Certain Investigations on Energy-Efficient Fault Detection and Recovery Management in Underwater Wireless Sensor Networks

Abstract

In recent years, underwater wireless sensor networks (UWSNs) have been widely applied to aquatic and military applications. Network survivability is an essential attribute to be considered in UWSN circumstance and various stratifications like node survivability, connectivity and rapid fault node detection and recovery. However, efficient and accurate fault tolerance mechanisms are required to prolong the network survivability in UWSN. In this research work, the energy-efficient fault detection and recovery management (EFRM) approach is proposed for the UWSN with relatively better network survivability. The hidden Poisson Markov model has been incorporated in EFRM to achieve efficient fault detection throughout the whole network. Thereafter, the recovered node can be selected by using the analytical network process model which facilitates to recover the larger number of nodes in the damaged region. The simulation results manifest that when the fault probability is 40%, the detection accuracy of the proposed EFRM is over 99%, and the false positive rate is below 2%. The detection accuracy is improved by up to 12% when compared with the existing state-of-the-art schemes.

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

“…This work considered six performance metrics to compute the performance of the proposed ensemble classifier. The mathematical formulation of these metrics is as follows [35][36][37][38] :…”

Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks

“…As per the comparison of the performance, the EEHRC protocol did not reduce the considerable amount of energy consumption of the UWSN which is the main disadvantage of the EEHRC protocol. Prasanth [19] presented the Energy-efficient Fault detection and Recovery Management (EFRM) method for selfish node detection and recovery of the secure data transmission in UWSNs. The EFRM method utilized the hidden poisson markov model to improve the performance of the fault node detection.…”

“…The packet delivery ratio directly represents the efficiency of the overall network which is given in Eq. (19).…”

“…Packet delivery ratio ¼ number of packages received number of packages transmitted (19) Fig. 3 represents the packet delivery ratio for E-ACO protocol and other methods such as SAPDA [16], SEECR [17], FRR-MFO [23] and IACO [24].…”

Reinforcement learning-based dynamic position control of mobile node for ocean sensor networks

“…Ocean sensor networks (OSNs) have been extensively used in marine research, oceanography, ocean monitoring, and offshore exploration (Prasanth, 2021) , in which the mobile node (MN) plays a significant role and is usually considered as an intelligent node to carry out specific tasks, such as node maintenance and target observation, as shown in Figure 1. When performing an observation task or acting as a fixed node, the MN needs to maintain a stable position.…”