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- Title
Distributed resource management in wireless sensor networks using reinforcement learning.
- Authors
Shah, Kunal; Di Francesco, Mario; Kumar, Mohan
- Abstract
In wireless sensor networks (WSNs), resource-constrained nodes are expected to operate in highly dynamic and often unattended environments. Hence, support for intelligent, autonomous, adaptive and distributed resource management is an essential ingredient of a middleware solution for developing scalable and dynamic WSN applications. In this article, we present a resource management framework based on a two-tier reinforcement learning scheme to enable autonomous self-learning and adaptive applications with inherent support for efficient resource management. Our design goal is to build a system with a bottom-up approach where each sensor node is responsible for its resource allocation and task selection. The first learning tier (micro-learning) allows individual sensor nodes to self-schedule their tasks by using only local information, thus enabling a timely adaptation. The second learning tier (macro-learning) governs the micro-learners by tuning their operating parameters so as to guide the system towards a global application-specific optimization goal (e.g., maximizing the network lifetime). The effectiveness of our framework is exemplified by means of a target tracking application built on top of it. Finally, the performance of our scheme is compared against other existing approaches by simulation. We show that our two-tier reinforcement learning scheme is significantly more efficient than traditional approaches to resource management while fulfilling the application requirements.
- Subjects
WIRELESS sensor networks; DISTRIBUTED resources (Electric utilities); REINFORCEMENT learning; RESOURCE management; QUALITY of service
- Publication
Wireless Networks (10220038), 2013, Vol 19, Issue 5, p705
- ISSN
1022-0038
- Publication type
Article
- DOI
10.1007/s11276-012-0496-2