Node discovery and interpretation in unstructured resource-constrained environments

A main characteristic of the Internet of Things networks is the large number of resource-constrained nodes, which, however, are required to perform reliable and fast data exchange; often of critical nature; over highly unpredictable and dynamic connections and network topologies. Reducing the number of message exchanges and retransmission of data, while guaranteeing the lifetime of the data session duration as per service requirements are vital for enabling scenarios such as smart home, intelligent transportation systems, eHealth, etc. This paper proposes a novel theoretical model for the discovery, linking and interpretation of nodes in unstructured and resource-constrained network environments and their interrelated and collective use for the delivery of smart services. The model is based on a basic mathematical approach, which describes and predicts the success of human interactions in the context of long-term relationships and identifies several key variables in the context of communications in resource-constrained environments. The general theoretical model is described and several algorithms are proposed as part of the node discovery, identification, and linking processes in relation to the key variables. The algorithms are each evaluated by simulations to determine which parameters are key for optimal node grouping.