An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks
Abstract
:1. Introduction
- We propose an adaptive network coding scheme for vehicle-to-cloud multipath communication in cellular-based wireless networks. When compared with the current NC schemes, ANC significantly improves network throughput performance and reduces multipath transmission packet loss.
- We propose a novel combination of network coding and packet dispatch to reduce packet caching time. Packet dispatch is realized in a group aspect to determine the distribution proportion in each available link according to measured bandwidth and Round-Trip Time (RTT).
- We introduce wavelets into Hidden Markov Model (HMM) to fit in with the rapid change of link quality in the cellular-based vehicular wireless networks. This estimation method effectively reduces the error range of estimated PLR.
2. Related Work
2.1. Multipath Transmission Schemes in Cellular-Based Vehicular Networks
2.2. NC Researches
3. ANC Scheme Overview
4. The Detail of ANC Scheme
4.1. Preliminaries and Definition
- Coding rate: the coding rate is the ratio of the number of redundant packets to the total number of coded packets. For example, if k raw packets are encoded into n coded packets, then, there are redundant packets generated to combat the packet loss of wireless links. In this case, the coding rate is . When becomes higher, it means NC generates more redundant packets. When becomes lower, it means that NC generates fewer redundant packets.
- TFR: TFR is the ratio of the number of coded groups that fail to be decoded to the total number of the coded groups. Although NC can reduce the probability of packet loss, it can not completely solve the problem of packet loss in wireless transmission. There are still some packets lost in transmission. When there are too many lost packets in transmission, the receiver can not obtain enough coded packets to support the decoding process. TFR can well quantify the ability of the NC scheme in order to overcome the packet loss problem of a wireless link in the different communication environments. Let denote the number of the coded group that fails to be decoded. We use to represent the total number of groups transmitted over a certain link. Accordingly, can be calculated as .
4.2. Network Topology Model
4.3. The Coding and Decoding Processes of ANC
Algorithm 1 Coded packets distribution |
Input: measured RTT, measured BW, estimated next moment PLR Output: packets distribution proportion
|
4.4. Mathematical PLR Estimation Model
4.4.1. Average PLR Acquisition
Algorithm 2 Average PLR acquisition |
Input: GN, LN, SSN, CPL Output: averPLRset
|
4.4.2. Raw PLR Estimation
- A={} is the matrix of state transition conditional probability, where has been defined in Equation (11).
- N={} is the set of PDF of the observed variable corresponding to different latent variable states, where has been defined in Equation (13).
- are the initial state probability distribution, is the probability that the system starts from latent variable state .
- Model learning: we use 10 observed raw PLR points to train this model by Baum-Welch algorithm.
- Prediction: we estimate the classification of 10 raw PLR points by the Viterbi algorithm and calculate the expectation of next moment raw PLR according to the state transition probability matrix and the conditional probability density function of the observed variable.
- Update: we combine the estimated raw PLR point with the previous nine data points into a new training set, go to step 1 until the data set runs out.
Algorithm 3 Next moment raw PLR estimation |
Input: average raw PLRs Output: estimated next moment raw PLRs
|
5. Results
5.1. Simulation Setup
5.2. Network Performance of Different Network Coding Schemes in Simulations
5.3. Real Experiment Setup
5.4. Network Performance of Different Network Coding Schemes in Real Experiments
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Awan, K.A.; Ud Din, I.; Almogren, A.; Guizani, M.; Khan, S. StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks. IEEE Access 2020, 8, 21159–21177. [Google Scholar] [CrossRef]
- Du, Z.; Wu, C.; Yoshinaga, T.; Yau, K.A.; Ji, Y.; Li, J. Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues. IEEE Open J. Comput. Soc. 2020, 1, 45–61. [Google Scholar] [CrossRef] [PubMed]
- Peng, C.; Wu, C.; Gao, L.; Zhang, J.; Alvin Yau, K.L.; Ji, Y. Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues. Sensors 2020, 20, 5079. [Google Scholar] [CrossRef] [PubMed]
- Gu, J.; Sun, B.; Du, X.; Wang, J.; Zhuang, Y.; Wang, Z. Consortium Blockchain-Based Malware Detection in Mobile Devices. IEEE Access 2018, 6, 12118–12128. [Google Scholar] [CrossRef]
- He, W.; Yan, G.; Xu, L.D. Developing Vehicular Data Cloud Services in the IoT Environment. IEEE Trans. Ind. Inform. 2014, 10, 1587–1595. [Google Scholar] [CrossRef]
- Su, Y.; Lu, X.; Huang, L.; Du, X.; Guizani, M. A Novel DCT-Based Compression Scheme for 5G Vehicular Networks. IEEE Trans. Veh. Technol. 2019, 68, 10872–10881. [Google Scholar] [CrossRef]
- Duo, R.; Wu, C.; Yoshinaga, T.; Zhang, J.; Ji, Y. SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks. Sensors 2020, 20, 1082. [Google Scholar] [CrossRef] [Green Version]
- Mandala, D.; Dai, F.; Du, X.; You, C. Load Balance and Energy Efficient Data Gathering in Wireless Sensor Networks. In Proceedings of the 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems, Vancouver, BC, Canada, 9–12 October 2006; pp. 586–591. [Google Scholar]
- Brown, J.; Du, X. Detection of Selective Forwarding Attacks in Heterogeneous Sensor Networks. In Proceedings of the 2008 IEEE International Conference on Communications, Beijing, China, 19–23 May 2008; pp. 1583–1587. [Google Scholar]
- Xu, C.; Jin, W.; Zhao, G.; Tianfield, H.; Yu, S.; Qu, Y. A Novel Multipath-Transmission Supported Software Defined Wireless Network Architecture. IEEE Access 2017, 5, 2111–2125. [Google Scholar] [CrossRef]
- Wu, J.; Yuen, C.; Wang, M.; Chen, J. Content-Aware Concurrent Multipath Transfer for High-Definition Video Streaming over Heterogeneous Wireless Networks. IEEE Trans. Parallel Distrib. Syst. 2016, 27, 710–723. [Google Scholar] [CrossRef]
- Wang, W.; Wang, X.; Wang, D. Handover optimisation for multipath transmission control protocol-based concurrent multipath transfer in heterogeneous networks. Electron. Lett. 2019, 55, 715–716. [Google Scholar] [CrossRef]
- Li, M.; Lukyanenko, A.; Cui, Y. Network coding based multipath TCP. In Proceedings of the 2012 Proceedings IEEE INFOCOM Workshops, Orlando, FL, USA, 25–30 March 2012; pp. 25–30. [Google Scholar]
- Palash, M.R.; Chen, K.; Khan, I. Bandwidth-Need Driven Energy Efficiency Improvement of MPTCP Users in Wireless Networks. IEEE Trans. Green Commun. Netw. 2019, 3, 343–355. [Google Scholar] [CrossRef]
- Dong, P.; Zheng, T.; Du, X.; Zhang, H.; Guizani, M. SVCC-HSR: Providing Secure Vehicular Cloud Computing for Intelligent High-Speed Rail. IEEE Netw. 2018, 32, 64–71. [Google Scholar] [CrossRef]
- Zhang, Y.; Dong, P.; Yu, S.; Luo, H.; Zheng, T.; Zhang, H. An Adaptive Multipath Algorithm to Overcome the Unpredictability of Heterogeneous Wireless Networks for High-Speed Railway. IEEE Trans. Veh. Technol. 2018, 67, 11332–11344. [Google Scholar] [CrossRef]
- Li, L.; Xu, K.; Wang, D.; Peng, C.; Zheng, K.; Mijumbi, R.; Xiao, Q. A Longitudinal Measurement Study of TCP Performance and Behavior in 3G/4G Networks Over High Speed Rails. IEEE/ACM Trans. Netw. 2017, 25, 2195–2208. [Google Scholar] [CrossRef]
- Kafaie, S.; Chen, Y.; Dobre, O.A.; Ahmed, M.H. Joint Inter-Flow Network Coding and Opportunistic Routing in Multi-Hop Wireless Mesh Networks: A Comprehensive Survey. IEEE Commun. Surv. Tutor. 2018, 20, 1014–1035. [Google Scholar] [CrossRef] [Green Version]
- Chakchouk, N. A Survey on Opportunistic Routing in Wireless Communication Networks. IEEE Commun. Surv. Tutor. 2015, 17, 2214–2241. [Google Scholar] [CrossRef]
- Bassoli, R.; Marques, H.; Rodriguez, J.; Shum, K.W.; Tafazolli, R. Network Coding Theory: A Survey. IEEE Commun. Surv. Tutorials 2013, 15, 1950–1978. [Google Scholar] [CrossRef]
- Cui, Y.; Wang, L.; Wang, X.; Wang, H.; Wang, Y. FMTCP: A Fountain Code-Based Multipath Transmission Control Protocol. IEEE/ACM Trans. Netw. 2015, 23, 465–478. [Google Scholar] [CrossRef]
- Xu, C.; Li, Z.; Zhong, L.; Zhang, H.; Muntean, G. CMT-NC: Improving the Concurrent Multipath Transfer Performance Using Network Coding in Wireless Networks. IEEE Trans. Veh. Technol. 2016, 65, 1735–1751. [Google Scholar] [CrossRef]
- Zhang, Y.; Dong, P.; Yu, Y.; Du, X.; Luo, H.; Zheng, T.; Guizani, M. A Bignum Network Coding Scheme for Multipath Transmission in Vehicular Networks. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE, 9–13 December 2018; pp. 206–212. [Google Scholar]
- Xu, C.; Li, Z.; Li, J.; Zhang, H.; Muntean, G. Cross-Layer Fairness-Driven Concurrent Multipath Video Delivery Over Heterogeneous Wireless Networks. IEEE Trans. Circ. Syst. Video Technol. 2015, 25, 1175–1189. [Google Scholar]
- Huang, C.-M.; Lin, M.-S. RG-SCTP: Using the relay gateway approach for applying SCTP in vehicular networks. In Proceedings of the IEEE symposium on Computers and Communications, Riccione, Italy, 22–25 June 2010; pp. 139–144. [Google Scholar]
- Pang, S.; Yao, J.; Wang, X.; Ding, T.; Zhang, L. Transmission Control of MPTCP Incast Based on Buffer Balance Factor Allocation in Data Center Networks. IEEE Access 2019, 7, 183428–183434. [Google Scholar] [CrossRef]
- Dong, P.; Song, B.; Zhang, H.; Du, X. Improving Onboard Internet Services for High-Speed Vehicles by Multipath Transmission in Heterogeneous Wireless Networks. IEEE Trans. Veh. Technol. 2016, 65, 9493–9507. [Google Scholar] [CrossRef]
- Xu, Z.; Tang, J.; Yin, C.; Wang, Y.; Xue, G. Experience-Driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning. IEEE J. Sel. Areas Commun. 2019. [Google Scholar] [CrossRef]
- Naeem, F.; Srivastava, G.; Tariq, M. A Software Defined Network based Fuzzy Normalized Neural Adaptive Multipath Congestion Control for Internet of Things. IEEE Trans. Netw. Sci. Eng. 2020. [Google Scholar] [CrossRef]
- Arianpoo, N.; Aydin, I.; Leung, V.C.M. Network Coding as a Performance Booster for Concurrent Multi-Path Transfer of Data in Multi-Hop Wireless Networks. IEEE Trans. Mob. Comput. 2017, 16, 1047–1058. [Google Scholar] [CrossRef]
- Ahlswede, R.; Cai, N. Network information flow. IEEE Trans. Inf. Theory 2000, 46, 1204–1216. [Google Scholar] [CrossRef]
- Kim, S.; Cho, H.; Yang, T.; Kim, C.; Kim, S. Low-Cost Multipath Routing Protocol by Adapting Opportunistic Routing in Wireless Sensor Networks. In Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, 19–22 March 2017; pp. 1–6. [Google Scholar]
- Lin, Y.; Liang, B.; Li, B. SlideOR: Online Opportunistic Network Coding in Wireless Mesh Networks. In Proceedings of the 2010 Proceedings IEEE INFOCOM, San Diego, CA, USA, 14–19 March 2010; pp. 1–5. [Google Scholar]
- Xu, C.; Wang, P.; Xiong, C.; Wei, X.; Muntean, G. Pipeline Network Coding-Based Multipath Data Transfer in Heterogeneous Wireless Networks. IEEE Trans. Broadcast. 2017, 63, 376–390. [Google Scholar] [CrossRef]
- Jiang, H.; Chen, S.; Yang, Y.; Jie, Z.; Leung, H.; Xu, J.; Wang, L. Estimation of Packet Loss Rate at Wireless Link of VANET–RPLE. In Proceedings of the 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu, China, 23–25 September 2010; pp. 1–5. [Google Scholar]
Symbol | Description |
---|---|
B | Column vector of raw packets |
CM | Coding matrix |
IM | Identity matrix |
E | Column vector of coded packets |
Column vector of received coded packets after transmission | |
Reconstructed coding matrix in receiver | |
D | Decoding matrix |
The length of raw packets group in the i th link | |
The length of coded packets group in the i th link | |
The number of row vectors of in the i th link | |
Packet Loss Rate of the i th link | |
Bandwidth of the i th link | |
Round-trip time of the i th link |
The Number of Training Points | Real Time Consumption for Single Link |
---|---|
10 | 0.01332179 s |
11 | 0.01466731 s |
100 | 0.02417990 s |
110 | 0.02523301 s |
1000 | 0.03267589 s |
1100 | 0.03368711 s |
Configuration | Link A | Link B | Link C |
---|---|---|---|
1 | 0 | 0 | 0.1% |
2 | 0.5% | 0.6% | 0.8% |
3 | 1% | 0.5% | 3% |
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Yin, C.; Dong, P.; Du, X.; Zheng, T.; Zhang, H.; Guizani, M. An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks. Sensors 2020, 20, 5902. https://doi.org/10.3390/s20205902
Yin C, Dong P, Du X, Zheng T, Zhang H, Guizani M. An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks. Sensors. 2020; 20(20):5902. https://doi.org/10.3390/s20205902
Chicago/Turabian StyleYin, Chenyang, Ping Dong, Xiaojiang Du, Tao Zheng, Hongke Zhang, and Mohsen Guizani. 2020. "An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks" Sensors 20, no. 20: 5902. https://doi.org/10.3390/s20205902