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 applica-tions 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.
Highlights
Internet of Things (IoT) provides an integration of multiple controllers, IoT nodes, servers, and gateways which contains embedded technologies to be logically connected and enables them to sense and interact to the real world and among themselves
In an IoT network, each sensor node works with a limited power source and when the sensor stops working the network cannot process data that they received this may affect the prediction of network health and reliability which leads to network failure
This work was analyzed properly to extend the existing algorithms works towards 150 IoT nodes with network lifetime, Mean Time to Data Loss (MTTDL), Reliability, and throughput of the network
Summary
IoT provides an integration of multiple controllers, IoT nodes, servers, and gateways which contains embedded technologies to be logically connected and enables them to sense and interact to the real world and among themselves. Data quality estimation mechanism depends on three stages[4], data reliability, device availability, and overall quality of data.Sensors are small devices with limited resources like memory space, battery, and processing power. These resourceconstrained nodes pose multiple challenges for network designers’ accurate usage of scarce resources. In an IoT network, each sensor node works with a limited power source and when the sensor stops working the network cannot process data that they received this may affect the prediction of network health and reliability which leads to network failure When this situation happens RAID structures are very useful for maintaining the redundant copies of data.
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