Home
Call For Papers
Submission
Author
Registration
Publications
About
Contact Us

  Analysis of Bio-inspired Scalable Routing Protocol for Cognitive Radio Sensor Networks  
  Authors : Amit N. Thakare; Dr. Latesh Bhagat (Malik); Achamma Thomas
  Cite as:

 

Wireless sensor network (WSN) is an autonomous system of number of sensor nodes deployed in the region and connected by the wireless links. The sensor nodes are battery powered node with some limited energy to utilize in network for various applications. The nodes are changing the position frequently and forward the packets from source to the destination. The routing is the main issue in wireless sensor networks. Thus there is need of designing the routing protocol for WSN. The next generation of wireless sensor network is the improvement in the hybridization techniques. The MAC and network layer is to be improved for the next generation wireless technology. The new idea behind the routing techniques is by sending the packets whenever the communication required with available spectrum holes. The next generation WSN is the CRSN (Cognitive radio sensor network) with cognitive capabilities. It is a possibly new routing philosophy that provides a scalable so lution to relatively large network in CRSN. An inspired from swarm colony optimization as artificial intelligence techniques likely to be ACO (Ant colony optimization) method used to find the shortest route with reliable routing in CRSN. In this paper an attempt has been made to compare the performance of routing methods for CRSN using biologically inspired methods. As per finding the differences in the protocol provides better solution for routing techniques with high density network. The performance differentials are analyzed using varying number of packets and pause time. The simulation is carried out using ns2 simulation tool. The results illustrate the importance of implementing the routing protocols in CRSN environment for large network.

 

Published In : IJCSN Journal Volume 6, Issue 6

Date of Publication : December 2017

Pages : 661-669

Figures :04

Tables : 03

 

Amit N. Thakare : He is pursuing the PhD.(Computer Science & Engineering)from G. H. Raisoni College of Engineering, Nagpur, M.Tech (Computer Science & Engineering)in 2008 from MGMs College of Engineering,Shree Ramanand Teerth Marathwada University, Nanded, B.E.(Computer Technology) in 2002, from Manoharbhai Patel Institute of Engineering & Technology, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur (Maharashtra), India, He is life member of ISTE, Member of CSI and IEI,Paper presented and Published more than 15 papers.

Dr. Latesh G. Bhagat(Malik) : She has completed Ph.D. (Computer Science & Engineering) from Visveyaraya National Institute of Technology in 2010, M.Tech. (Computer Science & Engineering) from Banasthali Vidyapith, Rajasthan, India and B.E. (Computer Engineering) from University of Rajasthan, India . She is gold medalist in B.E. and M.Tech. She is currently working as Associate Professor & Head of Department in Department of Computer Science & Engineering at Government College of Engineering, Nagpur, MS, India. She has teaching experience of 19 years. Mrs. Latesh Bhagat (Malik) is life member of ISTE, CSI, ACM and presented 46 papers in international journal and 57 papers in international conference. She is recipient of 2 RPS and 1 MODROBs by AICTE. She guided 31 PG projects and 8 Ph.D. students are registered under RTM Nagpur University.

Achamma Thomas : received M.Tech(Computer Science & Engineeirng) in 2013. She is currently the Head of Computer Science Department at G.H.Raisoni College of Engineering, Nagpur. She has completed M.Phil(Computer Science) in the year 2011.Her area of specialization is soft Computing and Data mining.

 

Routing; scalability; ant colony optimization; cognitive radio sensor network; performance analysis

In this paper the objective is to improve the scalability issue. To improve the scalability we consider the 25 to 100 sensor nodes for our experiment as node density. As observed that the increasing number of nodes may cause the reducing the performance of individual nodes in the network. The cognitive radio sensor networks which are the increasing network for the next generation wireless network also perform better in packet delivery ratio, throughput, and end2end delay with proper energy consumption of individual nodes, as per total energy by above equation. Here we consider the two routing protocol AODV and DSR with cognitive capabilities as AODV (cogns) and DSR (cogns) and observed that the DSR(cogns) perform better as compare to AODV(cogns) in parameter such as Throughput. They solve the complex NP-hard combinatorial optimization problem based on different application. The hands-off techniques from primary user to secondary user with available spectrum holes in the network occupied the secondary user for some duration of time and changing the route from primary user to secondary with efficiently. It solves and optimized different issues and challenges, which is the need of today era and solves the telecommunication problems.

 

[1] I. F. Akyilidiz et al, “A survey of wireless sensor network”, 2002. [2] W. Heinzelman, et al., “An application specific protocol architecture for wireless microsensor networks”, in press: IEEE Transaction on Wireless Networking”. [3] S.Muruganatha, D. Ma, eta l., “A Centralized Energy- Efficient Routing Protocol for wireless Sensor Networks”, IEEE Commun. Mag., 2005, Vol. 43, Issue3, pp. 8-13. [4] S. Lindsey and C. Raghavendra , “PEGASIS: Power- Efficient Gathering in Sensor Information Systems,” IEEE Aerospace Conf. proc., 2002, Vol. 3, 9-16, pp. 1125-30 . [5] E. Egea-Lopez, J.Vales-Alonso et al, “Simulation scalability issues in wireless sensor networks. IEEE Communication Magazine, pages 64-73, July 2006. [6] L. Alazzawi, A. Elkateeb, “Performance Evaluation of the WSN Routing Protocols Scalability,” Journal of Computer Systems. [7] A. Abbasi, M. Younis, A survey on clustering algorithms for wireless sensor networks, Journal of Computer Communications, Special Issues on Network Coverage and Routing Schemes for Wireless Sensor Networks, In Press. [8] M. Paone, A. Cucinotta, et al., “A bio-inspired distributed routing protocol for wireless sensor networks: performance evalution,” in Proc. IEEE. Inst. Conf. Distrib. Comput. Syst., 2010, pp. 247-255. [9] Oey, C. H.; Christian, et al., “Energy-and cognitive-radioaware routing in cognitive radio sensor networks. Int. J. Distr. Sen. Netw. 2012, 1-11. [10] Essam H. Hussein, et al., “Ant-Hoc: A swarm intelligence-based routing protocol for Adhoc networks,” IRACST-International Journal of Computer networks and Wireless Communications (IJCNWC), ISSN: 2250-3501, Vol. 6, No 1, Jan-Feb 2016. [11] Mohammad Saleem et al, “BeeSensor: A bee-inspired power aware routing protocol for wireless sensor networks. In:Applications of evolutionary computing. LNCS. Vol. 4448/2007. pp. 81-90. [12] Darigo, M.(1992), “Optimization, learning and natural algorithms”, Ph.D. dissertation. [13] Brand, M., Masuda, M., Wehner, N., & Yu, X. H.(2010) , “Ant colony optimization algorithm for robot path planning”, International Conference on Computer Design and Applications (ICCDA 2010), vol. 3 , pp. 436–440. [14] Kashif Saleem., et al.(2009), “Ant based self-organized routing protocol for wireless sensor networks”, IJCNIS 1(2). [15] Kwang Mong Sim et al., “Ant Colony optimization . for Routing and Load-balancing: Survey and New Direction”, IEEE Transaction on systems, Man, and Cybernetics-PartA: Systems and humans, Vol. 33, No. 5, September 2003. [16] R. Misra, and C. mandal, “Minimum Connected Dominating Set Using a Collaborative Cover Heuristic for Ad Hoc Sensor networks,” IEEE Transaction on Parallel and Distributed Systems, Vol. 21, pp. 292-302, 2010. [17] Yang, W. Markov Chains and Ant Colony Optimization, in Proceeding Yang 2013, Markov, CA. [18] VINT Project, “The network simulator-NS-2, “http://www.isi.edu/nsnam/ns. [19] A. N. Thakare and L. G. Malik, ”A Review of Routing Protocols in Wireless Sensor Networks Using Biologically Inspired Methods,” 6th International Conference on Emerging Trends in Engineering and Technology (ICETET), IEEExplore, pp. 103-104, Dec. 2013. [20] Thakare A.N., Malik L.G.(2014) Design Approach of Self-Organized Routing Protocol in Wireless Sensor Networks Using Biologically Inspired Methods. In: Maringanti R., Tiwari M. Arora A. (eds) Proceedings of Ninth International Conference on Wireless Communication and Sensor Networks. Lecture Notes in Electrical Engineering, Vol. 299, Springer, New Delhi. [21] Thakare, A.N., Bhagat, L.and Thomas, A. (2017) ‘A self-organised routing algorithm for cognitiveradiobased wireless sensor networks using biologicallyinspired method’,Int. J. Artificial Intelligence and Soft Computing, Vol. 6, No. 2, pp.148–169. [22] A. O. Bicen, O. B. Akan, “Reliability and Congestion Control in Cognitive Radio Sensor Networks,” to appear in Ad Hoc Networks Journal (Elsevier), 2011.