6G-Enabled Short-Term Forecasting for Large-Scale Traffic Flow in Massive IoT Based on Time-Aware Locality-Sensitive Hashing

Abstract

With the advent of the Internet of Things (IoT) and the increasing popularity of the Intelligent Transportation System, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real-time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data.Analyzing and mining such a big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decisionmaking often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and non-parametric model aided by 6G is proposed in this paper to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware LSH (Locality-Sensitive Hashing). We design a wide range of experiments based on a real-world big traffic dataset to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.

Privacy-Aware Content-of-Interest Search and Recommendation in Internet of Things for Cross-Dressers

“…In addition, there are many other privacy-preservation techniques. Therefore, in the future work, we will further refine our method by integrating more privacy-preserving techniques such as Blockchain [30][31] , Differential Privacy [32][33][34] , Locality-Sensitive Hashing [35][36] and so on. At last, balancing multiple conflicting performances is necessary for a decision-making problem [37][38][39] .…”

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

“…Many articles [191][192][193][194] in big data-driven and nonparametric model supported by 6G is suggested to extract comparable traffic patterns over time for accurate and efficient short-term traffic flow prediction in enormous IoT, which is mostly based on time-aware LSH, which is mainly based on time-aware LSH.…”

Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

“…In general, there are always some noises caused by uncontrollable factors in traffic profiles. Therefore, we adopt the data preprocessing method in [15] and Z-score standardization technology [18] to reduce noises.…”

“…Besides, we adopt KNN (K-nearest neighbor method; benchmark method that cannot protect privacy), enhanced KNN [8] (based on KNN method enhanced by multiple technologies), and TracForet ime-LSH [15] (based on time-aware LSH) as comparative methods for experimental comparisons. Moreover, four competitive methods are measured in terms of four metrics, i.e., MAE (Mean Absolute Error, defined by Eq.…”