Wuhua Hu

Multi-Hop Diffusion LMS for Energy-Constrained Distributed Estimation

We propose a multihop diffusion strategy for a sensor network to perform distributed least mean-squares (LMS) estimation under local and network-wide energy constraints. At each iteration of the strategy, each node can combine intermediate parameter estimates from nodes other than its physical neighbors via a multi-hop relay path. We propose a rule to select combination weights for the multi-hop neighbors, which can balance between the transient and the steady-state network mean-square deviations (MSDs). We study two classes of networks: simple networks with a unique transmission path from one node to another, and arbitrary networks utilizing diffusion consultations over at most two hops. We propose a method to optimize each nodes information neighborhood subject to local energy budgets and a network-wide energy budget for each diffusion iteration. This optimization requires the network topology, and the noise and data variance profiles of each node, and is performed offline before the diffusion process. In addition, we develop a fully distributed and adaptive algorithm that approximately optimizes the information neighborhood of each node with only local energy budget constraints in the case where diffusion consultations are performed over at most a predefined number of hops. Numerical results suggest that our proposed multi-hop diffusion strategy achieves the same steady-state MSD as the existing one-hop adapt-then-combine diffusion algorithm but with a lower energy budget.

Network infection source identification under the SIRI model

We study the problem of identifying a single infection source in a network under the susceptible-infected-recovered-infected (SIRI) model. We describe the infection model via a state-space model, and utilizing a state propagation approach, we derive an algorithm known as the heterogeneous infection spreading source (HISS) estimator, to infer the infection source. The HISS estimator uses the observations of node states at a particular time, where the elapsed time from the start of the infection is unknown. It is able to incorporate side information (if any) of the observed states of a subset of nodes at different times, and of the prior probability of each infected or recovered node to be the infection source. Simulation results suggest that the HISS estimator outperforms the dynamic message passing and Jordan center estimators over a wide range of infection and reinfection rates.

Multitask diffusion LMS with optimized inter-cluster cooperation

We consider a multitask network where nodes are divided into several connected clusters, with each cluster performing a least mean squares estimation of a different random parameter vector. Inspired by the adapt-then-combine strategy, we propose a multitask diffusion strategy whose mean and mean-square stability can be achieved independent of the inter-cluster cooperation weights. We develop a distributed optimization algorithm that allows each node in the network to locally optimize its inter-cluster cooperation weights. Simulation results demonstrate that our approach leads to a lower average steady-state network MSD, compared with the multitask diffusion strategy using an averaging rule for the inter-cluster cooperation.

An energy-efficient diffusion strategy over adaptive networks

We propose an energy-efficient diffusion strategy where each node performs the combination step in the diffusion LMS strategy only at every L time slots. This proposed strategy reduces the inter-node communication overhead by L-fold, but yields a steady-state network mean-square deviation (MSD) higher than the traditional diffusion LMS strategy. To improve the steady-state network MSD performance, we further propose a step-size switching mechanism that switches to a decaying step-size in the adaptation step of the diffusion LMS strategy as the MSD converges. To ensure adaptability of the new strategy, we also incorporate a parameter change detection method to switch the step-size back to a constant value if the underlying parameter varies. Simulation results suggest that the proposed method can effectively reduce the network energy consumption without sacrificing steady-state network MSD performance.

Localization of a moving non-cooperative RF target in NLOS environment using RSS and AOA measurements

We propose an alternating optimization algorithm for localizing a mobile non-cooperative target using a wireless sensor network. We consider the scenario where sensors receive single-bounce non-line-of-sight signals from the moving target. Each sensor is able to measure the target signals angle-of-arrival and received signal strength. The transmit powers of the non-cooperative target at different locations are unknown, and estimated jointly with its locations and the orientations of the scatterers off which the target signals are reflected before reaching the sensors. We formulate the problem as a non-convex least squares problem, and then transform and approximate it into a form that is solvable by an alternating algorithm. We show that our algorithm converges, and simulation results demonstrate that our algorithm is able to localize the target with good accuracy.

A Multitask Diffusion Strategy With Optimized Inter-cluster Cooperation

We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.

Joint scheduling and localization in UWB networks

Ultra-wide bandwidth (UWB) systems allow for accurate localization to tackle and complement the GPS-aided solutions, which are impractical in weak signal environments. We consider the problem of fast link scheduling in the medium access control (MAC) layer for UWB localization. We present an optimization strategy to perform robust ranging scheduling with localization constraints. Given the complexity of the optimal strategy, two different MAC-aware link selection heuristics are also proposed. Our results show that significant MAC delay reductions are possible through the use of simple local heuristics.

An integer linear programming approach for a class of bilinear integer programs

Abstract We propose an Integer Linear Programming (ILP) approach for solving integer programs with bilinear objectives and linear constraints. Our approach is based on finding upper and lower bounds for the integer ensembles in the bilinear objective function, and using the bounds to obtain a tight ILP reformulation of the original problem, which can then be solved efficiently. Numerical experiments suggest that the proposed approach outperforms a latest iterative ILP approach, with notable reductions in the average solution time.