In wireless sensor networks, the energy signal strength of the cloud platform node can represent the end-to-end distance between the sensor node and the receiving node. The delay jitter of the node of the cloud platform reflects the quality and stability of the wireless sensor network link. The distance of the end to end can be judged through the signal strength of the sensor node in the opportunistic calculation, but the error of the distance is larger. By detecting the attenuation degree of the crowd signal, the mobile behavior and link quality of sensor nodes are deduced. It is difficult to accurately reflect the relative position of nodes in the wireless transmission hop count information, which is a single cloud platform. When the wireless transmission path is complex and the distance between the adjacent nodes is larger, the elimination of the link instability may lead to the elimination of the link between the crowd signal and the cloud platform.
In wireless sensor networks, the data transfer path which does not conform to the constraint condition is transformed by the cloud platform which is exhausted by the active resource depletion. This transformation can avoid the loss caused by the use of multiple paths. In addition, the wireless sensor network through the sensor node physical layer signal receiving strength and the cloud platform to map the fusion degree of the main node of the rational planning of wireless sensor network cluster intelligence cloud routing.
In wireless sensor network cloud platform, the wireless signal transmission driven model of the core node has two kinds of opportunistic space model and crowd mapping model. The opportunistic space model is used to detect the non-blocking distance between the sender and the receiver. In wireless communication, the path between the transmitter and the receiver is random. Crowd mapping model is based on the combination of end-to-end wireless path and crowd cloud propagation path. In the fusion crowd cloud routing, the signal power received by the receiving party is as follows:
$$ \left\{\begin{array}{l}{\displaystyle {P}_r}=\frac{{\displaystyle \sum_{i=1}^N{\mathrm{t}\mathrm{D}}_{\mathrm{i}}}{P}_{\mathrm{R}}{G}_{\mathrm{r}}}{h_{\mathrm{t}}^2{h}_{\mathrm{r}}^2{\displaystyle \sum_{i=1}^N{C}_i}}\sqrt{f(T)}\\ {}f(T)={P}_{\mathrm{R}}\le {\displaystyle \sum_{i=1}^{N_{\mathrm{S}}}{P}_{\mathrm{R}}(i)}/{N}_{\mathrm{S}}\end{array}\right. $$
(5)
The crowd cloud platform receiver antenna gain is G
r, h
t, and h
r as the transmitter and the pick-up of the crowd antenna height. The crowd mapping model, the attenuation of the signal power along the crowd cloud path distance. Thus, the distance between sender and receiver is as follows:
$$ \left\{\begin{array}{l}d=\frac{{\mathrm{LG}}_{\mathrm{r}}\sqrt{{\displaystyle \sum_{i=1}^N{\mathrm{tD}}_i}{P}_{\mathrm{R}}}}{h_t^2{h}_r^2}\\ {}L=\frac{2N}{T}\sqrt{Nt}\end{array}\right. $$
(6)
The parameter values are determined by the wireless sensor nodes and the cloud platform server. Wireless sensor network and wireless mobile nodes in the network interface configuration are exactly the same.
In the process of the request message and the response message of the crowd cloud routing, the cloud platform is effective from the node according to the resource loss and propagation distance opportunistic to calculate the weight of the cloud path W, that is, the cloud platform:
$$ \left\{\begin{array}{l}w={\displaystyle \underset{i=1}{\overset{N}{\int }}\frac{\varphi -L}{\varphi }}\\ {}L=\frac{{\displaystyle \prod_{i\to N}\frac{2N}{T}}}{\sqrt{Nt}}\end{array}\right. $$
(7)
Here, parameter i represents the sensor node. φ represents the transmitting radius of the crowd cloud node signal. In the reliability of w measurement path and real-time path, we use the value of intellectual property group.