TY - GEN
T1 - Remote tracking of dynamic sources under sublinear communication costs
AU - Yun, Jihyeon
AU - Eryilmaz, Atilla
AU - Joo, Changhee
N1 - Funding Information:
The work of A. Eryilmaz is supported by the ONR Grant N00014-19-1-2621; NSF grants: CNS-NeTS-1717045, CNS-ICN-WEN-1719371, CNS-SpecEES-1824337, CNS-NeTS-2007231; and the DTRA grant: HDTRA1-18-1-0050.
Funding Information:
The work of J. Yun and C. Joo is supported in part by the NRF grant funded by the Korea government (MSIT) (No. NRF-2017K1A3A1A19070720 and No. NRF-2017R1E1A1A03070524), and in part by a Korea University Grant.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/10
Y1 - 2021/5/10
N2 - We study the remote monitoring of multiple sensors with evolving states following a Wiener Process under communication cost. We assume that the communication cost is sublinear such that the cost decreases with the number of simultaneous state updates. Such sublinear structures emerge in various settings, such as frame aggregation, and give rise to interesting unexplored tradeoffs between: updating a smaller subset of the processes earlier at a higher cost-per-process; and updating a larger subset of them later at a lower cost-per-process. We attack this problem by first providing two competitive benchmark strategies of All-at-once and Multi-threshold policies. Then, we propose a novel strategy of MAX-k policy that not only includes the two benchmark threshold-based policies as special cases, but also improves over them by better exploiting the aforementioned tradeoff. Further, we develop the GPSO optimization technique to develop an online learning algorithm that adaptively optimizes the parameters of MAX-k policy. We demonstrate that the proposed scheme outperforms the well-known online learning algorithm based on UCB index.
AB - We study the remote monitoring of multiple sensors with evolving states following a Wiener Process under communication cost. We assume that the communication cost is sublinear such that the cost decreases with the number of simultaneous state updates. Such sublinear structures emerge in various settings, such as frame aggregation, and give rise to interesting unexplored tradeoffs between: updating a smaller subset of the processes earlier at a higher cost-per-process; and updating a larger subset of them later at a lower cost-per-process. We attack this problem by first providing two competitive benchmark strategies of All-at-once and Multi-threshold policies. Then, we propose a novel strategy of MAX-k policy that not only includes the two benchmark threshold-based policies as special cases, but also improves over them by better exploiting the aforementioned tradeoff. Further, we develop the GPSO optimization technique to develop an online learning algorithm that adaptively optimizes the parameters of MAX-k policy. We demonstrate that the proposed scheme outperforms the well-known online learning algorithm based on UCB index.
UR - http://www.scopus.com/inward/record.url?scp=85113319694&partnerID=8YFLogxK
U2 - 10.1109/INFOCOMWKSHPS51825.2021.9484446
DO - 10.1109/INFOCOMWKSHPS51825.2021.9484446
M3 - Conference contribution
AN - SCOPUS:85113319694
T3 - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
BT - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
Y2 - 9 May 2021 through 12 May 2021
ER -