Skip to main content
Top
Published in: Microsystem Technologies 3/2017

30-05-2016 | Technical Paper

Enhancing data delivery with density controlled clustering in wireless sensor networks

Published in: Microsystem Technologies | Issue 3/2017

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Wireless sensor networks (WSN) are primarily used for sensing and collecting the information from environment. This information is sent to base station (BS), where, it is processed and analyzed by the underlying application. Functioning of WSN highly depend on the deployment strategy and the coordination among the sensor nodes. Preserving the energy is an important goal that must be considered when developing a routing protocol for WSNs. Radio communication in sensor nodes is highly expensive operation in terms of energy usage. Energy can be conserved in an efficient way with specifically customized routing techniques. Data aggregation methods when applied to sensor nodes, clusters are created where data generated from cluster members is aggregated. This kind of data collection strategy results into energy efficient communication. Integration of data aggregation and clustering approach is enabled through customized hierarchical routing strategies. Clustering technique is key to apply and exploit, the advantages in-network data processing offers. This paper compares various clustering protocols like LEACHC, K-means and its variants. The protocols are compared with respect to network lifetime, data delivery and energy consumption. These protocols only consider intra cluster distance of members while doing clustering, due to this non-uniform clusters are created. These kind of clusters do not deliver data periodically and uniformly from every corner of the field. A density control based approach is proposed which does balancing of cluster members assignment in a loose way. A loose density control (LDC) based approach is integrated with K-means clustering method. LDC based approach exhibits improved average data delivery per node, ensuring regular data availability from every corner of the deployment area as long as the nodes are alive. Not only it improves the amount of data delivered, it also improves the network lifetime considerably. This performance improvement is achieved with almost similar energy expenditure compared to other protocols.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
go back to reference Attea AA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12(7):1950–1957, 2012. ISSN 1568-4946. Soft Computing Approaches in the design of energy-efficient wireless systems Attea AA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12(7):1950–1957, 2012. ISSN 1568-4946. Soft Computing Approaches in the design of energy-efficient wireless systems
go back to reference Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(23):191–203CrossRef Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(23):191–203CrossRef
go back to reference Singh B, Lobiyal DK (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Hum Centric Comput Inf Sci 2(1):1–18CrossRef Singh B, Lobiyal DK (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Hum Centric Comput Inf Sci 2(1):1–18CrossRef
go back to reference Camilo T, Silva JS, Rodrigues A, Boavida F (2007) Gensen: a topology generator for real wireless sensor networks deployment. In: Software technologies for embedded and ubiquitous systems, Springer, pp 436–445 Camilo T, Silva JS, Rodrigues A, Boavida F (2007) Gensen: a topology generator for real wireless sensor networks deployment. In: Software technologies for embedded and ubiquitous systems, Springer, pp 436–445
go back to reference El-Din AE, Ramadan RA, Fayek MB (2014) Fuzzy-based clustering and data aggregation for multimodal wsn (c-damm). Int J Robot Autom 29 (3) El-Din AE, Ramadan RA, Fayek MB (2014) Fuzzy-based clustering and data aggregation for multimodal wsn (c-damm). Int J Robot Autom 29 (3)
go back to reference Feng CH, Heinzelman WB (2009) Rbmulticast: Receiver based multicast for wireless sensor networks. In Wireless Communications and Networking Conference, 2009. WCNC 2009. IEEE, pp 1–6 Feng CH, Heinzelman WB (2009) Rbmulticast: Receiver based multicast for wireless sensor networks. In Wireless Communications and Networking Conference, 2009. WCNC 2009. IEEE, pp 1–6
go back to reference Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRef Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRef
go back to reference Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRef Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRef
go back to reference Hoang DC, Yadav P, Kumar R, Panda SK (2014) Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Trans Ind Inf 10 (1):774–783CrossRef Hoang DC, Yadav P, Kumar R, Panda SK (2014) Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Trans Ind Inf 10 (1):774–783CrossRef
go back to reference Hoang DC, Kumar R, Panda SK (2013) Realisation of a cluster-based protocol using fuzzy c-means algorithm for wireless sensor networks. IET Wirel Sens Syst 3(3): 163–171 Hoang DC, Kumar R, Panda SK (2013) Realisation of a cluster-based protocol using fuzzy c-means algorithm for wireless sensor networks. IET Wirel Sens Syst 3(3): 163–171
go back to reference Hoang DC, Yadav P, Kumar R, Panda SK (2010a) A robust harmony search algorithm based clustering protocol for wireless sensor networks. In Communications Workshops (ICC), 2010 IEEE International Conference on, pp 1–5 Hoang DC, Yadav P, Kumar R, Panda SK (2010a) A robust harmony search algorithm based clustering protocol for wireless sensor networks. In Communications Workshops (ICC), 2010 IEEE International Conference on, pp 1–5
go back to reference Hoang DC, Kumar R, Panda SK (2010b) Fuzzy c-means clustering protocol for wireless sensor networks. In Industrial Electronics (ISIE), 2010 IEEE International Symposium on, pp 3477–3482 Hoang DC, Kumar R, Panda SK  (2010b) Fuzzy c-means clustering protocol for wireless sensor networks. In Industrial Electronics (ISIE), 2010 IEEE International Symposium on, pp 3477–3482
go back to reference Kannan AA, Mao G, Vucetic B (2006) Simulated annealing based wireless sensor network localization. J Comput 1(2):15–22CrossRef Kannan AA, Mao G, Vucetic B (2006) Simulated annealing based wireless sensor network localization. J Comput 1(2):15–22CrossRef
go back to reference Karp B, Kung HT (2000) Gpsr: Greedy perimeter stateless routing for wireless networks. In: Proceedings of the 6th annual international conference on Mobile computing and networking, ACM, pp 243–254 Karp B, Kung HT (2000) Gpsr: Greedy perimeter stateless routing for wireless networks. In: Proceedings of the 6th annual international conference on Mobile computing and networking, ACM, pp 243–254
go back to reference Kennedy J, Eberhart R (1995) Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pp 1942–1948 vol.4. doi:10.1109/ICNN.1995.488968 Kennedy J, Eberhart R (1995) Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pp 1942–1948 vol.4. doi:10.​1109/​ICNN.​1995.​488968
go back to reference Khalil EA, Bara AA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation, 1(4):195–203. ISSN 2210-6502 Khalil EA, Bara AA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation, 1(4):195–203. ISSN 2210-6502
go back to reference Kulkarni RV, Förster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. Commun Surveys Tutorials IEEE 13 (1):68–96 Kulkarni RV, Förster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. Commun Surveys Tutorials IEEE 13 (1):68–96
go back to reference Kumarawadu P, Dechene DJ, Luccini M, Sauer A (2008) Algorithms for node clustering in wireless sensor networks: a survey. In: Information and automation for sustainability. ICIAFS 2008. 4th International Conference on IEEE, pp 295–300 Kumarawadu P, Dechene DJ, Luccini M, Sauer A (2008) Algorithms for node clustering in wireless sensor networks: a survey. In: Information and automation for sustainability. ICIAFS 2008. 4th International Conference on IEEE, pp 295–300
go back to reference Manjarres D, Landa-Torres I, Gil-Lopez S, Del Ser J, Bilbao MN, Salcedo-Sanz S, Geem ZW (2013) A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence 26(8):1818–1831CrossRef Manjarres D, Landa-Torres I, Gil-Lopez S, Del Ser J, Bilbao MN, Salcedo-Sanz S, Geem ZW (2013) A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence 26(8):1818–1831CrossRef
go back to reference Manjula SH , Reddy EB , Shaila K , Nalini L , Venugopal KR, Patnaik LM (2008) Base-station controlled clustering scheme in wireless sensor networks. In Wireless Days, 2008. WD’08. 1st IFIP, pp 1–5 Manjula SH , Reddy EB , Shaila K , Nalini L , Venugopal KR, Patnaik LM (2008) Base-station controlled clustering scheme in wireless sensor networks. In Wireless Days, 2008. WD’08. 1st IFIP, pp 1–5
go back to reference Medhat F, Ramadan RA, Talkhan I (2012) Smart clustering for multimodal wsns. In: Proceedings of the 2012 Seventh International Conference on Broadband, Wireless Computing, Communication and Applications, IEEE Computer Society, pp 367–372 Medhat F, Ramadan RA, Talkhan I (2012) Smart clustering for multimodal wsns. In: Proceedings of the 2012 Seventh International Conference on Broadband, Wireless Computing, Communication and Applications, IEEE Computer Society, pp 367–372
go back to reference Montalvo I, Izquierdo J, Iglesias PL (2008) A diversity-enriched variant of discrete pso applied to the design of water distribution networks. Eng Optim 40(7):655–668CrossRef Montalvo I, Izquierdo J, Iglesias PL (2008) A diversity-enriched variant of discrete pso applied to the design of water distribution networks. Eng Optim 40(7):655–668CrossRef
go back to reference Muruganathan SD, Ma DC, Bhasin RI, Fapojuwo A (2005) A centralized energy-efficient routing protocol for wireless sensor networks. Communications Magazine IEEE 43(3): S8–13 Muruganathan SD, Ma DC, Bhasin RI, Fapojuwo A (2005) A centralized energy-efficient routing protocol for wireless sensor networks. Communications Magazine IEEE 43(3): S8–13
go back to reference Naeimi S, Ghafghazi H, Chow CO, Ishii H (2012) A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors 12(6):7350–7409CrossRef Naeimi S, Ghafghazi H, Chow CO, Ishii H (2012) A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors 12(6):7350–7409CrossRef
go back to reference Raghuvanshi AS, Tiwari S, Tripathi R, Kishor N (2012) Optimal number of clusters in wireless sensor networks: a fcm approach. Int J Sens Netw 12(1):16–24CrossRef Raghuvanshi AS, Tiwari S, Tripathi R, Kishor N (2012) Optimal number of clusters in wireless sensor networks: a fcm approach. Int J Sens Netw 12(1):16–24CrossRef
go back to reference Raghuvanshi AS, Tiwari S, Tripathi R, Kishor N (2010) Optimal number of clusters in wireless sensor networks: an fcm approach. In: Computer and communication technology (ICCCT), 2010 International Conference on, pp 817–823 Raghuvanshi AS, Tiwari S, Tripathi R, Kishor N (2010) Optimal number of clusters in wireless sensor networks: an fcm approach. In: Computer and communication technology (ICCCT), 2010 International Conference on, pp 817–823
go back to reference Raval G, Bhavsar M (2015) : Improving energy estimation based clustering with energy threshold for wireless sensor networks. Int J Comput Appl 113(19):41–47 Raval G, Bhavsar M (2015) : Improving energy estimation based clustering with energy threshold for wireless sensor networks. Int J Comput Appl 113(19):41–47
go back to reference Raval G, Bhavsar M, Patel N (2015) Analyzing the performance of centralized clustering techniques for realistic wireless sensor network topologies. Procedia Computer Science 57:1026–1035, 2015. ISSN 1877-0509. 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015) Raval G, Bhavsar M, Patel N (2015) Analyzing the performance of centralized clustering techniques for realistic wireless sensor network topologies. Procedia Computer Science 57:1026–1035, 2015. ISSN 1877-0509. 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015)
go back to reference Sanchez JA, Ruiz PM, Stojmenovic I (2006) Gmr: Geographic multicast routing for wireless sensor networks. In Sensor and Ad Hoc Communications and Networks, 2006. SECON’06. 2006 3rd Annual IEEE Communications Society on, volume 1, pp 20–29 Sanchez JA, Ruiz PM, Stojmenovic I (2006) Gmr: Geographic multicast routing for wireless sensor networks. In Sensor and Ad Hoc Communications and Networks, 2006. SECON’06. 2006 3rd Annual IEEE Communications Society on, volume 1, pp 20–29
go back to reference Sasikumar P , Sibaram Khara (2012) K-means clustering in wireless sensor networks. In: Computational intelligence and communication networks (CICN), 2012 Fourth International Conference on IEEE, pp 140–144 Sasikumar P , Sibaram Khara (2012) K-means clustering in wireless sensor networks. In: Computational intelligence and communication networks (CICN), 2012 Fourth International Conference on IEEE, pp 140–144
go back to reference Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid hsa and pso algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary Computation, Article in Press. ISSN 2210-6502 Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid hsa and pso algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary Computation, Article in Press. ISSN 2210-6502
go back to reference Singh AK, Purohit N, Varma S (2013) Fuzzy logic based clustering in wireless sensor networks: a survey. Int J Electron 100(1):126–141CrossRef Singh AK, Purohit N, Varma S (2013) Fuzzy logic based clustering in wireless sensor networks: a survey. Int J Electron 100(1):126–141CrossRef
go back to reference Murata T, Ishibuchi H (1994) Performance evaluation of genetic algorithms for flowshop scheduling problems. In Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on, vol.2, pp 812–817 Murata T, Ishibuchi H (1994) Performance evaluation of genetic algorithms for flowshop scheduling problems. In Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on, vol.2, pp 812–817
go back to reference Tan L, Gong Y, Chen G (2008) A balanced parallel clustering protocol for wireless sensor networks using k-means techniques. In: Sensor technologies and applications. SENSORCOMM’08. Second International Conference on IEEE, pp 300–305 Tan L, Gong Y, Chen G (2008) A balanced parallel clustering protocol for wireless sensor networks using k-means techniques. In: Sensor technologies and applications. SENSORCOMM’08. Second International Conference on IEEE, pp 300–305
go back to reference Thilagavathi S, Gnanasambandan Geetha B (2015) Energy aware swarm optimization with intercluster search for wireless sensor network. Sci World J Thilagavathi S, Gnanasambandan Geetha B (2015) Energy aware swarm optimization with intercluster search for wireless sensor network. Sci World J
go back to reference Transier M, Füßler H, Widmer J, Mauve M, Effelsberg W (2004) Scalable position-based multicast for mobile ad-hoc networks. In BroadWim, number LCA-CONF-2004-026 Transier M, Füßler H, Widmer J, Mauve M, Effelsberg W (2004) Scalable position-based multicast for mobile ad-hoc networks. In BroadWim, number LCA-CONF-2004-026
go back to reference Tyagi S, Kumar N (2013) A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks. J Netw Comput Appl 36(2):623–645CrossRef Tyagi S, Kumar N (2013) A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks. J Netw Comput Appl 36(2):623–645CrossRef
go back to reference Wendi Beth Heinzelman (2000) Application-specific protocol architectures for wireless networks. PhD thesis, Massachusetts Institute of Technology Wendi Beth Heinzelman (2000) Application-specific protocol architectures for wireless networks. PhD thesis, Massachusetts Institute of Technology
go back to reference Xu Y, Heidemann J, Estrin D (2001) Geography-informed energy conservation for ad hoc routing. In: Proceedings of the 7th annual international conference on Mobile computing and networking, ACM, pp 70–84 Xu Y, Heidemann J, Estrin D (2001) Geography-informed energy conservation for ad hoc routing. In: Proceedings of the 7th annual international conference on Mobile computing and networking, ACM, pp 70–84
Metadata
Title
Enhancing data delivery with density controlled clustering in wireless sensor networks
Publication date
30-05-2016
Published in
Microsystem Technologies / Issue 3/2017
Print ISSN: 0946-7076
Electronic ISSN: 1432-1858
DOI
https://doi.org/10.1007/s00542-016-2990-4

Other articles of this Issue 3/2017

Microsystem Technologies 3/2017 Go to the issue