Data Recovery Approach for Fault-Tolerant IoT Node

Abstract

Internet of Things (IoT) has a wide range of applications in many sectors like industries, health care, homes, militarily, and agriculture. Especially IoT-based safety and critical applications must be more securable and reliable. Such type of applications needs to be operated continuously even in the presence of errors and faults. In safety and critical IoT applications maintaining data reliability and security is the critical task. IoT suffers from node failures due to limited resources and the nature of deployment which results in data loss consequently. This paper proposes a Data Recovery Approach for Fault Tolerant (DRAFT) IoT node algorithm, which is fully distributed, data replication and recovery implemented through redundant local database storage of other nodes in the network. DRAFT ensures high data availability even in the presence of node failures to preserve the data. When an IoT node fails in any cluster in the network data can be retrieved through redundant storage with the help of neighbor nodes in the cluster. The proposed algorithm is simulated for 100-150 IoT nodes which enhances 5% of network lifetime, and throughput. The performance metrics such as Mean Time to Data Loss (MTTDL), throughput, Network lifetime, and reliability are computed and results are found to be improved.

A Deep Learning Based Data Recovery Approach for Missing and Erroneous Data of IoT Nodes

“…In [ 28 ], the authors proposed an algorithm to predict the incomplete fields in the vehicle parking duration in the survey data. The authors implemented a data recovery approach for a fault-tolerant IoT node (DRAFT) algorithm based on a redundant array of independent disks (RAID5) structures in [ 29 ] to recover the missing data for a fault-tolerant IoT node. Missing data can be recovered with the help of direct neighbor nodes in the cluster.…”