TY - GEN
T1 - Establishing multi-cast groups in computational robotic materials
AU - Ma, Shang
AU - Hosseinmardi, Homa
AU - Farrow, Nicholas
AU - Han, Richard
AU - Correll, Nikolaus
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Approximate match query
KW - Bloom filter
KW - Component
KW - Cyberphysical systems
UR - http://www.scopus.com/inward/record.url?scp=84875481288&partnerID=8YFLogxK
U2 - 10.1109/GreenCom.2012.74
DO - 10.1109/GreenCom.2012.74
M3 - Conference proceeding contribution
AN - SCOPUS:84875481288
SN - 9780769548654
T3 - Proceedings - 2012 IEEE Int. Conf. on Green Computing and Communications, GreenCom 2012, Conf. on Internet of Things, iThings 2012 and Conf. on Cyber, Physical and Social Computing, CPSCom 2012
SP - 311
EP - 316
BT - Proceedings - 2012 IEEE Int. Conf. on Green Computing and Communications, GreenCom 2012, Conf. on Internet of Things, iThings 2012 and Conf. on Cyber, Physical and Social Computing, CPSCom 2012
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
T2 - 2012 IEEE International Conference on Green Computing and Communications, GreenCom 2012, 2012 IEEE International Conference on Internet of Things, iThings 2012 and 5th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012
Y2 - 20 November 2012 through 23 November 2012
ER -