An Efficient Scyphozoa Swarm Optimization and Fuzzy Density Based Clustering Routing for Underwater Wireless Sensor Networks

Abstract

Underwater Wireless Sensor Networks (UWSNs) are placed in waterways including oceans, seas, rivers to keep an eye on military actions, carry out rescue missions, and mine for resources. Security is essential in UWSN system because the UWSN environment is vulnerable to several security threats. It is also susceptible to malicious assaults and threats. Earlier cluster-based routing protocol was working better but Heavy transmission use of energy puts nodes' lifespans in danger. Thus, it became crucial to provide solutions for network-based issues like level of service, protection, network variability, congestion prevention, efficient routing, and energy efficiency. The design of Scyphozoa Swarm Optimization (SSO) and Fuzzy Density Based Clustering (FDC) secure energy efficient data aggregation Routing protocols (SEDARP) is the solution to extending the lifespan of the network and named as SSOFDCSEDARP RP. This work describes routing protocol for UWSNs using multi-agent reinforcement learning. The two primary stages of the suggested hybrid routing technique are intra/extra cluster routing and cluster creation. A cluster head selection technique utilizing SSA is suggested, which allows nodes to independently determine that they can operate as cluster heads cantered on routing and environment data, hence reducing the likelihood of hotspot production without requiring extra network overhead. The suggested adaptive clustering routing protocol exceeds current methods according to routing efficacy, power usage, and network longevity, according to simulation findings.

Keywords

• cluster-based routing protocol
• data aggregationp
• fuzzy density based clustering
• scyphozoa swarm optimization
• underwater wireless sensor network