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In a future internet of things, an increasing number of every-day objects becomes interconnected with each other. Current network solutions are not designed to connect a large number of co-located devices with different characteristics and network requirements. To cope with increasingly large and heterogeneous networks, this paper presents an 'incentive driven' networking approach that optimizes the network performance by taking into account the network goals ('incentives') of all individual devices. Incentive driven networking consists of the following steps. First, devices dynamically search for co-located devices with similar network preferences and hardware and/or software capabilities. Next, if such devices are found, communities consisting of interconnected objects with similar network expectations are formed on an ad hoc basis. Due to the similarities between the involved devices, it is easier to optimize the network performance of each individual community. Finally, different communities can cooperate with each other by activating and sharing (software or hardware) network resources. The paper describes which (future) research is needed to realize this vision and illustrates the concepts with a number of simple algorithms. Through an experimental proof-of-concept implementation with two networks of resource-constrained embedded devices, it is shown that even these simple algorithms already result in improved network performance. Finally, the paper describes a large number of example use cases that can potentially benefit from our innovative networking methodology.