A tabu search heuristic for the multi-depot vehicle routing problem
Computers and Operations Research
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Tabu Search
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
Probability, Statistics, and Queueing Theory with Computer Science Applications
Probability, Statistics, and Queueing Theory with Computer Science Applications
Lightweight detection and classification for wireless sensor networks in realistic environments
Proceedings of the 3rd international conference on Embedded networked sensor systems
Energy-aware lossless data compression
ACM Transactions on Computer Systems (TOCS)
The design and implementation of a self-calibrating distributed acoustic sensing platform
Proceedings of the 4th international conference on Embedded networked sensor systems
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Dynamic agent classification and tracking using an ad hoc mobile acoustic sensor network
EURASIP Journal on Applied Signal Processing
Bird species recognition using support vector machines
EURASIP Journal on Applied Signal Processing
Design and evaluation of a hybrid sensor network for cane toad monitoring
ACM Transactions on Sensor Networks (TOSN)
Frog classification using machine learning techniques
Expert Systems with Applications: An International Journal
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SolarStore: enhancing data reliability in solar-powered storage-centric sensor networks
Proceedings of the 7th international conference on Mobile systems, applications, and services
Competitive simulated annealing and Tabu Search algorithms for the max-cut problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Monitoring frog communities: an application of machine learning
IAAI'96 Proceedings of the eighth annual conference on Innovative applications of artificial intelligence
TinyEARS: spying on house appliances with audio sensor nodes
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
In-situ soil moisture sensing: measurement scheduling and estimation using compressive sensing
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Efficient cross-correlation via sparse representation in sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Fast Solution of -Norm Minimization Problems When the Solution May Be Sparse
IEEE Transactions on Information Theory
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
Efficient background subtraction for real-time tracking in embedded camera networks
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environment monitoring, as well as indoor activity monitoring. In-network classification is critically important in ASNs because wireless transmission costs several orders of magnitude more energy than computation. The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based feature-less, low computational cost, and noise resilient framework for in-network classification in ASNs. The key component of Sparse Approximation based Classification (SAC), ℓ1 minimization, is a convex optimization problem, and is known to be computationally expensive. Furthermore, SAC algorithms assumes that the test samples are a linear combination of a few training samples in the training sets. For acoustic applications, this results in a very large training dictionary, making the computation infeasible to be performed on resource constrained ASN platforms. Therefore, we propose several techniques to reduce the size of the problem, so as to fit SAC for in-network classification in ASNs. Our extensive evaluation using two real-life datasets (consisting of calls from 14 frog species and 20 cricket species respectively) shows that the proposed SAC framework outperforms conventional approaches such as Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) in terms of classification accuracy and robustness. Moreover, our SAC approach can deal with multi-label classification which is common in ASNs. Finally, we explore the system design spaces and demonstrate the real-time feasibility of the proposed framework by the implementation and evaluation of an acoustic classification application on an embedded ASN testbed.