Standard remote sensing observation (RSO) access and formulization is essential to Internet of Things (IoT) data management, such as in precision agriculture (PA). Because of the heterogeneous characteristics and the petabyte data size of RSO, massive remote sensing processing in RSO management has been hampered. Here, we present a heterogeneous access metamodel for efficient RSO management (HAMERM) and verify it in PA. The structure of basic metadata components is defined. A five-tuple metadata structure based on the metaobject facility is designed. HAMERM consists of identification, platform, observation, product, and access, which represent the five aspects of RSO metadata information. In addition, the flatMap/reduceByKey algorithms and the table structure have been proposed under Sensor Web and Geographic Information Science (GIS) techniques. Intensive experiments in Guangdong Province, China are conducted to test the proposed method. Two RSO metadata formulization instances were conducted to examine the ability of sheltering the differences of multisource and heterogeneous RSO. Experiments containing data storage and data soil moisture (SM) mapping were performed. The results suggest that the HAMERM method achieved a performance 30.1 times higher than that of Hadoop and three times higher than that of Spark (stand-alone). Consequently, the proposed HAMERM can be applied to achieve efficient SM mapping within PA, which is helpful for efficient RSO management for the IoT.

Comprehensive surface soil moisture (SM) monitoring is a vital task in precision agriculture applications. SM monitoring includes remote sensing imagery monitoring and in situ sensor-based observational monitoring. Cloud computing can increase computational efficiency enormously. A geographical web service was developed to assist in agronomic decision making, and this tool can be scaled to any location and crop. By integrating cloud computing and the web service-enabled information infrastructure, this study uses the cloud computing-enabled spatio-temporal cyber-physical infrastructure (CESCI) to provide an efficient solution for soil moisture monitoring in precision agriculture. On the server side of CESCI, diverse Open Geospatial Consortium web services work closely with each other. Hubei Province, located on the Jianghan Plain in central China, is selected as the remote sensing study area in the experiment. The Baoxie scientific experimental field in Wuhan City is selected as the in situ sensor study area. The results show that the proposed method enhances the efficiency of remote sensing imagery mapping and in situ soil moisture interpolation. In addition, the proposed method is compared to other existing precision agriculture infrastructures. In this comparison, the proposed infrastructure performs soil moisture mapping in Hubei Province in 1.4 min and near real-time in situ soil moisture interpolation in an efficient manner. Moreover, an enhanced performance monitoring method can help to reduce costs in precision agriculture monitoring, as well as increasing agricultural productivity and farmers’ net-income.

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.

Daily trajectory data scale of vehicle monitoring networks in smart cities is growing rapidly, reaching daily volumes of 1 billion. Accessing hyper massive spatiotemporal trajectory data (HMSTD) in transport, the Internet of Things, or other fields is difficult and limited based on the current spatiotemporal data index techniques. Therefore, we propose path-divided Hadoop Distributed File System (HDFS) data blocking (PDDB) based on the Apache Impala (PDDB-Impala) method to optimize the efficient access manner of HMSTD to enhance the efficiency of hyper data sharing. Moreover, PDDB parquet data partitioning rules are proposed. In experiments, 35,809 buses equipped with BD positioning sensors, creating 1.03 billion data records each day. The bus distribution in Shenzhen city is collected from 7:00 a.m. to 9:00 a.m. and 11:00 a.m. to 01:00 p.m. Moreover, PDDB-Impala achieves about 8 times, 9 times, 29 times, and 110 times higher performances than those in MongoDB or HBase for data scales of 1 billion, 10 billion, 50 billion, and 100 billion, the results of which outperform those of the equipartition in the Impala, MongoDB, and HBase methods.

The efficient data access of streaming vehicle data is the foundation of analyzing, using and mining vehicle data in smart cities, which is an approach to understand traffic environments. However, the number of vehicles in urban cities has grown rapidly, reaching hundreds of thousands in number. Accessing the mass streaming data of vehicles is hard and takes a long time due to limited computation capability and backward modes. We propose an efficient streaming spatio-temporal data access based on Apache Storm (ESDAS) to achieve real-time streaming data access and data cleaning. As a popular streaming data processing tool, Apache Storm can be applied to streaming mass data access and real time data cleaning. By designing the Spout/bolt workflow of topology in ESDAS and by developing the speeding bolt and other bolts, Apache Storm can achieve the prospective aim. In our experiments, Taiyuan BeiDou bus location data is selected as the mass spatio-temporal data source. In the experiments, the data access results with different bolts are shown in map form, and the filtered buses’ aggregation forms are different. In terms of performance evaluation, the consumption time in ESDAS for ten thousand records per second for a speeding bolt is approximately 300 milliseconds, and that for MongoDB is approximately 1300 milliseconds. The efficiency of ESDAS is approximately three times higher than that of MongoDB.