Abstract
The Internet of Things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such a large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this article, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.
- M. E. Alexander, S. M. Moghadas, P. Rohani, and A. R. Summers. 2006. Modeling the effect of a booster vaccination on disease epidemiology. J. Math. Biol 52 (2006), 290--306.Google Scholar
Cross Ref
- R. M. Anderson and R. M. May. 1999. Population biology of infectious disease. I Nature 180 (1999), 361--367.Google Scholar
- L. Atzori, A. Iera, and G. Morabito. 2010. The internet of things: A survey. Comput. Netw. 54, 15 (2010), 2787--2805. Google Scholar
Digital Library
- T. Chen and N. Jamil. 2006. Effectiveness of quarantine in malicious codes epidemic. IEEE ICC (2006), 2142--2147.Google Scholar
- R. Di Pietro, L. V. Mancini, C. Soriente, A. Spognardi, and G. Tsudik. 2008. Catch me (if you can): Data survival in unattended sensor networks. In Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom’08). 185--194. Google Scholar
Digital Library
- R. Di Pietro and N. V. Verde. 2011. Epidemic data survivability in unattended wireless sensor networks. In Proceedings of the Fourth ACM Conference on Wireless Network Security (Wisec’11). 11--22. DOI:http://dx.doi.org/10.1145/1998412.1998417 Google Scholar
Digital Library
- M. Draief, A. Ganesh, and L. Massouili. 2008. Thresholds for virus spread on network. Ann. Appl. Probab 18, 2 (2008), 359--369.Google Scholar
Cross Ref
- C. Gan, X. Yang, W. Liu, Q. Zhu, J. Jin, and L. He. 2014. Propagation of computer virus both across the Internet and external computers: A complex-network approach. Comm. Nonlinear Sci. Numer. Sim. 19, 8 (2014), 2785--2792.Google Scholar
Cross Ref
- E. Gelenbe, V. Kaptan, and Y. Wang. 2004. Biological metaphors for agent behavior. In 19th International Symposium on Computer and Information Sciences (ISCIS’04) 3280 (2004), 667--675.Google Scholar
- M. J. Keeling and K. T. D. Eames. 2005. Network and epidemic models. J. R. Soc. Interface 2, 4 (2005), 295--307.Google Scholar
Cross Ref
- W. O. Kermack and A. G. McKendrick. 1933. Contributions of mathematical theory to epidemics. Proc. Royal Soc. London Series A 141 (1933), 94--122.Google Scholar
Cross Ref
- W. O. Kermack and A. McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proc. Royal Soc. London Series A, Containing Papers of a Mathematical and Physical Character 115, 772 (Aug. 1927), 700--721.Google Scholar
- B. K. Mishra and N. Jha. 2010. SEIQRS model for the transmission of malicious objects in computer network. Appl. Math. Model 34 (2010), 710--715.Google Scholar
Cross Ref
- B. K. Mishra and S. K. Pandey. 2011. Dynamic model of worms with vertical transmission in computer network. Appl. Math. Comput. 217, 21 (2011), 8438--8446.Google Scholar
- B. K. Mishra and S. K. Pandey. 2014. Dynamic model of worm propagation in computer network. Appl. Math. Model. 38, 7--8 (2014), 2173--2179.Google Scholar
Cross Ref
- M. E. J. Newman, S. Forrest, and J. Balthrop. 2002. Email networks and the spread of computer virus. Phys. Rev. E 66 (2002), 232-- 369.Google Scholar
Cross Ref
- R. Di Pietro and N. V. Verde. 2013. Epidemic theory and data survivability in unattended wireless sensor networks: Models and gaps. Pervasive Mobile Comput. 9, 4 (2013), 588--597. DOI:http://dx.doi.org/10.1016/j.pmcj.2012.07.010Google Scholar
Cross Ref
- W. T. Richard and J. C. Mark. 2005. Modeling virus propagation in peer-to-peer networks. In Proceedings of the IEEE International Conference on Information, Communication and Signal Processing (2005), 981--985.Google Scholar
- B. Shen, Z. Wang, and Y. S. Hung. 2010. Distributed consensus H-infinity filtering in sensor networks with multiple missing measurements: The finite-horizon case. Automatica 55, 7 (2010), 1682--1688. Google Scholar
Digital Library
- C. Tsai, C. Lai, and V. Vasilakos. 2014. Future internet of things: Open issues and challenges. ACM/Springer Wireless Netw. 20, 8 (2014), 2201--2217. Google Scholar
Digital Library
- J. Wan, H. Yan, H. Suo, and F. Li. 2011. Advances in cyber-physical systems research. KSII Trans. Internet Inf. Syst. 5, 11 (2011), 1891--1908.Google Scholar
Cross Ref
- Z. Wang, D. W. C. Ho, H. Dong, and H. Gao. 2010. Robust H-infinity finite-horizon control for a class of stochastic nonlinear time-varying systems subject to sensor and actuator saturations. IEEE Trans. Automat. Control 55, 7 (2010), 1716--1722.Google Scholar
Cross Ref
- L.-X. Yang and X. Yang. 2012a. The spread of computer viruses under the influence of removable storage devices. Appl. Math. Comput. 219, 8 (2012), 3914--3922.Google Scholar
Cross Ref
- L.-X. Yang, X. Yang, J. Liu, Q. Zhu, and C. Gan. 2013. Epidemics of computer viruses: A complex-network approach. Appl. Math. Comput. 219, 16 (2013), 8705--8717. Google Scholar
Digital Library
- L.-X. Yang, X. Yang, L. Wen, and J. Liu. 2012. A novel computer virus propagation model and its dynamics. Int. J. Comput. Math. 89, 17 (2012), 2307--2314. Google Scholar
Digital Library
- L.-X. Yang, X. Yang, Q. Zhu, and L. Wen. 2013. A computer virus model with graded cure rates. Nonlinear Anal.: Real World Appl. 14, 1 (2013), 414--422.Google Scholar
Cross Ref
- M. Yang, Z. Zhang, Q. Li, and G. Zhang. 2012. An SLBRS model with vertical transmission of computer virus over the internet. Discrete Dyn. Nat. Soc. 2012 (2012), Article ID 925648, 17 pages. DOI:10.1155/2012/925648Google Scholar
- X. Yang and L.-X. Yang. 2012b. Towards the epidemiological modeling of computer viruses. Discrete Dyn. Nat. Soc. 2012 (2012), Article ID 259671, 11 pages. DOI:10.1155/2012/259671Google Scholar
- C. Zhang, Y. Zhao, and Y. Wu. 2012a. An impulse model for computer viruses. Discrete Dyn. Nat. Soc. 2012 (2012), Article ID 260962, 13 pages. DOI:10.1155/2012/260962Google Scholar
- C. Zhang, Y. Zhao, Y. Wu, and S. Deng. 2012b. A stochastic dynamic model of computer viruses. Discrete Dyn. Nat. Soc. 2012 (2012), Article ID 264874, 16 pages. DOI:10.1155/2012/264874Google Scholar
- Q. Zhu, X. Yang, L.-X. Yang, and J. Ren. 2012a. Modeling and analysis of the spread of computer virus. Commun. Nonlinear Sci. Numer. Simu. 17, 2012 (2012), 5117--5124.Google Scholar
Cross Ref
- Q. Zhu, X. Yang, L.-X. Yang, and C. Zhang. 2012b. Optimal control of computer virus under a delayed model. Appl. Math. Comput. 218, 23 (2012), 11613--11619.Google Scholar
- C. C. Zou, W. B. Gong, D. Towsley, and L. X. Gao. 2005. The monitoring and early detection of internet malicious codes. IEEE/ACM Trans. Netw. 13, 5 (2005), 961--974. Google Scholar
Digital Library
Index Terms
Using an Epidemiological Approach to Maximize Data Survival in the Internet of Things
Recommendations
Cyberentity Security in the Internet of Things
A proposed Internet of Things system architecture offers a solution to the broad array of challenges researchers face in terms of general system security, network security, and application security.
Security of the Internet of Things: perspectives and challenges
Internet of Things (IoT) is playing a more and more important role after its showing up, it covers from traditional equipment to general household objects such as WSNs and RFID. With the great potential of IoT, there come all kinds of challenges. This ...
Perception layer security in Internet of Things
AbstractInternet of Things (IoT) is one of the rising innovations of the current era that has largely attracted both the industry and the academia. Life without the IoT is entirely indispensable. To dispel the doubts, if any, about the ...
Highlights- WSNs, RFID and RSN are the key enabling technologies of IoT.
- Security at ...






Comments