Energy-Optimal Data Aggregation and Dissemination for the Internet of Things
Fitzgerald, Emma LU
; Pioro, Michal
LU
and Tomaszewski, Artur
(2018)
In IEEE Internet of Things Journal
5(2).
p.955-969
- Abstract
- Established approaches to data aggregation in wireless
sensor networks (WSNs) do not cover the variety of new use
cases developing with the advent of the Internet of Things. In particular,
the current push towards fog computing, in which control,
computation, and storage are moved to nodes close to the network
edge, induces a need to collect data at multiple sinks, rather
than the single sink typically considered in WSN aggregation
algorithms. Moreover, for machine-to-machine communication
scenarios, actuators subscribing to sensor measurements may also
be present, in which case data should be not only aggregated and
processed in-network, but also disseminated to actuator nodes. In
this paper, we... (More) - Established approaches to data aggregation in wireless
sensor networks (WSNs) do not cover the variety of new use
cases developing with the advent of the Internet of Things. In particular,
the current push towards fog computing, in which control,
computation, and storage are moved to nodes close to the network
edge, induces a need to collect data at multiple sinks, rather
than the single sink typically considered in WSN aggregation
algorithms. Moreover, for machine-to-machine communication
scenarios, actuators subscribing to sensor measurements may also
be present, in which case data should be not only aggregated and
processed in-network, but also disseminated to actuator nodes. In
this paper, we present mixed-integer programming formulations
and algorithms for the problem of energy-optimal routing and
multiple-sink aggregation, as well as joint aggregation and
dissemination, of sensor measurement data in IoT edge networks.
We consider optimisation of the network for both minimal total
energy usage, and min-max per-node energy usage. We also
provide a formulation and algorithm for throughput-optimal
scheduling of transmissions under the physical interference model
in the pure aggregation case. We have conducted a numerical
study to compare the energy required for the two use cases, as
well as the time to solve them, in generated network scenarios
with varying topologies and between 10 and 40 nodes. Although
aggregation only accounts for less than 15% of total energy
usage in all cases tested, it provides substantial energy savings.
Our results show more than 13 times greater energy usage for
40-node networks using direct, shortest-path flows from sensors
to actuators, compared with our aggregation and dissemination
solutions. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7f227e8b-6164-452a-ba8b-fb5057d912c0
- author
- Fitzgerald, Emma
LU
; Pioro, Michal
LU
and Tomaszewski, Artur
- organization
- publishing date
- 2018-02-08
- type
- Contribution to journal
- publication status
- published
- subject
- in
- IEEE Internet of Things Journal
- volume
- 5
- issue
- 2
- pages
- 15 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85041513689
- ISSN
- 2327-4662
- DOI
- 10.1109/JIOT.2018.2803792
- project
- ELLIIT LU P01: WP2 Networking solutions
- language
- English
- LU publication?
- yes
- id
- 7f227e8b-6164-452a-ba8b-fb5057d912c0
- date added to LUP
- 2018-02-06 09:37:15
- date last changed
- 2022-05-03 00:55:03
@article{7f227e8b-6164-452a-ba8b-fb5057d912c0,
abstract = {{Established approaches to data aggregation in wireless<br/>sensor networks (WSNs) do not cover the variety of new use<br/>cases developing with the advent of the Internet of Things. In particular,<br/>the current push towards fog computing, in which control,<br/>computation, and storage are moved to nodes close to the network<br/>edge, induces a need to collect data at multiple sinks, rather<br/>than the single sink typically considered in WSN aggregation<br/>algorithms. Moreover, for machine-to-machine communication<br/>scenarios, actuators subscribing to sensor measurements may also<br/>be present, in which case data should be not only aggregated and<br/>processed in-network, but also disseminated to actuator nodes. In<br/>this paper, we present mixed-integer programming formulations<br/>and algorithms for the problem of energy-optimal routing and<br/>multiple-sink aggregation, as well as joint aggregation and<br/>dissemination, of sensor measurement data in IoT edge networks.<br/>We consider optimisation of the network for both minimal total<br/>energy usage, and min-max per-node energy usage. We also<br/>provide a formulation and algorithm for throughput-optimal<br/>scheduling of transmissions under the physical interference model<br/>in the pure aggregation case. We have conducted a numerical<br/>study to compare the energy required for the two use cases, as<br/>well as the time to solve them, in generated network scenarios<br/>with varying topologies and between 10 and 40 nodes. Although<br/>aggregation only accounts for less than 15% of total energy<br/>usage in all cases tested, it provides substantial energy savings.<br/>Our results show more than 13 times greater energy usage for<br/>40-node networks using direct, shortest-path flows from sensors<br/>to actuators, compared with our aggregation and dissemination<br/>solutions.}},
author = {{Fitzgerald, Emma and Pioro, Michal and Tomaszewski, Artur}},
issn = {{2327-4662}},
language = {{eng}},
month = {{02}},
number = {{2}},
pages = {{955--969}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Internet of Things Journal}},
title = {{Energy-Optimal Data Aggregation and Dissemination for the Internet of Things}},
url = {{https://lup.lub.lu.se/search/files/38124953/Fitzgerald_Pioro_Tomaszewski_data_aggregation.pdf}},
doi = {{10.1109/JIOT.2018.2803792}},
volume = {{5}},
year = {{2018}},
}