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Analysis of underwater target detection probability by using autonomous underwater vehicles

Publication ,  Conference
Sun, P; Boukerche, A
Published in: Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-located with MSWiM 2017
November 21, 2017

Due to the trend of under-ocean exploration, realtime monitoring or long-term surveillance of the under-ocean environment, e.g., real-time monitoring for under-ocean oil drilling, is imperative. Underwater wireless sensor networks could provide an optimal option, and have recently attracted intensive attention from researchers. Nevertheless, terrestrial wireless sensor networks (WSNs) have been well investigated and solved by many approaches that rely on the electromagnetic/optical transmission techniques. Deploying an applicable underwater wireless sensor network is still a big challenge. Due to critical conditions of the underwater environment (e.g., high pressure, high salinity, limited energy etc), the cost of the underwater sensor is significant. The dense sensor deployment is not applicable in the underwater condition. Therefore, Autonomous Underwater Vehicle (AUV) becomes an alternative option for implementing underwater surveillance and target detection. In this article, we present a framework to theoretically analyze the target detection probability in the underwater environment by using AUVs. The experimental results further verify our theoretical results.

Duke Scholars

Published In

Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-located with MSWiM 2017

DOI

Publication Date

November 21, 2017

Start / End Page

39 / 42
 

Citation

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Sun, P., & Boukerche, A. (2017). Analysis of underwater target detection probability by using autonomous underwater vehicles. In Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-located with MSWiM 2017 (pp. 39–42). https://doi.org/10.1145/3132114.3132729
Sun, P., and A. Boukerche. “Analysis of underwater target detection probability by using autonomous underwater vehicles.” In Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-Located with MSWiM 2017, 39–42, 2017. https://doi.org/10.1145/3132114.3132729.
Sun P, Boukerche A. Analysis of underwater target detection probability by using autonomous underwater vehicles. In: Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-located with MSWiM 2017. 2017. p. 39–42.
Sun, P., and A. Boukerche. “Analysis of underwater target detection probability by using autonomous underwater vehicles.” Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-Located with MSWiM 2017, 2017, pp. 39–42. Scopus, doi:10.1145/3132114.3132729.
Sun P, Boukerche A. Analysis of underwater target detection probability by using autonomous underwater vehicles. Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-located with MSWiM 2017. 2017. p. 39–42.

Published In

Q2SWinet 2017 - Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Co-located with MSWiM 2017

DOI

Publication Date

November 21, 2017

Start / End Page

39 / 42