These can be considered as anomalies in the data ( Adkisson et al., 2021).
#Moso inshape series
Missing or misrepresented data is significantly different from normal data in the time series data collected by the sensors ( Moso et al., 2021). This can also expose the network to attacks that could lead to data tampering ( Abdallah et al., 2021). Increasingly complex IoT systems bring technical complexity and therefore make the design of privacy and security mechanisms more difficult. Poor communication quality can lead to data loss and misrepresentation. In addition, the heterogeneous nature of network devices makes it difficult to design protocols, and the transmission of data is easily compromised ( Pundir and Sandhu, 2021). However, IoT devices in smart agriculture are usually exposed to harsh environments and are highly susceptible to damage due to cost control ( Rafii and Kechadi, 2019 Abdallah et al., 2021). The sensors in different application scenarios are shown in Figure 1. Thus, environmental monitoring and data analysis play an important role in increasing crop yields.
These data are collected by a large number of various types of sensors and provide information about different environmental conditions. Smart agriculture is a deep combination of IoT technology and modern agriculture, which mainly takes modern agriculture as an application scenario and applies IoT technology to achieve a goal of scientific cultivation and precise control ( Farooq et al., 2019).įor smart agriculture IoT systems, automated management and smart decision of IoT applications are driven by the detailed analysis of data ( Cao et al., 2021). To address these challenges, smart agriculture solutions based on real-time monitoring and decision-making have been received increasing attention. As for serious challenges in environmental pollution, energy depletion, and water shortage in the whole world, there is an urgent need for the agriculture industry to move toward digitalization ( Cao et al., 2021). Nowadays, Internet of Things (IoT) technology has been obtained rapidly developments, as a paradigm, to drive the evolution of modern industries and smart cities. The experimental precision, recall, and F1 score exceeded the counterpart models by reaching 0.9351, 0.9625, and 0.9482, respectively. Finally, based on three smart agriculture-related datasets, experimental results show that our proposed model can accurately achieve anomaly detection. In addition, we also present a new reconstruction error calculation method that measures the error in terms of both point-wise difference and curve similarity to improve the detection effect.
#Moso inshape generator
For the problem of generator inversion, an encoder–decoder structure incorporating the attention mechanism is designed to improve the performance of the model in learning normal data. GAN is a deep learning model to learn the distribution patterns of normal data and capture the temporal dependence of time series and the potential correlations between features through learning. To address the above problems, this article proposes a new anomaly detection model based on generative adversarial networks (GAN), which can process the multidimensional time series data generated by smart agricultural IoT.
Moreover, some intelligent decision-makings for agricultural management also require the detailed analysis of data. However, due to the limitation of applied scenarios, smart agricultural IoT often suffers from data loss and misrepresentation. As a result, smart agricultural IoT generated a large amount of multidimensional time series data. To achieve the aim of scientific cultivation and precise control, the agricultural environments are monitored in real time by using various types of sensors. More recently, smart agriculture has received widespread attention, which is a deep combination of modern agriculture and the Internet of Things (IoT) technology.
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