Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.

[1]  Eugene M. Izhikevich,et al.  Weakly pulse-coupled oscillators, FM interactions, synchronization, and oscillatory associative memory , 1999, IEEE Trans. Neural Networks.

[2]  Keiji Konishi,et al.  Experimental verification of synchronization in pulse-coupled oscillators with a refractory period and frequency distribution. , 2011, Chaos.

[3]  Keping Long,et al.  On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches , 2014, IEEE Communications Surveys & Tutorials.

[4]  Saurabh Ganeriwal,et al.  Timing-sync protocol for sensor networks , 2003, SenSys '03.

[5]  Cheng Yang,et al.  Multiscale cycle synchronization strategy for WSN based on firefly-inspired algorithm , 2015, 2015 IEEE International Conference on Information and Automation.

[6]  Yongqiang Wang,et al.  Synchronization of pulse-coupled oscillators to a global pacemaker , 2016, Syst. Control. Lett..

[7]  Christian Bettstetter,et al.  Self-organizing synchronization with inhibitory-coupled oscillators: Convergence and robustness , 2012, TAAS.

[8]  Takashi Morie,et al.  Parameterized digital hardware design of pulse-coupled phase oscillator networks , 2015, Neurocomputing.

[9]  Djamel Djenouri,et al.  Synchronization Protocols and Implementation Issues in Wireless Sensor Networks: A Review , 2016, IEEE Systems Journal.

[10]  Lars Michael Kristensen,et al.  An Industrial Perspective on Wireless Sensor Networks — A Survey of Requirements, Protocols, and Challenges , 2014, IEEE Communications Surveys & Tutorials.

[11]  C. Morawetz.  The Courant Institute of Mathematical Sciences , 1988 .

[12]  Anthony Rowe,et al.  FireFly: a cross-layer platform for real-time embedded wireless networks , 2007, Real-Time Systems.

[13]  Meng Zhang,et al.  An Improved Flooding Time Synchronization Protocol for Industrial Wireless Networks , 2009, 2009 International Conference on Embedded Software and Systems.

[14]  Dong Yue,et al.  Finite-time distributed event-triggered consensus control for multi-agent systems , 2016, Inf. Sci..

[15]  S. Strogatz,et al.  Synchronization of pulse-coupled biological oscillators , 1990 .

[16]  Deborah Estrin,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Fine-grained Network Time Synchronization Using Reference Broadcasts , 2022 .

[17]  Lin Cui,et al.  Reachback Firefly Synchronicity with Late Sensitivity Window in Wireless Sensor Networks , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[18]  Yongqiang Wang,et al.  Synchronization of Pulse-Coupled Oscillators on (Strongly) Connected Graphs , 2015, IEEE Transactions on Automatic Control.

[19]  Yi Zhao,et al.  Synchronization in Moving Pulse-Coupled Oscillator Networks , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[20]  Radhika Nagpal,et al.  Kilobot: A low cost robot with scalable operations designed for collective behaviors , 2014, Robotics Auton. Syst..

[21]  Radhika Nagpal,et al.  Firefly-inspired sensor network synchronicity with realistic radio effects , 2005, SenSys '05.

[22]  Alexey N. Averkin,et al.  Soft Computing in Wireless Sensors Networks , 2007, EUSFLAT Conf..

[23]  Athanasios Tsakonas,et al.  From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors , 2014, IDA.

[24]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[25]  Henrik Jeldtoft Jensen,et al.  Synchronization by small time delays , 2015 .

[26]  Chuan Zhang,et al.  A Reconfigurable Smart Sensor Interface for Industrial WSN in IoT Environment , 2014, IEEE Transactions on Industrial Informatics.

[27]  César Cruz-Hernández,et al.  Outer Synchronization of Simple Firefly Discrete Models in Coupled Networks , 2015 .

[28]  Wilfried Elmenreich,et al.  Firefly Clock Synchronization in an 802.15.4 Wireless Network , 2009, EURASIP J. Embed. Syst..

[29]  Gunther Auer,et al.  Emergent Slot Synchronization in Wireless Networks , 2010, IEEE Transactions on Mobile Computing.

[30]  Li Li,et al.  Convergence Analysis and Accelerating Design for Distributed Consensus Time Synchronization Protocol in Wireless Sensor Networks: Convergence Analysis and Accelerating Design for Distributed Consensus Time Synchronization Protocol in Wireless Sensor Networks , 2010 .

[31]  Y. Bar-Ness,et al.  Distributed synchronization in wireless networks , 2008, IEEE Signal Processing Magazine.

[32]  R. Merrill.  SYNCHRONOUS FLASHING OF FIREFLIES. , 1930, Science.