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An Autonomous Connectivity Restoration Algorithm Based on Finite State Machine for Wireless Sensor-Actor Networks.
Zhang, Ying; Wang, Jun; Hao, Guan.
Afiliação
  • Zhang Y; College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. yingzhang@shmtu.edu.cn.
  • Wang J; Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA. yingzhang@shmtu.edu.cn.
  • Hao G; Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA. Jun.Wang@ucf.edu.
Sensors (Basel) ; 18(1)2018 Jan 08.
Article em En | MEDLINE | ID: mdl-29316702
With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article