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Abstract
The Internet of Things (IoT) networks have become the infrastructure to enable the detection and reaction of anomalies in various domains, where an efficient sensory data gathering mechanism is fundamental since IoT nodes are typically constrained in their energy and computational capacities. Besides, anomalies may occur occasionally in most applications, while the majority of time durations may reflect a healthy situation. In this setting, the range, rather than an accurate value of sensory data, should be more interesting to domain applications, and the range is represented in terms of the category of sensory data. To decrease the energy consumption of IoT networks, this paper proposes an energy-efficient sensory data gathering mechanism, where the category of sensory data is processed by adopting the compressed sensing algorithm. The sensory data are forecasted through a data prediction model in the cloud, and sensory data of an IoT node is necessary to be routed to the cloud for the synchronization purpose, only when the category provided by this IoT node is different from the category of the forecasted one in the cloud. Experiments are conducted and evaluation results demonstrate that our approach performs better than state-of-the-art techniques, in terms of the network traffic and energy consumption.
Highlights
The Internet of Things (IoT) networks, as a promising and fast-developing research area in recent years, have been applied to support various kinds of domain applications, like traffic flow monitoring in Intelligent Transportation Systems (ITS) [1], where continuous sensory data gathering is fundamental to support the environmental monitoring and anomaly detection in industrial applications
Data compression in edge nodes After the category of sensory data is sparsely encoded, there are a lot of sparse binary data in IoT networks, which facilitates the application of the Clusterbased Compressive Sensing algorithm (CCS) [13] technique in the process of data gathering
The results show that our mechanism consumes less energy than the Derivative-based prediction (DBP) strategy with the same skewness of IoT nodes and location of the cloud
Summary
The Internet of Things (IoT) networks, as a promising and fast-developing research area in recent years, have been applied to support various kinds of domain applications, like traffic flow monitoring in Intelligent Transportation Systems (ITS) [1], where continuous sensory data gathering is fundamental to support the environmental monitoring and anomaly detection in industrial applications. CCS algorithm [13] is adopted to encode the category of sensory data of IoT nodes, which can reduce network traffic significantly when there are strong spatial-temporal correlations between sensory data. An accurate data prediction model is adopted in the cloud for forecasting sensory data of IoT nodes in edge networks, while the category of sensory data is compressed, and routed to the cloud and used as a criterion for judging the accuracy of predicted values. Data compression in edge nodes After the category of sensory data is sparsely encoded, there are a lot of sparse binary data in IoT networks, which facilitates the application of the CCS [13] technique in the process of data gathering. Ensure: Sensory data are gathered to the cloud without lowering the satisfiability of certain requirements
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