On the Development of Distributed Estimation
Techniques for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) have lately witnessed tremendous demand, as evidenced
by the increasing number of day-to-day applications. The sensor nodes aim
at estimating the parameters of their corresponding adaptive filters to achieve the
desired response for the event of interest. Some of the burning issues related to linear
parameter estimation in WSNs have been addressed in this thesis mainly focusing
on reduction of communication overhead and latency, and robustness to noise. The
first issue deals with the high communication overhead and latency in distributed
parameter estimation techniques such as diffusion least mean squares (DLMS) and
incremental least mean squares (ILMS) algorithms. Subsequently the poor performance
demonstrated by these distributed techniques in presence of impulsive noise
has been dealt separately. The issue of source localization i.e. estimation of source
bearing in WSNs, where the existing decentralized algorithms fail to perform satisfactorily,
has been resolved in this thesis. Further the same issue has been dealt
separately independent of nodal connectivity in WSNs.
This thesis proposes two algorithms namely the block diffusion least mean squares
(BDLMS) and block incremental least mean squares (BILMS) algorithms for reducing
the communication overhead in WSNs. The theoretical and simulation studies
demonstrate that BDLMS and BILMS algorithms provide the same performances as
that of DLMS and ILMS, but with significant reduction in communication overheads
per node. The latency also reduces by a factor as high as the block-size used in the
proposed algorithms.
With an aim to develop robustness towards impulsive noise, this thesis proposes
three robust distributed algorithms i.e. saturation nonlinearity incremental LMS
(SNILMS), saturation nonlinearity diffusion LMS (SNDLMS) and Wilcoxon norm
diffusion LMS (WNDLMS) algorithms. The steady-state analysis of SNILMS algorithm
is carried out based on spatial-temporal energy conservation principle. The
theoretical and simulation results show that these algorithms are robust to impulsive
noise. The SNDLMS algorithm is found to provide better performance than
SNILMS and WNDLMS algorithms.
In order to develop a distributed source localization technique, a novel diffusion
maximum likelihood (ML) bearing estimation algorithm is proposed in this thesis
which needs less communication overhead than the centralized algorithms. After
forming a random array with its neighbours, each sensor node estimates the source
bearing by optimizing the ML function locally using a diffusion particle swarm
optimization algorithm. The simulation results show that the proposed algorithm
performs better than the centralized multiple signal classification (MUSIC) algorithm
in terms of probability of resolution and root mean square error. Further,
in order to make the proposed algorithm independent of nodal connectivity, a distributed
in-cluster bearing estimation technique is proposed. Each cluster of sensors
estimates the source bearing by optimizing the ML function locally in cooperation
with other clusters. The simulation results demonstrate improved performance of
the proposed method in comparison to the centralized and decentralized MUSIC
algorithms, and the distributed in-network algorithm.
Keywords: Wireless Sensor Network, Distributed Estimation, Diffusion LMS,
Incremental LMS, Error Saturation Nonlinearity, Wilcoxon Norm, Direction of
Arrival, Maximum likelihood Estimation, Particle Swarm Optimization.