Data-Driven Modelling and Prediction of CoAP Throughput in a Grid Network Topology

Öz

In this study, we propose new models for predicting the average throughput in a 4x4 grid Constrained Application Protocol (CoAP)-based IoT network using Support Vector Machine (SVM) and Multiple Linear Regression (MLR). Two different CoAP congestion control mechanisms have been considered: the default CoAP congestion control (CC) and the CoAP Simple Congestion Control/Advanced (CoCoA). On the client-side, we run 3, 6, 9, 12 or 15 CoAP clients requesting packets, sized with 12, 24, 36 or 48 bytes, from different CoAP servers over 4x4 grid IoT network configured with packet delivery ratios of 90, 95 or 100. In total, 60 different experimental scenarios, each of which was run 10 times to determine the average throughput of default CoAP CC and CoCoA clients, were created. Using 10-fold cross-validation, the performance of the prediction models has been evaluated using several performance metrics. The results show that combining packet delivery ratio and number of concurrently sending clients in a model leads to the highest correlation with the average CoAP throughput of the IoT network. Particularly, this model produces the lowest prediction error among all SVM-based and MLR-based models, regardless of whether the default CoAP CC or CoCoA is used as the congestion control mechanism. 

Anahtar Kelimeler

• Internet of Things,Throughput Prediction

Bir Izgara Ağı Topolojisi Üzerinde CoAP’ın Veriye Dayalı Modellemesi ve Verimliliğinin Tahmini

Öz

Bu çalışmada, Destek Vektör Makinesi (SVM) ve Çoklu Doğrusal Regresyon (MLR) kullanılarak 4x4’lük ızgara topolojisi üzerinde Kısıtlı Uygulama Protokolü (CoAP) tabanlı bir IoT ağındaki ortalama verimi tahmin etmek için yeni modeller önerilmektedir. İki farklı CoAP tıkanıklık kontrol mekanizması dikkate alınmıştır: mevcut CoAP tıkanıklık kontrolü (default CoAP CC) ve CoAP Simple Congestion Control/Advanced (CoCoA). İstemci tarafında, 90, 95 veya 100 paket teslimat oranları ile yapılandırılmış 4x4’lük ızgara IoT ağı üzerindeki farklı CoAP sunucularından, 12, 24, 36 veya 48 bayt boyutunda paket talep eden 3, 6, 9, 12 veya 15 CoAP istemcisi çalıştırılmıştır. Toplamda, mevcut CoAP CC ve CoCoA istemcilerinin ortalama verimini belirlemek için her biri 10 kez çalıştırılan 60 farklı deneysel senaryo oluşturulmuştur. 10 katlı çapraz doğrulama kullanılarak, tahmin modellerinin performansı çeşitli performans ölçümleri kullanılarak değerlendirilmiştir. Sonuçlar, paket teslim oranının ve aynı anda gönderen istemci sayısının aynı modelde birleştirilmesinin IoT ağının ortalama CoAP verimi ile en yüksek korelasyona sahip olduğunu göstermektedir. Özellikle, bu model varsayılan CoAP CC veya CoCoA'nın tıkanıklık kontrol mekanizmalarından bağımsız olarak, tüm SVM tabanlı ve MLR tabanlı modeller arasında en düşük tahmin hatasını üretmektedir.

Anahtar Kelimeler

• Nesnelerin İnterneti

Kaynakça

1. [1] Atzori L.; Iera A.; Morabito G., The Internet of Things: A survey. Computer Networks, 2010, 54(15): 2787–2805.
2. [2] Bandyopadhyay D.; Sen J., Internet of Things: Applications and Challenges in Technology and Standardization. Wireless Personal Communications, 2011, 58(1): 49–69.
3. [3] Li S.; Xu L. Da; Zhao S., The internet of things: a survey. Information Systems Frontiers, 2015, 17(2): 243–259.
4. [4] Shelby Z.; Hartke K.; Bormann C., Constrained Application Protocol. RFC 7252. 2014. https://tools.ietf.org/html/rfc7252.
5. [5] Betzler A.; Gomez C.; Demirkol I.; Paradells J., CoAP congestion control for the internet of things. IEEE Communications Magazine, 2016, 54(7): 154–160.
6. [6] Bormann C.; Betzler A.; Gomez C.; Demirkol I., CoAP Simple Congestion Control/Advanced. 2018. https://tools.ietf.org/html/draft-ietf-core-cocoa-03.
7. [7] Betzler A.; Gomez C.; Demirkol I.; Paradells J., CoCoA+: An advanced congestion control mechanism for CoAP. Ad Hoc Networks, 2015, 33: 126–139.
8. [8] Liu Y.; Lee J.Y.B., An Empirical Study of Throughput Prediction in Mobile Data Networks. In: 2015 IEEE Global Communications Conference (GLOBECOM). IEEE; 2015: 1–6.

9. [9] Samba A.; Busnel Y.; Blanc A.; Dooze P.; Simon G., Instantaneous throughput prediction in cellular networks: Which information is needed? In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE; 2017: 624–627.
10. [10] Rygielski P.; Kounev S.; Zschaler S., Model-based throughput prediction in data center networks. In: 2013 IEEE International Workshop on Measurements & Networking (M&N). IEEE; 2013: 167–172.
11. [11] Gao K.; Zhang J.; Yang Y.R.; Bi J., Prophet: Fast, Accurate Throughput Prediction with Reactive Flows. In: International Conference on Computer Communications (INFOCOM18); 2018: 1–9.
12. [12] Mirza M.; Springborn K.; Banerjee S.; Barford P.; Blodgett M.; Zhu X., On The Accuracy of TCP Throughput Prediction for Opportunistic Wireless Networks. In: 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. IEEE; 2009: 1–9.
13. [13] Kandasamy S.; Morla R.; Ramos P.; Ricardo M., Predicting throughput in IEEE 802.11 based wireless networks using directional antenna. Wireless Networks, 2017: 1–18.
14. [14] Lu D.; Qiao Y.; Dinda P.A.; Bustamante F.E., Characterizing and Predicting TCP Throughput on the Wide Area Network. 25th IEEE International Conference on Distributed Computing Systems (ICDCS’05), 2005: 414–424.
15. [15] Mirza M.; Sommers J.; Barford P.; Zhu X., A machine learning approach to TCP throughput prediction. IEEE/ACM Transactions on Networking, 2010, 18(4): 1026–1039.
16. [16] Demir A.K.; Abut F., Data-Driven Modelling and Prediction of CoAP Throughput in A Grid Network Topology. In: Cilicia International Symposium on Engineering and Technology; 2018: 69–73.
17. [17] Kovatsch M.; Lanter M.; Shelby Z., Californium: Scalable cloud services for the Internet of Things with CoAP. In: 2014 International Conference on the Internet of Things (IoT). IEEE; 2014: 1–6.
18. [18] Kovatsch M.; Duquennoy S.; Dunkels A., A Low-Power CoAP for Contiki. In: 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems. IEEE; 2011: 855–860.
19. [19] Dunkels A.; Gronvall B.; Voigt T., Contiki - a lightweight and flexible operating system for tiny networked sensors. In: 29th Annual IEEE International Conference on Local Computer Networks. IEEE (Comput. Soc.); 2004: 455–462.