Abstract
Industrial Internet of Things has been widely used to facilitate disaster monitoring applications, such as liquid leakage and toxic gas detection. Since disasters are usually harmful to the environment, detecting accurate boundary regions for continuous objects in an energy-efficient and timely fashion is a long-standing research challenge. This article proposes a novel mechanism for continuous object boundary region detection in a fog computing environment, where sensing holes may exist in the deployed network region. Leveraging sensory data that have been gathered, interpolation algorithms have been applied to estimate sensory data at certain geographical locations, in order to estimate a more accurate boundary line. To examine whether estimated sensory data reflect that fact, mobile sensors are adopted to traverse these locations for gathering their sensory data, and the boundary region is calibrated accordingly. Experimental evaluation shows that this technique can generate a precise object boundary region with certain time constraints, and the network lifetime can be prolonged significantly.
Highlights
The concept of fog computing has attracted more and more attention nowadays, since fog computing promises to provide a relatively low latency and high-efficiency service [1, 2]
The results show that the proposed mechanism for object boundary region detection can effectively find a precise object boundary
We propose a simple method by deploying a certain number of mobile sensors to ensure that all of the stop stations could be traversed on time
Summary
The concept of fog computing has attracted more and more attention nowadays, since fog computing promises to provide a relatively low latency and high-efficiency service [1, 2]. These schemes adopt partial of sensor nodes for data gathering purpose and, for decreasing the network overhead This strategy may lead to a coarse object boundary detection, since many sensor nodes may not report their sensory data to the sink to support the decision-making. Note that a static sensor network could hardly generate a more precise object boundary when the object keeps relatively stable, since there may be very few sensor nodes deployed in the boundary region In this proposed scheme, mobile sensors traversing along an interpolationbased estimated boundary would detect a more precise object boundary. (i) Spatial interpolation algorithms are adopted to estimate the object boundary in the network region, where sensory data provided by static sensor nodes serve as the foundation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
