Data replication and collection framework for enhanced data availability in IoT-based sensor systems

Internet of Things (IoT) is an emerging technology aimed at bridging the gap between physical and digital worlds. IoT envisions to provide a seamless and efficient way of interacting with real-world physical objects through the Internet which highly impacts modern day life. Wireless Sensor Networks (WSNs) are primarily responsible for embedding intelligence and turning physical objects into IoT-enabled communicating entities that can be remotely accessed and controlled, thus, providing the basis for smart solutions to all the human needs. To realize the concept of IoT, efficient data generation, collection, and presentation through WSNs are crucial issues. However, WSNs face numerous challenges mainly because of the resource-constrained and unreliable nature of nodes which may result in loss of valuable sensed data. Data replication is a promising technique to facilitate efficient data management in IoT-based sensor systems. This thesis initially proposes a taxonomy of state-of-the-art data replication protocols for IoT-based sensor systems highlighting their pros and cons by classifying them into sub-categories of data retrieval, query balancing, system robustness, and data availability. Then, we propose a data replication and collection (DRACO) framework, composed of a fully distributed hop-by-hop data replication technique for IoT-based sensor systems to avoid data loss due to node failures and enhance data availability in the network. Additionally, the proposed data replication scheme in DRACO is coupled with an efficient data collection mechanism where a mobile sink node visits a relatively smaller percentage of network nodes to collect most of the network data. Thus, no routing structure is needed to enable communication among network nodes which completely eliminates the routing overhead which is a key feature of the proposed approach. Extensive simulation experiments using network simulator (NS-3) show that DRACO ensures high data availability in the presence of node failures as well as provides maximum data collection efficiency. Comparative simulation results reveal that in comparison to two other state-of-the-art approaches, namely, greedy and random replication techniques, DRACO improves data availability with maximum gains of about 15% and 34%, respectively. Similarly, it improves average replicas created in the network with maximum gains of about 18% and 40%, respectively. Furthermore, DRACO ensures a better replica spread which determines the quality of data dissemination in the network as well as facilitates efficient data collection.