Current trends in Wireless Sensor Networks are faced with the challenge of shifting from testbeds in controlled environments to real-life deployments, characterized by unattended and long-term operation. The network performance in such settings depends on various factors, ranging from the operational space, the behavior of the protocol stack, the intra-network dynamics, and the status of each individual node. As such, characterizing the network's high-level performance based exclusively on link-quality estimation, can yield episodic snapshots on the performance of specific, point-to-point links. The objective of this work is to provide an integrated framework for the unsupervised selection of the dominant features that have crucial impact on the performance of end-to-end links, established over a multi-hop topology. Our focus is on compressing the original feature vector of network parameters, by eliminating redundant network attributes with predictable behavior. The proposed approach is implemented alongside different cases of protocol stacks and evaluated on data collected from real-life deployments in rural and industrial environments. Discussions on the efficacy of the proposed scheme, and the dominant network characteristics per deployment are offered.
['Athanasia Panousopoulou', 'Mikel Azkune', 'Panagiotis Tsakalides']
Feature selection for performance characterization in multi-hop wireless sensor networks