Affiliations: [a] Department of Artificial Intelligence and Machine Learning, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India
| [b] Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, India
| [c] Department of Computer Science and Engineering, Sri Venkateshewara College of Engineering, Bengaluru, India
| [d] Department of Information Technology, MLR Institute of Technology, Hyderabad, Telangana, India
| [e] Department of CSE, KL Deemed to be University, Vaddeswaram, A.P., India
Correspondence:
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Corresponding author. N. Poongavanam, Department of Artificial Intelligence and Machine Learning, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu. E-mail: [email protected].
Abstract: In this research, –coverage –connected problem is viewed as multi-objective problem and shuffling frog leaps algorithm is proposed to address multi-objective optimization issues. The shuffled frog leaping set of rules is a metaheuristic algorithm that mimics the behavior of frogs. Shuffled frog leaping algorithms are widely used to seek global optimal solutions by executing the guided heuristic on the given solution space. The basis for the success of this SFL algorithm is the ability to exchange information among a group of individuals which phenomenally explores the search space. SFL improves the overall lifespan of the network, the cost of connection among the sensors, to enhance the equality of coverage among the sensors and targets, reduced sensor count for increased coverage, etc. When it comes to coverage connectivity issues, each target has to be covered using k sensors to avoid the loss of data and m sensors connected enhance the lifespan of the network. When the targets are covered by k sensors then the loss of data will be reduced to an extended manner. When the sensors are connected with m other sensors then the connectivity among the sensors will not go missing and hence the lifespan of the network will be improved significantly. Therefore, the sensor node number in coverage indicates the total number of sensor nodes utilised to cover a target, and the number of sensor nodes in connected reflects the total number of sensor nodes that provide redundancy for a single failed sensor node. Connectivity between sensor nodes is crucial to the network’s longevity. The entire network backbone acts strategically when all the sensors are connected with one or the other to pertain to the connectivity of the network. Coverage is yet another key issue regarding the loss of data. The proposed algorithm solves the connectivity of sensors and coverage of targets problems without weighted sum approach. The proposed algorithm is evaluated and tested under different scenarios to show the significance of the proposed algorithm.
