Wanzhi Qiu

Enhanced tree routing for wireless sensor networks

Tree routing (TR) is a low-overhead routing protocol designated for simple, low-cost and low-power wireless sensor networks. It avoids flooding the network with path search and update messages in order to conserve bandwidth and energy by using only parent-child links for packet forwarding. The major drawback of TR is the increased hop-counts as compared with more sophisticated path search protocols. We propose an enhanced tree routing (ETR) strategy for sensor networks which have structured node address assignment schemes. In addition to the parent-child links, ETR also uses links to other one-hop neighbours if it is decided that this will lead to a shorter path. It is shown that such a decision can be made with minimum storage and computing cost by utilizing the address structure. Detailed algorithms for applying ETR to ZigBee networks are also presented. Simulation results reveal that ETR not only outperforms TR in terms of hop-counts, but also is more energy-efficient than TR.

A GCD Method for Blind Channel Identification

Abstract Using the second-order statistics (SOS) for the estimation of impulse responses of multiple FIR channels driven by an unknown input has become a topic of great interest since Tong-Xu-Kailath proposed a SOS-based matrix pair (MP) method in 1991. The MP method exploits a pair of covariance matrices of the channel output vectors to identify the channel impulse responses. In this paper, we show that one of the two covariance matrices contains all the information about the channel impulse responses, and this information can be retrieved by a simple application of a notion called the greatest common divisor (GCD). The GCD method is shown to be much more robust to noise than the MP method.

A hybrid routing protocol for wireless sensor networks

The ZigBee standard which has been widely adopted for wireless sensor networks specifies two routing protocols. One is a path search protocol which finds the best path by flooding whole or part of the network with path search messages costing significant amount of energy and bandwidth. The other is a simple tree routing protocol which eliminates path search by solely following the parent-child links and, therefore, conserves energy and bandwidth but generates inferior routes. We propose a hybrid routing protocol which improves the efficiency of tree routing with minimum additional storage and computational cost. Particularly, in addition to the parent-child links, the proposed protocol also uses links to other one-hop neighbors if they are identified to be able to provide a route shorter than the tree path. The address assignment scheme of ZigBee networks is exploited to make such identification possible. Simulation results confirm the performance of the proposed protocol.

Real-Time Optimal Control of River Basin Networks

River basins are key components of water supply grids. River basin operators must handle a complex set of objectives including runoff storage, flood control, supply for consumptive use, hydroelectric power generation, silting management, and maintenance of river basin ecology. At present, operators rely on a combination of simulation and optimization tools to help them make operational decisions. The complexity associated with this approach makes it suitable for long term planning but not daily or hourly operation. The consequence is that between longerterm optimized operation points, river basins are largely operated in open loop. This leads to operational inefficiencies most notably wasted water and poor ecological outcomes. This paper proposes a systematic approach using optimal control based on simple low order models for the real-time operation of entire river basin networks.

Detection of Protein Conformational Changes with Multilayer Graphene Nanopore Sensors

Detecting conformational change in protein or peptide is imperative in understanding their dynamic function and diagnosing diseases. Existing techniques either rely on ensemble average that lacks the necessary sensitivity or require florescence labeling. Here we propose to discriminate between different protein conformations with multiple layers of graphene nanopore sensors by measuring the effect of protein-produced electrostatic potential (EP) on electric transport. Using conformations of the octapeptide Angiotensin II obtained through molecular dynamics simulations, we show that the EP critically depends on the geometries of constituent atoms and each conformation carries a unique EP signature. We then, using quantum transport simulations, reveal that these characteristic EP profiles cause distinctive modulation to electric charge densities of the graphene nanopores, leading to distinguishable changes in conductivity. Our results open the potential of label-free, single-molecule, and real-time detection of protein conformational changes.

Modeling and Estimating Simulated DNA Nanopore Translocation Signals

Solid-state nanopores have been proposed for rapid and inexpensive deoxyribonucleic acid (DNA) sequencing and analysis. This technology is primarily based on characterizing the ionic current flowing through the pore as DNA translocates from one side of the pore to the other side under the influence of an electric field. The magnitude of the DNA-induced current blockade is an important analytical feature for these applications. However, it remains a challenging task to accurately determine the ionic current levels due to small signal-to-noise ratios. In order to facilitate reliable analysis it is necessary to understand the noise statistics and develop effective signal estimation techniques. In this paper, we conduct a molecular dynamics simulation of DNA translocations through a solid-state nanopore and reveal that the simulated ionic current signals contain both thermal and shot noise. We then develop a model for these signals and propose a maximum likelihood estimator (MLE) for estimating the ionic current levels. We show that the MLE has the potential to significantly outperform the classic sample mean estimator.

Distributed source localization based on TOA measurements in wireless sensor networks

We study the problem of source localization in multihop wireless sensor networks. A fully distributed algorithm based on sensor measurements of time of arrivals (TOAs) is proposed. In contrast to centralized methods where all TOA measurements are transmitted via certain routes to a central location (the sink) for processing, the proposed method distributes the processing among the relay nodes on the routes to the sink. Fusion strategies are proposed so that the raw and intermediate data are progressively processed, and only the refined results are further relayed. As a result, the proposed scheme has improved flexibility and scalability since it does not impose any special requirements on the sink node. The proposed distributed strategy also has the potential to save energy and bandwidth due to reduced radio transmissions.

Quantum conductance of armchair graphene nanopores with edge impurities

The quantum conductance of armchair graphene nanopores (aGNPs) with edge impurities is investigated using the tight-binding model and non-equilibrium Greens function method. We find that aGNPs are particularly interesting since their transmission spectra can be easily tuned by pore-edge shaping to produce a variety of electronic transport characteristics. We first examine the local density of states at individual impurity sites. We then study the quantum conductance of aGNPs with various transmission spectra in response to perturbations to on-site energies and hopping coefficients of edge atoms. Insights into transport properties of aGNPs are provided and implications of these findings for designing aGNP devices in interconnection and sensing applications are discussed.

Graphene nanopores as negative differential resistance devices

We present graphene nanopores as new negative differential resistance (NDR) devices, and study their quantum transport properties using non-equilibrium Greens function and the density functional tight binding method. The proposed device structure is created on intrinsic armchair-edged graphene nanoribbons with uniform widths, where the central scattering region has a nanopore in the interior, and the two ends of the nanoribbon act naturally as connecting electrodes. We show that nitrogen-passivated scattering regions generally result in pronounced NDR properties, while hydrogen-passivated ones do not. This NDR effect occurs at low bias voltages, below 1 V, and achieves extraordinarily high peak-to-valley current ratio, while still attaining very high peak current densities. In addition, very sharp current peaks in the μA range can occur in the I-V curves, and through varying structural dimensions of the proposed structure multiple NDR regions can be realized. These results suggest that the device has promising potential in applications such as high frequency oscillators, memory devices, and fast switches.

Identification of MIMO systems with sparse transfer function coefficients

We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.