Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse
Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things (IoT) technology, which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless s...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-08-01
|
| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864821000948 |
| _version_ | 1811282145597980672 |
|---|---|
| author | Yihong Yang Sheng Ding Yuwen Liu Shunmei Meng Xiaoxiao Chi Rui Ma Chao Yan |
| author_facet | Yihong Yang Sheng Ding Yuwen Liu Shunmei Meng Xiaoxiao Chi Rui Ma Chao Yan |
| author_sort | Yihong Yang |
| collection | DOAJ |
| description | Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things (IoT) technology, which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness, correlations, and concept drift, which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency. First, the data stream is usually generated quickly, which means that the data stream is infinite and enormous. Hence, any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, and traditional algorithms hardly consider these correlations. Third, the underlying data generation process or distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency. |
| first_indexed | 2024-04-13T01:46:29Z |
| format | Article |
| id | doaj.art-665d21332f7141f196ab47d38a3faf11 |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-04-13T01:46:29Z |
| publishDate | 2022-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-665d21332f7141f196ab47d38a3faf112022-12-22T03:08:00ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482022-08-0184498507Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouseYihong Yang0Sheng Ding1Yuwen Liu2Shunmei Meng3Xiaoxiao Chi4Rui Ma5Chao Yan6School of Computer Science, Qufu Normal University, Rizhao, 276826, ChinaShandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang, Weifang, 262700, ChinaSchool of Computer Science, Qufu Normal University, Rizhao, 276826, ChinaDepartment of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210000, ChinaSchool of Computer Science, Qufu Normal University, Rizhao, 276826, ChinaGeneral Education Department, Shandong First Medical University (Shandong Academy Of Medical Sciences), Taian, 271000, ChinaSchool of Computer Science, Qufu Normal University, Rizhao, 276826, China; Corresponding author.Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things (IoT) technology, which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness, correlations, and concept drift, which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency. First, the data stream is usually generated quickly, which means that the data stream is infinite and enormous. Hence, any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, and traditional algorithms hardly consider these correlations. Third, the underlying data generation process or distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.http://www.sciencedirect.com/science/article/pii/S2352864821000948Anomaly detectionData streamDLSHiForestSmart greenhouseEdge computing |
| spellingShingle | Yihong Yang Sheng Ding Yuwen Liu Shunmei Meng Xiaoxiao Chi Rui Ma Chao Yan Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse Digital Communications and Networks Anomaly detection Data stream DLSHiForest Smart greenhouse Edge computing |
| title | Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse |
| title_full | Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse |
| title_fullStr | Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse |
| title_full_unstemmed | Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse |
| title_short | Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse |
| title_sort | fast wireless sensor for anomaly detection based on data stream in an edge computing enabled smart greenhouse |
| topic | Anomaly detection Data stream DLSHiForest Smart greenhouse Edge computing |
| url | http://www.sciencedirect.com/science/article/pii/S2352864821000948 |
| work_keys_str_mv | AT yihongyang fastwirelesssensorforanomalydetectionbasedondatastreaminanedgecomputingenabledsmartgreenhouse AT shengding fastwirelesssensorforanomalydetectionbasedondatastreaminanedgecomputingenabledsmartgreenhouse AT yuwenliu fastwirelesssensorforanomalydetectionbasedondatastreaminanedgecomputingenabledsmartgreenhouse AT shunmeimeng fastwirelesssensorforanomalydetectionbasedondatastreaminanedgecomputingenabledsmartgreenhouse AT xiaoxiaochi fastwirelesssensorforanomalydetectionbasedondatastreaminanedgecomputingenabledsmartgreenhouse AT ruima fastwirelesssensorforanomalydetectionbasedondatastreaminanedgecomputingenabledsmartgreenhouse AT chaoyan fastwirelesssensorforanomalydetectionbasedondatastreaminanedgecomputingenabledsmartgreenhouse |