Comparative Analysis of Bio-Inspired Algorithms for Underwater Wireless Sensor Networks


Mobile nodes in underwater wireless sensor networks are becoming very important as they not only enable flexible sensing areas but also entails the ability to provide means for data and energy sharing among existing static sensor nodes. In this paper, three efficient meta-heuristic evolutionary algorithms ant colony optimization, artificial bees colony and firefly algorithm, inspired by swarm intelligence are being compared with an objective to achieve the shortest path for the mobile node in traversing the complete sensing network. We transform this problem into the traveling salesman problem. It is the most famous and commonly used nondeterministic-polynomial combinatorial optimization problem in which an artificial agent is set to travel between different cities and calculate distance or time consumed to travel between these nodes or cities for best route selection. Heuristic and meta-heuristic algorithms are being used for decades to solve such type of problems. In this comparative study, an analysis of meta-heuristic algorithms for obtaining results in less processing time while searching for the optimal solution has been done. Moreover, this paper provides a classification of mentioned algorithms and highlights their characteristics. The experiment has been carried out on these algorithms by manipulating different parameters such as population and number of iteration.

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Correspondence to Syeda Sundus Zehra.

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Zehra, S.S., Qureshi, R., Dev, K. et al. Comparative Analysis of Bio-Inspired Algorithms for Underwater Wireless Sensor Networks. Wireless Pers Commun 116, 1311–1323 (2021).

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  • Underwater wireless sensor network (UWSN)
  • Meta-heuristics
  • Evolutionary algorithms
  • Traveling salesman problem (TSP)
  • Swarm intelligence (SI)
  • Non-deterministic polynomial-hard problem (NP-hard)
  • Combinatorial optimization problem (COP)
  • Ant colony optimization (ACO)
  • Artificial bees colony (ABC)
  • Firefly algorithm (FFA)