TY - JOUR
T1 - Delay-Minimized Routing in Mobile Cognitive Networks for Time-Critical Applications
AU - Tang, Feilong
AU - Tang, Can
AU - Yang, Yanqin
AU - Yang, Laurence T.
AU - Zhou, Tong
AU - Li, Jie
AU - Guo, Minyi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/6
Y1 - 2017/6
N2 - Cognitive radio significantly mitigates the spectrum scarcity for various applications built on wireless communication. Current techniques on mobile cognitive ad hoc networks (MCADNs), however, cannot be directly applied to time-critical applications due to channel interference, node mobility as well as unexpected primary user activities. In multichannel multiflow MCADNs, it becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs. First, we formulate the DMR problem with the objective of delay minimization. Next, we propose a delay prediction model based on a conflict probability. Finally, we design the minimized path delay as a routing metric, and propose a heuristic joint routing and channel assignment algorithm to solve the DMR problem. Our DMR can find out the path with a minimal end-to-end (e2e) delay for time-critical data transmission. NS2-based simulation results demonstrate that our DMR protocol significantly outperforms related proposals in terms of average e2e delay, throughput, and packet loss rate.
AB - Cognitive radio significantly mitigates the spectrum scarcity for various applications built on wireless communication. Current techniques on mobile cognitive ad hoc networks (MCADNs), however, cannot be directly applied to time-critical applications due to channel interference, node mobility as well as unexpected primary user activities. In multichannel multiflow MCADNs, it becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs. First, we formulate the DMR problem with the objective of delay minimization. Next, we propose a delay prediction model based on a conflict probability. Finally, we design the minimized path delay as a routing metric, and propose a heuristic joint routing and channel assignment algorithm to solve the DMR problem. Our DMR can find out the path with a minimal end-to-end (e2e) delay for time-critical data transmission. NS2-based simulation results demonstrate that our DMR protocol significantly outperforms related proposals in terms of average e2e delay, throughput, and packet loss rate.
KW - Signal collision
KW - channel assignment
KW - delay prediction
KW - mobile cognitive radio network
KW - routing
UR - http://www.scopus.com/inward/record.url?scp=85020637892&partnerID=8YFLogxK
U2 - 10.1109/TII.2016.2610408
DO - 10.1109/TII.2016.2610408
M3 - Article
SN - 1551-3203
VL - 13
SP - 1398
EP - 1409
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 3
M1 - 7569107
ER -