Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.
Barrier coverage is a widely adopted coverage model for intruder surveillance application in wireless sensor networks. However, when sensor nodes are deployed outdoors, they are subject to environmental detriments and will be failed while operating in the rain. Thus, one barrier is not robust to provide barrier coverage under both sunny and rainy weather. In this paper, we study the barrier coverage problem in a mobile survivability-heterogeneous wireless sensor network, which is composed of sensor nodes with environmental survivabilities to make them robust to environmental conditions and with motion capabilities to repair the barrier when sensors are dead. Our goal is to keep field to be monitored continuously under both sunny and rainy weather and to prolong the network lifetime as much as possible. We propose a novel greedy barrier construction algorithm to solve the problem. The algorithm adopts weather forecast to direct the barrier construction under sunny and rainy weather, and the energy consumption of construction is minimized. Simulation results show that our algorithm efficiently solves the problem and outperforms other alternatives.
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