Insights into the Design of Congestion Control Protocols for Multi-Hop Wireless Mesh Networks

The widespread deployment of multi-hop wireless mesh networks will depend on the performance seen by the user. Unfortunately, the most predominant transport protocol, TCP, performs poorly over such networks, even leading to starvation in some topologies. In this work, we characterize the root causes of starvation in 802.11 scheduled multi-hop wireless networks via simulations. We analyze the performance of three categories of transport protocols. (1) end-to-end protocols that require implicit feedback (TCP SACK), (2) Explicit feedback based protocols (XCP and VCP) and (3) Open-loop protocol (UDP). We ask and answer the following questions in relation to these protocols: (a) Why does starvation occur in different topologies? Is it intrinsic to TCP or, in general, to feedback-based protocols? or does it also occur in the case of open-loop transfers such as CBR over UDP? (a) What is the role of application behavior on transport layer performance in multi-hop wireless mesh networks? (b) Is sharing congestion in the wireless neighborhood essential for avoiding starvation? (c) For explicit feedback based transport protocols, such as XCP and VCP, what performance can be expected when their capacity estimate is inaccurate? Based on the insights derived from the above analysis, we design a rate-based protocol called VRate that uses the two ECN bits for conveying load feedback information. VRate achieves near optimal rates when configured with the correct capacity estimate

Towards a power consumption estimation model for routers over TCP and UDP protocols

Due to the growing development in the information and communication technology (ICT) industry, the usage of routers has increased rapidly. Meanwhile, these devices that are produced and developed today consume a definite amount of power, Furthermore, with limited focus on power estimation techniques and the increased demands of networking devices, it led to an increase of the vitality consumption as a result. While new high capacity router components are installed, energy intake in system elements will be rising due to the higher capability network consuming larger component of the vitality. This study considers providing estimating power model in different traffic settings over TCP and UDP protocols, this study is mainly concerned about the transport protocols power consumption. Isolating the power consuming components within an electronic system is a very precise process that requires deep understanding of the role of each component within the system and a thorough study of the component datasheet. The study started by simulating the protocols mechanism then followed by protoclos power measurements, a simple simulation has been provided for Xilinx Virtex-5, it is very complicated to simulate the whole system due to the need of an external devices, so the simulation focused on wavelengths, frequencies and traffic types. This study found that the estimated power stokes was high when the 1480nm, 1580nm, and 1750nm power source increase. while there were differrence in the consumed power while transiting different types of traffic such as CBR and HTTP through UDP and TCP. The effect of different frequencies has been noticed also while applying different frequencies to the protocols. So it is believed that this study may enhance the power scenarios in the network and routers throug applying different techniques to UDP and TC

Effective Retransmission in Network Coding for TCP

Incorporating network coding into TCP has the advantage of masking packet losses from the congestion control algorithm. It could make a lossy channel appear as a lossless channel for TCP, therefore the transport protocol can only focus on handling congestion. However, most schemes do not consider the decoding delay, thus are not suitable to be implemented in practical systems. We propose a novel feedback based network coding (FNC) retransmission scheme which has high throughput and quite low decoding delay without sacrificing throughput. It uses the implicit information of the seen scheme to acquire the exact number of packets the receiver needs for decoding all packets based on feedback. We also change the encoding rules of retransmission, so as to decode part of packets in advance. The scheme can work well on handling not only random losses but also bursty losses. Our scheme also keeps the end-to-end philosophy of TCP that the coding operations are only performed at the end hosts. Thus it is easier to be implemented in practical systems. Simulation results show that our scheme significantly outperforms the previous coding approach in reducing decoding delay, and obtains the throughput which is close to the scenarios where there is zero error loss. It is particularly useful for streaming applications

On the design of load factor based congestion control protocols for next-generation networks

Load factor based congestion control schemes have shown to enhance network performance, in terms of utilization, packet loss and delay. In these schemes, using more accurate representation of network load levels is likely to lead to a more efficient way of communicating congestion information to hosts. Increasing the amount of congestion information, however, may end up adversely affecting the performance of the network. This paper focuses on this trade-off and addresses two important and challenging questions: (i) How many congestion levels should be represented by the feedback signal to provide near-optimal performance? and (ii) What window adjustment policies must be in place to ensure robustness in the face of congestion and achieve efficient and fair bandwidth allocations in high Bandwidth-Delay Product (BDP) networks, while keeping low queues and negligible packet drop rates? Based on theoretical analysis and simulations, our results show that 3-bit feedback is sufficient for achieving near-optimal rate convergence to an efficient bandwidth allocation. While the performance gap between 2-bit and 3-bit schemes is large, gains follow the law of diminishing returns when more than 3 bits are used. Further, we show that using multiple levels for the multiplicative decrease policy enables the protocol to adjust its rate of convergence to fairness, rate variations and responsiveness to congestion based on the degree of congestion at the bottleneck. Based on these fundamental insights, we design Multi-Level feedback Congestion control Protocol (MLCP). In addition to being efficient, MLCP converges to a fair bandwidth allocation in the presence of diverse RTT flows while maintaining near-zero packet drop rate and low persistent queue length. These features coupled with MLCP's smooth rate variations make it a viable choice for many real-time applications. Using extensive packetlevel simulations we show that the protocol is stable across a diverse range of network scenarios. A fluid model for the protocol shows that MLCP remains globally stable for the case of a single bottleneck link shared by identical round-trip time flows

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

[EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607S12386Rahem, A. A. T., Ismail, M., Najm, I. A., & Balfaqih, M. (2017). Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET. Telecommunication Systems, 65(4), 739-754. doi:10.1007/s11235-016-0242-7Aalsalem, M. Y., Khan, W. Z., Gharibi, W., Khan, M. K., & Arshad, Q. (2018). Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges. Journal of Network and Computer Applications, 113, 87-97. doi:10.1016/j.jnca.2018.04.004Sunny, A., Panchal, S., Vidhani, N., Krishnasamy, S., Anand, S. V. R., Hegde, M., … Kumar, A. (2017). A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs. Journal of Network and Computer Applications, 93, 13-26. doi:10.1016/j.jnca.2017.05.006Jain, R. (1990). Congestion control in computer networks: issues and trends. IEEE Network, 4(3), 24-30. doi:10.1109/65.56532Kafi, M. A., Djenouri, D., Ben-Othman, J., & Badache, N. (2014). Congestion Control Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 16(3), 1369-1390. doi:10.1109/surv.2014.021714.00123Floyd, S. (2000). Congestion Control Principles. doi:10.17487/rfc2914Qazi, I. A., & Znati, T. (2011). On the design of load factor based congestion control protocols for next-generation networks. Computer Networks, 55(1), 45-60. doi:10.1016/j.comnet.2010.07.010Katabi, D., Handley, M., & Rohrs, C. (2002). Congestion control for high bandwidth-delay product networks. ACM SIGCOMM Computer Communication Review, 32(4), 89-102. doi:10.1145/964725.633035Wang, Y., Rozhnova, N., Narayanan, A., Oran, D., & Rhee, I. (2013). An improved hop-by-hop interest shaper for congestion control in named data networking. ACM SIGCOMM Computer Communication Review, 43(4), 55-60. doi:10.1145/2534169.2491233Mirza, M., Sommers, J., Barford, P., & Zhu, X. (2010). A Machine Learning Approach to TCP Throughput Prediction. IEEE/ACM Transactions on Networking, 18(4), 1026-1039. doi:10.1109/tnet.2009.2037812Taherkhani, N., & Pierre, S. (2016). Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3275-3285. doi:10.1109/tits.2016.2546555Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. doi:10.1109/comst.2017.2707140Gonzalez-Landero, F., Garcia-Magarino, I., Lacuesta, R., & Lloret, J. (2018). PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks. IEEE Access, 6, 14141-14150. doi:10.1109/access.2018.2811900Lloret, J., Parra, L., Taha, M., & Tomás, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks, 129, 340-351. doi:10.1016/j.comnet.2017.05.018Khan, I., Zafar, M., Jan, M., Lloret, J., Basheri, M., & Singh, D. (2018). Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems. Entropy, 20(2), 92. doi:10.3390/e20020092Elappila, M., Chinara, S., & Parhi, D. R. (2018). Survivable Path Routing in WSN for IoT applications. Pervasive and Mobile Computing, 43, 49-63. doi:10.1016/j.pmcj.2017.11.004Singh, K., Singh, K., Son, L. H., & Aziz, A. (2018). Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks, 138, 90-107. doi:10.1016/j.comnet.2018.03.023Shelke, M., Malhotra, A., & Mahalle, P. N. (2017). Congestion-Aware Opportunistic Routing Protocol in Wireless Sensor Networks. Smart Innovation, Systems and Technologies, 63-72. doi:10.1007/978-981-10-5544-7_7Godoy, P. D., Cayssials, R. L., & García Garino, C. G. (2018). Communication channel occupation and congestion in wireless sensor networks. Computers & Electrical Engineering, 72, 846-858. doi:10.1016/j.compeleceng.2017.12.049Najm, I. A., Ismail, M., Lloret, J., Ghafoor, K. Z., Zaidan, B. B., & Rahem, A. A. T. (2015). Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications, 58, 119-129. doi:10.1016/j.jnca.2015.09.003Najm, I. A., Ismail, M., & Abed, G. A. (2014). High-Performance Mobile Technology LTE-A using the Stream Control Transmission Protocol: A Systematic Review and Hands-on Analysis. Journal of Applied Sciences, 14(19), 2194-2218. doi:10.3923/jas.2014.2194.2218Katuwal, R., Suganthan, P. N., & Zhang, L. (2018). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing, 70, 1146-1153. doi:10.1016/j.asoc.2017.09.020Gómez, S. E., Martínez, B. C., Sánchez-Esguevillas, A. J., & Hernández Callejo, L. (2017). Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal. Computer Networks, 127, 68-80. doi:10.1016/j.comnet.2017.07.018Hasan, M., Hossain, E., & Niyato, D. (2013). Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches. IEEE Communications Magazine, 51(6), 86-93. doi:10.1109/mcom.2013.6525600Liang, D., Zhang, Z., & Peng, M. (2015). Access Point Reselection and Adaptive Cluster Splitting-Based Indoor Localization in Wireless Local Area Networks. IEEE Internet of Things Journal, 2(6), 573-585. doi:10.1109/jiot.2015.2453419Park, H., Haghani, A., Samuel, S., & Knodler, M. A. (2018). Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident Analysis & Prevention, 112, 39-49. doi:10.1016/j.aap.2017.11.025Shu, J., Liu, S., Liu, L., Zhan, L., & Hu, G. (2017). Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine. Chinese Journal of Electronics, 26(2), 377-384. doi:10.1049/cje.2017.01.013Riekstin, A. C., Januário, G. C., Rodrigues, B. B., Nascimento, V. T., Carvalho, T. C. M. B., & Meirosu, C. (2016). Orchestration of energy efficiency capabilities in networks. Journal of Network and Computer Applications, 59, 74-87. doi:10.1016/j.jnca.2015.06.015Adi, E., Baig, Z., & Hingston, P. (2017). Stealthy Denial of Service (DoS) attack modelling and detection for HTTP/2 services. Journal of Network and Computer Applications, 91, 1-13. doi:10.1016/j.jnca.2017.04.015Stimpfling, T., Bélanger, N., Cherkaoui, O., Béliveau, A., Béliveau, L., & Savaria, Y. (2017). Extensions to decision-tree based packet classification algorithms to address new classification paradigms. Computer Networks, 122, 83-95. doi:10.1016/j.comnet.2017.04.021Singh, D., Nigam, S. P., Agrawal, V. P., & Kumar, M. (2016). Vehicular traffic noise prediction using soft computing approach. Journal of Environmental Management, 183, 59-66. doi:10.1016/j.jenvman.2016.08.053Xia, Y., Chen, W., Liu, X., Zhang, L., Li, X., & Xiang, Y. (2017). Adaptive Multimedia Data Forwarding for Privacy Preservation in Vehicular Ad-Hoc Networks. IEEE Transactions on Intelligent Transportation Systems, 18(10), 2629-2641. doi:10.1109/tits.2017.2653103Tariq, F., & Baig, S. (2017). Machine Learning Based Botnet Detection in Software Defined Networks. International Journal of Security and Its Applications, 11(11), 1-12. doi:10.14257/ijsia.2017.11.11.01Wu, T., Petrangeli, S., Huysegems, R., Bostoen, T., & De Turck, F. (2017). Network-based video freeze detection and prediction in HTTP adaptive streaming. Computer Communications, 99, 37-47. doi:10.1016/j.comcom.2016.08.005Pham, T. N. D., & Yeo, C. K. (2018). Adaptive trust and privacy management framework for vehicular networks. Vehicular Communications, 13, 1-12. doi:10.1016/j.vehcom.2018.04.006Mohamed, M. F., Shabayek, A. E.-R., El-Gayyar, M., & Nassar, H. (2019). An adaptive framework for real-time data reduction in AMI. Journal of King Saud University - Computer and Information Sciences, 31(3), 392-402. doi:10.1016/j.jksuci.2018.02.012Louvieris, P., Clewley, N., & Liu, X. (2013). Effects-based feature identification for network intrusion detection. Neurocomputing, 121, 265-273. doi:10.1016/j.neucom.2013.04.038Verma, P. K., Verma, R., Prakash, A., Agrawal, A., Naik, K., Tripathi, R., … Abogharaf, A. (2016). Machine-to-Machine (M2M) communications: A survey. Journal of Network and Computer Applications, 66, 83-105. doi:10.1016/j.jnca.2016.02.016Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26. doi:10.9781/ijimai.2018.02.004Lavanya, D. (2012). Ensemble Decision Tree Classifier For Breast Cancer Data. International Journal of Information Technology Convergence and Services, 2(1), 17-24. doi:10.5121/ijitcs.2012.2103Polat, K., & Güneş, S. (2007). Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Applied Mathematics and Computation, 187(2), 1017-1026. doi:10.1016/j.amc.2006.09.022Cayirci, E., Tezcan, H., Dogan, Y., & Coskun, V. (2006). Wireless sensor networks for underwater survelliance systems. Ad Hoc Networks, 4(4), 431-446. doi:10.1016/j.adhoc.2004.10.008Mezzavilla, M., Zhang, M., Polese, M., Ford, R., Dutta, S., Rangan, S., & Zorzi, M. (2018). End-to-End Simulation of 5G mmWave Networks. IEEE Communications Surveys & Tutorials, 20(3), 2237-2263. doi:10.1109/comst.2018.282888

On the design of load factor based congestion control protocols for next-generation networks

Load factor based congestion control schemes have shown to enhance network performance, in terms of utilization, packet loss and delay. In these schemes, using more accurate representation of network load levels is likely to lead to a more efficient way of communicating congestion information to hosts. Increasing the amount of congestion information, however, may end up adversely affecting the performance of the network. This paper focuses on this trade-off and addresses two important and challenging questions: (i) How many congestion levels should be represented by the feedback signal to provide near-optimal performance? and (ii) What window adjustment policies must be in place to ensure robustness in the face of congestion and achieve efficient and fair bandwidth allocations in high Bandwidth-Delay Product (BDP) networks, while keeping low queues and negligible packet drop rates? Based on theoretical analysis and simulations, our results show that 3-bit feedback is sufficient for achieving near-optimal rate convergence to an efficient bandwidth allocation. While the performance gap between 2-bit and 3-bit schemes is large, gains follow the law of diminishing returns when more than 3 bits are used. Further, we show that using multiple back-off factors enables the protocol to adjust its fairness convergence rate, rate variations and responsiveness to congestion based on the degree of congestion at the bottleneck. Based on these insights, we design Multi-Level feedback Congestion control Protocol (MLCP). In addition to being efficient, MLCP converges to a fair bandwidth allocation in the presence of diverse RTT flows while maintaining near-zero packet drop rate and low persistent queue length. A fluid model for the protocol reinforces the stability properties that we observe in our simulations and provides a good theoretical grounding for MLCP