Jianwen Guo

Ecosystem monitoring involves fully understanding the complex interactions of ecosystem progress and providing massive scientific data to answer research questions. Fetching data in extreme areas without a public network largely relies on human efforts. Along with the ongoing advances of the Internet of Things (IoT), the Internet of Remote Things (IoRT), as a new paradigm, is being pursued. In this paper, we improve the previous drone-enabled IoRT system and innovatively integrate the system with ecological monitoring devices for wildlife, phenology and environmental monitoring, and the monitored data are remotely retrieved by drones. The experimental results indicate that the data transmission rate between the drone relay and the terrestrial terminal reaches up to 10-15 MB/s. Experiments further demonstrate that in terms of wildlife monitoring, the required time to retrieve an image cached on a terrestrial terminal is approximately 0.35 s, the time required to transfer a set of phenology data is 0.29 and 0.34 s, and in regard to environmental monitoring data, the average time consumption reaches approximately 0.0302 s. Moreover, we introduce a signal strength-based priority strategy that can reduce the time consumption of data transmission between the drone relay and the terrestrial terminal. The demonstrated applications reveal that monitoring devices deployed in remote extreme regions can be connected in the IoRT network, and the way to retrieve data from these deployed devices is being revolutionized by the drone-enabled IoRT network.

In this article, we employed a satellite-enabled Internet of Remote Things (IoRT) network as a promising solution to retrieve data in the most remote areas of interest, where public networks are absent. This article presents a system network based on the satellite-enabled IoRT, a new paradigm that defines a network where each environmental monitoring device can autonomously establish a network with a remote data center. The Xingyun satellite constellation was employed for data retrieval on the Tibetan Plateau (TP). The monitoring system was mainly composed of a ground Internet of Things (IoT) terminal that was built with satellite transceivers, environmental monitoring devices, and system software. We deployed five of these newly developed terminals in harsh areas to monitor environmental variables, and accordingly, air temperature and relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation were retrieved with the satellite-enabled IoRT network. Field experiments were conducted to evaluate the performance of the proposed system network, and the results indicated that the average time delay with and without the packet creation mode reached 32 and 32.7 s, respectively, and the average packet loss rate with and without the packet creation mode reached 5.63% and 4.48%, respectively. The successful implementation of the satellite-enabled IoRT network for the rapid retrieval of monitoring data in remote glacier, forestland, and canyon areas at very high altitudes on the TP provides an entirely new and revolutionary data retrieval means for backhauling data from remote areas of interest.

River basin cyberinfrastructure with the Internet of Things (IoT) as the core has brought watershed data science into the big data era, greatly improving data acquisition and sharing efficiency. However, challenges in analyzing, processing, and applying very large quantities of observational data remain. Given the observational needs in watershed research, we studied the construction of river basin cyberinfrastructure and developed an integrated observational data control system (IODCS). The IODCS is an important platform for processing large quantities of observational data, including automated collection, storage, analysis, processing, and release. This paper presents various aspects of the IODCS in detail, including the system’s overall design, function realization, big data analysis methods, and integrated models. We took the middle reaches of the Heihe River Basin (HRB) as the application research area to show the performance of the developed system. Since the system began operation, it has automatically received, analyzed, and stored more than 1.4 billion observational data records, with an average of more than 14 million observational data records processed per month and up to 21,011 active users. The demonstrated results show that the IODCS can effectively leverage the processing capability of massive observational data and provide a new perspective for facilitating ecological and hydrological scientific research on the HRB.

The rapid development of Internet of Things (IoT) technology and the widespread deployment of various sensors around the world have produced a large number of data streams. Thus, current computing systems face the challenge of quickly receiving and managing these large-scale streaming data. This study builds an efficient distributed database based on Greenplum (GP) and focuses on solving the problem of the low efficiency of structured data queries for observed ecological data collected from fragile areas in Northwest China’s desert oasis. First, a distributed database is designed and deployed at the physical storage structure level. A database table structure is then established based on the characteristics of the streaming data. On this basis, the data storage strategy is optimized at the data table level. Additionally, the query efficiency of the distributed database is compared with the query efficiency of traditional standalone databases. The results show that the distributed database significantly improves the data query efficiency. The greater the amount of data stored, the better the improvement in efficiency. Finally, based on the optimized distributed database, we develop a data sharing system for streaming data from ecologically fragile areas in the desert oasis in Northwest China, which provides a new approach for the efficient sharing of massive amounts of IoT streaming data for ecological monitoring. Our storage system is still currently working normally, which is highly important to both data managers and users.

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F₂ score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.