The predominate traffic patterns in a wireless sensor network are many-to-one and one-to-many communication. Hence, the performance of wireless sensor networks is characterized by the rate at which data can be disseminated from or aggregated to a data sink. In this paper, the problem of data aggregation at a single sink is considered. It is shown that a data aggregation rate of Θ(logn/n) per node is optimal and that this rate can be achieved in regular wireless sensor networks using a generalization of cooperative beamforming called cooperative time-reversal communication.

This paper presents the results of research exploring compact antenna design techniques for a desired 10:1 bandwidth in the Very High Frequency (VHF) band for remote sensing applications. A relatively compact antenna array has been developed to efficiently receive arbitrary RF signals across the entire 30 to 300 MHz VHF spectrum. Specifically, the antenna fits within a 3 meter cube, is capable of receiving both horizontally and vertically polarized signals, and has a single main beam that consistently maintains a beamwidth close to 45deg. Additionally it has only one output, and it is entirely passive, i.e. no power is provided to the antenna.

Data gathering is one of the most important services provided by wireless sensor networks (WSNs). Since the predominant traffic pattern in data gathering services is many-to-one communication, it is critical to understand the limitations of many-to-one information flows and devise efficient data aggregation protocols to support prolonged operations in WSNs. In this paper, we provide a theoretical characterization of data aggregation processes under different communication modalities in WSNs. We demonstrate that data aggregation rates of Theta(log(n)/n) and Theta(1) are optimal when operating in fading environments with power path-loss exponents that satisfy 24, respectively. Furthermore, the optimal rate can be achieved using a generalization of cooperative beam-forming called cooperative time-reversal communication. In contrast, the non-cooperative multihop relay strategies widely adopted in literature are shown to be suboptimal in the low-to-medium attenuation regime (for 2 < alpha < 4).

The properties and performance of a technique, called time reversal, for co-operative communication on power-constrained wireless sensor networks are studied. A brief discussion of the optimality properties of this approach is presented, and performance is studied experimentally via a simulated indoor environment containing multiple wireless sensors. Using numerical simulation, the behaviour of the peak power received at a target sensor as a function of the number of co-operating transmitting sensors as well as the level of transmitted signal distortion and timing synchronisation errors, is studied. The simulation results demonstrate that, subject to some rather stringent synchronisation requirements, time reversal is an effective generalisation of beamforming that provides an efficient basis for co-operative communication on broadband multipath channels.

Data gathering is one of the most important services provided by wireless sensor networks (WSNs). The predominant traffic pattern in data gathering services is many-to-one communication. Hence, it is critical to understand the limitations of many-to-one information flows and devise efficient data aggregation protocols to support prolonged operations in WSNs. In this paper, we demonstrate that a data aggregation rate of p(logn/n) is optimal and that this rate can be achieved using a generalization of cooperative beam forming called cooperative time-reversal communication. In contrast, the non-cooperative multihop relay strategies widely adopted in the literature are shown to be suboptimal in both capacity and network lifetime

Spread-spectrum Rayleigh fading channels with imperfect channel state information (CSI) is studied. We assume a multidimensional signal subspace, and study the effects of both the subspace dimension and the probability distribution of the transmitted symbols on the mutual information between the channel input and output in the presence of uncertainty regarding the true state of the channel.

We consider the problem of adaptive modulation for broadband DS-CDMA fading channels with imperfect channel state information. The goal is to adapt the dimension and geometry of the signal constellation within the subspace spanned by a collection of random spreading codes (multicoding) in order to improve system performance in the presence of uncertainty regarding the true state of the channel.

The application of RKHS (reproducing kernel Hilbert space) theory to the problems of robust signal detection and estimation is investigated. It is shown that this approach provides a general and unified framework in which to analyze the problems of L/sup 2/ estimation, matched filtering, and quadratic detection in the presence of uncertainties regarding the second-order structure of the random processes involved.>

Several results related to the reproducing kernel Hilbert space of fractional Brownian motion are presented to facilitate the study of signal detection in additive fractional Gaussian noise. This Hilbert space is completely characterized, and an alternative characterization for the restriction of this class of functions to a compact interval (0.T) is given. Infinite- and finite-interval whitening filters for fractional Brownian motion are also developed. The application of these results to the signal detection problem yields necessary and sufficient conditions for a deterministic or stochastic signal to produce a nonsingular shift when embedded in additive fractional Gaussian noise. A formula for the likelihood ratio corresponding to any deterministic nonsingular shift is developed.>