A Distributed Congestion Control Strategy Using Harmonic Search Algorithm in Internet of Vehicles

Abstract

Aiming at the diversified requirements of network application QoS (Quality of Service) in the terminal equipment of Internet of Vehicles, this paper proposes a distributed congestion control strategy based on harmony search algorithm and the Throughput Evaluation Priority Adjustment Model (TEPAM) to ensure real-time transmission of high-priority data messages related to security applications. Firstly, the channel usage rate is periodically detected and the congestion is judged; then, in order to minimize delay and delay jitter as the goal, harmony search algorithm is utilized to perform global search to obtain a better solution for the transmission range and transmission rate. Secondly, packet priority and the TEPAM are applied to indicate the sending right of each packet. The data message priority and throughput percentage factor are used to express the transmission weight of each data message. Besides, the real-time evaluation of path state in MPTCP is carried out by the batch estimation theory model, which realizes the on-demand dynamic adjustment of the network congestion time window. Finally, SUMO, MOVE, and NS2 tools are used to create a VANET-like environment to evaluate the performance of the proposed congestion control strategy. Experimental results show that the proposed method is superior to other three methods in the four indicators of average delay time, average transmission rate, number of retransmissions, and packet loss rate compared with other advanced methods.

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

“…This method prevents the starvation phenomenon of vehicles with high mobility. Also, prioritizing messages will be a good solution for high‐weighted vehicles with the same conditions in congested networks 47,67 …”

“…In vehicular networks, data management refers to the application of methods to manage data of interest to drivers, including suitable mechanisms for retrieving and transferring data, specific data filtering, the handling of data queries, data collection, and efficient data exploitation while attempting to avoid possible risks. [39][40][41][42][43][44][45][46][47][48][49][51][52][53][54][55][58][59][60][65][66][67][68] Researchers in congestion control-enabled data management domain in VANETs and IoV networks focus more on solving challenges such as improving network performance 48,60,65 , better RT 42,66 , strengthening PDR 67,68 , simplifying throughput 51,58,67,68 , better E2E delay 39,40,43,48,67,68 , increasing channel utilization 44,67 , reducing communication costs 39,40 , reducing the average WT 41,…”

“…Also, prioritizing messages will be a good solution for high-weighted vehicles with the same conditions in congested networks. 47,67 In the IoV networks, vehicles can limit V2V communications to manage data traffic by changing communication protocols to connect to remote infrastructure using cellular networks. Furthermore, if some messages are more crucial, vehicles that use cellular networks like 5G and are directly connected to the Internet send messages through V2I communication instead of V2V.…”

“…Pang et al 47 introduced a method for managing distributed congestion that relies on the harmony search algorithm and guarantees the timely availability of quality data packets regarding security concerns. The global search was conducted using the harmony search algorithm to find a more effective way to increase the transfer rate and range to reduce delay and delay jitter.…”

“…VANETs and IoV networks place a high priority on data management, wherein network nodes continually create, collect, or propagate data with details about their position and the time it was stored, which is then processed locally in each vehicle's internal processors. In vehicular networks, data management refers to the application of methods to manage data of interest to drivers, including suitable mechanisms for retrieving and transferring data, specific data filtering, the handling of data queries, data collection, and efficient data exploitation while attempting to avoid possible risks 39–49,51–55,58–60,65–68 . Researchers in congestion control‐enabled data management domain in VANETs and IoV networks focus more on solving challenges such as improving network performance 48,60,65 , better RT 42,66 , strengthening PDR 67,68 , simplifying throughput 51,58,67,68 , better E2E delay 39,40,43,48,67,68 , increasing channel utilization 44,67 , reducing communication costs 39,40 , reducing the average WT 41,52 , and effective use of resources and reducing casualties in congested areas.…”