I-Jeng Wang

Decentralized synchronization protocols with nearest neighbor communication

A class of synchronization protocols for dense, large-scale sensor networks is presented. The protocols build on the recent work of Hong, Cheow, and Scaglione [5, 6] in which the synchronization update rules are modeled by a system of pulse-coupled oscillators. In the present work, we define a class of models that converge to a synchronized state based on the local communication topology of the sensor network only, thereby lifting the all-to-all communication requirement implicit in [5, 6]. Under some rather mild assumptions of the connectivity of the network over time, these protocols still converge to a synchronized state when the communication topology is time varying.

Slip surface localization in wireless sensor networks for landslide prediction

A landslide occurs when the balance between a hills weight and the countering resistance forces is tipped in favor of gravity. While the physics governing the interplay between these competing forces is fairly well understood, prediction of landslides has been hindered thus far by the lack of field measurements over large temporal and spatial scales necessary to capture the inherent heterogeneity in a landslide. We propose a network of sensor columns deployed at hills with landslide potential with the purpose of detecting the early signals preceding a catastrophic event. Detection is performed through a three-stage algorithm: First, sensors collectively detect small movements consistent with the formation of a slip surface separating the sliding part of hill from the static one. Once the sensors agree on the presence of such a surface, they conduct a distributed voting algorithm to separate the subset of sensors that moved from the static ones. In the second phase, moved sensors self-localize through a trilateration mechanism and their displacements are calculated. Finally, the direction of the displacements as well as the locations of the moved nodes are used to estimate the position of the slip surface. This information along with collected soil measurements (e.g. soil pore pressures) are subsequently passed to a finite element model that predicts whether and when a landslide will occur. Our initial results from simulated landslides indicate that we can achieve accuracy in the order of cm in the localization as well as the slip surface estimation steps of our algorithm. This accuracy persists as the density and the size of the sensor network decreases as well as when considerable noise is present in the ranging estimates. As for our next step, we plan to evaluate the performance of our system in controlled environments under a variety of hill configurations

Two-timescale simultaneous perturbation stochastic approximation using deterministic perturbation sequences

Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effective for high-dimensional simulation optimization problems. The main idea is to estimate the gradient using simulation output performance measures at only two settings of the N-dimensional parameter vector being optimized rather than at the N + 1 or 2N settings required by the usual one-sided or symmetric difference estimates, respectively. The two settings of the parameter vector are obtained by simultaneously changing the parameter vector in each component direction using random perturbations. In this article, in order to enhance the convergence of these algorithms, we consider deterministic sequences of perturbations for two-timescale SPSA algorithms. Two constructions for the perturbation sequences are considered: complete lexicographical cycles and much shorter sequences based on normalized Hadamard matrices. Recently, one-simulation versions of SPSA have been proposed, and we also investigate these algorithms using deterministic sequences. Rigorous convergence analyses for all proposed algorithms are presented in detail. Extensive numerical experiments on a network of M/G/1 queues with feedback indicate that the deterministic sequence SPSA algorithms perform significantly better than the corresponding randomized algorithms.

An experiment for sensing-based opportunistic spectrum access in CSMA/CA networks

Opportunistic access to spectrum and secondary allocation of spectrum are topics being studied by regulatory bodies and organizations with interest in spectrum utilization. The DARPA ATO neXt Generation (XG) Program is investigating opportunistic use of spectrum wherein users would dynamically access spectrum based on its availability. Such access may embody changes to regulatory policies governing access to the RF spectrum. Additionally, the methods studied by the XG program could be used for secondary access within a fixed portion of spectrum. Opportunistic access would open spectrum that is sparsely used (temporally and spatially) to users who otherwise would be confined to inadequate frequency bands. An XG-enabled radio would sense and characterize spectral activity, identify spectral opportunities for use, and coordinate access, with the goal of not interfering with the primary, non-XG, and users. This paper describes a sensor suite and media access control (MAC) concepts representative of XG. A prototypical experiment has been conducted with the XG MAC in an environment of 802.11b radios. Results are presented that illustrate the operation of the MAC concept and demonstrate performance parameterized by the load carried by both 802.11b and XG

A macroeconomic model for resource allocation in large-scale distributed systems

In this paper we discuss an economic model for resource sharing in large-scale distributed systems. The model captures traditional concepts such as consumer satisfaction and provider revenues and enables us to analyze the effect of different pricing strategies upon measures of performance important for the consumers and the providers. We show that given a particular set of model parameters the satisfaction reaches an optimum; this value represents the perfect balance between the utility and the price paid for resources. Our results confirm that brokers play a very important role and can influence positively the market. We also show that consumer satisfaction does not track the consumer utility; these two important performance measures for consumers behave differently under different pricing strategies. Pricing strategies also affect the revenues obtained by providers, as well as, the ability to satisfy a larger population of users.

A constrained simultaneous perturbation stochastic approximation algorithm based on penalty functions

We present a stochastic approximation algorithm based on the penalty function method and a simultaneous perturbation gradient estimate for solving stochastic optimization problems with general inequality constraints. We also presents a very general convergence result for the proposed algorithm.

Stochastic optimization with inequality constraints using simultaneous perturbations and penalty functions

We present a stochastic approximation algorithm based on penalty function method and a simultaneous perturbation gradient estimate for solving stochastic optimization problems with general inequality constraints. We present a general convergence result that applies to a class of penalty functions including the quadratic penalty function, the augmented Lagrangian, and the absolute penalty function. We also establish an asymptotic normality result for the algorithm with smooth penalty functions under minor assumptions. Numerical results are given to compare the performance of the proposed algorithm with different penalty functions.

Monotone Optimal Policies for a Transient Queueing Staffing Problem

We consider the problem of determining the optimal policy for staffing a queueing system over multiple periods, using a model that takes into account transient queueing effects. Formulating the problem in a dynamic programming setting, we show that the optimal policy follows a monotone optimal control by establishing the submodularity of the objective function with respect to the staffing level and initial queue size in a period. In particular, this requires proving that the system occupancy in aG/M/s queue is submodular in the number of servers and initial system occupancy.

Surveillance Camera Coordination Through Distributed Scheduling

A challenge to scaling a video surveillance system is the amount of human supervision required for control of the cameras. In this paper we consider the problem of coordinating a network of video cameras for the purpose of identifying people. We pose the problem as a machine scheduling problem where each person is a job that should be scheduled before a deadline. To ensure scalability, we propose a distributed algorithm that only depends on neighbor to neighbor communication. We compare the performance of this algorithm to a localized scheduling approach.

Consensus with robustness to outliers via distributed optimization

Over the past few years, a number of distributed algorithms have been developed for integrating the measurements acquired by a wireless sensor network. Among them, average consensus algorithms have drawn significant attention due to a number of practical advantages, such as robustness to noise in the measurements, robustness to changes in the network topology and guaranteed convergence to the centralized solution. However, one of the main drawbacks of existing consensus algorithms is their inability to handle outliers in the measurements. This is because they are based on minimizing a Euclidean (L2) loss function, which is known to be sensitive to outliers. In this paper, we propose a distributed optimization framework that can handle outliers in the measurements. The proposed framework generalizes consensus algorithms to robust loss functions that are strictly convex or convex, such as the Huber loss or the L1-loss. This generalization is achieved by posing the robust consensus problem as a constrained optimization problem, which is solved using distributed versions of classical primal-dual and augmented Lagrangian optimization methods. The resulting algorithms include the classical average consensus as a particular case. Synthetic experiments evaluate our robust consensus framework for several robust cost functions and show their advantages over the classical average consensus algorithm.