Abstract
With the scale of data generated and the increasing computing capabilities at the IoT Edge, moving intelligence to end-devices and gateways in IoT networks is within the realms of possibility. There are tremendous hopes on how self-awareness in IoT networks could be helpful to solve a variety of smart city use case problems. Self-awareness implies self-teaching; the network devices autonomously become smarter with AI. Collective intelligence is a subset of self-awareness but not yet formalized and exploited at the Edge of IoT networks. In this paper, current mathematical models for Collective Intelligence are studied and compared. Their suitability for on-device machine learning deployment is also explored. Moreover, it attempts to lay down a framework using graph theory coupled with artificial neural networks to model a collective intelligence system applicable to a specific scenario.
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