Abstract
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.
Highlights
Data acquisition is the foundation of soft sensor and data fusion
Synchronization is a key feature of Wireless Sensor Networks (WSNs), and it has been used in many wireless protocols
Once the time synchronization is achieved, the synchronization mainly depends on the internal accuracy of the components or clock drifts of the clock system given by the manufacturing process
Summary
Data acquisition is the foundation of soft sensor and data fusion. Data often comes from multiple sources, which may be separated physically or virtually, and need to be synchronized. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors [1,2]. Wireless Sensor Networks (WSNs), as an important distributed data acquisition technology, have been widely used in industry [3,4,5]. Synchronization is a key feature of WSNs, and it has been used in many wireless protocols. The synchronization algorithm can be classified according to the centrality.
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