Convergecast with aggregatable data classes

Fangfei Chen, Matthew P. Johnson, Amotz Bar-Noy, Thomas F. La Porta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Data-gathering or convergecast problems have traditionally been studied in two combinations of settings: one-shot scheduling of data items with no aggregation, and periodic scheduling of data items with full aggregation meaning that any number of unit-size data items can, if available, be aggregated into a single (unit-size) data item (e.g., by summing or averaging values). In this paper, we extend beyond these problem settings in two ways. First, we study a) one-shot throughput maximization in settings with aggregation and b) periodic scheduling in settings without aggregation. Second, we generalize the notion of aggregatability in both one-shot and periodic scheduling beyond the binary choice of either all sets of items being aggregatable or none being so. Modeling the presence of multiple semantic data types (e.g., target counts to be summed and temperature readings to be averaged), we partition data items into classes, whereby items are aggregatable if they belong to the same class, in both periodic and non-periodic settings. For these two problems we provide guaranteed approximations and heuristics, for a variety of general and special cases. We then evaluate the algorithms in a systematic simulation study, both under the conditions in which our provable guarantees apply and in more general settings, where we find the algorithms continue to perform well on typical problem inputs.

Original languageEnglish (US)
Title of host publication2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012
Pages148-156
Number of pages9
Volume1
DOIs
StatePublished - Nov 1 2012
Event2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012 - Seoul, Korea, Republic of
Duration: Jun 18 2012Jun 21 2012

Other

Other2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012
CountryKorea, Republic of
CitySeoul
Period6/18/126/21/12

Fingerprint

Agglomeration
Scheduling
Semantics
Throughput
Temperature

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Chen, F., Johnson, M. P., Bar-Noy, A., & La Porta, T. F. (2012). Convergecast with aggregatable data classes. In 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012 (Vol. 1, pp. 148-156). [6275771] https://doi.org/10.1109/SECON.2012.6275771
Chen, Fangfei ; Johnson, Matthew P. ; Bar-Noy, Amotz ; La Porta, Thomas F. / Convergecast with aggregatable data classes. 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012. Vol. 1 2012. pp. 148-156
@inproceedings{0d2d8bbe8d39467a8a51d8cd54bc13c1,
title = "Convergecast with aggregatable data classes",
abstract = "Data-gathering or convergecast problems have traditionally been studied in two combinations of settings: one-shot scheduling of data items with no aggregation, and periodic scheduling of data items with full aggregation meaning that any number of unit-size data items can, if available, be aggregated into a single (unit-size) data item (e.g., by summing or averaging values). In this paper, we extend beyond these problem settings in two ways. First, we study a) one-shot throughput maximization in settings with aggregation and b) periodic scheduling in settings without aggregation. Second, we generalize the notion of aggregatability in both one-shot and periodic scheduling beyond the binary choice of either all sets of items being aggregatable or none being so. Modeling the presence of multiple semantic data types (e.g., target counts to be summed and temperature readings to be averaged), we partition data items into classes, whereby items are aggregatable if they belong to the same class, in both periodic and non-periodic settings. For these two problems we provide guaranteed approximations and heuristics, for a variety of general and special cases. We then evaluate the algorithms in a systematic simulation study, both under the conditions in which our provable guarantees apply and in more general settings, where we find the algorithms continue to perform well on typical problem inputs.",
author = "Fangfei Chen and Johnson, {Matthew P.} and Amotz Bar-Noy and {La Porta}, {Thomas F.}",
year = "2012",
month = "11",
day = "1",
doi = "10.1109/SECON.2012.6275771",
language = "English (US)",
isbn = "9781467319058",
volume = "1",
pages = "148--156",
booktitle = "2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012",

}

Chen, F, Johnson, MP, Bar-Noy, A & La Porta, TF 2012, Convergecast with aggregatable data classes. in 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012. vol. 1, 6275771, pp. 148-156, 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012, Seoul, Korea, Republic of, 6/18/12. https://doi.org/10.1109/SECON.2012.6275771

Convergecast with aggregatable data classes. / Chen, Fangfei; Johnson, Matthew P.; Bar-Noy, Amotz; La Porta, Thomas F.

2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012. Vol. 1 2012. p. 148-156 6275771.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Convergecast with aggregatable data classes

AU - Chen, Fangfei

AU - Johnson, Matthew P.

AU - Bar-Noy, Amotz

AU - La Porta, Thomas F.

PY - 2012/11/1

Y1 - 2012/11/1

N2 - Data-gathering or convergecast problems have traditionally been studied in two combinations of settings: one-shot scheduling of data items with no aggregation, and periodic scheduling of data items with full aggregation meaning that any number of unit-size data items can, if available, be aggregated into a single (unit-size) data item (e.g., by summing or averaging values). In this paper, we extend beyond these problem settings in two ways. First, we study a) one-shot throughput maximization in settings with aggregation and b) periodic scheduling in settings without aggregation. Second, we generalize the notion of aggregatability in both one-shot and periodic scheduling beyond the binary choice of either all sets of items being aggregatable or none being so. Modeling the presence of multiple semantic data types (e.g., target counts to be summed and temperature readings to be averaged), we partition data items into classes, whereby items are aggregatable if they belong to the same class, in both periodic and non-periodic settings. For these two problems we provide guaranteed approximations and heuristics, for a variety of general and special cases. We then evaluate the algorithms in a systematic simulation study, both under the conditions in which our provable guarantees apply and in more general settings, where we find the algorithms continue to perform well on typical problem inputs.

AB - Data-gathering or convergecast problems have traditionally been studied in two combinations of settings: one-shot scheduling of data items with no aggregation, and periodic scheduling of data items with full aggregation meaning that any number of unit-size data items can, if available, be aggregated into a single (unit-size) data item (e.g., by summing or averaging values). In this paper, we extend beyond these problem settings in two ways. First, we study a) one-shot throughput maximization in settings with aggregation and b) periodic scheduling in settings without aggregation. Second, we generalize the notion of aggregatability in both one-shot and periodic scheduling beyond the binary choice of either all sets of items being aggregatable or none being so. Modeling the presence of multiple semantic data types (e.g., target counts to be summed and temperature readings to be averaged), we partition data items into classes, whereby items are aggregatable if they belong to the same class, in both periodic and non-periodic settings. For these two problems we provide guaranteed approximations and heuristics, for a variety of general and special cases. We then evaluate the algorithms in a systematic simulation study, both under the conditions in which our provable guarantees apply and in more general settings, where we find the algorithms continue to perform well on typical problem inputs.

UR - http://www.scopus.com/inward/record.url?scp=84867972703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867972703&partnerID=8YFLogxK

U2 - 10.1109/SECON.2012.6275771

DO - 10.1109/SECON.2012.6275771

M3 - Conference contribution

SN - 9781467319058

VL - 1

SP - 148

EP - 156

BT - 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012

ER -

Chen F, Johnson MP, Bar-Noy A, La Porta TF. Convergecast with aggregatable data classes. In 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012. Vol. 1. 2012. p. 148-156. 6275771 https://doi.org/10.1109/SECON.2012.6275771