Litcius/Paper detail

FGOR: Flow-Guided Opportunistic Routing for Intrabody Nanonetworks

Xin‐Wei Yao, Yi-wei Chen, Yi Wu, Kai Zhao, Josep Miquel Jornet

2022IEEE Internet of Things Journal20 citationsDOI

Abstract

The advancement of nano communication has opened the door for the development of intrabody medical application services. Flow-guided nano-communication networks have gained major attraction in recent years as an effective solution for intrabody sensing and actuation. This article builds a three-layer vertical network structure for intrabody nanonetworks, i.e., nano nodes, nano routers, and gateway, where data packets generated by nano nodes are relayed to the gateway through nano routers or other nodes. However, how to guarantee the data transmission through the way of multiple hops in such a scenario is an unsolved challenge. In order to improve the throughput and reduce the energy consumption of intrabody nanonetworks in a single-flow environment where the nano devices are restricted, a flow-guided opportunistic routing (FGOR) protocol is proposed. In FGOR, a relative position (RP) model is proposed to formulate the criterion for candidate relay selection (CRS) and enable the nodes’ direction awareness to the gateway. Moreover, the CRS criterion is redesigned through a mobility gradient (MG) model further derived from the RP model. The candidate nodes are prioritized based on node ID, available energy, and RP information of nodes to perform backoff forwarding for decreasing transmission redundancy. Simulation results show that the RP model improves the throughput and significantly extends the lifecycle of intrabody nanonetwork by reducing the energy consumption. Compared with the RP model, the MG model performs better in terms of delay and successful transmission rate, especially within the circulation environment of intrabody.

Topics & Concepts

Computer scienceRouting (electronic design automation)Computer networkFlow (mathematics)Distributed computingMathematicsGeometryMolecular Communication and NanonetworksAdvanced Memory and Neural ComputingNeuroscience and Neural Engineering