Improving In-Network Computing in IoT Through Degeneracy

We present a novel way of considering in-network computing (INC), using ideas from statistical mechanics. We model the execution of a distributed computation with graphs called functional topologies, which allows us to provide a formal definition for degeneracy and redundancy in the context of INC. Degeneracy for INC is defined as the structural multiplicity of possible options available within the network to perform the same function with a given macroscopic property (e.g., delay). Two degenerate structures can partially overlap. Redundancy, on the other hand, does not allow overlapping between the functional graphs. We present an efficient algorithm to determine all these multiple options and compute both degeneracy and redundancy. Our results show that by exploiting the set of possible degenerate alternatives, we can significantly improve the successful computation rate of a symmetric function, while still being able to satisfy requirements such as delay or energy consumption.