Adaptive protocols for information dissemination in wireless sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Data Gathering in SEnsor Networks using the Energy Delay Metric
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
Self-organization in ad hoc sensor networks: an empirical study
ICAL 2003 Proceedings of the eighth international conference on Artificial life
TiNA: a scheme for temporal coherency-aware in-network aggregation
Proceedings of the 3rd ACM international workshop on Data engineering for wireless and mobile access
An optimized in-network aggregation scheme for data collection in periodic sensor networks
ADHOC-NOW'12 Proceedings of the 11th international conference on Ad-hoc, Mobile, and Wireless Networks
Description and composition of bio-inspired design patterns: a complete overview
Natural Computing: an international journal
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Sensor Networks have recently emerged as a ubiquitous computing platform. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. In this work, we propose a self-organizing method for aggregating data in ad-hoc wireless sensor networks. We present new network architecture, CODA (Cluster-based self-Organizing Data Aggregation), based on the Kohonen Self-Organizing Map to aggregate sensor data in cluster. Before deploying the network, we train the nodes to have the ability to classify the sensor data. Thus, it increases the quality of data and reduces data traffic as well as energy-conserving. Our simulation results show that CODA increases the accuracy of data than traditional aggregation of database system. Finally, we show a real-world platform, TIP, on that we will implement the idea.