ICDCSW '04 Proceedings of the 24th International Conference on Distributed Computing Systems Workshops - W7: EC (ICDCSW'04) - Volume 7
Wireless sensor networks
Towards Autonomic Computing: Adaptive Network Routing and Scheduling
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Experiences creating three implementations of the repast agent modeling toolkit
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Classifier fitness based on accuracy
Evolutionary Computation
Two-Phase Stochastic Optimization to Sensor Network Localization
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
A Characterization of Key Properties of Environment-Mediated Multiagent Systems
Engineering Environment-Mediated Multi-Agent Systems
Towards an Organic Network Control System
ATC '09 Proceedings of the 6th International Conference on Autonomic and Trusted Computing
Neural network Based secure media access control protocol for wireless sensor networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Proactive reconfiguration of wireless sensor networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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In recent years many algorithms and protocols for applications in wireless sensor networks (WSN) have been introduced. These include, e.g, solutions for routing and event notifications. Common among them is the need to adjust the basic operation to particular operating conditions by means of changing algorithmic parameters. In most applications, parameters have to be set carefully before nodes are deployed to a particular environment. But what happens to the system performance, if the operating conditions change to unforeseen situations at runtime? In this paper, we present the Organic Network Control (ONC) system and its application to WSNs. ONC is a system for adapting network protocols in response to environmental changes at runtime. Being generic in nature, ONC regards existing protocols as black box systems with an interface to changeable protocol parameters. ONC detects environmental changes locally at each node and applies changes to the protocol parameters by means of lightweight machine learning techniques. More complex exploration of possible parameters is transferred to powerful nodes, such as sink nodes. As an example we show how ONC can be applied to an exemplary WSN protocol for event detection and how performance in the ONC controlled system increases over fixed settings of the protocol.