Symbol G represents the density of the mobile crowd network. Symbol B represents the density of the covered area. R
_{
G
} represents the maximum radius of the rule coverage area. The sensing radius of the sensor node is R
_{
S
}. Mobile crowd network signal empty vector is C
_{
S
}. The core idea of mobile crowd network signal hole repair strategy is as follows: Firstly, the coverage area and density of the sensor nodes are obtained. Then, by using the ratio of the Poisson distribution and the area density, the density coverage performance of the sensor deployment is accurately evaluated. Then, through the analysis of the linear resolution and the signal detection of the mobile crowd network, the signal holes would be found. Finally, the connectivity graph of the mobile crowd network is erotic, and the hole is repaired with the fusion degree of the crowd signal.
The specific implementation steps are as follows:
The first step: the sensor nodes would form a Kdimensional connectivity map, which should satisfy the deployment density requirements.
The second step: based on the combination of the linear time resolution characteristics, the sensor network with the orientation, direction, and custom direction would be reconstructed.
The third step: the minimum node number of the coverage requirement is calculated by using the density of the covered area and the maximum radius of the rules:
$$ {N}_G=\left\lfloor B\frac{R_G}{\varphi {L}_p}\right\rfloor $$
(7)
The fourth step: in order to establish the network, the sensor nodes should be distributed as evenly as possible in each signal hole through the introduction of crowd network signal detection. Nodes with high degree of integration are distributed in the mobile crowd signal holes. In order to fill the void of the cover signal, nodes with a high degree of convergence and the crowd distribution are used.
The fifth step: in order to reduce the correlation degree between the network signal holes, state transition of the crowd network nodes would be completed according to the status vector.
The signal hole would be repaired and sensor nodes would be active by using the similar computation of Poisson distribution. At the same time, the probability density of the position of these nodes is calculated. Based on the sensing radius of the sensor nodes and the empty vector of the crowd network, the probability density is optimized. This can increase the sensor network coverage and sensor density sensing accuracy.
The sixth step: through the mobile crowd network signal detection, the network signal hole would be found and located.
Algorithm is described as follows:

Algorithm: mobile crowd network signal hole repair strategy denoted as HRDAC

Input: G, B, R
_{
G
}, R
_{
S
}, C
_{
S
}, d

Output: DES, L
_{
p
}, h(t, φ), F(hole)

1
initialize D
_{
F
}, k;

2
measure the Sp;

3
\( {L}_p= \exp \left(\frac{k}{\left\Vert {S}_p\right\Vert}\right) \);

4
\( p\left(\mathrm{D}\mathrm{E}\mathrm{S}(F)={D}_F\right)=\frac{\sqrt{\left(\left\Vert {S}_p\right\Vert {L}_p\right)}}{k} \log \left(k\left\Vert {S}_p\right\Vert \right) \);

5
measure S
_{0};

6
compute EF(S
_{0}) based on density trend;

7
compute g for evaluating the network coverage ratio;

8
Network signal detection based on region shape;

9
IF d < d
_{TH}, go to 10; else go to 11;

10
h(t, φ) = D
_{
t
}
φL
_{
p
}
^{ε};

11
\( h\left(t,\varphi \right)={\displaystyle \sum_{i=1}^n{D}_t(n)}{\displaystyle \prod_t{L_p}^{\varphi }} \);

12
\( y={\displaystyle \sum_{i=1}^L{L}_p\left({h}_i\right)}+{\displaystyle \sum_{j=1}^M{h}_j^k} \);

13
Finding signal holes;

14
repair signal hole with N
_{
G
}.