Abstract
Energy efficient routing in wireless sensor networks (WSNs) has been studied widely to enhance the network performance. Various nature-inspired routing mechanisms are proposed to achieve scalable solutions. However, conventional nature-inspired optimization algorithms are insufficient to solve discrete routing optimization problems. In this study, a new discrete particle swarm optimization algorithm (PSO) based routing protocol is designed to achieve better performance. In the new protocol, firstly, two new fitness functions with energy awareness for clustering and routing are formulated respectively. Secondly, a novel greedy discrete PSO with memory (GMDPSO) is put forward to build optimal routing tree. In GMDPSO, particle’s position and velocity are redefined under a discrete scenario; particle update rules are reconsidered based on the network topology; a greedy search strategy is designed to drive particles to find better positon quickly. Besides, searching histories are memorized to accelerate convergence. Simulations results show the efficiency and effectiveness of the new protocol.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Alkhatib AA (2012) Mac layer overview for wireless sensor networks. In: International Conference on Computer Networks and Communication Systems
Amgoth T, Jana PK (2015) Energy-aware routing algorithm for wireless sensor networks. Comput Electr Eng 41:357–367
Attea BA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl Soft Comput 12(7):1950–1957
Azharuddin M, Kuila P (2015) Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Comput Electr Eng 41:177–190
Bin G, Victor SS et al (2015) Incremental learning for v-support vector regression. Neural Netw 67:140–150
Chu X, Sethu H (2015) Cooperative topology control with adaptation for improved lifetime in wireless sensor networks. Ad Hoc Netw 30:99–114
Deng X, He L, Zhu C, Dong M, Ota K, Cai L (2016) Qos-aware and load-balance routing for ieee 802.11s based neighborhood area network in smart grid. Wirel Person Commun 89(4):1065–1088
Dorigo M, Birattari M (2010) Ant colony optimization. Springer US, Boston, pp 36–39. doi:10.1007/978-0-387-30164-8_22
Elhabyan Riham RSY, Yagoub MCE (2015) Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J Netw Comput Appl 52:116–128
Gu B, Sheng VS (2016) A robust regularization path algorithm for v-support vector classification. IEEE Trans Neural Netw Learn Syst 99:1–8. doi:10.1109/TNNLS.2016.2527796 (ISSN 2162-237X)
Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst 99:1–11. doi:10.1109/TNNLS.2016.2544779 (ISSN 2162-237X)
Guo W, Wei Z (2014) A survey on intelligent routing protocols in wireless sensor networks. J Netw Comput Appl 38:185–201
Guo Z, Wu Z, Dong X, Zhang K, Wang SN, Li Y (2014) Component thermodynamical selection based gene expression programming for function finding. Math Probl Eng 2:1–16
Guo Z, Liu G, Li D, Wang S (2016a) Self-adaptive differential evolution with global neighborhood search. Soft Comput 1–10
Guo Z, Yue X, Yang HN, Liu K, Liu X (2016b) Enhancing social emotional optimization algorithm using local search. Soft Comput 1–12
Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670. doi:10.1109/TWC.2002.804190 (ISSN 1536-1276)
Khalil EA, Bara’a AA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evolut Comput 1(4):195–203
Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007
Kuila P, Jana PK (2014) Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng Appl Artif Intell 33(1):127–140
Kumar D (2014) Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wirel Sensor Syst 4(1):9–16. doi:10.1049/iet-wss.2012.0150 (ISSN 2043-6386)
Long J, Liu A, Dong M, Li Z (2015) An energy-efficient and sink-location privacy enhanced scheme for wsns through ring based routing. J Parallel Distrib Comput 81–82:47–65
Ming T, Yuan H, Wei W, Qu C (2013) Feature-aware cooperative relaying for multiflow wireless sensor networks. Int J Distrib Sensor Netw 2:1–7
Rahmanian A, Omranpour H, Akbari M, Raahemifar K (2011) A novel genetic algorithm in leach-c routing protocol for sensor networks. In: 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE), pp 001096–001100. doi:10.1109/CCECE.2011.6030631
Rani S, Malhotra J, Talwar R (2015) Energy efficient chain based cooperative routing protocol for wsn. Appl Soft Comput 35:386–397
Rault T, Bouabdallah A, Challal Y (2014) Energy efficiency in wireless sensor networks: a top–down survey. Comput Netw 67:104–122
Senouci M, Mellouk A, Senouci H, Aissani A (2012) Performance evaluation of network lifetime spatial–temporal distribution for wsn routing protocols. J Netw Comput Appl 35(35):1317–1328
Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178
Smaragdakis G, Matta I, Bestavros A (2004) Sep: a stable election protocol for clustered heterogeneous wireless sensor networks. In: Proceeding of 2nd International Workshop on Sensor and Actor Network Protocol and Applications (SANPA)
Tang D, Cai Y, Zhao J, Xue Y (2014) A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems. Inf Sci 289(24):162–189
Tao M, Lu D, Yang J (2012) An adaptive energy-aware multi-path routing protocol with load balance for wireless sensor networks. Wirel Person Commun 63(4):823–846
Tao M, Yuan H, Wei W, Li Z (2013) Geographic information assisted routing flexibility control in hierarchical ad hoc networks. In: 32nd Chinese Control Conference(CCC), pp 2190–2194
Thakkar A, Kotecha K (2014) Cluster head election for energy and delay constraint applications of wireless sensor network. Sens J IEEE 14(8):2658–2664
Thakkar A, Kotecha K (2015) A new bollinger band based energy efficient routing for clustered wireless sensor network. Appl Soft Comput 32:144–153
Tyagi S, Gupta SK, Tanwar S, Kumar N (2013) Ehe-leach: enhanced heterogeneous leach protocol for lifetime enhancement of wireless sns. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp 1485–1490. doi:10.1109/ICACCI.2013.6637399
Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345
Xu J, Wei L, Lang F, Zhang Y, Wang C (2010) Distance measurement model based on rssi in wsn. Wirel Sens Netw 2(8):606–611
Xu X, Ansari R, Khokhar A, Vasilakos AV (2015) Hierarchical data aggregation using compressive sensing (hdacs) in wsns. ACM Trans Sen Netw 11(3):45:1–45:25. doi:10.1145/2700264 (ISSN 1550-4859)
Xuezhi W, Ling S, Yu X, Wei F (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406
Yang J, Liu F, Cao J, Wang L (2016) Discrete particle swarm optimization routing protocol for wireless sensor networks with multiple mobile sinks. Sensors 16(7):1081
Song Y, Gui C, Lu X, Chen H, Sun B (2015) A genetic algorithm for energy-efficient based multipath routing in wireless sensor networks. Wirel Person Commun 1–12
Yongjun R, Jian S, Jin W, Jin H, Sungyoung L (2015) Mutual verifiable provable data auditing in public cloud storage. J Internet Technol 16(2):317–323
Younis O, Fahmy S (2004) Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379. doi:10.1109/TMC.2004.41
Zambrano-Bigiarini M, Clerc M, Rojas R (2013) Standard particle swarm optimisation 2011 at cec-2013: a baseline for future pso improvements. In: 2013 IEEE Congress on Evolutionary Computation, pp 2337–2344. doi:10.1109/CEC.2013.6557848
Acknowledgements
The authors would like to thank the anonymous reviewers and guest editor for their valuable reviews that are very useful for the improvement of quality of this paper. This work was partially supported by the Engineering and Technology Research Center of Guangdong Province for Logistics Supply Chain and Internet of Things (No. GDDST[2016]176); Engineering and Technology Research Center of Guangdong Province for Big Data Intelligent Processing under Grant no. GDDST[2013]1513-1-11; the Natural Science Foudation of Guangdong Province, under Grant No. 2014A030313585; Guang Dong Provincial Natural fund project, under Grant No. 2016A030310300.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, J., Liu, F. & cao, J. Greedy discrete particle swarm optimization based routing protocol for cluster-based wireless sensor networks. J Ambient Intell Human Comput 15, 1277–1292 (2024). https://doi.org/10.1007/s12652-017-0515-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-017-0515-3