Energy Budget Based Multiple Attribute Decision Making (EB-MADM) Algorithm for Cooperative Clustering in Wireless Body Area Networks

  • Amit ChoudharyEmail author
  • M. Nizamuddin
  • Manish Kumar Singh
  • Vibhav Kumar Sachan
Original Article



A wireless network of biosensor nodes, attached to different parts of the patient body, is termed as Wireless Body Area Network (WBAN). WBANs offer a real-time data monitoring platform for biological health parameters like blood pressure, heart rate, and glucose level, etc.


Low-power consumption is an essential WBAN design requirement due to limited power resources of biosensor nodes.


Present work proposes the design of low power, clustering based data routing protocol for WBANs. Proposed protocol incorporates a novel “Energy Budget based Multiple Attributes Decision Making Algorithm (EB-MADM)” for dynamic cluster head selection. The algorithm selects an optimum node as cluster head which has the higher residual energy level and performs data routing at the cost of least network residual energy loss. EB-MADM selects a new cluster head for each transmission round and distributes cluster head load evenly among cluster nodes. It results in enhanced network lifetime. Proposed protocol incorporates another low power technique termed as “Cooperative effort of cluster nodes”. This technique saves node transmission energy by prohibiting redundant data from transmission.


The proposed protocol is simulated using MATLAB tool and the performance results are compared with existing WBAN protocols.


Proposed protocol shows better performance in terms of network lifetime, stability period, throughput, and propagation delay.


Wireless body area network Routing protocol Clustering Network lifetime Residual energy Throughput 


  1. 1.
    Wu T, Wu F, Redouté JM, Yuce MR (2017) An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access 5:11413–11422. CrossRefGoogle Scholar
  2. 2.
    Liu J, Sohn J, Kim S (2017) Classification of daily activities for the elderly using wearable sensors. J Healthc Eng 2017:7. Article ID 8934816.
  3. 3.
    Imam SA, Choudhary A, Sachan VK (2015) Design issues for wireless sensor networks and smart humidity sensors for precision agriculture: a review. In: Proceedings of the 2015 IEEE international conference on so computing techniques and implementations (ICSCTI), Faridabad, India, 2015, pp 181–187.
  4. 4.
    Kumar A et al (2015) A Zigbee-based animal health monitoring system. IEEE Sens J 15(1):61–617CrossRefGoogle Scholar
  5. 5.
    Challoo R et al (2012) An overview and assessment of wireless technologies and co-existence of ZigBee, Bluetooth and Wi-Fi devices. Elsevier Proc Comput r Sci 12:386–391CrossRefGoogle Scholar
  6. 6.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422. ISSN 1389-1286Google Scholar
  7. 7.
    Kone CT, Hafid A, Boushaba M (2015) Performance management of IEEE 802.15.4 wireless sensor network for precision agriculture. IEEE Sens J 15(10):5734–5747CrossRefGoogle Scholar
  8. 8.
    Sachan VK, Imam SA, Beg MT (2012) Energy-efficient communication methods in wireless sensor networks: a critical review. Int J Comput Appl 39(17). ISSN 0975-8887Google Scholar
  9. 9.
    Kaur N, Singh S (2017) Optimized cost effective and energy efficient routing protocol for wireless body area networks. Ad Hoc Netw. Google Scholar
  10. 10.
    Cavallari R, Martelli F, Rosini R, Buratti C, Verdone R (2014) A survey on wireless body area networks: technologies and design challenges. In: IEEE communications surveys and tutorials, accepted for publication, pp 1–23Google Scholar
  11. 11.
    El Azhari M, El Moussaid N, Toumanari A, Latif R (2017) Equalized energy consumption in wireless body area networks for a prolonged network lifetime. Hindawi Wirel Commun Mobile Comput 2017:9. Article ID 4157858.
  12. 12.
    Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaii international conference on system sciences-(HICSS ‘00), Washington DC, USA, 2000, vol 2, pp 10. IEEE Computer Society.
  13. 13.
    Smaragdakis G, Matta I, Bestavros A (2004) SEP: a stable election protocol for clustered heterogeneous wireless sensor networks. In: Proceedings of the second international workshop on sensor and actor network protocols and applications (SANPA 2004), Boston, MA, August 2004Google Scholar
  14. 14.
    Yahiaoui S, Omar M, Bouabdallah A, Natalizio E, Challal Y (2017) An energy efficient and QoS aware routing protocol for wireless sensor and actuator networks. Int J Electron Commun. Google Scholar
  15. 15.
    Qing L, Zhu Q, Wang M (2006) Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput Commun 29(12):2230–2237CrossRefGoogle Scholar
  16. 16.
    Sabet M, Naji HR (2015) A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. Int J Electron Commun (AEÜ). Google Scholar
  17. 17.
    Azad P, Sharma V (2013) Cluster head selection in wireless sensor networks under fuzzy environment. In: Hindawi ISRN sensor networks, vol 2013, Article ID 909086, p 8.
  18. 18.
    Ullah Z, Ahmed I, Razzaq K, Naseer MK, Ahmed N (2017) DSCB: dual sink approach using clustering in body area network. Peer-to-Peer Netw Appl. Google Scholar
  19. 19.
    Nadeem Q, Javaid N, Mohammad SN, Khan MY, Sarfraz S, Gull M (2013) SIMPLE: stable increased-throughput multi-hop protocol for link efficiency in wireless body area networks. In: Proceedings of the 2013 eighth international conference on broadband and wireless computing, communication and applications, Compiegne, Compiegne, France, 2013, pp 221–226.
  20. 20.
    Ahmed S, Javaid N, Akbar M, Iqbal A, Khan Z, Qasim U (2014) LAEEBA: link aware and energy efficient scheme for body area networks. In: Proceedings of the IEEE international conference on advanced information networking and applications (AINA), Victoria, BC, Canada, pp 435–440.
  21. 21.
    Javaid N, Ahmad A, Nadeem Q, Imran M, Haider N (2018) iM-SIMPLE: iMproved stable increased-throughput multi-hop link efficient routing protocol for Wireless Body Area Networks. Comput Human Behav (Article in press) Google Scholar
  22. 22.
    Kumar D, Aseri TC, Patel RB (2009) EEHC: energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput Commun 32(4):662–667CrossRefGoogle Scholar
  23. 23.
    Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mobile Comput 3(4):366–379. CrossRefGoogle Scholar
  24. 24.
    Javaid N, Abbas Z, Fareed MS, Khan ZA, Alrajeh N (2013) M-ATTEMPT: a new energy-efficient routing protocol for wireless body area sensor networks. In: Elsevier procedia computer science, vol 19, pp 224–231. ISSN 1877-0509Google Scholar
  25. 25.
    Singh S, Negi S, Uniyal A, Verma S (2016) Modified new-attempt routing protocol for wireless body area network. In: Proceedings of the 2nd IEEE international conference on advances in computing, communication, and automation (ICACCA), Bareilly, India, 2016, pp 1–5.
  26. 26.
    Katayama N, Takizawa K, Aoyagi T, Takada J, Li H, Kohno R (2008) Channel model on various frequency bands for wearable body area network. In: Proceedings of the first international symposium on applied sciences on biomedical and communication technologies, Aalborg, 2008, pp 1–5.

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Amit Choudhary
    • 1
    Email author
  • M. Nizamuddin
    • 1
  • Manish Kumar Singh
    • 2
  • Vibhav Kumar Sachan
    • 2
  1. 1.Department of Electronics and Communication EngineeringJamia Millia Islamia-A Central UniversityNew DelhiIndia
  2. 2.Department of Electronics and Communication EngineeringKIET Group of InstitutionsGhaziabadIndia

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