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In this paper we propose a technique to determine the decision thresholds in multi-bit distributed detection. Detection thresholds are required to quantize the acquired information from the environment to send them to a fusion center. In multi-bit detection, decision making is complicated and in most cases, methods based on simulation or Person by Person Optimizations are applied to find the thresholds...
The advanced wireless communications such as the sensor networks and ad-hoc networks requires the communications system that is flexible about the transmission power control and robust over multiple access interference. We have showed that the throughput of Code Shift Keying (CSK) random access network is high with the transmission power control. However, it is difficult to use transmission power...
This work proposes a distributed sampling design for the signal detection application in the cluster-based wireless sensor networks (WSNs). Considering the energy saving requirement in the cluster-based WSNs, a linear weighting data fusion scheme for data reduction at the cluster head is also developed in this paper. Both the distributed sampling and the data reduction schemes are designed based on...
We consider the problem of event detection in wireless sensor networks (WSNs) that are large in the sense that an event affects the statistics of the observations of a small number of sensors in the vicinity of where it occurs. An event occurs at a random time at a random location in the region (called the region of interest, ROI) covered by the WSN. We consider a distance based sensing model in which...
Sensing the radio spectrum is an essential feature of cognitive radio. What is mostly important for a cognitive transmitter is to know the power spectral density at the location of its intended receiver and of the primary receivers. This requires, as a whole, knowledge of the spatial distribution of the power spectral density, an information that could be delivered by a network of sensors distributed...
In this paper, we address the problem of decentralized parameter estimation with a Wireless Sensor Network (WSN). The network is hierarchical in that sensors are grouped into clusters, being each cluster under the supervision of a cluster-head (CH). The CH is also in charge of consolidating the sensor observations into a local estimate before its transmission to the Fusion Center (FC). In this context,...
Over the last few years, target tracking in wireless sensor networks has become a topic of particular interest. This paper presents a tracking system intended for deployment in distributed wireless sensor networks. The approach is inspired from the concept of deploying sensor nodes in an ad-hoc manner and based on the aggregate amount of information they provide, perform tracking of mobile objects...
With the aging population and increased need to care for the elderly there are fewer of the younger generation to administer the necessary care and supervision. This condition is one of the reasons many researchers devote their time in evolving smart homes. These homes offer the occupant(s) a level of convenience not seen in traditional homes by using technology to create an environment that is aware...
The observations gathered by the individual nodes of a sensor network may be unreliable due to malfunctioning, observation noise or low battery level. Global reliability is typically recovered by collecting all the measurements in a fusion center which takes proper decisions. However, centralized networks are more vulnerable and prone to congestion around the sink nodes. To relax the congestion problem,...
Proper Power allocation can improve energy efficiency in wireless sensor networks (WSNs). Previous works considered only the channel statistics and did not take the instantaneous observations into consideration. Applying the instantaneous quality of an observation to the transmission power control of a node can improve the overall energy efficiency. In this work, we proposed nearly optimal power allocation...
Recently, energy-based localization using acoustic energy measurements has received much attention in wireless sensor networks. Since the objective function of the energy-based maximum likelihood (ML) localization is non-convex, the global solutions are hardly obtained without good initial estimates. In this paper, we relax this non-convex problem as a convex semidefinite programming (SDP), based...
The problem of distributed average consensus with quantized data is considered in this paper. We firstly propose a simple modification to the classical consensus protocol. Under a condition that the quantization noise variance converges to zero, the proposed protocol achieves a consensus in a mean squared sense and the consensus value is equal to the average of the initial state. Based on this result,...
Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for sparse signals. For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Because of deployment cost, the number of sensors is limited, and due to energy constraint, not all the sensors are turned on all the time. In this paper, the first contribution...
We consider a network of sensors whose decisions are used to discriminate between two alternative hypotheses, after communication over a multiaccess channel. Each sensor makes independent local binary decisions. Using a distance measure, we consider optimizing the entire network under the assumption that sensors are grouped into classes with identical characteristics. We prove that it can be optimal...
This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise, they remain silent (negative observations). Based on the...
Nowadays, the source localization has been widely applied for wireless sensor networks. The Gaussian mixture model has been adopted for maximum-likelihood (ML) source localization schemes. However, this model does not match the statistics of the real data in practice. In this paper, we study the probability density function of the sensor signals and demonstrate that the distribution is not Gaussian...
In this paper, we derive the Cramer-Rao lower bound (CRLB) for the 2-dimensional (2D) time-of-arrival (TOA) based localization. Unlike previous work on the CRLB, we consider a more practical propagation channel and relate it to inter-node range estimate through a distance-dependent variance model. We demonstrate that this will impact the derivation of the Fisher information matrix (FIM), eventually...
In this paper we consider the problem of binary hypothesis detection in wireless sensor networks under power constraint. The objective is to solve the resource allocation problem for this distributed detection problem and find a suitable operating point for the network. We consider the Neyman-Pearson (NP) criteria and design suitable transmission schemes and a fusion center (FC) detector, so that...
In this paper, we consider the distributed parameter estimation problem using one-bit quantized data from local sensors. Nonparametric distributed estimators are proposed based on knowledge of the moments of sensor noise. These estimators are shown to be either unbiased or asymptotically unbiased with bounded estimation variance for all possible parameter values. Relationship between the proposed...
Ever since the Federal Communications Commision (FCC) announced that it would open the television (TV) spectrum for opportunistic access, there has been an increased interest in industry as well as academia to come up with proposals that would fit the criteria laid by FCC. To this end, IEEE 802.22 Working Group (WG) is formulating the first worldwide standard for cognitive radio to operate in the...
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