TY - GEN
T1 - Into the SMOG
T2 - 13th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2016
AU - Corbalán, Pablo
AU - Marfievici, Ramona
AU - Cionca, Victor
AU - O'Shea, Donna
AU - Pesch, Dirk
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/11
Y1 - 2017/1/11
N2 - Previous research has shown that centralized network control in Wireless Sensor Networks (WSNs) can lead to improved network lifetime, benefit reliability, help to diagnose and localize network failures, assist network recovery, and lead to optimal routing and transmission scheduling. A stepping stone to centralized network control is to build and maintain a complete network topology model that scales and reacts to the network dynamics that occur in low-power wireless networks. We propose SMOG as a mechanism to build and maintain a centralized full network topology model using probabilistic data structures. Extensive analysis of the proposed approach in both simulation and two testbeds shows that SMOG can build a complete model of a WSN of over 100 nodes with 98% accuracy in less than four minutes. Our approach also offers fast recovery from heavy network interference, recovering model accuracy to 98% in less than two and a half minutes.
AB - Previous research has shown that centralized network control in Wireless Sensor Networks (WSNs) can lead to improved network lifetime, benefit reliability, help to diagnose and localize network failures, assist network recovery, and lead to optimal routing and transmission scheduling. A stepping stone to centralized network control is to build and maintain a complete network topology model that scales and reacts to the network dynamics that occur in low-power wireless networks. We propose SMOG as a mechanism to build and maintain a centralized full network topology model using probabilistic data structures. Extensive analysis of the proposed approach in both simulation and two testbeds shows that SMOG can build a complete model of a WSN of over 100 nodes with 98% accuracy in less than four minutes. Our approach also offers fast recovery from heavy network interference, recovering model accuracy to 98% in less than two and a half minutes.
UR - https://www.scopus.com/pages/publications/85013324761
U2 - 10.1109/MASS.2016.025
DO - 10.1109/MASS.2016.025
M3 - Conference proceeding
AN - SCOPUS:85013324761
T3 - Proceedings - 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2016
SP - 118
EP - 126
BT - Proceedings - 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 October 2016 through 13 October 2016
ER -