Kaur, P.; Kaur, K.; Singh, K.; Bharany, S.; Almazyad, A.S.; Xiong, G.; Mohamed, A.W.; Shokouhifar, M.; Werner, F. Acoustic Monitoring in Underwater Wireless Sensor Networks using Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP). Preprints2023, 2023101325. https://doi.org/10.20944/preprints202310.1325.v1
APA Style
Kaur, P., Kaur, K., Singh, K., Bharany, S., Almazyad, A.S., Xiong, G., Mohamed, A.W., Shokouhifar, M., & Werner, F. (2023). Acoustic Monitoring in Underwater Wireless Sensor Networks using Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP). Preprints. https://doi.org/10.20944/preprints202310.1325.v1
Chicago/Turabian Style
Kaur, P., Mohammad Shokouhifar and Frank Werner. 2023 "Acoustic Monitoring in Underwater Wireless Sensor Networks using Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP)" Preprints. https://doi.org/10.20944/preprints202310.1325.v1
Abstract
Underwater wireless sensor networks (UWSNs) represent a specialized category of WSNs with versatile applications including acoustic monitoring, oil and gas exploration, and military surveillance. UWSNs face formidable challenges such as limited energy resources, extended propagation delays, and harsh conditions. Existing clustering and multi-hop routing protocols often unevenly distribute nodes geographically, causing network fragmentation and disproportionately draining the battery life of nodes near the sink due to higher data transmission demands. In this paper, we introduce an Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP), inspired by the collective behavior of fish swarms. EAFSCP is a decentralized clustering algorithm designed for acoustic monitoring in UWSNs. Its decentralized nature makes it particularly well-suited for large-scale UWSNs, where centralized algorithms may not be feasible. Through comprehensive comparisons with existing cluster-based routing protocols, our findings indicate that EAFSCP consistently outperforms them across multiple key performance metrics, including network lifetime, energy consumption, packet delivery ratio, packet loss rate, and throughput. According to the results, EAFSCP represents an effective clustering algorithm that enhances network performance, prolongs network lifespan by reducing energy consumption, promotes scalability, and provides valuable guidance for emergency response efforts.
Keywords
underwater wireless sensor network (UWSN); acoustic monitoring; energy efficiency; clustering; routing; artificial fish swarm algorithm (AFSA)
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
*
