The distributed boosting algorithm
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Learning Ensembles from Bites: A Scalable and Accurate Approach
The Journal of Machine Learning Research
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Energy aware lossless data compression
Proceedings of the 1st international conference on Mobile systems, applications and services
Lightweight detection and classification for wireless sensor networks in realistic environments
Proceedings of the 3rd international conference on Embedded networked sensor systems
Networked infomechanical systems: a mobile embedded networked sensor platform
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
The design and evaluation of a hybrid sensor network for Cane-Toad monitoring
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
The design and implementation of a self-calibrating distributed acoustic sensing platform
Proceedings of the 4th international conference on Embedded networked sensor systems
Sparse data aggregation in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection in sensor networks
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Bird species recognition using support vector machines
EURASIP Journal on Applied Signal Processing
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
SolarStore: enhancing data reliability in solar-powered storage-centric sensor networks
Proceedings of the 7th international conference on Mobile systems, applications, and services
Data fusion improves the coverage of wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Mercury: a wearable sensor network platform for high-fidelity motion analysis
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Discovery of frequent distributed event patterns in sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Identification of low-level point radioactive sources using a sensor network
ACM Transactions on Sensor Networks (TOSN)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Towards optimal rate allocation for data aggregation in wireless sensor networks
MobiHoc '11 Proceedings of the Twelfth ACM International Symposium on Mobile Ad Hoc Networking and Computing
Parametric Representations of Bird Sounds for Automatic Species Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Real-time classification via sparse representation in acoustic sensor networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
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The main challenge of designing classification algorithms for sensor networks is the lack of labeled sensory data, due to the high cost of manual labeling in the harsh locales where a sensor network is normally deployed. Moreover, delivering all the sensory data to the sink would cost enormous energy. Therefore, although some classification techniques can deal with limited label information, they cannot be directly applied to sensor networks since they are designed for centralized databases. To address these challenges, we propose a hierarchical aggregate classification (HAC) protocol which can reduce the amount of data sent by each node while achieving accurate classification in the face of insufficient label information. In this protocol, each sensor node locally makes cluster analysis and forwards only its decision to the parent node. The decisions are aggregated along the tree, and eventually the global agreement is achieved at the sink node. In addition, to control the tradeoff between the communication energy and the classification accuracy, we design an extended version of HAC, called the constrained hierarchical aggregate classification (cHAC) protocol. cHAC can achieve more accurate classification results compared with HAC, at the cost of more energy consumption. The advantages of our schemes are demonstrated through the experiments on not only synthetic data but also a real testbed.