10,919 research outputs found
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
The problem of modeling and predicting spatiotemporal traffic phenomena over
an urban road network is important to many traffic applications such as
detecting and forecasting congestion hotspots. This paper presents a
decentralized data fusion and active sensing (D2FAS) algorithm for mobile
sensors to actively explore the road network to gather and assimilate the most
informative data for predicting the traffic phenomenon. We analyze the time and
communication complexity of D2FAS and demonstrate that it can scale well with a
large number of observations and sensors. We provide a theoretical guarantee on
its predictive performance to be equivalent to that of a sophisticated
centralized sparse approximation for the Gaussian process (GP) model: The
computation of such a sparse approximate GP model can thus be parallelized and
distributed among the mobile sensors (in a Google-like MapReduce paradigm),
thereby achieving efficient and scalable prediction. We also theoretically
guarantee its active sensing performance that improves under various practical
environmental conditions. Empirical evaluation on real-world urban road network
data shows that our D2FAS algorithm is significantly more time-efficient and
scalable than state-of-the-art centralized algorithms while achieving
comparable predictive performance.Comment: 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012),
Extended version with proofs, 13 page
Robust Environmental Mapping by Mobile Sensor Networks
Constructing a spatial map of environmental parameters is a crucial step to
preventing hazardous chemical leakages, forest fires, or while estimating a
spatially distributed physical quantities such as terrain elevation. Although
prior methods can do such mapping tasks efficiently via dispatching a group of
autonomous agents, they are unable to ensure satisfactory convergence to the
underlying ground truth distribution in a decentralized manner when any of the
agents fail. Since the types of agents utilized to perform such mapping are
typically inexpensive and prone to failure, this results in poor overall
mapping performance in real-world applications, which can in certain cases
endanger human safety. This paper presents a Bayesian approach for robust
spatial mapping of environmental parameters by deploying a group of mobile
robots capable of ad-hoc communication equipped with short-range sensors in the
presence of hardware failures. Our approach first utilizes a variant of the
Voronoi diagram to partition the region to be mapped into disjoint regions that
are each associated with at least one robot. These robots are then deployed in
a decentralized manner to maximize the likelihood that at least one robot
detects every target in their associated region despite a non-zero probability
of failure. A suite of simulation results is presented to demonstrate the
effectiveness and robustness of the proposed method when compared to existing
techniques.Comment: accepted to icra 201
Federated Embedded Systems – a review of the literature in related fields
This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous
computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways
Active Learning of Gaussian Processes for Spatial Functions in Mobile Sensor Networks
This paper proposes a spatial function modeling approach using mobile sensor networks, which potentially can be used for environmental surveillance applications. The mobile sensor nodes are able to sample the point observations of an 2D spatial function. On the one hand, they will use the observations to generate a predictive model of the spatial function. On the other hand, they will make collective motion decisions to move into the regions where high uncertainties of the predictive model exist. In the end, an accurate predictive model is obtained in the sensor network and all the mobile sensor nodes are distributed in the environment with an optimized pattern. Gaussian process regression is selected as the modeling technique in the proposed approach. The hyperparameters of Gaussian process model are learned online to improve the accuracy of the predictive model. The collective motion control of mobile sensor nodes is based on a locational optimization algorithm, which utilizes an information entropy of the predicted Gaussian process to explore the environment and reduce the uncertainty of predictive model. Simulation results are provided to show the performance of the proposed approach. © 2011 IFAC
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