COBAC: An Adaptive Transhipment Station Localization for Reducing IUU Fishing Practices

The global consumption of fish products is increasing on the scale of millions of tonnes every year. This makes the aquaculture industry as one of the leading sectors to provide food, employment, and ensuring a sustainable livelihood. The implication of rapid growth in global fish production and massive consumption is causing productivity burden on the fisheries management to meet the market demands. This eventually leads to aggravated competition within the fishing networked community. For surviving the competition and increased pressure, few people from fishing community often indulge in various kinds of illegal fishing activities. Illegal, Unreported and Unregulated (IUU) fishing happens to be a major problem plaguing the fish production. Our research proposes a solution based on official transhipment station that solves the problems of illegal transhipment activities, thereby allowing transhipment to continue in a legal and safe manner. We have proposed Cost Optimisation Based Adaptive clustering (COBAC) algorithm that takes into consideration various operational cost and provides the location of establishment of the wirelessly operating transhipment stations in the ocean. The performance of the proposed transhipment was compared with random, greedy and heuristic approaches. Also, the experimentation results show that our proposed COBAC algorithm consumes \(1/{10}^{{\text{th}}}\) $1/{10}^{\text{th}}$ of the execution time as compared to Brute force clustering and produced result with only 0.1% relative error. Our COBAC algorithm was capable to locate the optimal number of stations in \(1/{18}^{{\text{th}}}\) $1/{18}^{\text{th}}$ of iterations as compared to Brute force clustering. Moreover, owing to the usage of grid structure, the proposed algorithm executes in a time complexity of \(O(1)\) $O(1)$ .