Simultaneous placement and scheduling of sensors
We consider the problem of monitoring spatial phenomena, such as road speeds on a highway, using wireless sensors with limited battery life. A central question is to decide where to locate these sensors to best predict the phenomenon at the unsensed locations. However, given the power constraints, w...
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| Published in: | 2009 International Conference on Information Processing in Sensor Networks pp. 181 - 192 |
|---|---|
| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
Washington, DC, USA
IEEE Computer Society
13.04.2009
IEEE |
| Series: | ACM Conferences |
| Subjects: |
Theory of computation
> Design and analysis of algorithms
> Approximation algorithms analysis
> Scheduling algorithms
Theory of computation
> Design and analysis of algorithms
> Online algorithms
> Online learning algorithms
> Scheduling algorithms
|
| ISBN: | 1424451086, 9781424451081 |
| Online Access: | Get full text |
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| Abstract | We consider the problem of monitoring spatial phenomena, such as road speeds on a highway, using wireless sensors with limited battery life. A central question is to decide where to locate these sensors to best predict the phenomenon at the unsensed locations. However, given the power constraints, we also need to determine when to selectively activate these sensors in order to maximize the performance while satisfying lifetime requirements. Traditionally, these two problems of sensor placement and scheduling have been considered separately from each other; one first decides where to place the sensors, and then when to activate them. In this paper, we present an efficient algorithm, ESPASS, that simultaneously optimizes the placement and the schedule. We prove that ESPASS provides a constant-factor approximation to the optimal solution of this NP-hard optimization problem. A salient feature of our approach is that it obtains “balanced” schedules that perform uniformly well over time, rather than only on average. We then extend the algorithm to allow for a smooth power-accuracy tradeoff. Our algorithm applies to complex settings where the sensing quality of a set of sensors is measured, e.g., in the improvement of prediction accuracy (more formally, to situations where the sensing quality function is submodular). We present extensive empirical studies on several sensing tasks, and our results show that simultaneously placing and scheduling gives drastically improved performance compared to separate placement and scheduling (e.g., a 33% improvement in network lifetime on the traffic prediction task). |
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| AbstractList | We consider the problem of monitoring spatial phenomena, such as road speeds on a highway, using wireless sensors with limited battery life. A central question is to decide where to locate these sensors to best predict the phenomenon at the unsensed locations. However, given the power constraints, we also need to determine when to selectively activate these sensors in order to maximize the performance while satisfying lifetime requirements. Traditionally, these two problems of sensor placement and scheduling have been considered separately from each other; one first decides where to place the sensors, and then when to activate them. In this paper, we present an efficient algorithm, ESPASS, that simultaneously optimizes the placement and the schedule. We prove that ESPASS provides a constant-factor approximation to the optimal solution of this NP-hard optimization problem. A salient feature of our approach is that it obtains ldquobalancedrdquo schedules that perform uniformly well over time, rather than only on average. We then extend the algorithm to allow for a smooth power-accuracy tradeoff. Our algorithm applies to complex settings where the sensing quality of a set of sensors is measured, e.g., in the improvement of prediction accuracy (more formally, to situations where the sensing quality function is submodular). We present extensive empirical studies on several sensing tasks, and our results show that simultaneously placing and scheduling gives drastically improved performance compared to separate placement and scheduling (e.g., a 33% improvement in network lifetime on the traffic prediction task). We consider the problem of monitoring spatial phenomena, such as road speeds on a highway, using wireless sensors with limited battery life. A central question is to decide where to locate these sensors to best predict the phenomenon at the unsensed locations. However, given the power constraints, we also need to determine when to selectively activate these sensors in order to maximize the performance while satisfying lifetime requirements. Traditionally, these two problems of sensor placement and scheduling have been considered separately from each other; one first decides where to place the sensors, and then when to activate them. In this paper, we present an efficient algorithm, ESPASS, that simultaneously optimizes the placement and the schedule. We prove that ESPASS provides a constant-factor approximation to the optimal solution of this NP-hard optimization problem. A salient feature of our approach is that it obtains “balanced” schedules that perform uniformly well over time, rather than only on average. We then extend the algorithm to allow for a smooth power-accuracy tradeoff. Our algorithm applies to complex settings where the sensing quality of a set of sensors is measured, e.g., in the improvement of prediction accuracy (more formally, to situations where the sensing quality function is submodular). We present extensive empirical studies on several sensing tasks, and our results show that simultaneously placing and scheduling gives drastically improved performance compared to separate placement and scheduling (e.g., a 33% improvement in network lifetime on the traffic prediction task). |
| Author | Gupta, Anupam Krause, Andreas Rajagopal, Ram Guestrin, Carlos |
| Author_xml | – sequence: 1 givenname: Andreas surname: Krause fullname: Krause, Andreas organization: Caltech, USA – sequence: 2 givenname: Ram surname: Rajagopal fullname: Rajagopal, Ram organization: UC Berkeley, USA – sequence: 3 givenname: Anupam surname: Gupta fullname: Gupta, Anupam organization: Carnegie Mellon University, USA – sequence: 4 givenname: Carlos surname: Guestrin fullname: Guestrin, Carlos organization: Carnegie Mellon University, USA |
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| PublicationTitle | 2009 International Conference on Information Processing in Sensor Networks |
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| Snippet | We consider the problem of monitoring spatial phenomena, such as road speeds on a highway, using wireless sensors with limited battery life. A central question... |
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| SubjectTerms | Accuracy Approximation algorithms Batteries Computer systems organization -- Dependable and fault-tolerant systems and networks General and reference -- Cross-computing tools and techniques -- Performance Mathematics of computing -- Mathematical analysis -- Functional analysis -- Approximation Mathematics of computing -- Mathematical analysis -- Mathematical optimization Monitoring Networks -- Network performance evaluation Networks -- Network services -- Network management Processor scheduling Road transportation Scheduling algorithm Sensor networks Sensor phenomena and characterization Telecommunication traffic Theory of computation -- Design and analysis of algorithms -- Approximation algorithms analysis Theory of computation -- Design and analysis of algorithms -- Approximation algorithms analysis -- Scheduling algorithms Theory of computation -- Design and analysis of algorithms -- Mathematical optimization Theory of computation -- Design and analysis of algorithms -- Online algorithms -- Online learning algorithms -- Scheduling algorithms Theory of computation -- Theory and algorithms for application domains -- Machine learning theory -- Reinforcement learning -- Sequential decision making Wireless sensor networks |
| Title | Simultaneous placement and scheduling of sensors |
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