TY - GEN
T1 - On the optimal blacklisting threshold for link selection in wireless sensor networks
AU - Fabbri, Flavio
AU - Zuniga, Marco
AU - Puccinelli, Daniele
AU - Marrón, Pedro
PY - 2012
Y1 - 2012
N2 - Empirical studies on link blacklisting show that the delivery rate is sensitive to the calibration of the blacklisting threshold. If the calibration is too restrictive (the threshold is too high), all neighbors get blacklisted. On the other hand, if the calibration is too loose (the threshold is too low), unreliable links get selected. This paper investigates blacklisting analytically. We derive a model that accounts for the joint effect of the wireless channel (signal strength variance and coherence time) and the network (node density). The model, validated empirically with mote-class hardware, shows that blacklisting does not help if the wireless channel is stable or if the network is relatively sparse. In fact, blacklisting is most beneficial when the network is relatively dense and the channel is unstable with long coherence times.
AB - Empirical studies on link blacklisting show that the delivery rate is sensitive to the calibration of the blacklisting threshold. If the calibration is too restrictive (the threshold is too high), all neighbors get blacklisted. On the other hand, if the calibration is too loose (the threshold is too low), unreliable links get selected. This paper investigates blacklisting analytically. We derive a model that accounts for the joint effect of the wireless channel (signal strength variance and coherence time) and the network (node density). The model, validated empirically with mote-class hardware, shows that blacklisting does not help if the wireless channel is stable or if the network is relatively sparse. In fact, blacklisting is most beneficial when the network is relatively dense and the channel is unstable with long coherence times.
UR - http://www.scopus.com/inward/record.url?scp=84857200871&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28169-3_10
DO - 10.1007/978-3-642-28169-3_10
M3 - Conference contribution
AN - SCOPUS:84857200871
SN - 9783642281686
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 162
BT - Wireless Sensor Networks - 9th European Conference, EWSN 2012, Proceedings
T2 - 9th European Conference on Wireless Sensor Networks, EWSN 2012
Y2 - 15 February 2011 through 17 February 2011
ER -