The rapid development of Internet of Things (IoT) technology can significantly promote the development and deployment of smart roads, enabling efficient and reliable road information sensing and analysis. As an important part of smart roads, timely and accurate detection of road cracks can improve service life of roads and reduce road management and operating costs. In this paper, we propose a vibration-sensor-based crack detection scheme for smart roads. In this scheme, by deploying the vibration sensor on the roadside, the changes in the vibration signals caused by the vehicle passing through the range of the sensor can be collected in real time. Then, considering that the seismic waves caused by vehicle driving are mostly distributed in the low-frequency range, we perform low-pass filtering on the collected vibration signals to retain the low-frequency vibration signals. After that, in order to distinguish the crack state of the road, we extract the vibration signal features of the normal road and the cracked road in the time domain, frequency domain and time-frequency domain, respectively. Based on the extracted features, we use logistic regression (LR), support vector machine (SVM) and random forest classification (RFC) machine learning algorithms to realize road crack detection. Finally, we conduct experiments to evaluate the performance of the proposed road crack detection scheme. The experimental results verify the high accuracy of the proposed scheme, and the accuracy of LR, SVM and RFC are 93.3%, 93.3% and 96.7%, respectively.

The traffic in wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications and the complexity of scenarios. The purpose of this paper is to quantitatively analyze the impact of the wireless traffic, which fluctuates both spatially and temporally, on the performance of the wireless networks. Specially, we propose to combine the tools from stochastic geometry and queueing theory to model the spatial and temporal fluctuation of traffic, which to our best knowledge has seldom been evaluated analytically. We derive the spatial and temporal statistics, the total arrival rate, the stability of queues and the delay of users by considering two different spatial properties of traffic, i.e., the uniformly and non-uniformly distributed cases. The numerical results indicate that although the fluctuation of traffic (reflected by the variance of total arrival rate) when the users are clustered is much fiercer than that when the users are uniformly distributed, the unstable probability is smaller. Our work provides a useful reference for the design of wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.

Emerging wireless applications starve for the realization of the ultra-reliable and low-latency communication (URLLC). The reliability and latency requirements of URLLC for a specific single link have been explored extensively, but a comprehensive evaluation of the URLLC in a large-scale wireless network is still lacking. In this paper, by using the point process theory we evaluate the probability that the delay and reliability requirement of a typical URLLC user can be satisfied in the large-scale wireless network. This probability is also the ratio of users with satisfactory delay and reliability in the wireless network. In order to improve the performance of URLLC, we propose two retransmission policies, corresponding to time diversity and frequency diversity, in which the retransmissions are silenced randomly to reduce the interference correlation in different frames so to reduce the effect of correlations between different retransmissions. Simulation and numerical results reveal that both of the proposed two retransmission policies improve the performance of URLLC, and the retransmission policy of frequency diversity is better than that of the time diversity.

The traffic in the wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications. The purpose of this paper is to explore the impact of the wireless traffic with violent fluctuation both spatially and temporally on the performance of the wireless networks. We analyze the spatial statistics, the stability of queues and the delay of users by considering two different spatial distributions of traffic, i.e., the uniformly and non-uniformly distributed cases. The simulation results indicate that although the fluctuations of traffic when the users are clustered, reflected by the variance of total arrival rate, is much fiercer than that when the users are uniformly distributed, the stability performance is much better. Our work provides a useful reference for the design of the wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.