A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks. (4th December 2017) - Goyal, Nitin; Dave, Mayank; Verma, Anil Kumar
Summary: The performance of underwater wireless sensor network gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster‐based network in this paper. Primarily, while selecting the CH, a backup cluster head (BCH) is selected using fuzzy logic technique based on parameters such as node density, residual energy, load, distance to sink, and link quality. Then, failure of CH, BCH, and CM is detected. If fault is detected at CH, then the BCH will start performing the task of failed CH. Simultaneously, when BCH failed, any other CM will be elected as BCH. If any of the CM appears to be nonperforming, then CH will detect the communication failure and request BCH to transfer the data from the failed CM to CH. The comparison of proposed FDRT is performed with existing FDRTs EDETA, RCH, and SDMCGC on the basis of packet drop, end‐to‐end delay, energy consumption, and delivery ratio of data packets. By simulation results, it is shown that FDRT for cluster‐based underwater wireless sensor network results in quicker detection of failures and recovery of the network along with the reduction in energy consumption, thereby increasing the lifespan of the network. Abstract : The performance of underwater wireless sensor network (UWSN) gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster‐based network and compare with EDETA, RCH, and SDMCGC based on QoS parameters.Our contributions in this paper are briefed as follows: We propose an efficient fault management scheme that guarantees to detect and recover the CH and CM faults collectively with better network performance. We adapt the concept of BCH that is formed along with CH using fuzzy logic to obtain good accuracy. To get faster results, data aggregation and data transmission scheduling based on TDMA technique is applied. The proposed technique consumes less energy along with better results.