2018
DOI: 10.1002/asjc.1795
|View full text |Cite
|
Sign up to set email alerts
|

A Filter‐based Clock Synchronization Protocol for Wireless Sensor Networks

Abstract: Clock parameters in wireless sensors experience slow changes due to low-cost construction and environmental conditions. In this paper, a filter-based distributed protocol, called FBP, is proposed to dynamically achieve clock synchronization for wireless sensor networks. The idea of FBP is derived from a first-order filter which is robust to environmental noises. The proposed protocol is fully distributed, meaning that each node relies only on its local clock readings and reading announcements from its neighbou… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
3
2
1

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(3 citation statements)
references
References 33 publications
0
3
0
Order By: Relevance
“…In this paper, is defined to describe the packet loss event of the first round of information exchange [ 31 , 32 ]. When the nodes successfully exchange data, there is ; otherwise, .…”
Section: Kalman Filter Based Clock Modelmentioning
confidence: 99%
“…In this paper, is defined to describe the packet loss event of the first round of information exchange [ 31 , 32 ]. When the nodes successfully exchange data, there is ; otherwise, .…”
Section: Kalman Filter Based Clock Modelmentioning
confidence: 99%
“…In the last two decades, multi-agent systems (MASs) have received substantial research attention in a variety of fields including sensor networks [1], computer networks [2], clock synchronization [3], distributed decision making [4], distributed optimization [5], social networks [6], load balancing in power systems [7], formation-containment control [8,9], persistent monitoring [10], and robotics [11,12]. The consensus problem was investigated for the networks with first-order [13][14][15][16] and second-order agent dynamics [17][18][19][20] extensively.…”
Section: Introductionmentioning
confidence: 99%
“…Nonlinear dynamical networks (NDNs) are composed of multiple interconnected nodes, which are described by nonlinear dynamics and the interconnected structure among nodes is often modeled as a graph. Consensus problems of NDNs have attracted a great deal of research interest from a wide range variety of scientific communities disciplines due to its extensive applications in various domains, for example, smart grid [1][2][3], sensor networks [4][5][6], teaming of robots [7,8], distributed optimization [9,10], and block chain [11]. The core challenge of the consensus in NDNs is how to design appropriate controller Abbreviations: ANA, anti-nuclear antibodies; APC, antigen-presenting cells; IRF, interferon regulatory factor.…”
Section: Introductionmentioning
confidence: 99%