Data Aggregation Using Squirrel Search Algorithm in Wireless Sensor Networks
Keywords:
WSN-Energy Consumption-Network Lifetime-Data Aggregation-Squirrel Search AlgorithmAbstract
In a wireless sensor network (WSN), individual sensor nodes have limitations such as energy consumption, packet delivery, and delay and network lifetime. Data aggregation is an important method for lowering energy consumption of each sensor nodes in WSN. This helps in achieving increased network lifetime in WSN. Therefore, in order to improve the energy efficiency and lifespan of the network, Cluster-based data aggregation using Squirrel Search Algorithm is proposed in this paper.Cluster Head(CH) selection plays an important role for increasing the network lifetime. Criteria such as energy, distance are taken into consideration for selecting sensor nodes. For Cluster Head (CH) re-Electing, criteria such as its Residual energy and Received Signal Strength (RSS) are taken into consideration. Hence the sensor nodes with best CHs selected. Simulation results conducted in MATLAB shows that the Data Aggregation using Squirrel Search Algorithm (SSA) was able to improve the network lifetime, energy efficiency, delay compared with the Firefly Algorithm (FA) and Shuffled Frog Algorithm (SFA).
References
- K. Saleem, N. Fisal, S. Hafizah, S. Kamilah, R. Rashid, “Biological Inspired Self-Optimized Routing Algorithm for Wireless Sensor Networks,” Communications IEEE 9th Malaysia International Conference, 2009.
- M. Breza, & J.A., McCann, “Lessons in implementing bio-inspired algorithms on Wireless Sensor Networks”, IEEE-Adaptive Hardware and Systems, 2008.
- K. Aksa, and M. Benmohammed, “A Comparison between Geometric and Bio-Inspired Algorithms for Solving Routing Problem in Wireless Sensor Network”,International Journal of Networks and Communications, 2012.
- Randhawa,Sukchandan, Sushma Jain,” Data aggregation in Wireless Sensor networks”, 2017.
- Harb Hassan, et al., “A distance-based data aggregation technique for periodic sensor networks”, ACM Trans. Sensor Netw, 2017.
- Jiawei Tang, et al., “An aggregate signature-based trust routing for data gathering in sensor networks”, Secur. Commun. Netw, 2018.
- J. Wang, Y. Cao, B. Li, H. Kim, S. Lee, “Particle swarm optimization based clustering algorithm with mobile sink for WSNs”, Futur. Gener. Comput. Syst, 2017.
- H. Cheng and X. Jia, “An energy efficient routing algorithm for wireless sensor networks,” in Proc. IEEE WiCOM, 2005.
- Kim Kyung Tae and A YounHee Yong, “A stochastic and optimized energy efficient clustering protocol for wireless sensor networks,” Int. J. Distrib. Sensor Netw., 2014.
- X. Liu and J. Shi, “Clustering routing algorithms in wireless sensor networks: An overview,” KSII Trans. Internet Inform. Syst., 2012.
- S. Lindsey and C. S. Raghavendra, “PEGASIS: Power-efficient gathering in sensor information systems,” in Proc. IEEE Aerospace Conference, Big Sky, Montana, Mar. 2002.
- B. Zarei, M. Zeynali, and V. M. Nezhad, “Novel cluster based routing protocol in wireless sensor networks,” Int. J. Comput. Sci. Issues, 2010.
- Latiff, Tsimenidis, C.C., Sharif, B.S, “Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization”,IEEE, 2007.
- Cheng, Xia, “An effective cuckoo search algorithm for node localization in wireless sensor network”, sensors, 2016.
- X.liu, “Sensor deployment of wireless sensor network based on ant colony optimization with three classes of ant transitions”, IEEE commun.Lett.,2012.
- X.Liu, Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor network, J,Netw.comput.2014.
- M. M. Eusuff and K. E. Lansey, “Optimization of water distribution network design using the shuffled frog leaping algorithm,” Journal of Water Resources Planning and Management,2003.
- M. Eusuff, K. Lansey, and F. Pasha, “Shuffled frog- leaping algorithm: a memetic meta-heuristic for discrete optimization,” Engineering Optimization, 2006.
- H. Amirian and R. Sahraeian, “Solving a grey project selection scheduling using a simulated shuffled frog leaping algorithm,” Computers & Industrial Engineering, 2017.
- D. Lei and X. Guo, “A shuffled frog-leaping algorithm for job shop scheduling with outsourcing options,” International Journal of Production Research, 2016.
- D. Lei, Y. Zheng, and X. Guo, “A shuffled frog- leaping algorithm for flexible job shop scheduling with the consideration of energy consumption,” International Journal of Production Research, 2016.
- D. Lei and X. Guo, “A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents,” Expert Systems with Applications, 2015.
- Abirami, T., and S. Anandamurugan. "Data aggregation in wireless sensor network using shuffled frog algorithm." Wireless Personal Communications,(2016) [24].Hussain S, Matin AW, “Islam O. Genetic algorithm for energy efficient clusters in wireless sensor networks”, in Fourth International Conference on Information Technology,2007.
- Nikokheslat, H. D., &Ghafari, “Protocol for controlling congestion in wireless sensor networks”, Wireless Personal Communications, 2017.
- Yang, X.-S. (2009). “Firefyalgorithmsfor multimodaloptimization”, In International symposium on stochastic algorithms, 2009.
- M. Jain, V. Singh, and A. Rani, “A novel nature- inspired algorithm for optimization: Squirrel search algorithm,” Swarm and Evolutionary Computation, 2018.
- Mosavvar, Islam, and Ali Ghaffari. "Data aggregation in wireless sensor networks using firefly algorithm." Wireless Personal Com
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