TY - GEN
T1 - Frugal sensor assignment
AU - Johnson, Matthew P.
AU - Rowaihy, Hosam
AU - Pizzocaro, Diego
AU - Bar-Noy, Amotz
AU - Chalmers, Stuart
AU - La Porta, Thomas
AU - Preece, Alun
PY - 2008
Y1 - 2008
N2 - When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensor-assignment problems motivated by frugality, i.e., the conservation of resources, for both static and dynamic settings. In general, the problems we study are NP-hard even to approximate, and so we focus on heuristic algorithms that perform well in practice. In the static setting, we propose a greedy centralized solution and a more sophisticated solution that uses the Generalized Assignment Problem model and can be implemented in a distributed fashion. In the dynamic setting, we give heuristic algorithms in which available sensors propose to nearby missions as they arrive. We find that the overall performance can be significantly improved if available sensors sometimes refuse to offer utility to missions they could help based on the value of the mission, the sensor's remaining energy, and (if known) the remaining target lifetime of the network. Finally, we evaluate our solutions through simulations.
AB - When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensor-assignment problems motivated by frugality, i.e., the conservation of resources, for both static and dynamic settings. In general, the problems we study are NP-hard even to approximate, and so we focus on heuristic algorithms that perform well in practice. In the static setting, we propose a greedy centralized solution and a more sophisticated solution that uses the Generalized Assignment Problem model and can be implemented in a distributed fashion. In the dynamic setting, we give heuristic algorithms in which available sensors propose to nearby missions as they arrive. We find that the overall performance can be significantly improved if available sensors sometimes refuse to offer utility to missions they could help based on the value of the mission, the sensor's remaining energy, and (if known) the remaining target lifetime of the network. Finally, we evaluate our solutions through simulations.
UR - http://www.scopus.com/inward/record.url?scp=45849133076&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-69170-9_15
DO - 10.1007/978-3-540-69170-9_15
M3 - Conference contribution
AN - SCOPUS:45849133076
SN - 3540691693
SN - 9783540691693
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 219
EP - 236
BT - Distributed Computing in Sensor Systems - 4th IEEE International Conference, DCOSS 2008, Proceedings
T2 - 4th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2008
Y2 - 11 June 2008 through 14 June 2008
ER -