Recently, multi-access edge computing (MEC) cooperating with fifth-generation (5G) mobile communication technology or WiFi has been widely discussed for low-delay systems. However, for the Industrial Internet of Things, which raises higher requirements on system delay, security, capacity, etc., visible light communication (VLC) has better adaptability due to its controllable attocells. Therefore, we establish a computation and transmission integrated system with MEC-VLC as the main body. To solve the imbalance of resource utilization caused by users’ movement in intensive attocells, we propose a series of flexible design schemes based on access points’ cooperation in attocell overlapping areas. We formulate the overlap-based low-delay flexible system design as an optimization problem and then design the system based on it. Specifically, we first give an attocell-associated congestion judgment criterion and correspondingly propose a user discard algorithm. After that, we offer an iterative optimization method for task assignment, which adjusts computing-transmitting units’ cooperation mode to enhance the overall time delay. Then, the computing and transmitting resources are jointly allocated for delay reduction. Finally, our simulation demonstrates that the overlap-based design has a lower user discard ratio than the traditional distance-based system. The maximum delay and standard deviation are also reduced. Consequently, the flexible design based on attocell overlap can improve the reliability, capacity, and fairness of the low-delay integrating system.

In the industrial environment, the positioning of mobile terminals plays an important role in production scheduling. Visible light positioning (VLP) based on a CMOS image sensor has been widely considered as a promising indoor positioning technology. However, the existing VLP technology still faces many challenges, such as modulation and decoding schemes, and strict synchronization requirements. In this paper, a visible light area recognition framework based on convolutional neural network (CNN) is proposed, where the training data is the LED images acquired by the image sensor. The mobile terminal positioning can be realized from the perspective of recognition without modulating LED. The experimental results show that the mean accuracy of the optimal CNN model is as high as 100% for the two-class and the four-class area recognitions, and is more than 95% for the eight-class area recognition. These results are obviously superior to other traditional recognition algorithms. More importantly, the model has high robustness and universality, which can be applied to various types of LED lights.

No abstract