RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

Abstract

According to the loss network problems caused by limited resources and environment interference factors, we presented a survivability mechanism based on mobility prediction and based on the topological evolution of cloud computing wireless multimedia sensor network (WMSN). First, based on cooperative neural network and opportunity Markov chain model, the multimedia mobile sensor node state and state transition probability could be predicted. Then, the opportunity computational scheme was given based on mobility prediction of dynamic topology evolution mechanism. Finally, using the network topology reconfiguration and opportunities for cloud computing, an enhanced WMSN survivability and end-to-end quality of service guarantee mechanism was proposed. Experimental results show that compared with the static scheme for WMSNs, the proposed survivability mechanism has the obvious advantage in the node protection, data communication, network life cycle, and decodable frame rate and other aspects, and can effectively improve the invulnerability of WMSNs.

Introduction

Through the deployment of large-scale multimedia sensor nodes [1, 2], the information of audio, image, and video in the environment could be transmitted to a cloud platform with the multimedia stream transmission network. Nan X. M. et al. [3] investigated the fundamental concerns with queueing theory and optimization methods for multimedia cloud. Nan G.F. et al. [4] proposed a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution. A mobile cloud-based architecture for enabling remote-resident multimedia service discovery and access was proposed by Zheng X. H. et al. [5]. A new design for large-scale multimedia content protection systems was proposed by Hefeeda M. et al. [6]. However, the failure or damage of the networks may be caused by the long time transmission of multimedia stream, the limited resources of the multimedia sensor nodes, and the complexity of the cloud computing, etc.

The topology evolution and reconstruction have a lot of influence on the network status, so it has been a concern by some researchers. A novel scheme of how to efficiently and reliably deliver multimedia real-time streams in wireless sensor networks was proposed in [7]. Image data is transmitted to the base station via fuzzy logic with clustering infrastructure, which considered the nodes mobility [8]. So, Xu Y.F. et al. [9] proposed a prediction scheme, which could predict a large-scale spatial field using successive noisy measurements obtained by mobile-sensing agents. The correlation between some available design measurements and class stability over versions was investigated in article [10], which proposed a stability prediction model using such available measurements. Pirozmand P. et al. [11] studied the mobility characteristics, mobility models and traces, and mobility prediction techniques of human mobility. A multiclass support vector machine-based mobility prediction (Multi-SVMMP) scheme was proposed in [12], which could estimate the future location of mobile users according to the movement history information of each user in HetNets. A distributed bandwidth reservation scheme called the mobility-prediction-aware bandwidth reservation scheme was proposed by Nadembega A. et al. [13].

Although network and link layers were unified into a single communication module for quality of service (QoS) provisioning [14], the network is seriously damaged when the network cannot provide effective QoS protection. Hence, Chen S.M. et al. [15] proposed a train scheduling scheme with optimal invulnerability. A cascading failure model for scale-free multi-coupling-link coupled networks was built in [16] based on time-delay coupled map lattices model.

About the combination of cloud and multimedia communication, Shen H. et al. [17] proposed a QoS-aware multi-sink opportunistic routing to efficiently deliver multimedia information under QoS constraints. A channel characterization scheme combined to a cross-layer admission control was proposed in dynamic cloud-based multimedia sensor networks, which is used to share the network resources among any two nodes [18]. Baccarelli E. [19] reviewed the current state of the art in green quality of experience (QoE) with reference to mobile users and remote applications. Zhou Y. et al. [20] tackled the problem of concealing lost whole frames for multiview 3D video transmission over wireless multimedia sensor networks (WMSNs).

On the basis of the existing research results, the main contributions of our work are as follows: (1) the mobility prediction scheme was designed based on the cooperative neural network and opportunistic Markov model; (2) the opportunistic cloud computing based on topological evolution was built according to the results of mobility prediction; (3) the survivability mechanism based on topology reconstruction.

The rest of the paper is organized as follows. Section 2 describes the mobility prediction scheme. In Section 3, we design the opportunistic cloud computing based on topological evolution. In Section 4, we proposed the survivability mechanism based on topology reconstruction. Experiment results are given in Section 5. Finally, we conclude the paper in Section 6.

Mobility prediction scheme

In wireless multimedia sensor networks (WMSNs), N multimedia sensor nodes were deployed. Each node is randomly moved. A data transmission path was established between WMSNs and cloud platform. The routing request can be initiated by the cloud or any node, so as to establish a wireless transmission path between the nodes and the cloud, which was composed of M nodes. At this point, WMSNs have the following three characteristics:

• (1) The multimedia data transmission or node status has the equilibrium point. When the multimedia data is transferred, the WMSN gradually tends to balance with the nodes of the nodes, and it reaches a steady state.

• (2) There is the direct relationship between the speed and state of the WMSN equilibrium point and the state of the mobile node.

• (3) Data transfer between the nodes and the received data from the cloud can be effectively obtained by the mobile node’s collaboration.

According to the system characteristics, the WMSN can be defined as a cooperative neural network, as shown in Fig. 1. In WMSNs, the heptagonal cooperative neural networks are composed of data sending node and neighbor nodes as shown in Fig. 1, to provide reliable data transmission service. Data fusion after convergence evacuation was sent to the cloud. Users can obtain the multimedia data through the clouds. In the WMSNs, the mobile node’s 3D moving trajectory is shown in Fig. 2, which is divided into the time component, the moving vector x and the moving vector y. The whole system of WMSNs can be obtained by calculating the 3D vector product of N mobile node.

Fig. 1

Cooperative neural network

Fig. 2

Node moving trajectory

Therefore, the characteristics of the cooperative neural network are expressed as the formula (1a).

$$ \left\{\begin{array}{l}n={\displaystyle \sum_{i=1}^N{S}_{ik},\begin{array}{cc}\hfill \hfill & \hfill i\ne k\hfill \end{array}}\\ {}w\left|{}_{k=1\to N}\right.=\left\{\begin{array}{l}1,n\ge {S}_{i\to \mathrm{cloud}}\\ {}0,n<{S}_{i\to \mathrm{cloud}}\end{array}\right.\\ {}{S}_{\mathrm{CNN}}={\displaystyle \sum_{i=1}^N{w}_i{S}_i}\end{array}\right. $$

(1a)

Here, S _ik denotes the sending signal form i node to k node. n denotes the signal received by neighbor nodes after wireless broadcasting by any node. w denotes the cooperative weight. The node would join the cooperation when the signal of some neighbor node is larger than one between i node and cloud. S _CNN is the convergence signal after cooperation to reach the cloud.

Based on signal transmission, the 3D vector analysis can be implemented in the i node of the cooperative neural network, as shown in the formula (2a).

$$ \left\{\begin{array}{l}{t}_i=w\left|{}_{i-1\to N}\right.\sqrt{{x_{i-1}}^2+{y_{i-1}}^2}+w\left|{}_{i+1\to N}\right.\sqrt{\left|{x_{i+1}}^2-{y_{i+1}}^2\right|}\\ {}{x}_i={S}_{i\to \mathrm{cloud}}w\left|{}_{i-1\to N}\right.+\sqrt{\left|{x_{i+1}}^2-{y_{i+1}}^2\right|}w\left|{}_{i+1\to N}\right.\\ {}{y}_i=w\left|{}_{i-1\to N}\right.{S}_i{\displaystyle \sum_{j=1}^i\sqrt{\left|{x_{i+1}}^2-{y_{i+1}}^2\right|}}\end{array}\right. $$

(2a)

In WMSNs with 100 nodes, a data transmission network consisting of ten nodes was forecasted from sender to receiver using the cooperation-type neural network. The results are as shown in Fig. 3. Fig. 3a shows the actual node deployment plan. Fig. 3b is the forecast results. Fig. 3c gives the analysis results of prediction errors. The results showed that cooperative neural network prediction has the higher accuracy.

Fig. 3

Performance evaluation of prediction. a Real network state. b Cooperative neural network prediction. c Prediction error

Although the cooperative neural network can accurately predict the nodes in a data transmission network topology, there is a poor performance for the future state of the whole network. Therefore, combining with the opportunistic Markov chain model as shown in Fig. 4, state transition prediction model of WMSN topology dynamic was proposed according to the characteristics of dynamic Markov model, where the three transmission path multimedia is established between data sending node sender and receiving cloud.

Fig. 4

Opportunistic Markov model

The probability is determined by probabilistic chance and opportunity dimensions probability with full connection from the sender to the receiver. According to the three-dimensional prediction results of the cooperative neural network, an opportunity-type Markov chain model was defined. The 1D Markov chain model is applied to only one dimension to change, the success arrive probability of the cloud is P₁P_x. A two-dimensional Markov chain model is applied to only two dimensions to change, and the success arrive probability of the cloud is P₁P_xP_y. The three-dimensional Markov chain model is suitable to three-dimensional transfer, the success arrive probability is P₁P_xP_yP_t.

In the Markov chain model, the opportunistic probability sum of the three-dimensional transmission path is 1, as shown in formula (1). The end-to-end success rate of sending packet Ps can be obtained by formula (2), as well as the outage probability P _OUT of transmission path can be calculated by formula (3).

$$ {P}_1+{P}_2+{P}_3=1 $$

(1)

$$ {P}_S=\left\{\begin{array}{l}{P}_1{\displaystyle \prod_{i=1}^a{P}_x}\\ {}{P}_2{\displaystyle \prod_{i=1}^a{\displaystyle \prod_{j=1}^b{P}_x{P}_y}}\\ {}{P}_3{\displaystyle \prod_{i=1}^a{\displaystyle \prod_{j=1}^b{\displaystyle \prod_{k=1}^c{P}_x{P}_y{P}_t}}}\end{array}\right. $$

(2)

$$ {P}_{\mathrm{OUT}}=\left\{\begin{array}{l}{P}_1{\displaystyle \prod_{i=1}^a\left(1-{P}_x\right)}\\ {}{P}_2{\displaystyle \prod_{i=1}^a{\displaystyle \prod_{j=1}^b\left(1-{P}_x\right)\left(1-{P}_y\right)}}\\ {}{P}_3{\displaystyle \prod_{i=1}^a{\displaystyle \prod_{j=1}^b{\displaystyle \prod_{k=1}^c\left(1-{P}_x\right)\left(1-{P}_y\right)\left(1-{P}_t\right)}}}\end{array}\right. $$

(3)

The cooperative neural network will lead to WMSNs gradually reach a state of equilibrium. Based on the opportunistic Markov chain model, the equilibrium state probability P _balance can be obtained as formula (4).

$$ {P}_{\mathrm{balance}}={P}_S>{P}_{\mathrm{OUT}}\left|{}_{t\to \infty}\right. $$

(4)

A single mobile node status in WMSNs included the mobile speed V _E, topology robustness R _E, and cloud communication stability S _E. Therefore, the mobile node working state can be defined as {V _E, R _E, S _E}. The V _E could be calculated through cooperative neural network of two different direction vector component predictions. R _E could be calculated through the connectivity detection using opportunistic Markov chain model as shown in the formula (5). S _E could be calculated by detecting the cloud success probability using Markov chain model as shown in Eq. (6).

$$ {R}_E={\displaystyle \prod_{t=1}^{\infty }{\displaystyle \sum_{i=1}^n\sqrt{\left|{\left({S}_{i\to i+1}w\left|{}_t\right.\right)}^2-{\displaystyle \sum_{j=1}^i\sqrt{\left|{x_{i-1}}^2-{y_{i-1}}^2\right|}{S}_i}\right|}}} $$

(5)

$$ {S}_E={P}_{\mathrm{balance}}\left|{}_{\mathrm{cloud}}\right.{\left[\begin{array}{cc}\hfill \begin{array}{cc}\hfill {V}_{E1}\hfill & \hfill \cdots \hfill \end{array}\hfill & \hfill {V}_{Ea}\hfill \\ {}\hfill \begin{array}{cc}\hfill {V}_{E1}\hfill & \hfill \cdots \hfill \end{array}\hfill & \hfill {V}_{Eb}\hfill \end{array}\right]}_{a*b}{{\left[\begin{array}{c}\hfill {R}_{E1}\hfill \\ {}\hfill {R}_{Ec}\hfill \end{array}\right]}_{c*1}}^T $$

(6)

Opportunistic cloud computing based on topological evolution

On the basis of the mobility prediction model based on cooperative neural network and opportunistic Markov chain, analysis of WMSNs dynamic topology evolution could be obtained combined with the opportunity to cloud platform for real-time multimedia data calculation.

In dynamic topology evolution process, factors resulting in decline of multimedia communication performance and damage of WMSNs include the calculation complexity, multimedia data transmission delay, cloud equipment idle rate and efficiency of cloud computing and multimedia sensor energy consumption, and multimedia data scale. Therefore, starting from the study of WMSNs survivability perspective, the dynamic topology evolution analysis model was given.

Here, let E _encode denote the encoding energy consumption of multimedia data. Let E _prediction denote the energy consumption of mobility prediction. Let E _topology denote the topology evaluation energy consumption. Let E _fd denote the data transfer energy consumption. E _agr is the energy consumption of data fusion. Len_stream is the length of multimedia stream. L _packet is the number of transferring data packets.

In cloud platform, the computational complexity of multiple clouds with multimedia data can be obtained by formula (8).

$$ \mathrm{O}{\mathrm{C}}_{\mathrm{C}\mathrm{C}}=\left\{\begin{array}{l}{\displaystyle \sum_{i=1}^{L_{\mathrm{OC}}}\left({\mathrm{C}\mathrm{C}}_i\right){\displaystyle \sum_{j=1}^{L_{\mathrm{packet}}}\mathrm{E}{\mathrm{N}}_j}}\hfill \\ {}\mathrm{C}\mathrm{C}(x)={\displaystyle \sum_{k=1}^{L_{\mathrm{OC}}}\mathrm{F}{\mathrm{r}}_k\oplus \mathrm{F}{\mathrm{r}}_{k+1}}\hfill \\ {}\mathrm{E}\mathrm{N}(x)={\displaystyle \sum_{k=1}^{L_{\mathrm{OC}}}\mathrm{M}{\mathrm{P}}_k\oplus \mathrm{M}{\mathrm{P}}_{k+1}}\hfill \end{array}\right. $$

(8)

Here, let L _OC denote the cloud number of joining the cooperation. CC is the computational complexity. EN is encoding function of multimedia data packet fusion. Fr is the video frames. MP is the encoding function of single data packet. In the process of dynamic topology evolution, there are the following issues, which should be considered when combining the mobile node state with the opportunity of cloud computing.

• (1) The same multimedia data stream in different data packets in the opportunistic Markov chain model can choose different data transmission path.

• (2) In cooperative neural networks, the mobile nodes, which are in equilibrium state, are all in equilibrium, which can improve the efficiency of cloud computing.

• (3) To find and activate the mobile nodes with stable three-dimensional stability, which are used to establish the end-to-end communication between sending sensor and a cloud platform.

In dynamic topology evolution process, the transmission delay of multimedia data can be obtained by the formula (9). The delay includes the following three parts: (1) the prediction delay with opportunistic Markov chain model, (2) cooperative transmission delay of mobile node, and (3) opportunistic cloud computing delay.

$$ {T}_{\mathrm{C}\mathrm{C}}=\left\{\begin{array}{l}{T}_{C1}+{T}_{C2}+{T}_{C3}\\ {}{T}_{C1}={\displaystyle \sum_{i=1}^3{P}_i{T}_{\mathrm{MC}}\left({\mathrm{C}\mathrm{C}}_i\right)}\\ {}{T}_{C2}={\displaystyle \sum_{i=1}^N\mathrm{M}\mathrm{S}{\left(\mathrm{C}\mathrm{C}\right)}_i}\\ {}{T}_{C3}={\displaystyle \sum_{I=1}^{L_{OC}}{T}_{\mathrm{OC}\hbox{-} \mathrm{C}\mathrm{C}}\mathrm{C}{\mathrm{C}}_{\mathrm{i}}}\end{array}\right. $$

(9)

Cloud device idle rate as shown in formula (10).

$$ \left\{\begin{array}{l}{P}_{\mathrm{idle}}=\left(1-{\displaystyle \sum_{i=1}^{L_{\mathrm{OC}}}{P}_{\mathrm{ACTIVE}}}\right){\left.{P_{TR}}^Y\right|}_{V\to U}\\ {}{L}_{\mathrm{OC}}=U+V\end{array}\right. $$

(10)

Here, U is the active cloud number. V is the cloud number with opportunistic active status. P _TR ^Y| _V → U denote the probability from the waiting state to an active state of V opportunistic nodes.

Cloud computing efficiency can be obtained by formula (11).

$$ {\eta}_{\mathrm{OC}}=\frac{{\displaystyle \sum_{i=1}^{\mathrm{Len}}F{r}_i}-E\left({\displaystyle \sum_{j=1}^{L_{\mathrm{OC}}}F{r}_i}\right)}{\sqrt{D\left({\displaystyle \sum_{j=1}^{L_{\mathrm{OC}}}F{r}_i}\right)}}\frac{T_{C3}}{T_C}\left(1-{P}_{\mathrm{idle}}\right) $$

(11)

Survivability mechanism based on topology reconstruction

Based on opportunistic Markov chain model, the multimedia data calculation complexity, multimedia data transmission delay, and cloud computational efficiency of WMSN topology evolution would be analyzed in depth. Here, we considered two kinds of calculation schemes. The first is a static cloud computing program that is responsible for the calculation of the data of fixed cloud computing tasks, from the mobile node to the cloud transmission path is fixed. The second is the scheme opportunistic cloud computing scheme. The multimedia stream transmission path of the mobile node and the cloud is dynamic, which can choose the optimal transmission path according to the mobile node topology evolution state and provide transmission services of end-to-end communication.

Figure 5 shows the effect of multimedia mobile nodes of different cloud computing schemes of WMSNs. Here, based on the opportunity Markov chain model and the cooperative neural network prediction with topology evolution, the opportunistic cloud computing scheme can analyze the extension and connectivity of the mobile node according to the complexity, multimedia data transmission delay, cloud equipment idle rate and efficiency of cloud computing and multimedia sensor energy consumption, and dynamic evolution of network topology in real time. Hence, the best multimedia stream transmission path could be established on the basis of the prediction of mobile nodes.

Fig. 5

Topological evolution performance analysis. a Computational complexity of multimedia data. b Multimedia data transmission delay. c Cloud computing efficiency

From Fig. 5a, the calculation complexity of opportunity cloud computing scheme is not too sensitive to mobile node scale, which is difficult to decline because of the size of the mobile node influence and jitter significantly. The opportunistic cloud computing scheme can make full use of the mobile node state prediction, combined with the opportunity of Markov chain model to choose the best data transmission path. The real-time performance is better than the static cloud computing scheme, which is shown in Fig. 5b. With the increase of the scale of the mobile node, data transmission through multi-hop to the cloud platform significantly increased the computational complexity of multimedia data and reduced the efficiency of cloud computing. The cloud computing may be weakened. However, the multi-hop opportunity transmission and multiple data fusion could gradually improve the efficiency of cloud computing, as shown in Fig. 5c. Above the performance benefit is the opportunistic cloud calculation scheme using the network topology dynamic evolution can perceive the WMSN system state and the mobile node state and cause damage to the network, with strong survivability.

Figure 6 shows network survivability performance of the multimedia data scale and cloud number of multimedia sensor energy consumption and cloud idle equipment rate. In Fig. 6a, there are many video frames in multimedia data stream. With the increase in the video frame, static cloud computing scheme for mobile nodes energy consumption of linear increase, easily lead to WMSNs due to energy failure caused by network damaged, and opportunity cloud calculation scheme of energy consumption remains below the 100 kJ, have the ability to resist energy failure damaged. The damage analysis of WMSNs with the cloud device utilization rate was shown in Fig. 6b. Static cloud computing scheme cannot obtain the cloud equipment state in real time, which makes the cloud equipment in idle state for a long time. A large number of multimedia computing focused on fixed cloud equipment, easily lead to cloud device computational inefficiency, which caused the damage of WMSNs. The opportunistic cloud computing scheme can calculate the multimedia data with opportunistic computing, which can assign computing tasks to the newly increased computing equipment and always maintain high utilization. WMSNs based on the opportunity cloud computing scheme can well deal with the network topology evolution brings network damage factor, improve resource utilization, and reduce the damage probability.

Fig. 6

Invulnerability impact of cloud and multimedia data on WMSNs. a Multimedia sensor energy consumption. b Cloud device idle rate

About multimedia sensor mobile node, its failure generally occurs in the following circumstances:

• (1) The resource depletion of mobile nodes.

• (2) Mobile nodes cannot meet the needs of the opportunity cloud computing.

Similarly, the failure probability of a multimedia mobile node is shown in the formula (12).

$$ {\left.{P}_{FA}(MS)={P}_{MS}\right|}_{M{S}_{\mathrm{RE}}<T{H}_{\mathrm{RE}}}+{P}_{MS\to \mathrm{Cloud}}\left|{}_{M{S}_C<O{C}_{CC}}\right. $$

(12)

In the process of opportunity cloud computing, WMSN topology reconfiguration probability is shown in the formula (13).

$$ T{P}_{\mathrm{WMSNs}}={\displaystyle \sum_{i=1}^N\left(1-{P}_{\mathrm{OUT}}\right){\left.{P_{\mathrm{TR}}}^Y\right|}_{V\to U}}{\displaystyle \sum_{i=1}^{\mathrm{Len}}{P}_S\left({\mathrm{Fr}}_i\right)}\left|{P}_S<{P}_{\mathrm{OUT}}\left|{}_{t\to \infty}\right.\right. $$

(13)

To sum up, according to the forecast of WMSN node state, dynamic selection of sender and cloud multimedia data transmission path, the opportunity selection device cloud data calculation and promote the WMSN topology reconfiguration. Opportunity-based Markov chain model and the cooperation-type neural network, combined with the cloud platform resource state of WMSN topology reconfiguration and cloud decision calculation process such as that shown in Fig. 7, can improve the WMSN survivability by chance based on cloud computing and topology reconstruction.

Fig. 7

Survivability architecture of WMSNs mechanism based on topology reconfiguration

A mobility prediction and opportunistic cloud computing scheme network topology reconfiguration is proposed in WMSN survivability mechanism, which could real-time enhance network survivability.

Performance evaluation

In order to verify the I-MPOCCTE survivability mechanism performance and end-to-end multimedia communication quality of service support capability in WMSNs, we designed the experimental platform based on NS-2 simulation system. Through the C language, the proposed scheme would be embedded into the multimedia sensor nodes and compared with the performance of static WMSN scheme (I-S-WMSNs). The experimental time of video transmission is 60 min, the experimental area is a 600 × 800 m rectangular area, there is a total of 30 multimedia sensor nodes, and cooperative communication distance is 200 m.

In the experiment, the video data is transmitted and transmitted as the object, and the failure probability of WMSNs of the proposed scheme is analyzed, and the number of WMSN mobile nodes and the system execution efficiency are analyzed. Parameter settings for the multimedia sensor and settings of cloud platform are shown in Tables 1 and 2. Each mobile node’s residual energy, storage space, and computing power of different analysis of WMSNs to cope with node failure can be caused by random networks are impaired under the mechanism of survivability. In order to facilitate comparison, in the same environment and at the same time, the statistical static WMSN scheme performance (as shown in Fig. 8) and the service quality assurance capability (as shown in Fig. 9) were analyzed.

Table 1 Settings of multimedia sensor

Table 2 Cloud platform

Fig. 8

Performance evaluation of invulnerability. a Sending node failure probability. b Number of failure node. c System execution efficiency

Fig. 9

Quality of video at receiver

From Fig. 8a, it is found that for the I-MPOCCTE with large amounts of data transmission of multimedia application for a long time, multimedia data sending node failure probability was significantly lower than that in the I-S-WMSNs, and in coping with the task, resource depletion caused the transmitter failure caused by network damage has a significant advantage. This is because part of the node failure causes the topology to change dynamically, in the topology evolution process of the I-MPOCCTE according to the mobile forecast results real-time adjustment of transmission network. From Fig. 8b, it shows that the proposed I-MPOCCTE mobile node failure number increased slowly and at the end of the transmission node failure is less than four; and I-S-WMSNs, due to the failure of the scale of more than 50 %, caused paralysis, this is because the I-S-WMSNs scheme leads to tissue damage and cannot be repaired. And the I-MPOCCTE method combining network topology reconfiguration and opportunity keeps itself more available to the mobile node and the given effective protection, so as to the system for the high rate of implementation of the security, see Fig. 8c.

In addition, the user terminal can decode the frame as shown in Fig. 9. The high decoding frame rate I-MPOCCTE is mainly due to the opportunity for cloud computing, while maintaining low computational complexity and improving the efficiency of the multimedia data decoding and playback quality to provide effective protection.

Conclusions

In order to enhance the survivability performance of WMSNs, survivability mechanism based on mobility prediction and cloud computing with topological evolution for WMSN is proposed in this paper. Based on cooperative neural network and opportunity Markov chain model, the accurate prediction of multimedia mobile sensor node state and state transition probability then is given based on mobility prediction of dynamic topology evolution mechanism and the opportunity computational scheme. Finally, combined with network topology reconfiguration and opportunity calculation, it puts forward a kind of enhanced WMSNs survivability and end-to-end quality of service guarantee mechanism. Experimental results show that the sending node failure, playback quality, node lifetime, and the execution efficiency of the system, such as the survivability mechanism, have a significant advantage.

The future work is to study the cloud platform failure, repair of damaged node for enhancing the WMSN invulnerability, and seek for end-to-end QoS guarantee mechanism in the network with damaged case.