Document Type : Original Research Paper


1 Islamic Azad University North Tehran Branch

2 Department of electrical and computer engineering ,sciences and reseach branch Tehran Iran


Body area networks is one of the types of wireless area networks which has been created to optimize utilizing hospital resources and for earlier diagnosis of medical symptoms, and ultimately to reduce the cost of medical care. This network like most of the wireless networks is without infrastructure and the embedded sensor nodes in the body have limited energy. Hence, the early power completion of the wireless nodes based on the transmission of messages in the network can disrupt the entire network. In this study, a fuzzy clustering based routing is presented to overcome the mention challenge. In this method, the sensor nodes are allocated to the nearest cluster, based on their distance from the cluster head node, and exchange information with the cluster-head at the near distances, and the cluster-head node, due to its high initial energy, can transmit data to the remote server. In this study, due to the movement of the person and the position shift in the sensor nodes and the distances between the cluster-head nodes, sensor nodes belonging to the clusters are also updated and placed in their proper cluster and the transmission of sensory messages was done with its nearest cluster- head node. Simulation results show that the proposed method can be better than other existing methods in and equal condition.


Main Subjects

[1]     Gonzalez, S., Vasilakos, A., Cao, H., Victor, C. and Leung, M., 2011, Body Area Networks : A Survey.  Mobile Network Applications,16, pp.171–193.
[2]     Challal, Y., Heudiasyc, A. and Bouabdallah F., 2014, Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, pp.104–122.  
[3]     Cavallari, R., Buratti, C., 2014, A Survey on Wireless Body Area Networks: Technologies and Design Challenges. IEEE COMMUNICATIONS SURVEYS & TUTORIALS, 16, Issue 3, pp. 1635 - 1657.
[4]     Noorzaie, I., 2006, Survey paper: Medical applications of wireless networks , Doi:
[5]     Huang, X. and Fang Y., 2008, Multiconstrained QoS multipath routing in wireless sensor networks. Wireless Networks,14, pp.465–478.
[6]     Bangash, J.l., Abdullah, A. H.,  Anisi, M. H. and Waheed Khan A., 2014, A Survey of Routing Protocols in Wireless Body Sensor Networks, Sensors, 14, pp.1322-1357.
[7]      Aslam Khan, Z., Sivakumar, N. and Phillips, S., 2012, Energy-aware peering routing protocol for indoor hospital body area network communication. Procedia Computer Scirnce, 10, pp.188–196.38.
[8]      Culpepper, B.J., Dung, L. and Moh, M., 2005, Design and analysis of Hybrid Indirect Transmissions (HIT) for data gathering in wireless micro sensor networks. ACM SIGMOBILE Mobile Comput.
[9]     Tanvir, Md., Ishtaique, ul., Kumudu, H., Munasinghe, S. and Abolhasan, M., 2013, EAR-BAN: Energy efficient adaptive routing in Wireless Body Area Networks. In 7th International Conference on Signal Processing and Communication Systems (ICSPCS2013 ), Pages: 1 - 10.
[10]    Alim, Md. A., Wu, Y. and Wang, W., 2013, A Fuzzy Based Clustering Protocol for Energy-efficient Wireless Sensor Networks. In Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), pp.2874-2878.
[11]    Javaid, N., Abbas, Z., Fareed, M. S., Khan, Z. A. and Alrajeh, N., M-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless Body Area Sensor Networks, IN The 4th International Conference on Ambient Systems, Networks and Technologies
(ANT 2013), Procedia Computer Science, vol.19, pp.224 – 231.
[12]    ul Huque, M. T. I., Munasinghe, K. S., Abolhasan, M. and A. Jamalipour, 2013, SEA-BAN:  Semi-Autonomous  Adaptive  Routing  in  Wireless  Body  Area  Networks.  In 7th International Conference on Signal Processing and Communication Systems (ICSPCS), 2013.
[13]    Ning, T. P., Michael, S. and K. Vipin, 2006, Introduction to Data Mining, Pearson Addison-Wesley, chapter4.
[14]    Jun, S., Park, S. S. and Jang, D.S., 2014, Document clustering method using dimension reduction and support vector clustering to overcome sparseness. Expert Systems with Applications, 41, pp.3204–3212.
[15]    Naldia, M.C. and Campello, R.J.G.B., 2014, Evolutionary k-means for distributed data sets. Neurocomputing, 127, pp.30–42.
[16]    T.  Velmurugan, 2014, Performance  based  analysis  between  k-Means  and  Fuzzy C-Means  clustering  algorithms  for  connection  oriented telecommunication  data. Elsevier Applied  Soft  Computing, 19, pp.134–146