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Predicting performance-related behavior of the underlying network structurebecomes more and more indispensable in terms of the aspired application outcomequality. However, the reliable forecast of QoS metrics like packet transferdelay in wireless network systems is still a challenging task. Even thoughexisting approaches are technically capable of determining such networkproperties under certain assumptions, they mostly abstract away from primalaspects that inherently have an essential impact on temporal networkperformance dynamics. Also, they usually require auxiliary resources to beimplemented and deployed along with the actual network components. In thecourse of developing a lightweight measurement-based alternative for theself-inspection and prediction of volatile performance characteristics inenvironments of any kind, we selectively investigate the duration of messagedelivery and packet loss rate against various parameters peculiar to commonradio network technologies like Wireless Sensor Networks (WSNs). Our hands-onexperiments reveal the relations between the oftentimes underestimated mediumaccess delay and a variety of main influencing factors including packet size,backoff period, and number of neighbor nodes contending for the communicationmedium. A closed formulation of selected weighted drivers facilitates theaverage-case prediction of inter-node packet transfer delays for arbitraryconfigurations of given network parameters even on resource-scarce WSN devices.We validate our prediction method against basic multi-hop networking scenarios.Yield field test results proof the basic feasibility and high precision of ourapproach to network property estimation in virtue of self-governed localmeasurements and regression-based calculations paving the way for a prospectiveself-management of network properties based upon autonomous distributedcoordination.

Measurement and Prediction of Centrical/Peripheral Network Properties  based on Regression Analysis - A Parametric Foundation for Performance  Self-Management in WSNs