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In this paper, we propose a novel coverage maintenance scheme, scalable coverage maintenance (SCOM), which is scalable to sensor deployment density in terms of communication overhead (i.e., number of transmitted and received beacons) and computational complexity (i.e., time and space complexity).
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We assume a directional sensor network of an area A where a set (mathcal {S}) of directional sensor nodes are deployed with uniform random distribution, resulting in deployment density, rho = frac{|mathcal{S}|}{A}.
As previous related wake-up radios provide low sensitivity feature result in higher deployment density of sensor network, the sensitivity of wake-up radios should be improved to reduce the deployment density of sensor networks.
In this study, the optimum deployment density of the chrysanthemum flower model trap for reducing thrips infestation was investigated in commercial strawberry greenhouses.
This in turn effectively increases the deployment density, which is not suitable to sensor networks.
According to the analysis in Section 3, the FBS deployment density ({overline lambda _{text {FBS}}}) is one of the key factors that will affect the downlink capacity.
The relationship between the sensor deployment density sensing vector and the regional area is shown in formula (3).
Sensor deployment density sensing model is proposed.
Our design also reduces the deployment density.
Section 2 describes the sensor deployment density sensing model.
For the deployment density that we have chosen, a mapping between node speed and the average link changes per node per second is listed in Table 1.
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