CoNICE: Consensus in Intermittently-Connected Environments by Exploiting Naming With Application to Emergency Response

Abstract

In many scenarios, information must be disseminated over intermittently-connected environments when the network infrastructure becomes unavailable, e.g., during disasters where first responders need to send updates about critical tasks. If such updates pertain to a shared data set, dissemination consistency is important. This can be achieved through causal ordering and consensus. Popular consensus algorithms, e.g., Paxos, are most suited for connected environments. While some work has been done on designing consensus algorithms for intermittently-connected environments, such as the One-Third Rule (OTR) algorithm, there is still need to improve their efficiency and timely completion. We propose CoNICE, a framework to ensure consistent dissemination of updates among users in intermittently-connected, infrastructure-less environments. It achieves efficiency by exploiting hierarchical namespaces for faster convergence, and lower communication overhead. CoNICE provides three levels of consistency to users, namely replication, causality and agreement. It uses epidemic propagation to provide adequate replication ratios, and optimizes and extends Vector Clocks to provide causality. To ensure agreement, CoNICE extends OTR to also support long-term network fragmentation and decision invalidation scenarios; we define local and global consensus pertaining to within and across fragments respectively. We integrate CoNICE’s consistency preservation with a naming schema that follows a topic hierarchy-based dissemination framework, to improve functionality and performance. Using the Heard-Of model formalism, we prove CoNICE’s consensus to be correct. Our technique extends previously established proof methods for consensus in asynchronous environments. Performing city-scale simulation, we demonstrate CoNICE’s scalability in achieving consistency in convergence time, utilization of network resources, and reduced energy consumption.