IoT-Based Urban Traffic-Light Control: Modelling, Prototyping and Evaluation of MQTT Protocol

Most traffic light's control systems in smart cities are wired and have a semi-static behavior. They are time-based, with pre-configured pattern and expensive cameras. Although traffic lights can communicate wirelessly with incoming vehicles, they are less adapted to an urban environment. If we consider light signs as an Internet of Things (IoT) network, one issue is to model thoroughly the change of signs' states and the Quality of Service (QoS) of this network. In this paper, we propose a new architecture of Urban Traffic Light Control based on an IoT network (IoT-UTLC). The objective is to interconnect both roads' infrastructures and traffic lights through an IoT platform. We designed our IoT-UTLC by selecting motes and protocols of wireless sensor network (WSN). Message Queuing Telemetry Transport (MQTT) protocol has been integrated to manage QoS. It enables lights to adapt remotely to any situation and smoothly interrupt traffic light's classic cycles. Our experimental results show that the MQTT protocol is efficient when the packets rate exceeds 35% of traffic flow, it reduces traffic delay up to 0.05s at 90% of congestion. After verification and validation of our solution using a UPPAAL model checker, our system has been prototyped. Motes' functions have been implemented on Contiki OS and connected via a 6LoWPAN/IEEE 802.15.4 network. Timestamping messages have been performed throughout the system to evaluate the MQTT protocol with different QoS levels. In our experiments, we measured the Round-trip delay time (RTT) of messages exchanged between the WSN and IoT Cloud. The results show that MQTT decreases the RTT when the Cumulative Distributed Function (CDF) of generated messages exceeds 35%.

[1]  Rajkumar Buyya,et al.  Fog Computing: Helping the Internet of Things Realize Its Potential , 2016, Computer.

[2]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[3]  Mariagrazia Dotoli,et al.  An urban traffic network model via coloured timed Petri nets , 2004 .

[4]  Olivier Buffet,et al.  Decentralized traffic management: A synchronization-based intersection control , 2014, 2014 International Conference on Advanced Logistics and Transport (ICALT).

[5]  Nicola Sacco,et al.  On using petri nets for representing and controlling signalized urban areas: New model and results , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[6]  Li Zhao,et al.  Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G , 2017, IEEE Communications Standards Magazine.

[7]  Emiliano Sisinni,et al.  Latency evaluation for MQTT and WebSocket Protocols: an Industry 4.0 perspective , 2018, 2018 IEEE Symposium on Computers and Communications (ISCC).

[8]  Angela Di Febbraro,et al.  Traffic-responsive signalling control through a modular/switching model represented via DTPN , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[9]  Kim G. Larsen,et al.  Uppaal SMC tutorial , 2015, International Journal on Software Tools for Technology Transfer.

[10]  Mohsen Guizani,et al.  Toward better horizontal integration among IoT services , 2015, IEEE Communications Magazine.

[11]  Amitha Chalappuram,et al.  Development of 6LoWPAN in Embedded Wireless System , 2016 .

[12]  M. Stephens Tests of fit for the logistic distribution based on the empirical distribution function , 1979 .

[13]  MengChu Zhou,et al.  Modular Design of Urban Traffic-Light Control Systems Based on Synchronized Timed Petri Nets , 2014, IEEE Transactions on Intelligent Transportation Systems.

[14]  Jos Vrancken,et al.  A modular Petri net to modeling and scenario analysis of a network of road traffic signals , 2012 .

[15]  Lyman Chapin,et al.  THE INTERNET OF THINGS : AN OVERVIEW Understanding the Issues and Challenges of a More Connected World , 2015 .