Initial Phase Proximity for Reachback Firefly Synchronicity in WSNs: Node Clustering

  • Misbahuddin Misbahuddin Universitas Indonesia
  • Riri Fitri Sari Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia


Synchronicity is one of the essential basic services to support the main duties of Wireless Sensor Networks (WSNs). Synchronicity is the ability to arrange simultaneously collective actions in WSNs. A high-rate data sampling to analyze the seismic structure and volcanic monitoring is the important applications requiring a synchronicity. However, most of the existing synchronicity algorithm is still executed in a flat network, so that it requires a long time to achieve a synchronous condition. To increase the convergence rate, the synchronicity can be executed concurrently through a clustering scheme approach. In this work, the such scheme is called as the Node Clustering based on Initial Phase Proximity for Reachback Firefly Synchronicity (NCIPP-RFS). The NCIPP-RFS solves in three steps: (1) constructing the node clustering, (2) intra-cluster synchronicity, and (3) inter-cluster synchronicity. The NCIPP-RFS method is based on the firefly-inspired algorithm. The fireflies are a species in the natural system, which are able to manage their flashing for synchronicity in a distributed manner. The NCIPP-RFS was implemented in NS-3 and evaluated and compared with Reachback Firefly Algorithm (RFA). The simulation results show a significant increase in the convergence rate. The NCIPP-RFS can reach a convergence time shorter than the RFA. In addition, the NCIPP-RFS was compared in the various numbers of clusters, where the least number of clusters can reach the fastest convergence rate. Finally, it can also contribute significantly to the increase of the convergence rate if the number of nodes is greater than or equal to 50 nodes.

Author Biography

Misbahuddin Misbahuddin, Universitas Indonesia
Dept. Electrical Engineering,Faculty Of Engineering,University of Indonesia


[1] F. Lamonaca, A. Gasparri, E. Garone, and D. Grimaldi (2014), Clock Synchronization in Wireless Sensor Network With Selective Convergence Rate for Event Driven Measurement Applications, Instruments. IEEE Trans., 63(9): 2279-2287.

[2] F. Gielow, G. Jakllari, M. Nogueira, and A. Santos (2015), Data similarity aware dynamic node clustering in wireless sensor networks, Ad Hoc Networks, 24: 29-45.

[3] H. Araujo, W. L. T. de Castro, and R. Holanda Filho (2010), A proposal of self-configuration in Wireless Sensor Network for recovery of broken paths, Sensors Applications Symposium (SAS), 2010 IEEE, 245-250.

[4] K. Kyungmi, K. Hyunsook, and H. Youngchoi (2009), A Self Localization Scheme for Mobile Wireless Sensor Networks, Computer Sciences and Convergence Information Technology, 2009. ICCIT 09. Fourth International Conference on, 774-778.

[5] A. K. Mohapatra, N. Gautam, and R. L. Gibson (2013), Combined Routing and Node Replacement in Energy-Efficient Underwater Sensor Networks for Seismic Monitoring, Oceanic Engineering, IEEE Journal of, 38: 80-90.

[6] R. Lara, D. BenAtez, A. Caamato, M. Zennaro; J. L.Rojo-Alvarez (2015), On Real-Time Performance Evaluation of Volcano-Monitoring Systems With Wireless Sensor Networks, IEEE Sensors Journal, 15(6): 3514-3523.

[7] M. M. Afsar and M.-H. Tayarani-N. (2014), Clustering in sensor networks: A literature survey, J. Netw. Comput. Appl., 46: 198-226.

[8] R. E. Mirollo and S. H. Strogatz (1990), Synchronization of pulse-coupled biological oscillator, SIAM J. Appl. Math., 50(6): 1645-1662.

[9] N. Yu, B. J. d'Auriol, W. Xiaoling, W. Jin, C. Jinsung, and L. Sungyoung (2008), Selective Pulse Coupling Synchronicity for Sensor Network, Sensor Technologies and Applications, 2008. SENSORCOMM '08. Second International Conference on,123-128.

[10] G. Werner-Allen, G. Tewari, A. Patel, M. Welsh, and R. Nagpal (2005), Firefly-inspired sensor network synchronicity with realistic radio effects, Proc. of the 3rd international conference on Embedded networked sensor systems, ACM, San Diego, California, USA, 142-153.

[11] A. Tyrrell, G. Auer, and C. Bettstetter (2006), Fireflies as Role Models for Synchronization in Ad Hoc Networks, Bio-Inspired Models of Network, Information and Computing Systems, 1-7.

[12] J. Yanliang, M. Wei, B. Zhishu, and Z. Xuqin (2013), Blind and buffer phase area based on M&S model fireflies synchronization in WSNs, in Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech), 2013 First International Symposium on, 1-4.

[13] C. Lin and W. HongPeng (2009), Reachback Firefly Synchronicity with Late Sensitivity Window in Wireless Sensor Networks, Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on, 1: 451-456.

[14] F. A. dos S. Silva, S. R. Lopes, and R. L. Viana (2016), Synchronization of biological clock cells with a coupling mediated by the local concentration of a diffusing substance, Commun. Nonlinear Sci. Numer. Simul., 35: 37-52.

[15] Y. Yaniv, I. Ahmet, J. Liu, A. E. Lyashkov, T.-R. Guiriba, Y. Okamoto, B. D. Ziman, and E. G. Lakatta (2014), Synchronization of sinoatrial node pacemaker cell clocks and its autonomic modulation impart complexity to heart beating intervals, Heart Rhythm, 11(7):1210-1219. doi: 10.1016/j.hrthm.2014.03.049.

[16] D. Kim (2004), A spiking neuron model for synchronous flashing of fireflies., Biosystems, 76(1-3): 7-20.

[17] R. Leidenfrost and W. Elmenreich (2009), Firefly clock synchronization in an 802.15. 4 wireless network, EURASIP J. Embed. Syst., 1-17, DOI: 10.1155/2009/186406.

[18] S. Yi, J. Qing, and Z. Kai (2012), A clustering scheme for Reachback Firefly Synchronicity in wireless sensor networks, Network Infrastructure and Digital Content (IC-NIDC), 2012 3rd IEEE International Conference on, 27-312.
How to Cite
MISBAHUDDIN, Misbahuddin; SARI, Riri Fitri. Initial Phase Proximity for Reachback Firefly Synchronicity in WSNs: Node Clustering. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 1, p. 90-102, dec. 2016. ISSN 1841-9844. Available at: <>. Date accessed: 27 june 2022.


wireless sensor network, synchronicity, node clustering, phase proximity, firefly-inspired algorithm