Motivated by the computational, bandwidth and energy restrictions of wireless sensor network nodes and their need to, collectively, determine the presence of exogenous interference that could impair their communication, we consider schemes that could support the task of interference classification as a first step towards interference mitigation strategies. In particular, we examine the effectiveness of the Discrete Wavelet Transform (DWT) to communicate to other nodes the state of the channel, as sampled by a node, in a compressed, denoised form. We examine the suitability of different wavelet filters and thresholding methods in order to: (a) preserve key features of the interference, (b) denoise the noisy interference samples, and (c) reduce the amount of information that needs to be communicated to describe the interference.
['Aikaterini Vlachaki', 'Ioanis Nikolaidis', 'Janelle J. Harms']
Wavelet-Based Analysis of Interference in WSNs