Ultraviolet (UV) networks are important approaches to modern wireless communications owing to their unique characteristics, such as non-line-of-sight communication, strong anti-interference ability, and all-weather working mode. Given the lack of media access control (MAC) protocols suitable for UV networks at present, a novel UV lossless contention MAC (UVLLC-MAC) protocol is proposed herein based on UV physical properties. A fully connected finite-population multinode UV model is adopted, and its network performance is systematically derived and mathematically analyzed under the UVLLC-MAC protocol. Compared with the time-slot ALOHA protocol, the UVLLC-MAC protocol achieves 36.7% better throughputs, with 13.2% drop in packet loss rates, and the excellent performance of the protocol is obtained and fully verified. The UVLLC-MAC protocol can provide guidance for multinode and multiservice UV networking.Index Terms-multinode ultraviolet network, optical wireless communication, lossless contention access, media access control protocol. I. INTRODUCTIONLTHOUGH traditional optical wireless communication (OWC) has the advantages of large capacity, high speed, and long-distance transmission, it is difficult to set up the required automatic pointing, acquisition, and tracking (PAT) system, which is unfavorable for fast networking in complex electromagnetic environments [1]. However, ultraviolet (UV) communication networks have excellent features, such as non-line-of-sight (NLOS) communication, strong anti-interference ability, and flexible operation, in addition to the utilization of UV optoelectronic parts with small sizes, low cost, light weight, and high reliability, which have enabled UV communications to gradually gain interest in wireless networking research [2,3].At present, several studies have investigated the UV physical (PHY) layer properties and point-to-point communication systems [4][5][6][7][8][9][10][11][12][13], but there are relatively few recent studies on UV networking, especially on UV media access control (MAC) protocol. The problem of multiuser interference in NLOS UV
Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.
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