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Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks. / Kirton, Jack; Bradbury, Matthew; Jhumka, Arshad.
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017. p. 2200-2205.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Kirton, J, Bradbury, M & Jhumka, A 2017, Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks. in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp. 2200-2205. https://doi.org/10.1109/ICDCS.2017.171

APA

Kirton, J., Bradbury, M., & Jhumka, A. (2017). Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (pp. 2200-2205). IEEE. https://doi.org/10.1109/ICDCS.2017.171

Vancouver

Kirton J, Bradbury M, Jhumka A. Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE. 2017. p. 2200-2205 Epub 2017 Jun 5. doi: 10.1109/ICDCS.2017.171

Author

Kirton, Jack ; Bradbury, Matthew ; Jhumka, Arshad. / Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017. pp. 2200-2205

Bibtex

@inproceedings{4049e4ebe4a64bb3a4ccfbc330e4f5e1,
title = "Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks",
abstract = "Source location privacy (SLP) is an important property for the class of asset monitoring problems in wireless sensor networks (WSNs). SLP aims to prevent an attacker from finding a valuable asset when a WSN node is broadcasting information due to the detection of the asset. Most SLP techniques focus at the routing level, with typically high message overhead. The objective of this paper is to investigate the novel problem of developing a TDMA MAC schedule that can provide SLP. We make a number of important contributions: (i) we develop a novel formalisation of a class of eavesdropping attackers and provide novel formalisations of SLP-aware data aggregation schedules (DAS), (ii) we present a decision procedure to verify whether a DAS schedule is SLP-aware, that returns a counterexample if the schedule is not, similar to model checking, and (iii) we develop a 3-stage distributed algorithm that transforms an initial DAS algorithm into a corresponding SLP-aware schedule against a specific class of eavesdroppers. Our simulation results show that the resulting SLP-aware DAS protocol reduces the capture ratio by 50% at the expense of negligable message overhead.",
keywords = "Data aggregation, Monitoring, Protocols, Routing, Safety, Schedules, Wireless sensor networks, Data Aggregation Scheduling, Source Location Privacy, TDMA, Wireless Sensor Networks",
author = "Jack Kirton and Matthew Bradbury and Arshad Jhumka",
year = "2017",
month = jul,
day = "17",
doi = "10.1109/ICDCS.2017.171",
language = "English",
isbn = "9781538617939",
pages = "2200--2205",
booktitle = "2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Source Location Privacy-Aware Data Aggregation Scheduling for Wireless Sensor Networks

AU - Kirton, Jack

AU - Bradbury, Matthew

AU - Jhumka, Arshad

PY - 2017/7/17

Y1 - 2017/7/17

N2 - Source location privacy (SLP) is an important property for the class of asset monitoring problems in wireless sensor networks (WSNs). SLP aims to prevent an attacker from finding a valuable asset when a WSN node is broadcasting information due to the detection of the asset. Most SLP techniques focus at the routing level, with typically high message overhead. The objective of this paper is to investigate the novel problem of developing a TDMA MAC schedule that can provide SLP. We make a number of important contributions: (i) we develop a novel formalisation of a class of eavesdropping attackers and provide novel formalisations of SLP-aware data aggregation schedules (DAS), (ii) we present a decision procedure to verify whether a DAS schedule is SLP-aware, that returns a counterexample if the schedule is not, similar to model checking, and (iii) we develop a 3-stage distributed algorithm that transforms an initial DAS algorithm into a corresponding SLP-aware schedule against a specific class of eavesdroppers. Our simulation results show that the resulting SLP-aware DAS protocol reduces the capture ratio by 50% at the expense of negligable message overhead.

AB - Source location privacy (SLP) is an important property for the class of asset monitoring problems in wireless sensor networks (WSNs). SLP aims to prevent an attacker from finding a valuable asset when a WSN node is broadcasting information due to the detection of the asset. Most SLP techniques focus at the routing level, with typically high message overhead. The objective of this paper is to investigate the novel problem of developing a TDMA MAC schedule that can provide SLP. We make a number of important contributions: (i) we develop a novel formalisation of a class of eavesdropping attackers and provide novel formalisations of SLP-aware data aggregation schedules (DAS), (ii) we present a decision procedure to verify whether a DAS schedule is SLP-aware, that returns a counterexample if the schedule is not, similar to model checking, and (iii) we develop a 3-stage distributed algorithm that transforms an initial DAS algorithm into a corresponding SLP-aware schedule against a specific class of eavesdroppers. Our simulation results show that the resulting SLP-aware DAS protocol reduces the capture ratio by 50% at the expense of negligable message overhead.

KW - Data aggregation

KW - Monitoring

KW - Protocols

KW - Routing

KW - Safety

KW - Schedules

KW - Wireless sensor networks

KW - Data Aggregation Scheduling

KW - Source Location Privacy

KW - TDMA

KW - Wireless Sensor Networks

U2 - 10.1109/ICDCS.2017.171

DO - 10.1109/ICDCS.2017.171

M3 - Conference contribution/Paper

SN - 9781538617939

SP - 2200

EP - 2205

BT - 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

PB - IEEE

ER -