Abstract
Multipath TCP (MPTCP) is one of the most important extensions to TCP that enables the use of multiple paths in data transmissions for a TCP connection. In MPTCP, the end hosts transmit data across a number of TCP subflows simultaneously on one connection. MPTCP can sufficiently utilize the bandwidth resources to improve the transmission efficiency while providing TCP fairness to other TCP connections. Meanwhile, it also offers resilience due to multipath data transfers. MPTCP attracts tremendous attention from the academic and industry field due to the explosive data growth in recent times and limited network bandwidth for each single available communication interface. The vehicular Internet-of-Things systems, such as cooperative autonomous driving, require reliable high speed data transmission and robustness. MPTCP could be a promising approach to solve these challenges. In this paper, we first conduct a brief survey of existing MPTCP studies and give a brief overview to multipath routing. Then we discuss the significance technical challenges in applying MPTCP for vehicular networks and point out future research directions.
Highlights
In the past, people have mostly relied on newspapers, magazines, or books to acquire the information they need
Existing studies show that we can improve the performance of multipath transmission control protocol (TCP) (MPTCP) by leveraging other techniques
We first conducted a survey on MPTCP and compared MPTCP with single path TCP (SPTCP)
Summary
People have mostly relied on newspapers, magazines, or books to acquire the information they need. TCP utilizes a congestion control algorithm (CC) [12] to adjust sending rate based on packet loss or delay in order to avoid network congestion. The above-mentioned approaches generally adopt a pre-determined policy with fixed system parameters for implementing congestion control They are incapable of satisfying the performance requirements under dynamic network environments. QTCP is capable of adjusting congestion control corresponding to a new network scenario They developed a generalization-based Kanerva coding function approximation approach to reduce the computation complexity and necessary state space, reducing the training time. The TCP-RL dynamically adjusts the IW and congestion control to improve TCP flow transmission efficiency Such learning based TCP variants are able to provide a higher throughput than rule-based TCP variants.
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