TY - GEN
T1 - Softlora – A LorA-based platform for accurate and secure timing
AU - Gu, Chaojie
AU - Tan, Rui
AU - Huang, Jun
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2019/4/16
Y1 - 2019/4/16
N2 - LoRa is an emerging low-power wide-area network technology. Existing studies have focused on LoRa’s communication performance. Differently, we study two physical properties of LoRa, i.e., its performance in timing the signal propagation and the transmitters’ frequency traits. Signal timing is a basis for implementing clock synchronization, ranging, and advanced physical (PHY) layer techniques such as concurrent decoding. However, LoRa end devices do not provide PHY-layer timestamping that is needed for accurate timing. We propose a SoftLoRa design that integrates a low-power software-defined radio receiver with a LoRa transceiver to provide PHY-layer access. Experiments show that SoftLoRa achieves microseconds timing accuracy over one kilometer and in a multistory building with strong signal attenuation. Signal propagation timing is in general susceptible to a frame delay attack. We implement this attack against LoRa by a combination of stealthy jamming and delayed replay. To address the attack, we investigate the inherent frequency biases of LoRa transmitters. With an efficient signal processing algorithm, our frequency bias estimation achieves a resolution of 0.14 parts-per-million (ppm) of the channel’s central frequency. This resolution is sufficient to detect the attack that introduces an additional frequency bias of one or more ppm. In summary, this work provides an accurate and secure LoRa-based timing approach based on the SoftLoRa design.
AB - LoRa is an emerging low-power wide-area network technology. Existing studies have focused on LoRa’s communication performance. Differently, we study two physical properties of LoRa, i.e., its performance in timing the signal propagation and the transmitters’ frequency traits. Signal timing is a basis for implementing clock synchronization, ranging, and advanced physical (PHY) layer techniques such as concurrent decoding. However, LoRa end devices do not provide PHY-layer timestamping that is needed for accurate timing. We propose a SoftLoRa design that integrates a low-power software-defined radio receiver with a LoRa transceiver to provide PHY-layer access. Experiments show that SoftLoRa achieves microseconds timing accuracy over one kilometer and in a multistory building with strong signal attenuation. Signal propagation timing is in general susceptible to a frame delay attack. We implement this attack against LoRa by a combination of stealthy jamming and delayed replay. To address the attack, we investigate the inherent frequency biases of LoRa transmitters. With an efficient signal processing algorithm, our frequency bias estimation achieves a resolution of 0.14 parts-per-million (ppm) of the channel’s central frequency. This resolution is sufficient to detect the attack that introduces an additional frequency bias of one or more ppm. In summary, this work provides an accurate and secure LoRa-based timing approach based on the SoftLoRa design.
KW - Clock synchronization
KW - Jamming
KW - LoRa
KW - LoRaWAN
KW - Replay
KW - Timing
UR - http://www.scopus.com/inward/record.url?scp=85066607969&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85066607969&origin=recordpage
U2 - 10.1145/3302506.3312603
DO - 10.1145/3302506.3312603
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450362849
T3 - IPSN 2019 - Proceedings of the 2019 Information Processing in Sensor Networks
SP - 309
EP - 310
BT - IPSN 2019 - Proceedings of the 2019 Information Processing in Sensor Networks
PB - Association for Computing Machinery
T2 - 18th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2019
Y2 - 16 April 2019 through 18 April 2019
ER -