# Implementation of Connected Dominating Set in Fog Computing Using Knowledge-Upgraded IoT Devices

## Abstract

Wireless sensor networks (WSNs) have a worldwide attraction because of its increasing popularity. The key enablers for the Internet of Things (IoT) are WSN, which plays an important role in future by collecting information through the cloud. Fog Computing, the latest innovations, connects sensor-based IoT devices to the cloud. Fog Computing is a decentralized computing infrastructure in which the data, compute, storage, and applications are distributed efficiently between the data source and the cloud. The main aim of Fog Computing is to reduce the amount of data transported to the cloud and hence increase the efficiency. The knowledge-upgraded IoT devices will be embedded with a piece of software into it, which can able to understand the Distributed Denial of Service (DDoS). Such attacks are not forwarded to the cloud and thus the cloud server down problem is avoided. The IoT devices enabled with such knowledge is connected together to form a Connected Dominating Set (CDS). The data routed through only such IoT devices will be directly connected to the cloud. The CDS-based approach reduces the search for a minimum group of IoT devices called nodes, thus forming the backbone network. Various CDS algorithms have been developed for constructing CDSs with minimum number of nodes. However, most of the research work does not focus on developing a CDS based on application and requirement. In this chapter, a Gateway-based Strategic CDS (GWS-CDS) is constructed based on strategy and communication range. Here, any node in the network assigned a critical communication range, which is in a strong neighbourhood and which is within the communication range of more than one network, will be selected as the starting node, instead of the node with maximum connectivity. If a node is not within a critical communication range, then the following factors will be increased: the number of nodes that locally compete over a shared channel, access delay, network throughput and network partitioning. The other nodes for CDS construction are selected based on density and velocity. The focus of this research work was to construct a CDS in heterogeneous networks. The algorithm was tested with respect to three metrics—average CDS node size, average CDS Edge Size and average hop count per path. Simulation results showed that the proposed algorithm was better when compared to the existing algorithms.

### Keywords

Wireless sensor networks IoT Fog Computing Communication range CDS Strategy Density Velocity and gateway nodes## Notes

### Acknowledgement

The authors gratefully acknowledge the use of services and facilities of the Centre for Networking and Cyber Defense (CNCD) at Hindustan Institute of Technology and Science, Chennai, India.

### References

- 1.Baccarelli, Enzo, et al. (2017). Fog of everything: Energy-efficient networked computing architectures, research challenges and a case study.
*IEEE Access,**5,*9882–9910.CrossRefGoogle Scholar - 2.Marín-Tordera, E., et al. (2017). Do we all really know what a fog node is? Current trends towards an open definition.
*Computer Communications,**109,*117–130.CrossRefGoogle Scholar - 3.Verba, N., et al. (2016). Platform as a service gateway for the fog of things.
*Advanced Engineering Informatics,*Article in press.Google Scholar - 4.Arkian, H. R., Diyanat, A., & Pourkhalili, A. (2017). MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for iot crowdsensing applications.
*Journal of Network and Computer Applications,**82,*152–165.CrossRefGoogle Scholar - 5.Wu, J., Lou, W., & Dai, F. (2006). Extended multipoint relays to determine connected dominating sets in manets.
*IEEE Transactions on Computers,**55*(3), 334–347.CrossRefGoogle Scholar - 6.Sanchez, M., Manzoni, P., & Haas, Z. J. (1999). Determination of critical transmission range in ad-hoc networks.
*Multiaccess, Mobility and Teletraffic in Wireless Communications,**4,*293–304.CrossRefGoogle Scholar - 7.Santi, P. (2005). The critical transmitting range for connectivity in mobile ad hoc networks.
*IEEE Transactions on Mobile Computing,**4*(3), 310–317.CrossRefGoogle Scholar - 8.Deng, J., et al. (2007). Optimal transmission range for wireless ad hoc networks based on energy efficiency.
*IEEE Transactions on Communications,**55*(9), 1772–1782.CrossRefGoogle Scholar - 9.Sharmila, C., & George, A. (2014). Construction of strategic connected dominating set for mobile ad hoc networks.
*Journal of Computer Science,**10*(2), 285–295.CrossRefGoogle Scholar - 10.Akbari Torkestani, J., & Meybodi, M. R. (2010). An intelligent backbone formation algorithm for wireless ad hoc networks based on distributed learning automata.
*Computer Networks,**54*(5), 826–843.CrossRefMATHGoogle Scholar - 11.Hussain, S., Shafique .M. I., & Yang. L. T (2010).Constructing a CDS-based network backbone for energy efficiency in industrial wireless sensor network. In
*Proceedings of HPCC*(pp. 322–328).Google Scholar - 12.Das, B., Sivakumar, R., & Bhargavan, V. (1997). Routing in ad hoc networks using a spine. In
*International Conference on Computer Communications and Networks*(pp. 1–20).Google Scholar - 13.Guha, S., & Khuller, S. (1998). Approximation algorithms for connected dominating sets.
*Algorithmica,**20*(4), 374–387.MathSciNetCrossRefMATHGoogle Scholar - 14.Clark, Brent N., Colbourn, Charles J., & Johnson, David S. (1990). Unit disk graphs.
*Discrete Mathematics,**86*(1-3), 165–177.MathSciNetCrossRefMATHGoogle Scholar - 15.Garey, M. R., & Johnson, D. S. (1979).
*Computers and intractability*. San Francisco: W. H. Freeman, Print. Print.MATHGoogle Scholar - 16.Kim, D., et al. (2009). Constructing minimum connected dominating sets with bounded diameters in wireless networks.
*IEEE Transactions on Parallel and Distributed Systems,**20*(2), 147–157.CrossRefGoogle Scholar - 17.Meghanathan, N., & Terrell, M. (2012). An algorithm to determine stable connected dominating sets for mobile ad hoc networks using strong neighborhoods.
*International Journal of Combinatorial Optimization Problems and Informatics,**3*(2), 79–92.Google Scholar - 18.Misra, R., & Mandal, C. (2010). Minimum connected dominating set using a collaborative cover heuristic for ad hoc sensor networks.
*IEEE Transactions on Parallel and Distributed Systems,**21*(3), 292–302.CrossRefGoogle Scholar - 19.Meghanathan, N. (2012). Graph theory algorithm for mobile ad hoc networks.
*Informatica,**36,*185–200.MathSciNetGoogle Scholar - 20.Bannoura, A., et al. (2016). The wake up dominating set problem.
*Theoretical Computer Science,**608,*120–134.MathSciNetCrossRefMATHGoogle Scholar - 21.Thai, M. T., et al. (2007). Connected dominating sets in wireless networks with different transmission ranges.
*IEEE Transactions on Mobile Computing,**6*(7), 721–730.CrossRefGoogle Scholar - 22.Xiaohong, L. I., et al. (2014). A lifetime-extended size-bounded construction algorithm for connected dominating sets in heterogeneous wireless sensor networks.
*Journal of Computational Information Systems,**10*(16), 6973–6981.Google Scholar - 23.Deng, J., et al. (2007). Optimal transmission range for wireless ad hoc networks based on energy efficiency.
*IEEE Transactions on Communications,**55*(9), 1772–1782.CrossRefGoogle Scholar - 24.Wu J., & Li H. (1999). On calculating connected dominating set for efficient routing in ad hoc wireless networks. In
*Proceedings of the 3rd International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications*(pp. 7–14). August 1, 1999 ACM.Google Scholar - 25.Das, B., Sivakumar, E., & Bhargavan, V. (1997) Routing in ad-hoc networks using a virtual backbone. In
*Proceedings of the 6th International Conference on Computer Communications and Networks (IC3N‘97)*(pp. 1–20). September 1997.Google Scholar - 26.Wu, J., et al. (2002). On calculating power-aware connected dominating sets for efficient routing in ad hoc wireless networks.
*Journal of Communications and Networks,**4*(1), 59–70.CrossRefGoogle Scholar - 27.Yan, X., et al. (2004). A heuristic algorithm for minimum connected dominating set with maximal weight in ad hoc networks. In
*Grid and Cooperative Computing, Springer Berlin Heidelberg*(pp. 719–722).Google Scholar - 28.Gandhi, R., & Parthasarathy, S. (2007). Distributed algorithms for connected domination in wireless networks.
*Journal of Parallel and Distributed Computing,**67*(7), 848–862.CrossRefMATHGoogle Scholar - 29.Fly, P., & Meghanathan, N. (2010). Predicted link expiration time based connected dominating sets for mobile ad hoc networks.
*International Journal of Computer Science and Engineering,**2*(6), 2096–2103.Google Scholar