Abstract: The swarm intelligence has become an interesting topic for employing of multi-agent robotics with specific purpose. The capability of multi-coordination, scalability and goals-oriented control in spatial and temporal environment are already concerned and proven for several applications such as in military patrol, and drones leader-follower coordination. In marine-based environment, swarm intelligence adopted by ASV or ROV has been used for water quality and environment monitoring with sufficient optimized results making it convenient for rapid assessments. In this paper, the arrangement for building a trusted cyber-physical systems for algal bloom rapid assessment using swarm ship technology were explained in state-of-the-art perspectives. The minimum requirements for sensing, vehicle controlling, and communication of this system with others were explored as well as algorithm chosen for the best known configuration to monitor algal bloom events before spreading so fast to larger area. Some models were explained to show the robustness of autonomous unmanned ship control. From this point of view, we concluded that swarm ship technology has become an important potential implementation for near real time in situ monitoring compared to other decision making method such as laboratory examination or remote sensing-based results. The results of this review open the opportunity to realization of swarm ship technology in cyber physical system for monitoring algal bloom in specific area near real time efficiently.

Denny Darlis, Indra Jaya, Karlisa Priandana, Yopi Novita and Ayi Rachmat, “State-of-the-Art of the Swarm Ship Technology for Alga Bloom Rapid Monitoring” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140366

@article{Darlis2023,
title = {State-of-the-Art of the Swarm Ship Technology for Alga Bloom Rapid Monitoring},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140366},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140366},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Denny Darlis and Indra Jaya and Karlisa Priandana and Yopi Novita and Ayi Rachmat}
}