A common theme among previously proposed models for network epidemics is the assumption that the propagating object (e.g., a pathogen [in the context of infectious disease propagation] or a piece of information [in the context of information propagation]) is transferred across network nodes without going through any modification or evolutionary adaptations. However, in real-life spreading processes, pathogens often evolve in response to changing environments and medical interventions, and information is often modified by individuals before being forwarded. In this article, we investigate the effects of evolutionary adaptations on spreading processes in complex networks with the aim of 1) revealing the role of evolutionary adaptations on the threshold, probability, and final size of epidemics and 2) exploring the interplay between the structural properties of the network and the evolutionary adaptations of the spreading process.
The integration of cognitive radios and wireless sensor networks enables a new paradigm of communication in which, sensor nodes can avoid heavily crowded transmission bands by tuning their transmission parameters to less crowded bands thanks to the cognitive radio capabilities. In such setting, sensor nodes act as a secondary user, opportunistically accessing vacant channels within a band originally licensed to a primary user. In this paper, we discuss the problem of how to cluster cognitive radio sensor nodes in a dynamic frequency environment set by the primary users. We introduce Cognitive LEACH (CogLEACH), which is a spectrum-aware extension of the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CogLEACH is a fast, decentralized, spectrum-aware, and energy efficient clustering protocol for cognitive radio sensor networks. CogLEACH uses the number of vacant channels as a weight in the probability of each node to become a cluster head. We show that CogLEACH improves the throughput and lifetime of the network compared to the regular LEACH protocol that is operating in the same settings.Index Terms-clustering, cognitive radio, wireless sensor networks.
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