In this paper, we consider the distributed classification problem
in wireless sensor networks. The DCFECC-SD approach employing the
binary code matrix has recently been proposed to cope with the
errors caused by both sensor faults and the effect of fading
channels. The DCFECC-SD approach extends the DCFECC approach by
using soft decision decoding to combat channel fading. However,
the performance of the system employing the binary code matrix
could be degraded if the distance between different hypotheses can
not be kept large. This situation could happen when the number of
sensor is small or the number of hypotheses is large. In this
paper, we design the DCFECC-SD approach employing the D-ary code
matrix, where D>2. Simulation results show that the performance
of the DCFECC-SD approach employing the D-ary code matrix is
better than that of the DCFECC-SD approach employing the binary
code matrix. Performance evaluation of DCFECC-SD using different
number of bits of local decision information is also provided when
the total channel energy output from each sensor node is fixed.
In this paper we propose a new approach for distributed multiclass
classification using a hierarchical fusion architecture. Binary
decisions from local sensors, possibly in the presence of faults,
are fused locally. Locally fused results are forwarded to the
global fusion center that determines the final classification
result. Classification fusion in our approach is implemented via
error correcting codes to incorporate fault-tolerance capability.
This new approach not only provides an improved fault-tolerance
capability but also reduces bandwidth requirements as well as
computation time and memory requirements at the fusion center.
Numerical examples are provided to illustrate the performance of
this new approach.