Energy harvesting based routing protocol for underwater sensor networks

Abstract

Underwater sensor networks (UWSNs) are ad-hoc networks which are deployed at rivers, seas and oceans to explore and monitor the phenomena such as pollution control, seismic activities and petroleum mining etc. The sensor nodes of UWSNs have limited charging capabilities. UWSNs networks are generally operated under two deployment mechanisms i.e localization and non-localization based. However, in both the mechanisms, balanced energy utilization is a challenging issue. Inefficient usage of energy significantly affects stability period, packet delivery ratio, end-to-end delay, path loss and throughput of a network. To efficiently utilize and harvest energy, this paper present a novel scheme called EH-ARCUN (Energy Harvesting Analytical approach towards Reliability with Cooperation for UWSNs) based on cooperation with energy harvesting. The scheme employs Amplify-and-Forward (AF) technique at relay nodes for data forwarding and Fixed Combining Ratio (FCR) technique at destination node to select accurate signal. The proposed technique selects relay nodes among its neighbor nodes based on harvested energy level. Most cooperation-based UWSN routing techniques do not exhibit energy harvesting mechanism at the relay nodes. EH-ARCUN deploys piezoelectric energy harvesting at relay nodes to improve the working capabilities of sensors in UWSNs. The proposed scheme is an extension of our previously implemented routing scheme called ARCUN for UWSNs. Performance of the proposed scheme is compared with ARCUN and RACE (Reliability and Adaptive Cooperation for efficient Underwater sensor Networks) schemes in term of stability period, packet delivery ratio, network throughput and path loss. Extensive simulation results show that EH-ARCUN performs better than both previous schemes in terms of the considered parameters.

EH-IRSP: Energy Harvesting Based Intelligent Relay Selection Protocol

“…This behavior increases the number of data transmissions and extra energy is consumed by the source node. In EH-ARCUN (Energy Harvesting Analytical approach towards Reliability with Cooperation for UWSNs) [15], the authors deployed multiple energy harvesting relay nodes. The protocol works in cooperation based communication mode.…”

“…The Function of Angle FA for both the relays R 1 and R 2 are given in the equations (12) and (13) respectively. (12) and (13) represents Euclidian distance between the source node s and the destination node d. When a broadcast packet is received from source node s, both the relay nodes checks the cosine value, if the value is not below zero, both R 1 and R 2 will send response packet with radiuses 1 and 2 for relays R 1 and R 2 , which are calculated as in equation 14and (15) respectively [23]:…”

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

“…Furthermore, they used an amplified forwarding scheme for data forwarding. In addition, they used the piezoelectric effect-based harvesting scheme to increase the efficiency of sensors in UWSNs [26].…”

“…However, the coordination between static and dynamic nodes during communication is lacking in these three types of deployment architectures. To support these deployment approaches, a linear programming-centric approach has been suggested for selecting underwater relay nodes focusing on a longer network lifetime [25].In a similar work, node-level energy harvesting capability has been used as a parameter in relay node selection for a longer network lifetime [26]. However, the impact of self-mobility of underwater nodes due to underwater flow is not considered in both approaches, including deployment-centric network lifetime optimization and harvested energy level-centric network lifetime optimization.…”

“…In a similar work, node-level energy harvesting capability has been used as a parameter in relay node selection for a longer network lifetime [26]. However, the impact of self-mobility of underwater nodes due to underwater flow is not considered in both approaches, including deployment-centric network lifetime optimization and harvested energy level-centric network lifetime optimization.…”