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Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach

Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach
Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach
The proliferation of “Things” over a network creates the Internet of Things (IoT), where sensors integrate to collect data from the environment over long periods of time. The growth of IoT applications will inevitably involve co-locating multiple wireless sensor networks, each serving different applications with, possibly, different needs and constraints. Since energy is scarce in sensor nodes equipped with non-rechargeable batteries, energy harvesting technologies have been the focus of research in recent years. However, new problems arise as a result of their wide spatio-temporal variation. Such a shortcoming can be avoided if co-located networks cooperate with each other and share their available energy. Due to their unique characteristics and different owners, recently, we proposed a negotiation approach to deal with conflict of preferences. Unfortunately, negotiation can be impractical with a large number of participants, especially in an open environment. Given this, we introduce a new partner selection technique based on multi-armed bandits (MAB), that enables each node to learn the strategy that optimises its energy resources in the long term. Our results show that the proposed solution allows networks to repeatedly learn the current best energy partner in a dynamic environment. The performance of such a technique is evaluated through simulation and shows that a network can achieve an efficiency of 72% against the optimal strategy in the most challenging scenario studied in this work.
Reinforcement Learning, Automated negotiation, Wireless sensor networks, Agent-based sensor network, Energy management, Multi-armed bandit based learning
1570-8705
Ortega, Andre P.
f7290834-ef51-4e94-b8b0-d164485482c0
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Ortega, Andre P.
f7290834-ef51-4e94-b8b0-d164485482c0
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020

Ortega, Andre P., Ramchurn, Sarvapali, Tran-Thanh, Long and Merrett, Geoff (2021) Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach. Ad Hoc Networks, 112, [102354]. (doi:10.1016/j.adhoc.2020.102354).

Record type: Article

Abstract

The proliferation of “Things” over a network creates the Internet of Things (IoT), where sensors integrate to collect data from the environment over long periods of time. The growth of IoT applications will inevitably involve co-locating multiple wireless sensor networks, each serving different applications with, possibly, different needs and constraints. Since energy is scarce in sensor nodes equipped with non-rechargeable batteries, energy harvesting technologies have been the focus of research in recent years. However, new problems arise as a result of their wide spatio-temporal variation. Such a shortcoming can be avoided if co-located networks cooperate with each other and share their available energy. Due to their unique characteristics and different owners, recently, we proposed a negotiation approach to deal with conflict of preferences. Unfortunately, negotiation can be impractical with a large number of participants, especially in an open environment. Given this, we introduce a new partner selection technique based on multi-armed bandits (MAB), that enables each node to learn the strategy that optimises its energy resources in the long term. Our results show that the proposed solution allows networks to repeatedly learn the current best energy partner in a dynamic environment. The performance of such a technique is evaluated through simulation and shows that a network can achieve an efficiency of 72% against the optimal strategy in the most challenging scenario studied in this work.

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AdHocNetworks-AOSRLTGM - Accepted Manuscript
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Accepted/In Press date: 3 November 2020
e-pub ahead of print date: 7 November 2020
Published date: March 2021
Keywords: Reinforcement Learning, Automated negotiation, Wireless sensor networks, Agent-based sensor network, Energy management, Multi-armed bandit based learning

Identifiers

Local EPrints ID: 445733
URI: http://eprints.soton.ac.uk/id/eprint/445733
ISSN: 1570-8705
PURE UUID: c8133fa1-2344-491b-971b-e916318b063f
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894

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Date deposited: 06 Jan 2021 17:44
Last modified: 06 Jan 2021 17:44

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Contributors

Author: Andre P. Ortega
Author: Sarvapali Ramchurn ORCID iD
Author: Long Tran-Thanh ORCID iD
Author: Geoff Merrett ORCID iD

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