A Heterogeneous Streaming Vehicle Data Access Model for Diverse IoT Sensor Monitoring Network Management

With the coming of the era of Internet of Everything and 5G, streaming data access via a uniform approach is a great challenge with metadata heterogeneity, streaming characteristics, and data inconsistency. Therefore, streaming data access via a uniform approach is a great challenge. In our study, we propose a unified and efficient streaming vehicle data access self-adaption model (SVASM) to uniform the heterogeneity of streaming vehicle spatiotemporal data. The proposed meta-model has been divided into a nonstreaming meta-model and a streaming meta-model according to the static or dynamic characteristics. The nonstreaming meta-model is composed of basic data types, which are represented by seven tuples. Dynamic elements contain observation parts, and dynamic elements of streaming vehicle metadata consist of a streaming meta-model. By extending the observations and measurements (O&M) encoding profile, the streaming characteristics are supplemented, and the mandatory elements and extending elements have been defined. To achieve its practical application, the corresponding massive vehicle data access algorithm based on Apache Storm has been proposed. In experiments, data access via MongoDB is an ideal data access method with good performance, as applied in our tests. As the experiments demonstrates, the efficiency of SVASM is approximately 2.5 times that of MongoDB in entire or single bolts. In addition, the data access via different bolts can achieve an adaptive, customized, flexible access mechanism. Consequently, the proposed model is a unified, adaptive, efficient data access model for massive streaming vehicle data under an Internet of Things sensor network.