Directional sensing coverage enhancement method for agricultural wireless multimedia sensor network

Abstract

The invention provides a directional sensing coverage enhancement method for an agricultural wireless multimedia sensor network. The method realizes effective recognition on an invisible blind area by increasing a monitoring object description component for a directional sensing model, and realizes local fine adjustment by adopting a local algorithm without affecting the whole. The method has the advantage of avoiding large-scale algorithm and node posture adjustment overhead while the invisible blind area of directional sensing nodes is eliminated.

Description

[0001] The invention relates to the technical field of data information collection, and more specifically, to a method for enhancing directional sensing coverage of an agricultural wireless multimedia sensor network. Background technique [0002] Wireless sensor network technology is an emerging short-range, low-complexity, low-power, low-data-rate, and low-cost wireless network technology that is widely used in sensing and control applications. As a low-cost wireless transmission technology, it is also suitable for small-scale continuous data volume (image data) and non-strong real-time application scenarios under special conditions. As the monitoring environment becomes more and more complex and changeable, the simple data obtained by traditional wireless sensor networks cannot meet people's overall needs for environmental monitoring. It is urgent to introduce information-rich media such as images, audio, and video From the basic environmental monitoring activities, the envi...

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Problems solved by technology

[0008] However, many existing works are based on graph theory and probability theory to solve the directed problem of nodes. Through analysis, several shortcomings of existing work are summarized: 1) Assuming that the direction of the perception model cannot be continuously adjusted, given a limited A direction set composed of several directions, and the perceptual viewing angle must be divisible by 2π, these greatly limit the optimality of the solution results; 2) Most of the proposed coverage problems are NP-complete problems, which do not have the optimal sub- Structural characteristics, the result obtained by the classic greedy method will be very different from the actual optimal value; 3) Some algorithms have high time complexity and complex mathematical calculations, which are not suitable for sensor node calculations with limited energy; 4) Most Existing studies are based on a single perception model, such as the 0-1 model. In some studies, the node perception ability decreases with the increase of distance, and the two types of algorithms based on different models cannot be used universally.

For a real image / video sensor node, its perception ability will not only cause changes in the accuracy of monitoring results due to energy weakening, but also cause obvious perception differences due to obstacles within the perception range

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