TY - JOUR
T1 - Age of Information in Multihop Connections With Tributary Traffic and No Preemption
AU - Chiariotti, Federico
AU - Vikhrova, Olga
AU - Soret, Beatriz
AU - Popovski, Petar
PY - 2022/10/1
Y1 - 2022/10/1
N2 - Age of Information (AoI) has gained significant attention from the research community because of its applications to Internet of Things (IoT) monitoring and control. In this work, we treat multihop connections over queuing networks with tributary flows and non-preemptive service: packets cannot be discarded because they are utilized for other system objectives, such as data analytics. Without preemption, the key tool for optimizing AoI is then the scheduling policy between the different data flows at each intermediate node. This is the subject of our analysis, along with the impact of packet erasure on the age. We derive upper and lower bounds for the average AoI considering several queuing policies in arbitrary network topologies, and present the results in different scenarios. Network topology, tributary traffic load, and link characteristics such as packet erasure generate complex trade-offs, which affect the optimal operation point and the age performance. The scheduling strategy at each node can also affect performance and fairness among users, particularly at critical bottleneck links, which have a significant impact on the overall performance of the whole network.
AB - Age of Information (AoI) has gained significant attention from the research community because of its applications to Internet of Things (IoT) monitoring and control. In this work, we treat multihop connections over queuing networks with tributary flows and non-preemptive service: packets cannot be discarded because they are utilized for other system objectives, such as data analytics. Without preemption, the key tool for optimizing AoI is then the scheduling policy between the different data flows at each intermediate node. This is the subject of our analysis, along with the impact of packet erasure on the age. We derive upper and lower bounds for the average AoI considering several queuing policies in arbitrary network topologies, and present the results in different scenarios. Network topology, tributary traffic load, and link characteristics such as packet erasure generate complex trade-offs, which affect the optimal operation point and the age performance. The scheduling strategy at each node can also affect performance and fairness among users, particularly at critical bottleneck links, which have a significant impact on the overall performance of the whole network.
KW - Queueing analysis
KW - Analytical models
KW - Spread spectrum communication
KW - Computational modeling
KW - Satellites
KW - Internet of Things
KW - Routing
UR - https://ieeexplore.ieee.org/document/9869878/
U2 - 10.1109/TCOMM.2022.3202946
DO - 10.1109/TCOMM.2022.3202946
M3 - Journal article
SN - 1558-0857
VL - 70
SP - 6718
EP - 6733
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 10
M1 - 9869878
ER -