Reliable Path Selection and Opportunistic Routing Protocol for Underwater Wireless Sensor Networks

The increased need to gather scientific data and the renewed drive to explore underwater natural resources has led more and more researchers to study the underwater environment. This has resulted in enormous attention being given to Underwater Wireless Sensor Networks (UWSNs) all over the world. However, UWSNs are faced with some major challenges including harsh environment, higher propagation delay, and limited battery power of the sensor nodes. To address these challenges, several routing schemes have been proposed. In this paper, we propose a routing strategy, called Reliable Path Selection and Opportunistic Routing (RPSOR) for UWSNs, which is a significantly improved version of Weighting Depth and Forwarding Area Division Depth Based Routing (WDFAD-DBR). RPSOR is based on three main factors: Advancement factor (<inline-formula> <tex-math notation="LaTeX">$ADV_{f}$ </tex-math></inline-formula>), which depends on the depth of current as well as next hop forwarding node; Reliability index (<inline-formula> <tex-math notation="LaTeX">$REL_{i}$ </tex-math></inline-formula>), which depends on the energy of the current forwarder as well as average energy in the next expected forwarding region; and Shortest Path Index (<inline-formula> <tex-math notation="LaTeX">$SP_{i}$ </tex-math></inline-formula>), which is calculated on the basis of number of hops to the sink and average depth of neighbors in the next expected hop. To deal with the void hole problem and improve the Packet Delivery Ratio (PDR), we follow the more reliable path towards the sink by calculating <inline-formula> <tex-math notation="LaTeX">$REL_{i}$ </tex-math></inline-formula> for a node. At the end, we perform extensive simulations and compare our proposed scheme with WDFAD-DBR, the results of which prove that RPSOR shows better performance in terms of PDR and energy tax in comparison to WDFAD-DBR. However, the proposed work compromises end-to-end delay in sparse networks.