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In  this  paper  we  address  the  average  consensus problem in a Wireless Sensor-Actor Network with the particular focus  on  autonomous  fault  detection.  To  this  aim,  we  design  a distributed  clustering  procedure  that  partitions  the  network into  clusters  according  to  both  similarity  of  measurements and communication connectivity. The exploitation of clustering techniques   in   consensus   computation   allows   to   obtain   the detection   and   isolation   of   faulty   nodes,   thus   assuring   the convergence  of  the  other  nodes  to  the  exact  consensus  value. More  interestingly,  the  algorithm  can  be  integrated  into  a Kalman filtering framework to perform distributed estimation of  a  dynamic  quantity  in  presence  of  faults.  The  proposed approach is validated through numerical simulations and tests on a real world scenario dataset.

In this paper, we consider the problem of designing a distributed strategy to estimate the channel parameters for a generic Wireless Sensor-Actor Network. To this aim, we present a distributed least-square algorithm that complies with the constraint of transmitting only integer data through the wireless communication, which often characterizes Wireless Sensor-Actor Network embedded architectures. In this respect, we propose a quantized consensus strategy that mitigates the effects of the rounding operations applied to the wireless exchanged floating data. Moreover, the approach is based on a symmetric random gossip strategy, making it suitable for the actual deployment in multiagent networks. Finally, the effectiveness of the proposed algorithm and of its implementation as an open-source application is assessed and the employment of the procedure is illustrated through the application to radio-frequency localization experiments in a real world testbed.