# Data Gathering Techniques in WSN: A Cross-Layer View
## 2 WSN Architecture--Arising Platforms and Novel Infrastructure Concepts
### Application-Oriented Network Architecture  
We continue by covering special types of WSN platforms for data gathering and specialized application-driven architecture types. [PERSON] et al. [52] suggest an IoT network architecture for _wildlife monitoring systems_ (WMS) for scenarios in which animals exhibit sparse mobility, which results in sporadic wireless links. In addition, they suggest a data forwarding enhancement that adopts the flood-store-carry-and-forward paradigm suggested in the seminal ZebraNet study by [PERSON] et al. [53], in which in order to send data to the sink, the nodes disseminate it among themselves until it reaches the sink. Specifically, each node stores the data needing to be conveyed, waits for connectivity with other nodes, and distributes the data to them, and they repeat the same process. Accordingly, the data is spread throughout the entire network (i.e., flooding) and will eventually be received by the sink. The authors in [52] suggest leveraging locally available routing parameters to improve opportunistic data forwarding algorithms by managing the data replication decision.  
[PERSON] et al. [54] suggest extending the lifetime of a wireless sensor network used in mobile healthcare applications by increasing the number of bits transmitted per symbol, and specifically to rely on a quaternary interconnect scheme in which each transmitted symbol modulates two bits. A complementary neural network, static RAM-based architecture is suggested to reduce energy consumption in storage and transmissions during the data dissemination process. A WSN dedicated to home deployment for elderly healthcare and early health emergency alarm is discussed by [PERSON] et al. [55]. The authors first raise privacy concerns related to the monitoring, and accordingly, advocate that only sound-based surveillance aimed to merely indicate alarming situations is appropriate. In order to further conform to the privacy demands, they focus on distributed architecture (rather than on a centralized one), where each of the WSN sensors sends encrypted identifiers of their measurement. The identification of events is built on feature extraction. This is done on the frequency domain by first dividing the incoming signal into blocks with Hamming sliding window, then transforming into the frequency domain using Discrete Fourier Transform (DFT) to evaluate the contribution of every band of the spectrum. The final coefficients are obtained after Discrete Cosine Transform (DCT). The conclusive parts of the proposed algorithm classify the coefficients, feeding them into Support Vector Machines which classifies the estimated audio event. The authors assert that the classification results could be further improved by incorporating a deep artificial neural network (ANN) into their system.  
In [PERSON] et al. [56], a similar method was implemented for urban noise monitoring. Namely, while STFT was utilized for the noise preprocessing, the classification of noise levels and events was performed by convolutional neural networks (CNNs). The authors used several previously published networks; see references therein. Similar methods for noise monitoring WSN were introduced by [PERSON] et al. [57]. The frequency-domain analysis was performed. Then, classification by statistical methods was accomplished (Gaussian mixture model was used). In addition, the authors in [57] provide an elaborate WSN architecture, where energy-harvesting solar panels augment the sensors' lifetime and the sensors' state-of-charge is transmitted and tracked by central, more powerful nodes.