Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 1/2017

25.04.2017

Traffic Aware Channel Access Algorithm for Cluster Based Wireless Sensor Networks

verfasst von: Aarti Jain

Erschienen in: Wireless Personal Communications | Ausgabe 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Partition of nodes into clusters is one of the most accepted method for achieving energy efficiency and scalability in wireless sensor networks. In this paper, we have modified the Fuzzy C-Means algorithm to partition the network into clusters such as to ensure that the resulted clusters are both spatially efficient and are sharing equal data transmission load. Further in this paper, we have re-defined the medium access protocol for cluster heads. The proposed medium access protocol is dependent upon the data traffic at the Cluster heads. Cluster heads with high traffic are given preference to access the channel and cluster head(s) having low traffic are made to wait for comparatively higher back-off time. By giving more time to cluster heads with lower initial data to collect more data, energy efficiency of the system is increased and contention losses are decreased due to reduction in number of transmissions between cluster heads and sink. The proposed method has been simulated and compared with LEACH protocol, a FCM based clustering protocol and Zonal based Deterministic Energy Efficient Clustering Protocol. The simulation results show that our proposed method performs better in terms of network performance parameters viz. network lifetime, energy dissipation, throughput and packet delivery ratio.
Vorheriger Artikel Spectrum and Energy Efficient OFDM Multicarrier Modulation for an Underwater Acoustic Channel
Nächster Artikel Cost Reduction in Location Management Using Reporting Cell Planning and Particle Swarm Optimization

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRef
2.
Reinisch, C., Kastner, W., Neugschwandtner, G., & Granzer, W. (2007). Wireless technologies in home and building automation. In 2007 5th IEEE international conference on industrial informatics (Vol. 1, pp. 93–98). IEEE.
3.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.CrossRef
4.
Troubleyn, E., Moerman, I., & Demeester, P. (2013). QoS challenges in wireless sensor networked robotics. Wireless Personal Communications, 70(3), 1059–1075.CrossRef
5.
Manap, Z., Ali, B. M., Ng, C. K., Noordin, N. K., & Sali, A. (2013). A review on hierarchical routing protocols for wireless sensor networks. Wireless Personal Communications, 72(2), 1077–1104.CrossRef
6.
Lam, S. S. (1980). A carrier sense multiple access protocol for local networks. Computer Networks (1976), 4(1), 21–32.CrossRef
7.
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.CrossRef
8.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.CrossRef
9.
Dasgupta, S., & Dutta, P. (2011). An improved Leach approach for head selection strategy in a fuzzy-C means induced clustering of a wireless sensor network. In Proceedings of the IEMCON Organised by IEM in Collaboration with IEEE (pp. 203–208).
10.
Hoang, D. C., Kumar, R., & Panda, S. K. (2010). Fuzzy C-means clustering protocol for wireless sensor networks. In 2010 IEEE international symposium on industrial electronics (ISIE) (pp. 3477–3482). IEEE.
11.
Kaur, P., & Singh, R. (2015). Zonal based deterministic energy efficient clustering protocol for WSNs. International Journal of Computer Applications, 109(10), 1–5.
12.
Mhatre, V., & Rosenberg, C. (2004). Homogeneous vs heterogeneous clustered sensor networks: A comparative study. In 2004 IEEE international conference on communications (Vol. 6, pp. 3646–3651). IEEE.
13.
Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Ipdps (vol. 1, p. 189).
14.
Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Ipdps (Vol. 2, p. 48).
15.
Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.CrossRef
16.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000 (p. 10). IEEE.
17.
Handy, M. J., Haase M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Proceedings of the international workshop on mobile wireless communication networks (pp. 368–372).
18.
Ferng, H. W., Tendean, R., & Kurniawan, A. (2012). Energy-efficient routing protocol for wireless sensor networks with static clustering and dynamic structure. Wireless Personal Communications, 65(2), 347–367.CrossRef
19.
Baker, D. J., & Ephremides, A. (1981). The architectural organization of a mobile radio network via a distributed algorithm. IEEE Transactions on Communications, 29(11), 1694–1701.CrossRef
20.
Chatterjee, M., Das, S. K., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing, Special Issue on Mobile Ad hoc Networking, 5, 193–204.
21.
Cheng, H., Yang, S., & Cao, J. (2013). Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks. Expert Systems with Applications, 40, 1381–1392.CrossRef
22.
Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. Upper Saddle River, NJ: Prentice Hall.MATH
23.
Ross, T. (2004). Fuzzy logic with engineering applications (2nd ed.). Chichester: Wiley.MATH
24.
Gupta, I., Riordan, D., & Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the annual conference on communication network and services research (pp. 255–260).
25.
Kim, J., Park, S., Han, Y., & Chung, T. (2008): CHEF. Cluster head election mechanism using fuzzy logic in wireless sensor networks. In Proceedings of the international conference on advanced communication technology (pp. 654–659).
26.
Pires, A., Silva, C., Cerqueira, E., Monteiro, D., & Viegas, R. (2011). CHEATS: A cluster-head election algorithm for WSN using a Takagi-Sugeno fuzzy system. In 2011 IEEE Latin-American conference communications (LATINCOM) (pp. 1–6).
27.
Lee, J. S., Member, S., & Cheng, W. L. (2012). Fuzzy-logic-based clustering approach for energy predication. IEEE Sensors Journal, 12(9), 2891–2897.CrossRef
28.
Jain, A., & Reddy, B. R. (2015). A novel method of modeling wireless sensor network using fuzzy graph and energy efficient fuzzy based k-hop clustering algorithm. Wireless Personal Communications, 82(1), 157–181.CrossRef
29.
Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2011). A deterministic energy-efficient clustering protocol for wireless sensor networks. In 2011 Seventh international conference on intelligent sensors, sensor networks and information processing (ISSNIP) (pp. 341–346). IEEE.
30.
Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In INFOCOM 2003. Twenty-second annual joint conference of the IEEE computer and communications. IEEE Societies (Vol. 3, pp. 1713–1723). IEEE.
31.
Dasgupta, S., & Dutta, P. (2011). An improved Leach approach for head selection strategy in a fuzzy-C means induced clustering of a wireless sensor network. In Proceedings of the IEMCON organised by IEM in collaboration with IEEE (pp. 203–208).
32.
Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461.CrossRef
33.
Singh, A. K., Goutele, S., Verma, S., & Purohit, N. (2012). An energy efficient approach for clustering in WSN using fuzzy logic. International Journal of Computer Applications, 44(18), 8–12.CrossRef
34.
Hoang, D. C., Kumar, R., & Panda, S. K. (2013). Realisation of a cluster-based protocol using fuzzy C-means algorithm for wireless sensor networks. IET Wireless Sensor Systems, 3(3), 163–171.CrossRef
35.
Kone, C. T., David, M., & Lepage, F. (2010). Cluster-based multi-channel system for improving performance of large-scale wireless multi-sink sensor networks. In 2010 2nd international conference on future computer and communication (ICFCC) (Vol. 3, pp. V3–163). IEEE.
36.
Park, Y. K., Lee, M. G., Jung, K. K., Yoo, J. J., Lee, S. H., & Kim, H. S. (2011). Optimum sensor nodes deployment using fuzzy c-means algorithm. In 2011 international symposium on computer science and society (ISCCS) (pp. 389–392). IEEE.
37.
Raghuvanshi, A. S., Tiwari, S., Tripathi, R., & Kishor, N. (2012). Optimal number of clusters in wireless sensor networks: A FCM approach. International Journal of Sensor Networks, 12(1), 16–24.CrossRef
38.
Fouad, M. R., Fahmy, S., & Pandurangan, G. (2005). Latency-sensitive power control for wireless ad-hoc networks. In Proceedings of the 1st ACM international workshop on quality of service and security in wireless and mobile networks (pp. 31–38). ACM.
39.
Wang, X., & Berger, T. (2008). Spatial channel reuse in wireless sensor networks. Wireless Networks, 14(2), 133–146.CrossRef
40.
Jain, A., & Reddy, B. R. (2015). Eigenvector centrality based cluster size control in randomly deployed wireless sensor networks. Expert Systems with Applications, 42(5), 2657–2669.CrossRef
41.
Dutta, R., Gupta, S., & Das, M. K. (2014). Low-energy adaptive unequal clustering protocol using fuzzy c-means in wireless sensor networks. Wireless Personal Communications, 79(2), 1187–1209.CrossRef
42.
Förster, A., Förster, A., & Murphy, A. L. (2009). Optimal cluster sizes for wireless sensor networks: An experimental analysis. In International conference on ad hoc networks (pp. 49–63). Berlin: Springer.
43.
Ho, T. S., & Chen, K. C. (1996). Performance analysis of IEEE 802.11 CSMA/CA medium access control protocol. In Proceedings of the PIMRC (Vol. 96, pp. 407–411).
44.
Lu, G., Krishnamachari, B., & Raghavendra, C. S. (2004). Performance evaluation of the IEEE 802.15. 4 MAC for low-rate low-power wireless networks. In 2004 IEEE international conference on performance, computing, and communications (pp. 701–706). IEEE.
45.
Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. Doctoral dissertation, Massachusetts Institute of Technology.
46.
Karl, H., & Willig, A. (2005). Protocols and architectures for wireless sensor networks (pp. 36–44). Chichester: Wiley.CrossRef
Metadaten
Titel
Traffic Aware Channel Access Algorithm for Cluster Based Wireless Sensor Networks
verfasst von
Aarti Jain
Publikationsdatum
25.04.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 1/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4258-4

Weitere Artikel der Ausgabe 1/2017

Wireless Personal Communications 1/2017 Zur Ausgabe

OriginalPaper

A Demand-Based Structure for the Architecture of Wireless Networks on Chip

OriginalPaper

Hybrid STBC–SM Suitable for Multi-link Device-to-Device Communication in Cellular Networks

OriginalPaper

A Novel Grouping Slotted Aloha Scheme to Enhance Throughput Performance for Wireless Networks

OriginalPaper

Transforming the Statistical Distribution of Wireless OFDM Signal for PAPR Reduction

OriginalPaper

A 1-V 2.4 GHz Low-Power CMOS LNA Using Gain-Boosting and Derivative Superposition Techniques for WSN

Erratum

Erratum to: A 1-V 2.4 GHz Low-Power CMOS LNA Using Gain-Boosting and Derivative Superposition Techniques for WSN

Neuer Inhalt

    Bildnachweise