Gradient-Based Localization and Relay Nodes Selection in Delay Tolerant Mobile Opportunistic Networks for Emergency Rescue

摘要

Background In opportunistic networks, there is no complete path between source and destination and all the nodes are in mobility. Opportunistic networks exploit the potential capability of existing mobile nodes, such as data forwarding without any preplanted infrastructures. In gradient-based routing, relay nodes are selected using a gradient value that leads to less assurance to reaching the destination. So finding the location of the relay node is an important criterion, which resolves to loop the packets within the network. Localization is one of the critical issues for handling emergency rescue message in opportunistic networks. Method In gradient-based localization, choose the best neighbor node that is near to the sink as a relay node. Nodes are two types in the network; they are anchor nodes and sensor nodes. Anchor node is equipped with GPS receiver, and sensor nodes are randomly deployed in canvas. Anchor position is considered as a reference, and in turn sensor nodes position is computed using a hybrid approach. Hybrid approach maps the anchor node coordinates, ToT (time of transmission), AoA (angle of arrival), and ToA (time of arrival) into a Cartesian plane in order to localize the sensor node position. After finding the sensor position, the relay node selection was based on near to the sink, which maximizes the emergency message discrimination toward the sink. Results Proposed localization and relay node selection were extensively simulated in OMNeT++. The simulation profile consists of 20 anchor nodes and 30 sensor nodes in the considered terrain dimension. The simulation result shows that the performance metric such as a Packet Delivery Ratio, throughput, and End-to-End Delay is enhanced and compared with existing gradient-based approach. This proposed scheme is most suitable for dissemination of a rescue message with least delay during emergency cases such as floods, volcano, and earthquake scenario.