TY - GEN
T1 - Enabling reliable, asynchronous, and bidirectional communication in sensor networks over white spaces
AU - Saifullah, Abusayeed
AU - Lu, Chenyang
AU - Rahman, Mahbubur
AU - Liu, Jie
AU - Ismail, Dali
AU - Chandra, Ranveer
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Low-Power Wide-Area Network (LPWAN) heralds a promising class of technology to overcome the range limits and scalability challenges in traditional wireless sensor networks. Recently proposed Sensor Network over White Spaces (SNOW) technology is particularly attractive due to the availability and advantages of TV spectrum in long-range communication. This paper proposes a new design of SNOW that is asynchronous, reliable, and robust. It represents the first highly scalable LPWAN over TV white spaces to support reliable, asynchronous, bi-directional, and concurrent communication between numerous sensors and a base station. This is achieved through a set of novel techniques. This new design of SNOW has an OFDM based physical layer that adopts robust modulation scheme and allows the base station using a single antenna-radio (1) to send different data to different nodes concurrently and (2) to receive concurrent transmissions made by the sensor nodes asynchronously. It has a lightweight MAC protocol that (1) efficiently implements per-transmission acknowledgments of the asynchronous transmissions by exploiting the adopted OFDM design; (2) combines CSMA/CA and location-aware spectrum allocation for mitigating hidden terminal effects, thus enhancing the flexibility of the nodes in transmitting asynchronously. Hardware experiments through deployments in three radio environments - in a large metropolitan city, in a rural area, and in an indoor environment - as well as large-scale simulations demonstrated that the new SNOW design drastically outperforms other LPWAN technologies in terms of scalability, energy, and latency.
AB - Low-Power Wide-Area Network (LPWAN) heralds a promising class of technology to overcome the range limits and scalability challenges in traditional wireless sensor networks. Recently proposed Sensor Network over White Spaces (SNOW) technology is particularly attractive due to the availability and advantages of TV spectrum in long-range communication. This paper proposes a new design of SNOW that is asynchronous, reliable, and robust. It represents the first highly scalable LPWAN over TV white spaces to support reliable, asynchronous, bi-directional, and concurrent communication between numerous sensors and a base station. This is achieved through a set of novel techniques. This new design of SNOW has an OFDM based physical layer that adopts robust modulation scheme and allows the base station using a single antenna-radio (1) to send different data to different nodes concurrently and (2) to receive concurrent transmissions made by the sensor nodes asynchronously. It has a lightweight MAC protocol that (1) efficiently implements per-transmission acknowledgments of the asynchronous transmissions by exploiting the adopted OFDM design; (2) combines CSMA/CA and location-aware spectrum allocation for mitigating hidden terminal effects, thus enhancing the flexibility of the nodes in transmitting asynchronously. Hardware experiments through deployments in three radio environments - in a large metropolitan city, in a rural area, and in an indoor environment - as well as large-scale simulations demonstrated that the new SNOW design drastically outperforms other LPWAN technologies in terms of scalability, energy, and latency.
KW - LPWAN
KW - MAC Protoco
KW - OFDM
KW - Sensor Network
KW - White space
UR - https://www.scopus.com/pages/publications/85052024367
U2 - 10.1145/3131672.3131676
DO - 10.1145/3131672.3131676
M3 - Conference contribution
AN - SCOPUS:85052024367
T3 - SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems
BT - SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems
A2 - Eskicioglu, Rasit
PB - Association for Computing Machinery, Inc
T2 - 15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017
Y2 - 6 November 2017 through 8 November 2017
ER -