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Quality of Service (QoS) of disadvantaged networks is considered from a purely network standpoint in existing works. Adversarial intervention in such networks is not analyzed, nor is it possible to infer if a QoS loss is benign or otherwise. In this paper, we present a QoS loss inference module, where the end nodes can infer the nature of a QoS loss in a non-intrusive manner. The objective of this work is to develop a conceptual framework to model the inference module, and investigate its integration into existing platforms. We abstract the problem of link selection (as opposed to route selection) in disadvantaged networks as a resource selection problem, and apply a game theoretic model to set limits on the rate of convergence. Using this convergence rate, our loss inference model can distinguish between adversarial network manipulation and benign network loss. Such a module will help manage the operation of disadvantaged networks in a more effective manner in critical networks.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |