An efficient multiple access control protocol for directional dense urban traffic surveillance system

Abstract

Artificial intelligence (AI) methods for traffic video analysis have been widely identified as potential solutions for solving hard problems in intelligent transport systems (ITS). To exploit the advantages of AI, dense cameras to monitor the traffic are required to be deployed along the road and at the intersections. The captured videos of these cameras should be back-hauled to the control center, acting as the inputs of the AI methods. To bear such large data traffic load and to cover long transmission range, directional communication technology can be employed, which concentrate the energy of the wireless signal in a specified direction to provide high data rate and long transmission range (up to hundreds of kilometers). In this paper, the communication time extension problem (CTEP) is identified when directional transmission is applied to the dense urban traffic surveillance system, where the wireless signal propagation time approximates the data transmission time. A link distance division-based time division multiple access (LDD-TDMA) protocol is proposed to address the identified CTEP. Firstly, the directional wireless communication links are classified into categories according to the link distance, where nodes located in the same communication ring belong to the same category. Then a link distance aware (LDA)-based slot allocation algorithm is proposed to assign the time slots to the links. The optimal communication rings’ radius is derived in closed form formula, and the minimum average links’ distance is derived. Simulation results show that LDD-TDMA outperforms TDMA by 13.37% when the ring number is 4.

Keywords:

• artificial intelligence
• communication ring
• data transmission time extension
• directional networks
• traffic video analysis

Acknowledgement

The authors thank the reviewers and the editors for their valuable comments and suggestions, which help to improve the quality of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundations of China [grant number 61771392], [grant number 61771390], [grant number 61871322], [grant number 61501373], [grant number 61271279]; the National Science and Technology Major Project [grant number 2015ZX03002006-004], [grant number 2016ZX03001018-004]; the Fundamental Research Funds for the Central Universities [grant number 3102017ZY018] and the Science and Technology on Avionics Integration Laboratory [grant number 20185553035].