5 research outputs found
A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks
Wireless sensor networks consist of sensor nodes that are physically
distributed over different locations. Spatial filtering procedures exploit the
spatial correlation across these sensor signals to fuse them into a filtered
signal satisfying some optimality condition. However, gathering the raw sensor
data in a fusion center to solve the problem in a centralized way would lead to
high energy and communication costs. The distributed adaptive signal fusion
(DASF) framework has been proposed as a generic method to solve these signal
fusion problems in a distributed fashion, which reduces the communication and
energy costs in the network. The DASF framework assumes that there is a common
goal across the nodes, i.e., the optimal filter is shared across the network.
However, many applications require a node-specific objective, while all these
node-specific objectives are still related via a common latent data model. In
this work, we propose the DANSF algorithm which builds upon the DASF framework,
and extends it to allow for node-specific spatial filtering problems.Comment: 5 page
Distributed node-specific LCMV beamforming in wireless sensor networks
In this paper, we consider the linearly constrained distributed adaptive node-specific signal estimation (LC-DANSE) algorithm, which generates a node-specific linearly constrained minimum variance (LCMV) beamformer, i.e., with node-specific linear constraints, at each node of a wireless sensor network. The algorithm significantly reduces the number of signals that are exchanged between nodes, and yet obtains the optimal LCMV beamformers as if each node has access to all the signals in the network. We consider the case where all the steering vectors are known, as well as the blind beamforming case where the steering vectors are not known. We formally prove convergence and optimality for both versions of the LC-DANSE algorithm. We also consider the case where nodes update their local beamformers simultaneously instead of sequentially, and we demonstrate by means of simulations that applying a relaxation is often required to obtain a converging algorithm in this case. We also provide simulation results that demonstrate the effectiveness of the algorithm in a realistic speech enhancement scenario. © 2006 IEEE.status: publishe
Energy optimization for wireless sensor networks using hierarchical routing techniques
Philosophiae Doctor - PhDWireless sensor networks (WSNs) have become a popular research area that is widely
gaining the attraction from both the research and the practitioner communities due to their
wide area of applications. These applications include real-time sensing for audio delivery,
imaging, video streaming, and remote monitoring with positive impact in many fields such
as precision agriculture, ubiquitous healthcare, environment protection, smart cities and
many other fields. While WSNs are aimed to constantly handle more intricate functions
such as intelligent computation, automatic transmissions, and in-network processing, such
capabilities are constrained by their limited processing capability and memory footprint as
well as the need for the sensor batteries to be cautiously consumed in order to extend their
lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by
proposing a novel clustering approach for routing the sensor readings in wireless sensor
networks. The main contribution of this dissertation is to 1) propose corrective measures to
the traditional energy model adopted in current sensor networks simulations that
erroneously discount both the role played by each node, the sensor node capability and
fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at
maximizing sensor networks lifetime. We propose three energy models for sensor network:
a) a service-aware model that account for the specific role played by each node in a sensor
network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint. These two models are complemented by a load balancing
model structured to balance energy consumption on the network of cluster heads
that forms the backbone for any cluster-based hierarchical sensor network. We present two
novel approaches for clustering the nodes of a hierarchical sensor network: a) a distanceaware
clustering where nodes are clustered based on their distance and the residual energy
and b) a service-aware clustering where the nodes of a sensor network are clustered
according to their service offered to the network and their residual energy. These
approaches are implemented into a family of routing protocols referred to as EOCIT
(Energy Optimization using Clustering Techniques) which combines sensor node energy
location and service awareness to achieve good network performance. Finally, building
upon the Ant Colony Optimization System (ACS), Multipath Routing protocol based on
Ant Colony Optimization approach for Wireless Sensor Networks (MRACO) is proposed
as a novel multipath routing protocol that finds energy efficient routing paths for sensor
readings dissemination from the cluster heads to the sink/base station of a hierarchical
sensor network. Our simulation results reveal the relative efficiency of the newly proposed
approaches compared to selected related routing protocols in terms of sensor network
lifetime maximization