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Establishing Multi-cast Groups in Computational Robotic Materials

We study an efficient ad hoc multicast communication protocol for next-generation large-scale distributed cyber-physical systems that we dub Computational Robotic Materials (CRMs). CRMs tightly integrate sensing, actuation, computation and communication, and can enable materials that can change their shape, appearance and function in response to local sensing and distributed information processing. As CRMs potentially consist of thousands of nodes with limited processing power and memory, communication in such systems poses serious challenges. For example, when processing a gesture recorded by the CRM, only a subset of nodes involved in its detection should communicate amongst themselves for distributed proessing. In previous work, we proposed a Bloom filter-based approach to label the multicast group with an approximate error-resilient multicast tag that captures the temporal and spatial characteristics of the sensor group. A Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. We describe our Bloom filter-based multicast communication (BMC) protocol, and report experimental results using a 48-node Computational Robotic Material test-bed engaged in shape and gesture recognition.