Wireless Sensor Networks (WSN) are of significant importance with increasingly diverse and viable applications. They gained even more traction after the IEEE 802.15.4 standard was defined. Distributed adaptive filtering algorithms have added statistical inference to WSN applications, employing techniques that extract data from distributed devices. In contrast, most adaptive filtering contributions do not consider realistic features of the subjacent telecommunications network protocols. Similarly, the telecommunications area typically does not take into account interesting abilities of adaptive filtering algorithms. In this paper, we explore this gap between the two study areas, allowing the development of network-protocol-aware distributed adaptive filtering techniques. In order to explore network realistic behaviors, this paper focuses on distributed inference problems. More specifically, we propose two new diffuse adaptive algorithms, aware of the characteristics of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, namely: (i) Variant Reuse of Coefficients Least Mean Squares (VRC-LMS) algorithm; and the (ii) Reuse of Coefficients Least Mean Squares (RC-LMS) algorithm in the Adapt-Then-Combine (ATC) modality. These two new algorithms will bring some advantages, specifically when information is delayed because of too much packet loss. Another advantage will be the addition of the spatial information diversity contribution in the VRC-LMS algorithm.

Distributed inference tasks could be performed by adaptive filtering techniques. Several enhancement strategies for such techniques were proposed, such as sparsity-aware algorithms, coefficients reuse and correntropy-based cost functions in the case of impulsive noise. In this paper, a general framework based on La-grange multipliers for the derivation of sophisticated algorithms that incorporate most of these improvements is described. A new general identification algorithm is derived as an example of the proposed approach and its performance is assessed in a distributed setting.