Advertisement

Improved BEST-MAC protocol for WSN using optimal cluster head selection

  • Rakesh KumarEmail author
  • Manju Gangwar
Original Research
First Online:
  • 6 Downloads

Abstract

Use of wireless technology increases rapidly in present days because of convenient use for its users. Wireless sensor network is one of the widely used wireless technologies. Although wireless sensor network has many advantages over other communication technologies, but still anguish from some limitations like limited battery power, control overheads, scalability, data aggregation, throughput etc. MAC layer is considered as most power consuming part over communication. Numerous MAC protocols had been designed to enhance the lifetime of wireless sensor network. In this paper, we have chosen a TDMA based MAC protocol for our proposed work. In our proposed work we try to improve existing work i.e. BEST-MAC protocol using artificial intelligence. We considered two performance parameters that are energy efficiency and throughput of wireless sensor network to improve the life span of WSN using optimal cluster head selection process. Our proposed work is implemented with MATLAB version R2012a and results are included in this paper. The results shows the energy is saved by 3.085% and throughput is improved by 51.26% in the proposed work as compared to BEST-MAC protocol.

Keywords

Wireless sensor networks (WSN) Bitmap-assisted Cluster head Control overheads Energy efficiency MAC protocol Scalability 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Akyildiz IF, Kasimoglu IH (2004) Wireless sensor and actuator networks: research challenges. Ad Hoc Netw 2(4):351–367CrossRefGoogle Scholar
  2. 2.
    Kassim MRM, Harun AN (2016) Applications of WSN in agricultural environment monitoring systems. In: IEEE international conference on information and communication technology convergence (ICTC), 19–21 Oct 2016, Jeju, South Korea, pp 344–349Google Scholar
  3. 3.
    Murillo AF, Pena M, Martinez D (2012) Application of WSN in health and agriculture. In: IEEE Colombian communications conference (COLCOM), 16–18 May 2012, Cali, Colombia, pp 1–6Google Scholar
  4. 4.
    Vuletić P (2015) Application of WSN in railway intelligent transportation system (RITS). In: IEEE 23rd telecommunications forum telfor (TELFOR), 24–26 Nov 2015, Belgrade, Serbia, pp 103–105Google Scholar
  5. 5.
    Barón JCS, Barón MJS (2014) Application of SHT71 sensor to measure humidity and temperature with a WSN. In: IEEE 9th IberoAmerican congress on sensors, 15–18 Oct 2014, Bogota, Colombia, pp 1–7Google Scholar
  6. 6.
    Kaur G, Joshi S, Kaur J, Kaur S (2014) Going driverless with sensors. Int J Sci Eng Technol 2(5):298–305Google Scholar
  7. 7.
  8. 8.
    Boulfekhar S, Bouallouche L, Benmohammed M (2012) S-TDMA: a new MAC protocol for wireless sensor networks. In: IEEE/ACM 16th international symposium of distributed simulation real time applications, 25–27 Oct 2012, Dublin, Ireland, pp 29–35Google Scholar
  9. 9.
    Shanti C, Sahoo A (2010) DGRAM: a delay guaranteed routing and MAC protocol for wireless sensor networks. IEEE Commun Mob Comput 9(10):1407–1423Google Scholar
  10. 10.
    Shafiullah GM, Azad SA, Ali ABMS (2013) Energy-efficient wireless MAC protocols for railway monitoring applications. IEEE Trans Intell Transp Syst 14(2):649–659CrossRefGoogle Scholar
  11. 11.
    Sazak N, Erturk I, Koklukaya E, Cakiroglu M (2011) Impact of active node determination approach for energy efficiency in WSN MAC protocol design. In: International conference on Ultra modern telecommunications control systems and workshops, 5–7 Oct 2011, Budapest, Hungary, pp 1–5Google Scholar
  12. 12.
    Li J, Lazarou GY (2004) A bit-map-assisted energy-efficient MAC scheme for wireless sensor networks. In: International symposium of information processing in sensor networks, 27–27 April 2004, Berkeley, CA, USA, pp 55–60Google Scholar
  13. 13.
    Hsu T-H, Yen P-Y (2011) Adaptive time division multiple access-based medium access control protocol for energy conserving and data transmission in wireless sensor networks. IET Commun 5(18):2662–2672CrossRefGoogle Scholar
  14. 14.
    Jiang X, Du DHC (2016) PTMAC: a prediction-based TDMA MAC protocol for reducing packet collisions in VANET. IEEE Trans Veh Technol 65:9209–9223CrossRefGoogle Scholar
  15. 15.
    Philipose A, Rajesh A (2015) Performance analysis of an improved energy aware MAC protocol for railway systems. In: IEEE international conference on electronics and communication system, 26–27 Feb 2015, Coimbatore, India, pp 233–236Google Scholar
  16. 16.
    Balobaid A (2016) A survey and Comparative study on different energy efficient MAC-Protocols for Wireless Sensor Networks. In: IEEE international conference on internet of things and applications, 22–24 Jan 2016, Pune, India, pp 321–325Google Scholar
  17. 17.
    Liu K, Shan W, Huang B, Liu F, Zhen X (2016) A Power-optimized cooperative MAC protocol for lifetime extension in wireless sensor networks. NCBI J 16:1–28Google Scholar
  18. 18.
    Usha NS, Hossen M, Saha S (2017) Efficient duty cycle management for reduction of energy consumption in wireless sensor network. In: International conference on electrical & electronic engineering, 27–29 Dec. 2017 , Rajshahi, Bangladesh, pp 1–4Google Scholar
  19. 19.
    Quintero V, Estevez C, Orchard M, Pérez A (2018) Improvements of energy-efficient techniques in WSNs: a MAC-protocol approach. IEEE Commun Surv Tutor 21:1188–1208CrossRefGoogle Scholar
  20. 20.
    Pegatoquet A, Le TN, Magno M (2019) A wake-up radio-based MAC protocol for autonomous wireless sensor networks. IEEE/ACM Trans Netw 27:56–70CrossRefGoogle Scholar
  21. 21.
    Dattatraya KN, Rao KR (2019) Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. J King Saud Univ Comput Inf Sci 27(1):56–70Google Scholar
  22. 22.
    Alvi AN, Bouk SH, Ahmed SH, Yaqub MA, Sarkar M, Song H (2016) BEST-MAC: bitmap-assisted efficient and scalable TDMA-based WSN MAC protocol for smart cities. IEEE Access 4:312–322CrossRefGoogle Scholar
  23. 23.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nat Intern Wkly J Sci 5:533–536zbMATHGoogle Scholar
  24. 24.
    Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technical Teachers Training and ResearchChandigarhIndia

Personalised recommendations

Cite article

Buy options