Per-flow traffic measurement in the high-speed network plays an important role in many practical applications. Due to the limited on-chip memory and the mismatch between off-chip memory speed and line rate, sampling-based methods select and forward a part of flow traffic to off-chip memory, which complements sketch-based solutions in estimation accuracy and online query support. However, most current work uses the same sampling probability for all flows, leading to the waste in storage and communication resources. In practice, different flows often require different sampling rates to meet the same accuracy constraint. This paper presents self-adaptive sampling, a framework to sample each flow with a probability adapted to flow size/spread. Then we propose three algorithms, SAS-LC, SAS-LOG, and SAS-HYB. SAS-LC and SAS-LOG are geared towards per-flow spread estimation and per-flow size estimation by using different compression functions. SAS-HYB combines the advantages of SAS-LC and SAS-LOG, showing higher efficiency when both small flows and large flows are interested. We implement our estimators in hardware using NetFPGA. Experimental results based on real Internet traces show that, compared to the state-of-the-art in per-flow spread estimation, SAS-LC can save around 10% on-chip space and reduce up to 40% communication cost for large flows. In per-flow size estimation, SAS-LOG can save 40% on-chip space and reduce up to 96% communication costs for large flows. Moreover, SAS-HYB’s on-chip memory usage will not be larger than SAS-LC or SAS-LOG and can save up to 19% on-chip space than SAS-LOG when both small flows and large flows are interested.

Real-time road condition monitoring, especially of severe road damage, is critical for driving safety. In this work, we propose a crowdsensing-based pothole detection and measurement system, called CPDM, which can accurately measure the depth and length of detected potholes in urban roads. CPDM learns road surface information by collecting the sensor data from the smartphones belonging to the passing drivers and passengers. The proposed system can detect potholes by observing the vibration signal when the vehicle passes over the pothole, learn the vehicle parameters by fitting the underdamping vibration signal with stochastic gradient descent, and estimate the depth and length of the pothole. We have implemented a prototype system to prove the efficiency and adaptability of the proposed CPDM. The experimental results based on real traces show that CPDM has a high detection accuracy at different vehicle speeds.

Forecasting taxi demand is of great significance to the intelligent transportation systems in a smart city. Traditional demand prediction methods mostly considered about inter-regional traffic, events, activities, and weather, while they overlooked the influence of other travel modes, such as metro. In this paper, we propose a Deep Taxi-Metro Spatial-Temporal Network framework, namely TMST-Net, to model the spatiotemporal relationships between the taxi demand and the metro crowd flows. In detail, we apply residual neural networks to model temporal (current, day, and week) properties of the taxi demand in each area. For each feature, we apply residual convolutional units to handle the spatial properties of taxi demand. Likewise, we apply the same method to model the metro crowd flows. TMST-Net learns to assign different weights between taxi and metro by aggregating the output of the three residual neural networks and the external factors to forecast the final taxi demand for each area in the next timestamp. Experimental results on real taxi trajectory and the automatic fare collection (AFC) data in Shanghai show that our approach outperforms the state-of-the-art methods.