. Peng S. Chen Z. and Qin X. (2019). MAC protocol recognition based on LSTM network in cognitive radio. Journal of Signal Processing 35(5):837–842.Li Y. (2004). On incremental and robust subspace learning. Pattern recognition 37(7):1509–1518.Li Y. Jin D. Wang B. Su X. Riekki J. Sun C. Wei H. Wang H. and Han L. (2020). Predicting Internet of Things data traffic through lstm and autoregressive spectrum analysis. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium pages 1– 8. IEEE.Liang L. Xu L. Cao B. and Jia Y. (2018). A cluster-based congestion-mitigating access scheme for massive M2M communications in Internet of Things. IEEE Internet of Things Journal 5(3):2200–2211.Lien S.-Y. Liau T.-H. Kao C.-Y. and Chen K.-C. (2012). Cooperative access class barring for machine- to-machine communications. IEEE Transactions on Wireless Communications 11(1):27–32.Lin T.-M. Lee C.-H. Cheng J.-P. and Chen W.-T. (2014). PRADA: Prioritized random access with dynamic access barring for MTC in 3GPP LTE-A networks. IEEE Transactions on Vehicular Technology 63(5):2467–2472.Liu J. Song L. et al. (2017). A novel congestion reduction scheme for massive machine-to- machine communication. IEEE Access 5:18765–18777.Liu L. Larsson E. G. Yu W. Popovski P. Stefanovic C. and de Carvalho E. (2018). Sparse signal processing for grant-free massive connectivity: A future paradigm for random access protocols in the Internet of Things. IEEE Signal Processing Magazine 35(5):88–99.Liu Y. Yuen C. Cao X. Hassan N. U. and Chen J. (2014). Design of a scalable hybrid MAC protocol for heterogeneous M2M networks. IEEE Internet of Things Journal 1(1):99– 111.Lopez-Martin M. Carro B. and Sanchez-Esguevillas A. (2019). Neural network architecture based on gradient boosting for IoT traffic prediction. Future Generation Computer Systems 100:656–673.Lopez-Martin M. Carro B. and Sanchez-Esguevillas A. (2020). IoT type-of-traffic forecasting method based on gradient boosting neural networks. Future Generation Computer Systems 105:331–345.Nakip M. and Gelenbe E. (2021). Randomization of data generation times improves performance of predictive IoT networks. In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT) pages 350–355. IEEE.Nakip M. Gül B. C. Rodoplu V. and Güzeliş C. (2019a). Comparative study of forecasting schemes for IoT device traffic in machine-to-machine communication. In Proceedings of the 2019 4th International Conference on Cloud Computing and Internet of Things pages 102– 109.Nakip M. Helva A. Güzeliş C. and Rodoplu V. (2021a). Subspace-based emulation of the relationship between forecasting error and network performance in joint forecasting- scheduling for the Internet of Things. In IEEE World Forum on Internet of Things (WF-IoT) pages 247–252.Nakip M. Karakayali K. Güzeliş C. and Rodoplu V. (2021b). An end-to-end trainable feature selection-forecasting architecture targeted at the Internet of Things. IEEE Access 9:1–1.Nakip M. Rodoplu V. Güzeliş C. and Eliiyi D. T. (2019b). Joint forecasting-scheduling for the Internet of Things. In 2019 IEEE Global Conference on Internet of Things (GCIoT) pages 1–7. IEEE.[Narayanan N. S. Patnaik M. and Kamakoti V. (2016). ProMAC: A proactive model predictive control based MAC protocol for cognitive radio vehicular networks. Computer Communications 93:27–38.Pang Y.-C. Chao S.-L. Lin G.-Y. and Wei H.-Y. (2014). Network access for M2M/H2H hybrid systems: A game theoretic approach. IEEE Communications Letters 18(5):845–848.Park I. Kim D. and Har D. (2015). MAC achieving low latency and energy efficiency in hierarchical M2M networks with clustered nodes. IEEE Sensors Journal 15(3):1657–1661.Patnaik M. Kamakoti V. Matyáš V. & Řchák V. “Prolemus: A proactive learning based mac protocol against PUEA and SSDF attacks in Energy constrained cognitive radio networks” IEEE Transactions on Cognitive Communications and Networking vol.5 no.2 pp.400–4122019.Pehlevan C. Hu T. and Chklovskii D. B. (2015). A hebbian/anti-hebbian neural network for linear subspace learning: A derivation from multidimensional scaling of streaming data. Neural computation 27(7):1461–1495.Petkov V. and Obraczka K. (2011). The case for using traffic forecasting in schedule-based channel access. In Consumer Communications and Networking Conference (CCNC) 2011 IEEE pages 208–212. IEEE.Petkov V. and Obraczka K. (2012). Collision-free medium access based on traffic forecasting. In World of Wireless Mobile and Multimedia Networks (WoWMoM) 2012 IEEE International Symposium pages 1–9. IEEE.Qiao M. Zhao H. Huang S. Zhou L. and Wang S. (2018). An intelligent MAC protocol selection method based on machine learning in wireless sensor networks. KSII Transactions on Internet & Information Systems 12(11).Qiao M. Zhao H. Wang S. and Wei J. (2016). MAC protocol selection based on machine learning in cognitive radio networks. In 2016 19th International Symposium on Wireless Personal Multimedia Communications (WPMC) pages 453–458.Raca D. Zahran A. H. Sreenan C. J. Sinha R. K. Halepovic E. Jana R. and Gopalakrishnan V. (2020). On leveraging machine and deep learning for throughput prediction in cellular networks: Design performance and challenges. IEEE Communications Magazine 58(3):11–17.Rodoplu V. Nakip M. Eliiyi D. T. and Güzeliş C. (2020a). A multiscale algorithm for joint forecasting-scheduling to solve the massive access problem of IoT. IEEE Internet of Things Journal 7:8572–8589.Rodoplu V. Nakip M. Qorbanian R. and Eliiyi D. T. (2020b). Multi-channel joint forecasting-scheduling for the Internet of Things. IEEE Access 8:217324–217354.Ruan L. Dias M. P. I. and Wong E. (2019). Machine learning-based bandwidth prediction for low-latency H2M applications. IEEE Internet of Things Journal 6(2):3743–3752.Sha M. Dor R. Hackmann G. Lu C. Kim T.-S. and Park T. (2013). Self-adapting MAC layer for wireless sensor networks. In 2013 IEEE 34th Real-Time Systems Symposium pages 192–201. IEEE.Shahin N. Ali R. and Kim Y.-T. (2018). Hybrid slotted-CSMA/CA-TDMA for efficient massive registration of IoT devices. IEEE Access 6:18366–18382.Shehab M. Hagelskjær A. K. Kalør A. E. Popovski P. and Alves H. (2020). Traffic prediction based fast uplink grant for massive IoT. In 2020 IEEE 31st Annual International Symposium on Personal Indoor and Mobile Radio Communications pages 1–6. IEEE.Shirvanimoghaddam M. Dohler M. and Johnson S. J. (2017). Massive non- orthogonal multiple access for cellular IoT: Potentials and limitations. IEEE Communications Magazine 55(9):55–61.Si P. Yang J. Chen S. and Xi H. (2015). Adaptive massive access management for QoS guarantees in M2M communications. IEEE Transactions on Vehicular Technology 64(7):3152–3166.Soltanmohammadi E. Ghavami K. and Naraghi-Pour M. (2016). A survey of traffic issues in machine-to-machine communications over LTE. IEEE Internet of Things Journal 3(6):865– 884.Stallings W. (2009). Operating systems: internals and design principles. Upper Saddle River NJ: Pearson/Prentice Hall.Tello-Oquendo L. Leyva-Mayorga I. Pla V. Martinez-Bauset J. Vidal J.-R. Casares- Giner V. and Guijarro L. (2018a). Performance analysis and optimal access class barring parameter configuration in LTE-A networks with massive M2M traffic. IEEE Transactions on Vehicular Technology 67(4):3505–3520.Tello-Oquendo L. Pacheco-Paramo D. Pla V. and Martinez-Bauset J. (2018b). Reinforcement learning-based ACB in LTE-A networks for handling massive M2M and H2H communications. In 2018 IEEE International Conference on Communications (ICC) pages 1–7. IEEE.Tinnirello I. Garlisi D. Giuliano F. Syrotiuk V. R. and Bianchi G. (2016). MAC learning: Enabling automatic combination of elementary protocol components. In Proceedings of the Tenth ACM International Workshop on Wireless Network Testbeds Experimental Evaluation and Characterization pages 89–90.Tomforde S. and Hähner J. (2012). Organic network control: turning standard protocols into evolving systems. In Biologically Inspired Networking and Sensing: Algorithms and Architectures pages 11–35. IGI Global.Vieira T. P. d. B. (2019). On the subspace learning for network attack detection. Vodafone (Apr. 2010). RACH intensity of time controlled devices. 3GPP TSG RAN WG2 #69bis R2-102296.Wang K. He R. Wang L. Wang W. and Tan T. (2015). Joint feature selection and subspace learning for cross-modal retrieval. IEEE transactions on pattern analysis and machine intelligence 38(10):2010–2023.Wang W. Dong C. Wang H. and Jiang A. (2016). Design and implementation of adaptive MAC framework for UAV ad HOC networks. In 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) pages 195–201. IEEE.Yu X. Navaratnam P. and Moessner K. (2013). Resource reservation schemes for IEEE 802.11-based wireless networks: a survey. IEEE Communications Surveys & Tutorials 15(3):1042–1061.Zanella A. Zorzi M. dos Santos A. F. Popovski P. Pratas N. Stefanovic C. Dekorsy A. Bockelmann C. Busropan B. and Norp T. A. (2013). M2M massive wireless access: Challenges research issues and ways forward. In 2013 IEEE Globecom Workshops pages 151–156. IEEE.Zhang C. and Patras P. (2018). Long-term mobile traffic forecasting using deep spatio- temporal neural networks. In Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing pages 231–240.Zhang D.-g. Zhou S. and Tang Y.-m. (2018). A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mobile Networks and Applications 23(4):828–839.Zhang X. Shen W. Xu J. Liu Z. and Ding G. (2020). A MAC protocol identification approach based on convolutional neural network. In 2020 International Conference on Wireless Communications and Signal Processing (WCSP) pages 534–539. IEEE.Zhen J. and Rodoplu V. (2013). Automated MAC protocol generation under dynamic traffic conditions. In 2013 IEEE Global Communications Conference (GLOBECOM) pages 152– 157. IEEE
WoS Q
Scopus Q
OpenCitations Citation Count
N/A
Source
Volume
Issue
Start Page
End Page
Collections
