Abstract
In order to reduce the signal holes in wireless sensor networks, we proposed a sensor deployment density-aware signal hole repair strategy of mobile crowd network. On the one hand, based on the multi-dimensional connected graph, the density continuous jitter problem of the sensor network is solved effectively. Sensor density prediction method improves the efficiency of random deployment of sensor nodes. Based on the above schemes, the sensor deployment density sensing model is proposed. On the other hand, according to the regional geometry, diversity of the sensor network channel and density can detect the time domain channel impulse response signal successfully. Finally, the network signal is detected by the signal detection of the mobile crowd network. The signal holes can be detected and repaired based on coverage density and crowd. Experimental results show that the proposed algorithm has outstanding performance in terms of signal strength and signal void ratio compared with the energy-aware repair algorithm.
Highlights
Random deployment of wireless sensor networks [1] is used to generate coverage holes
The sensor nodes are randomly deployed by using the sensor density prediction method
In the diversity of sensor network channel model, the HR-DAC algorithm can detect time domain channel impulse response signal according to the regional geometry and density
Summary
Random deployment of wireless sensor networks [1] is used to generate coverage holes. There is difficulty in repairing the network coverage hole [4] and the signal cavity through the use of wireless sensor network in a timely manner. These problems lead to a sharp decline in the performance of wireless sensor networks. After the deep analysis of receiver operating characteristic analysis [6], received signal correlation [8], and bandwidth minimization [12], we study the sensor deployment density of mobile crowd network and proposed the signal hole repair strategy. The sensor deployment can be accurately evaluated by the density coverage performance
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