Abstract
Time synchronization is an essential problem for energy-harvesting wireless sensor networks (EH-WSNs), which is closely related to efficient resource schedules, energy harvesting, data fusion, location, etc. With the advantage of being more robust than master controlling synchronization, distributed time synchronization algorithms are usually used to EH-WSNs for cooperating sleeping nodes. This paper proposes a novel accelerated time co-synchronization algorithm based on the storage-and-prediction method to improve the convergence rate. In this algorithm, each node in the network first predicts the estimated current time state value according to previous time state values stored in the local node, and then adjusts the time state value according to the estimated time state value deviations between all its adjacent nodes. Theoretical analysis in a more general case shows that the proposed algorithm can improve the convergence rate of distributed time synchronization when selecting the appropriate parameter, and the closed-form solution of the optimal parameter is also given. Finally, the simulation of comparing the classical algorithm with the proposed algorithm based on different scenarios is completed.
Highlights
Wireless sensor networks (WSNs) have broad application prospects in various fields such as military, environmental, industrial, medical and many other fields
energy harvesting wireless sensor networks (EH-WSNs) collect energy from the environment by energy harvesting technology to extend the lifetime of nodes
Based on the above related work, this paper proposes a distributed time synchronization algorithm for EH-WSN, which uses storage and predictor
Summary
Wireless sensor networks (WSNs) have broad application prospects in various fields such as military, environmental, industrial, medical and many other fields. The spectral radius is constrained by the network topology and may be unpractical Another way to improve the convergence rate of distributed average algorithms is by using the information of second-order neighbors [37], [38]. In the paper [50], the convergence of the distributed average linear iterative algorithm has been theoretically analyzed based on second-order storage information under the condition that the network topology is fully-connected and the weight matrix is doubly stochastic, symmetric and nonnegative. Based on [50], paper [51] discussed a general case It theoretically analyzed the convergence performance of a new algorithm and the influence of different parameters on the convergence rate of the algorithm when the weight matrix is not necessarily non-negative.
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