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In Proceedings of the 5th international conference on Information processing in sensor networks (IPSN) (2006)
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In Proceedings of the International Conference on Information Technology: Research and Education (ITRE) (2003), pp. 306-310.
Abstract
Future large-scale networks, such as sensor networks, will consist of hundreds and even thousands of wirelessly connected sensor and actuator nodes. The nodes are typically highly resource-constrained (processor, memory, and power), have limited communication range, and are prone to failure. Furthermore, there is no global information about the exact location and connectivity of the nodes. Consequently, the explicit consideration of network and task constraints and objectives will be an important part of routing algorithms for these networks. We present a distributed ...
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In Proceedings of the IEEE Wireless Communications and Networking (WCNC), Vol. 3 (2003), pp. 1948-1953.
Abstract
The rapid advances in processor, memory, and radio technology have enabled the development of distributed networks of small, inexpensive nodes that are capable of sensing, computation, and wireless communication. Sensor networks of the future are envisioned to revolutionize the paradigm of collecting and processing information in diverse environments. However, the severe energy constraints and limited computing resources of the sensors, present major challenges for such a vision to become a... ...
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In Proceedings of the 6th IEEE International Conference on Computer and Information Technology (CIT) (2006), pp. 219-224.
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Intelligent Sensors, Sensor Networks and Information Processing Conference, 2005. Proceedings of the 2005 International Conference on In Proceedings of the 2nd International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2005), pp. 423-429.
Abstract
Implementing a multi-agent system (MAS) on a wireless sensor network comprising sensor-actuator nodes with processing capability enables these nodes to perform tasks in a coordinated manner to achieve some desired system-wide objective. In this paper, several distributed reinforcement learning (DRL) algorithms used in MAS are described. Next, we present our experience and results from the implementation of these DRL algorithms on actual Berkeley motes in terms of communication, computation and energy costs, and speed of convergence to optimal policies. We investigate ...
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In Proceedings of the 1st International Workshop on Energy in Wireless Sensor Networks (WEWSN) (2008)
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Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005)
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IEEE Signal Processing Magazine, Vol. 23, No. 4. (2006), pp. 56-69.
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IEEE Communications Surveys & Tutorials, Vol. {to be published} (2009)
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IEEE Transactions on Parallel and Distributed Systems, Vol. 19, No. 7. (2008), pp. 981-994.
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IEEE Transactions on Neural Networks, Vol. 19, No. 7. (July 2008), pp. 1154-1166.
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