GSTAT: a program for geostatistical modelling, prediction and simulation
Computers & Geosciences
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Backcasting: adaptive sampling for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Mobility improves coverage of sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Exploiting mobility for energy efficient data collection in wireless sensor networks
Mobile Networks and Applications
Robomote: enabling mobility in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
The variance quadtree algorithm: Use for spatial sampling design
Computers & Geosciences
Decision Analysis
Distributed Mobility Management for Target Tracking in Mobile Sensor Networks
IEEE Transactions on Mobile Computing
Representing soil pollution by heavy metals using continuous limitation scores
Computers & Geosciences
A conditioned Latin hypercube method for sampling in the presence of ancillary information
Computers & Geosciences
The BikeNet mobile sensing system for cyclist experience mapping
Proceedings of the 5th international conference on Embedded networked sensor systems
A public transport system based sensor network for road surface condition monitoring
Proceedings of the 2007 workshop on Networked systems for developing regions
The Journal of Machine Learning Research
The Rise of People-Centric Sensing
IEEE Internet Computing
Decentralized control of adaptive sampling in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Optimization of mobile radioactivity monitoring networks
International Journal of Geographical Information Science
Selection and navigation of mobile sensor nodes using a sensor network
Pervasive and Mobile Computing
A sensor network for high frequency estimation of water quality constituent fluxes using surrogates
Environmental Modelling & Software
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Energy-Balanced Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Network
IEEE Transactions on Parallel and Distributed Systems
Optimizing the spatial pattern of networks for monitoring radioactive releases
Computers & Geosciences
Editorial: Introduction to this special issue on geoinformatics for environmental surveillance
Computers & Geosciences
Mobility-based communication in wireless sensor networks
IEEE Communications Magazine
Hi-index | 0.00 |
Wireless sensor networks (WSNs) play a vital role in environmental monitoring. Advances in mobile sensors offer new opportunities to improve phenomenon predictions by adapting spatial sampling to local variability. Two issues are relevant: which location should be sampled and which mobile sensor should move to do it? This paper proposes a form of adaptive sampling by mobile sensors according to the expected value of information (EVoI) and mobility constraints. EVoI allows decisions to be made about the location to observe. It minimises the expected costs of wrong predictions about a phenomenon using a spatially aggregated EVoI criterion. Mobility constraints allow decisions to be made about which sensor to move. A cost-distance criterion is used to minimise unwanted effects of sensor mobility on the WSN itself, such as energy depletion. We implemented our approach using a synthetic data set, representing a typical monitoring scenario with heterogeneous mobile sensors. To assess the method, it was compared with a random selection of sample locations. The results demonstrate that EVoI enables selecting the most informative locations, while mobility constraints provide the needed context for sensor selection. This paper therefore provides insights about how sensor mobility can be efficiently managed to improve knowledge about a monitored phenomenon.