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Q-learning based vehicular ad hoc network routing method

A vehicle self-organization and network technology, applied in the field of Internet of Things communication, can solve complex and changeable problems, and achieve the effect of improving the success rate of transmission

Inactive Publication Date: 2015-05-20
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a vehicle-mounted self-organizing network routing method for the complex and changeable urban traffic network environment, which can improve the success rate of data packet delivery as much as possible regardless of the quality of the network connectivity

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specific Embodiment approach

[0024] This part will describe the Q-learning and grid-based routing routing selection method in detail in conjunction with the above-mentioned drawings. The specific implementation of each part included in this method is as follows:

[0025] Step 1: Divide the urban area into equal grids, and record the track information of the passing vehicles in each grid in the past period of time. Since the vehicles in the network are all equipped with GPS global positioning system, the vehicles obtain neighbor node information by transmitting Hello data packets to each other. figure 1 It is the change of the number of vehicle GPS records in different grids in the area around Shanghai Railway Station from February 1, 2007 to February 8, 2007. Among them, the area is 1200m×1200m, and the side length is 200m. Since the frequency of vehicles uploading GPS points is fixed, the number of GPS records of vehicles in the grid can roughly indicate the frequency of passing vehicles in the grid. F...

Embodiment

[0048] In order to verify the beneficial effects of the present invention, simulation verification is performed on this embodiment.

[0049] In some applications of the urban traffic network, there is a high requirement for the success rate of data packet transmission, but not high requirements for the transmission delay of data packets, so in this kind of network, the success rate of data packet transmission is a measure of the vehicle Core metrics for the performance of routing protocols in ad hoc networks.

[0050] The Q-learning and grid-based routing algorithm proposed in the present invention is named as QGrid, and is subdivided into QGrid_G and QGrid_M according to the next-hop vehicle greedy selection strategy and Markov selection strategy. In order to verify the data transmission success rate and transmission delay performance of the QGrid algorithm in the vehicle ad hoc network, the present invention compares it with GPSR and HarpiaGrid. GPSR is a classic routing pr...

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Abstract

The invention relates to a Q-learning based vehicular ad hoc network routing method and belongs to the technical field of Internet-of-things communication. The method includes that (1) a GPS (global positioning system) is loaded to each vehicle in a network, and the vehicles acquire neighbor node information by passing Hello messages therebetween; (2) a city region is divided into equal grids, the position of each grid represents a different state, and transferring from one grid to the adjacent grid represents an action; (3) a Q-value table is learnt; (4) parameters are set; (5) routing strategies QGrid_G and QGrid_M are selected. Vehicles newly added into the network acquire the Q-value table obtained by offline learning from the neighbor vehicles, and the vehicles can be informed of the optimal next-hop grid of message passing by querying the Q-value table of the message destination grid. The grid sequence that the vehicles mostly frequently travel is taken into consideration from a macroscopic point of view, the vehicle which is mostly likely to arrive at the optimal next-hop grid is selected by considering from a microcosmic point of view, and passing success rate of messages in the urban traffic network is increased effectively by the macroscopic and microcosmic combination mode.

Description

technical field [0001] The invention belongs to the technical field of Internet of things communication, and in particular relates to a routing selection method of a vehicle-mounted self-organizing network, which is used to solve the problem of routing selection of the vehicle-mounted Internet of Things in a complex and changeable environment. Background technique [0002] Vehicular Ad Hoc Networks (VANETs) is a high-speed mobile wireless network, which relies on short-range communication technology to realize communication between vehicles and between vehicles and roadside infrastructure. At present, the geographic location-based routing protocol algorithms suitable for the vehicle Internet of Things mainly include the following: 1) The GPSR (Greedy Perimeter Stateless Routing) protocol is based on geographic location and greedy forwarding mechanism. In the protocol, the current node always transmits the data packet to the nearest neighbor node from the destination node. H...

Claims

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Application Information

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IPC IPC(8): H04W40/04H04W84/18
CPCH04W4/046H04W40/04H04W40/20H04W40/32H04W84/18Y02D30/70
Inventor 李凡李瑞玲宋肖玉王昱
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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