Development of a Smart Traffic Light Control System With Real-Time Monitoring

Oliveira, Luiz Fernando Pinto de; Manera, Leandro Tiago; Luz, P. D. G.

doi:10.1109/jiot.2020.3022392

Cited by 78 publications

(32 citation statements)

References 27 publications

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“…Just as the concept of smart cities is strongly linked to the use of IoT technologies [115,116], the concept of smart agriculture must also be. The use of various artificial intelligence techniques, such as CNN, demonstrated a high degree of adaptation to the rapid variation in natural lighting, changing seasons and crop growing.…”

Section: Iot-based Smart Agriculturementioning

confidence: 99%

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

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Section: Iot-based Smart Agriculturementioning

confidence: 99%

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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“…We mainly focus on downlink transmission from BS to devices. The reason is that some emerging IIoT applications such as tactile Internet [19], augmented reality [20], real-time control [21], and hologram [22] impose stringent requirements on downlink data transmission. In such a downlink scenario, the data traffic source is IIoT application servers.…”

Section: System Modelmentioning

confidence: 99%

Two-timescale Resource Allocation for Automated Networks in IIoT

He¹,

Ren²,

Zhou³

et al. 2022

Preprint

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The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the twotimescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a lowcomplexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.

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“…The spoilage time is defined as three components' discrete-time summation (S): waiting time (T w ), loss of start-stop time (T q ), and time reward (T p ), as shown in (5).…”

Section: Objective Function Of the Simulation Modelmentioning

confidence: 99%

“…This increasing population has brought many problems to the developing cities, such as traffic congestions, environmental pollution, safety, and fuel wastage [2][3][4]. According to [5], in 2014, congestion cost North Americans approximately an extra 6.9 billion hours to travel, consuming around 3.1 billion litres of additional fuel, resulting in a waste of 160 billion dollars.…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control

Shi

et al. 2021

Machines

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Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.

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Development of a Smart Traffic Light Control System With Real-Time Monitoring

Cited by 78 publications

References 27 publications

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Two-timescale Resource Allocation for Automated Networks in IIoT

A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control

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