Avoiding traffic accidents is critical since the death of traffic accidents is the eighth among the top ten leading causes of death in 2018. This paper proposes a light-weight convolutional 3D (C3D) network with five 3D convolution layers and two fully-connected layers to predict overtaking behavior. This network utilizes the last layer of convolution layer to learn the overtaking object location in the final frame. Based on NVIDIA Jetson TX2, the proposed C3D network achieves 91.46% accuracy to detect overtaking behavior on rainy days. To generate this excellent deep learning model, we use an efficient labeling tool, called ezLabel, which is a free SaaS for academia group with 96,000 opened image data samples for deep learning. ezLabel owns outstanding route prediction and fitting functions, which speeds up with the factor of ten compared to traditional tools. Users only label the object in its first frame and in its final frame, and then ezLabel labels the object in all frames in between and fits the bounding box to the object. The ezLabel can be used to label objects captured with any moving or static cameras efficiently.

The Set -Cover problem aims to partition a set of nodes for the maximal number of covers. This problem is crucial for extending the lifetime of wireless sensor networks (WSNs) under the constraint of covering all targets. More specifically, the Set -Cover problem enables partitioning the set of sensors into several covers over all targets and activating the covers by turns to effectively extend the WSN lifetime. To resolve this problem, we propose a novel memetic algorithm (MA) based on integer-coded genetic algorithm and local search. This paper adapts the crossover and mutation operators to integer representation and, furthermore, designs a new fitness function that considers both the number of covers and the contribution of each sensor to covers. A local improvement method, called the recycling operator, is developed to enhance the performance on the Set -Cover problem. Experimental results show that the proposed MA significantly outperforms five evolutionary algorithms in terms of the number of covers obtained, hit rate (HR), and running time. In particular, the new MA increases 38.1% HR and saves 78.7% running time of state-of-the-art MA on average. The preferable results validate the effectiveness and efficiency of the proposed MA for the Set -Cover problem.

Lifetime is a critical issue at wireless sensor networks (WSNs). Partitioning the set of sensors into several covers over all targets and enabling the covers by turns can effectively extend the lifetime. The problem formulation regarding optimization of sensor partition commonly assumes static networks; however, the composition and topology of real-world WSNs can vary with time due to hardware failure or communication error. This study considers extending the lifetime of dynamic WSNs; specifically, some sensors may fail or recover during the lifetime. In addition, we propose two genetic algorithms (GAs) to deal with this dynamic optimization problem. A series of simulations was conducted to examine the performance of the proposed algorithms. The simulation results validate the effectiveness of the GAs on extending the lifetime under dynamic network environment.

Extending the lifetime is a key issue in wireless sensor networks. An effective way to extend the lifetime is to partition the sensors into several covers and activate the covers one by one. Thus, the more the covers, the longer the lifetime. To find the maximum number of covers has been modeled as the Set K-Cover problem. In this paper we propose using order-based genetic algorithm to solve the Set K-Cover problem for extending the lifetime of wireless sensor networks. The proposed algorithm needs neither an upper bound nor any assumption about the maximum number of covers. Experimental results show that the order-based genetic algorithm can achieve near-optimal solutions efficiently.