Name
Abstract | ||
|---|---|---|
Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network (LPWAN) radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this paper, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength (RSS). Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61bps in the Sub-1 GHz bands. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICNP.2019.8888145 | 2019 IEEE 27th International Conference on Network Protocols (ICNP) |
Keywords | Field | DocType |
Cross-technology Communication,LoRa,Zig-Bee,Internet of Things | Byte,Wireless,Computer science,Network packet,Computer network,Throughput,Wireless mesh network,RSS,LPWAN,Payload | Conference |
ISSN | ISBN | Citations |
1092-1648 | 978-1-7281-2701-9 | 3 |
PageRank | References | Authors |
0.38 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junyang Shi | 1 | 13 | 4.94 |
| Di Mu | 2 | 5 | 1.09 |
| Mo Sha | 3 | 251 | 23.52 |