TY - JOUR
T1 - A Bignum Network Coding Scheme for Multipath Transmission in Vehicular Networks
AU - Zhang, Yuyang
AU - Dong, Ping
AU - Yu, Yong
AU - Du, Xiaojiang
AU - Luo, Hongbin
AU - Zheng, Tao
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - The multipath transmission scheme in vehicular networks has become a hot topic. It is a great challenge to overcome the unreliability of wireless network in multipath transmission. Recently, scholars propose a lot of network coding schemes to solve this problem. These schemes implement network coding algorithms by bitwise X O R or Galois Field arithmetic. However, these schemes cannot take into account both coding flexibility and computational complexity. Therefore, we propose a BigNum Network Coding (BNNC) scheme. The core idea of the BNNC scheme is to treat a packet as an integer and implement the network coding through linear operations on the integer set. It replaces bitwise XOR and Galois Field arithmetic with integer arithmetic that guarantees high coding flexibility and low computational complexity. In this paper, first, we propose the BNN C scheme that can effectively improve the reliability of multipath transmission in vehicular networks with lower computational complexity than current network coding scheme. Second, we design the Independent Matrix that enables the coding process to improve coding efficiency without independent check. Third, we compare BNNC scheme with Earliest Completion First (ECF) and Galois Field network coding scheme through a lot of simulations and real tests. The results show that the BNN C scheme is significantly superior to the Galois Field network coding schemes in terms of computational performance. And in terms of the network performance, the BNNC scheme can overcome the unreliability of links in multipath transmission.
AB - The multipath transmission scheme in vehicular networks has become a hot topic. It is a great challenge to overcome the unreliability of wireless network in multipath transmission. Recently, scholars propose a lot of network coding schemes to solve this problem. These schemes implement network coding algorithms by bitwise X O R or Galois Field arithmetic. However, these schemes cannot take into account both coding flexibility and computational complexity. Therefore, we propose a BigNum Network Coding (BNNC) scheme. The core idea of the BNNC scheme is to treat a packet as an integer and implement the network coding through linear operations on the integer set. It replaces bitwise XOR and Galois Field arithmetic with integer arithmetic that guarantees high coding flexibility and low computational complexity. In this paper, first, we propose the BNN C scheme that can effectively improve the reliability of multipath transmission in vehicular networks with lower computational complexity than current network coding scheme. Second, we design the Independent Matrix that enables the coding process to improve coding efficiency without independent check. Third, we compare BNNC scheme with Earliest Completion First (ECF) and Galois Field network coding scheme through a lot of simulations and real tests. The results show that the BNN C scheme is significantly superior to the Galois Field network coding schemes in terms of computational performance. And in terms of the network performance, the BNNC scheme can overcome the unreliability of links in multipath transmission.
KW - multipath transmission
KW - network coding scheme
KW - Vehicular networks
KW - wireless networks
UR - http://www.scopus.com/inward/record.url?scp=85063505301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063505301&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2018.8647337
DO - 10.1109/GLOCOM.2018.8647337
M3 - Conference article
AN - SCOPUS:85063505301
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
M1 - 8647337
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
Y2 - 9 December 2018 through 13 December 2018
ER -