Abstract
Internet of Things (IoT) has been widely used to facilitate environmental perception, where detecting the boundary regions for continuous objects with energy-efficient manner is a challenge to be explored to support domain applications. This paper proposes a novel approach for continuous objects boundary prediction and detection in IoT sensing network, which is called Cloud Model-IoT Sensing Network Collaborative (CM-IoTSNC). Specifically, when an event is potentially occurred, the atmospheric dynamic diffusion model deployed on the cloud is adopted to predict gas diffusion trend in ideal and complex environments, moreover prediction results are transmitted to the IoT devices in the real-time fashion for scheduling security plans in advance. One-hop neighbor nodes are activated by abnormal nodes to determine a more accurate boundary region. Compared our technique with two traditional methods, namely Wireless Sensor Monitoring and Activating One-hop Neighbor Nodes, experimental results show that our method has a good performance in reducing the energy consumption and prolonging the lifetime of the network.
Highlights
With the new generation of information technology, Internet of Things (IoT) has developed rapidly and has been applied to support applications in various fields such as environmental protection and military monitoring [1], [2]
In view of the above problems, we propose a novel method of Cloud Model-IoT Sensing Network Collaborative (CM-IoTSNC) in this paper
We propose the method of CM-IoTSNC for tracking dangerous areas of toxic gas leakage
Summary
With the new generation of information technology, Internet of Things (IoT) has developed rapidly and has been applied to support applications in various fields such as environmental protection and military monitoring [1], [2]. IoT nodes connect and communicate with each other through sensing and perception. They can combine into a large scale of IoT sensing networks [3]. The collaboration and cooperation of IoT nodes sense the surrounding environment, which can effectively compensate for the shortcomings of current atmospheric environmental monitoring technologies, and achieve real-time monitoring in accident-prone areas, important industrial parks and densely populated areas.
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