- Research
- Open Access

# RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

- Jianming Zhou
^{1}Email author and - Tao Dong
^{2}

**2015**:243

https://doi.org/10.1186/s13638-015-0471-6

© Zhou and Dong. 2015

**Received:**23 July 2015**Accepted:**27 October 2015**Published:**10 November 2015

The Retraction Note to this article has been published in EURASIP Journal on Wireless Communications and Networking 2017 2017:25

## 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.

## Keywords

- Survivability
- Opportunistic cloud computing
- Mobility prediction
- Wireless multimedia sensor networks
- Topological evolution

## 1 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.

## 2 Mobility prediction scheme

*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.

*x*and the moving vector

*y*. The whole system of WMSNs can be obtained by calculating the 3D vector product of

*N*mobile node.

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.

*i*node of the cooperative neural network, as shown in the formula (2a).

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 P_{1}P_{x}. A two-dimensional Markov chain model is applied to only two dimensions to change, and the success arrive probability of the cloud is P_{1}P_{x}P_{y}. The three-dimensional Markov chain model is suitable to three-dimensional transfer, the success arrive probability is P_{1}P_{x}P_{y}P_{t}.

*P*

_{OUT}of transmission path can be calculated by formula (3).

*P*

_{balance}can be obtained as formula (4).

*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).

## 3 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.

*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.

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.

## 4 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.

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.

- (1)
The resource depletion of mobile nodes.

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

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

## 5 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.

Settings of multimedia sensor

Parameters | Value | Parameters | Value |
---|---|---|---|

Sending rate | 30 frames/s | End-to-end distance | 800 m |

Video resolution | 720 × 480 pixel | Bandwidth | 100 Mbit/s |

Working frequency | 1200 kHz | Video encoding algorithm | MPEG-4 |

Cloud platform

Parameters | Value | Parameters | Value |
---|---|---|---|

Number of clouds | 10 | Working frequency | 800 kHz |

Disk space | 100 TB | Computing error | <0.005 % |

Memory space | 1 TB | CPU power | <0.2 W |

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.

## 6 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.

## Notes

## Declarations

### Acknowledgements

This work is supported in part by the Open Research Fund of State Key Laboratory of Space-Ground Integrated Information Technology under grant no. 2014_CXJJ-TX_08.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

## References

- S Colonnese, F Cuomo, T Melodia. An empirical model of multiview video coding efficiency for wireless multimedia sensor networks. IEEE Trans. Multimedia 15(8), 1800–1814 (2013)Google Scholar
- Y Yang, Y Wang, D Pi, R Wang. Optimization of self-directed target coverage in wireless multimedia sensor network. Scientific World Journal 2014, 416218 (2014)Google Scholar
- X Nan, Y He, L Guan et al., Queueing model based resource optimization for multimedia cloud. J. Vis. Commun. Image Represent.
**25**(5), 928–942 (2014)View ArticleGoogle Scholar - G Nan, Z Mao, M Li, Y Zhang, S Gjessing, H Wang, et al. Distributed resource allocation in cloud-based wireless multimedia social networks. IEEE Netw. 28(4), 74–80 (2014)Google Scholar
- X Zheng, N Chen, Z Chen, C Rong, G Chen, W Guo. Mobile cloud based framework for remote-resident multimedia discovery and access. J. Internet Tech. 15(6), 1043–1050 (2014)Google Scholar
- M Hefeeda, T ElGamal, K Calagari, A Abdelsadek. Cloud-based multimedia content protection system. IEEE Trans. Multimedia 17(3), 420–433 (2015)Google Scholar
- K-I Kim, T-E Sung. Modeling and routing scheme for (m, k)-firm streams in wireless multimedia sensor networks. Wirel. Commun. Mob. Comput.
**15**(3), 475–483 (2015)View ArticleGoogle Scholar - A Alaybeyoglu. Transmission of image data using fuzzy logic based clustering infrastructure in mobile multimedia sensor networks. J. Intell. Fuzzy Syst.
**28**(3), 1235–1242 (2015)MathSciNetGoogle Scholar - Y Xu, J Choi, S Dass, T Maiti. Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. Automatica 49(12), 3520–3530 (2013)Google Scholar
- M Alshayeb, Y Eisa, A Ahmed Moataz et al., Object-oriented class stability prediction: a comparison between artificial neural network and support vector machine. Arab. J. Sci. Eng.
**39**(11), 7865–7876 (2014)View ArticleGoogle Scholar - P Pirozmand, G Wu, B Jedari, F Xia. Human mobility in opportunistic networks: characteristics, models and prediction methods. J. Netw. Comput. Appl. 42, 45–58 (2014)Google Scholar
- J Chen, L Ma, and Y Xu. Support vector machine based mobility prediction scheme in heterogeneous wireless networks. Math. Probl. Eng. (2015)Google Scholar
- A Nadembega, A Hafid, T Taleb et al., Mobility-prediction-aware bandwidth reservation scheme for mobile networks. IEEE Trans. Veh. Technol.
**64**(6), 2561–2576 (2015)View ArticleGoogle Scholar - K Demir Alper, H Demiray, S Baydere, Engin QoSMOS: cross-layer QoS architecture for wireless multimedia sensor networks. Wireless Network
**20**(4), 655–670 (2014)View ArticleGoogle Scholar - S Chen, J Jiang, S Pang, S Nie, Y Lai. Modeling and optimization of train scheduling network based on invulnerability analysis. Applied Mathematics & Information Sciences. 7(1), 113–119, (2013)Google Scholar
- P Xing-Zhao, Y Hong, D Jun, D Chao, Z Zhi-Hao. Study on cascading invulnerability of multi-coupling-links coupled networks based on time-delay coupled map lattices model. Acta Physica Sinica. 63(7), (2014)Google Scholar
- H Shen, G Bai, Z Tang, L Zhao. QMOR: QoS-aware multi-sink opportunistic routing for wireless multimedia sensor networks. Wirel. Pers. Commun. 75(2), 1307–1330 (2014)Google Scholar
- DP Mendes Lucas, JPC Rodrigues Joel, J Lloret, S Sendra. Cross-layer dynamic admission control for cloud-based multimedia sensor networks. IEEE Syst. J. 8(1), 235–246 (2014)Google Scholar
- E Baccarelli, F Chiti, N Cordeschi R Fantacci, D Marabissi, R Parisi. Green multimedia wireless sensor networks: distributed intelligent data fusion, in-network processing, and optimized resource management. IEEE Wirel. Commun. 21(4), 20–26 (2014)Google Scholar
- Y Zhou, W Xiang, G Wang. Frame loss concealment for multiview video transmission over wireless multimedia sensor networks. IEEE Sensors J.
**15**(3), 1892–1901 (2015)View ArticleGoogle Scholar