Abstract
We study how knowledge of a moving object's path can be used to select sensors in a network that maximize the coverage of its path. We propose a mobility model that combines the shortest path between two points with random movement. Given the mobility model, we have different knowledge levels in terms of knowing nothing, the start, destination, movement model, and the whole path. We present a framework to assign weights to points on the movement grid based on the knowledge level and to greedily select sensors to maximize weighted coverage of the grid. We show in simulations of random movement that knowing more information generally has better performance, but for certain levels of knowledge, this decreases as the randomness increases. We also find that it is possible to obtain the maximum coverage by assuming the target follows the shortest path when the randomness is below a certain threshold. We verified these results on real human mobility traces.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 103-110 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538654705 |
| DOIs | |
| State | Published - Oct 25 2018 |
| Event | 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018 - Bronx, United States Duration: Jun 18 2018 → Jun 19 2018 |
Other
| Other | 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018 |
|---|---|
| Country | United States |
| City | Bronx |
| Period | 6/18/18 → 6/19/18 |
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Keywords
- mobility model
- path coverage
- path exposure
- sensor coverage
- sensor selection
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems and Management
Cite this
Leveraging knowledge for path exposure. / Shamoun, Simon; Mei, Jie; Abdelzaher, Tarek; Bar-Noy, Amotz.
Proceedings - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 103-110 8510966.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Leveraging knowledge for path exposure
AU - Shamoun, Simon
AU - Mei, Jie
AU - Abdelzaher, Tarek
AU - Bar-Noy, Amotz
PY - 2018/10/25
Y1 - 2018/10/25
N2 - We study how knowledge of a moving object's path can be used to select sensors in a network that maximize the coverage of its path. We propose a mobility model that combines the shortest path between two points with random movement. Given the mobility model, we have different knowledge levels in terms of knowing nothing, the start, destination, movement model, and the whole path. We present a framework to assign weights to points on the movement grid based on the knowledge level and to greedily select sensors to maximize weighted coverage of the grid. We show in simulations of random movement that knowing more information generally has better performance, but for certain levels of knowledge, this decreases as the randomness increases. We also find that it is possible to obtain the maximum coverage by assuming the target follows the shortest path when the randomness is below a certain threshold. We verified these results on real human mobility traces.
AB - We study how knowledge of a moving object's path can be used to select sensors in a network that maximize the coverage of its path. We propose a mobility model that combines the shortest path between two points with random movement. Given the mobility model, we have different knowledge levels in terms of knowing nothing, the start, destination, movement model, and the whole path. We present a framework to assign weights to points on the movement grid based on the knowledge level and to greedily select sensors to maximize weighted coverage of the grid. We show in simulations of random movement that knowing more information generally has better performance, but for certain levels of knowledge, this decreases as the randomness increases. We also find that it is possible to obtain the maximum coverage by assuming the target follows the shortest path when the randomness is below a certain threshold. We verified these results on real human mobility traces.
KW - mobility model
KW - path coverage
KW - path exposure
KW - sensor coverage
KW - sensor selection
UR - http://www.scopus.com/inward/record.url?scp=85057136437&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057136437&partnerID=8YFLogxK
U2 - 10.1109/DCOSS.2018.00021
DO - 10.1109/DCOSS.2018.00021
M3 - Conference contribution
AN - SCOPUS:85057136437
SP - 103
EP - 110
BT - Proceedings - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -