A Decentralized Damage Detection System for Wireless Sensor and Actuator Networks

The miraculous capabilities of observing the physical world then processing the data, making decisions and performing appropriate actions of wireless sensor and actuator networks (WSAN) enable these networks to provide the several service oriented applications, such as environmental monitoring, industrial applications, security and surveillance, entertainment, smart building and healthcare(health monitoring and medical diagnostics). In such applications, our monitoring area empowered with wireless smart devices, are able to self-monitor and autonomously respond to situations using computational intelligence. This research focuses on developing a module to be setup in the hospitals to monitor the damage detection, which results delay on doctor and patient performances and also monitor WSAN sensor performances. This research work presents a decentralized algorithm for detecting damage by using a WSAN. Our algorithm makes use of cooperative information fusion for calculating a damage coefficient. Before the algorithm starts the network must be already deployed on the structure to be monitored. We found that our collaborative and information fusion-based approach ensures the accuracy of our algorithm . This is tolerable for maximum 100 nodes or less in the WSAN, operating system and protocols can last as long as 468 days.

[1]  John A. Stankovic,et al.  t-kernel: providing reliable OS support to wireless sensor networks , 2006, SenSys '06.

[2]  Jiangchuan Liu,et al.  A real-time communication framework for wireless sensor-actuator networks , 2006, 2006 IEEE Aerospace Conference.

[3]  Nadine Eberhardt,et al.  Wireless Sensor And Actuator Networks Technologies Analysis And Design , 2016 .

[4]  Deborah Estrin,et al.  Sympathy for the sensor network debugger , 2005, SenSys '05.

[5]  Roger Wattenhofer,et al.  Towards a zero-configuration wireless sensor network architecture for smart buildings , 2009, BuildSys '09.

[6]  Liang Zhang,et al.  Organizational memory: reducing source-sink distance , 1997, Proceedings of the Thirtieth Hawaii International Conference on System Sciences.

[7]  Frank L. Lewis,et al.  Energy-efficient wireless sensor network design and implementation for condition-based maintenance , 2007, TOSN.

[8]  R. Klempous,et al.  Byzantine Algorithms in Wireless Sensors Network , 2006, 2006 International Conference on Information and Automation.

[9]  Ms. S. Sudha,et al.  A Hospital Healthcare Monitoring System Using Wireless Sensor Networks , 2017 .

[10]  Chenyang Lu,et al.  A Holistic Approach to Decentralized Structural Damage Localization Using Wireless Sensor Networks , 2008, 2008 Real-Time Systems Symposium.

[11]  Mohammad Sadegh Besharati Moghaddam,et al.  Wireless sensors in agriculture and food industry-Recent development and future perspective , 2017 .

[12]  Jie Wu,et al.  DRBTS: Distributed Reputation-based Beacon Trust System , 2006, 2006 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing.

[13]  Azzedine Boukerche,et al.  An Efficient Directed Localization Recursion Protocol for Wireless Sensor Networks , 2009, IEEE Transactions on Computers.

[14]  Eduardo F. Nakamura,et al.  Information fusion for wireless sensor networks: Methods, models, and classifications , 2007, CSUR.

[15]  Mari Carmen Domingo,et al.  Energy analysis of routing protocols for underwater wireless sensor networks , 2008, Comput. Commun..

[16]  Kay Römer,et al.  The design space of wireless sensor networks , 2004, IEEE Wireless Communications.

[17]  Ian F. Akyildiz,et al.  Wireless sensor and actor networks: research challenges , 2004, Ad Hoc Networks.