I-Jeng Wang

Bio: I-Jeng Wang is an academic researcher from Johns Hopkins University Applied Physics Laboratory. The author has contributed to research in topics: Stochastic approximation & Wireless sensor network. The author has an hindex of 18, co-authored 92 publications receiving 1197 citations. Previous affiliations of I-Jeng Wang include Purdue University & Johns Hopkins University.

Decentralized synchronization protocols with nearest neighbor communication

Abstract: 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

Abstract: A landslide occurs when the balance between a hill's 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

Abstract: 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

Abstract: 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

Stochastic optimization with inequality constraints using simultaneous perturbations and penalty functions

Abstract: 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.

Wireless sensor network survey

Abstract: A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.

A survey of spectrum sensing algorithms for cognitive radio applications

Abstract: The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.

Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects

Abstract: Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating sociotechnical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value--and at the same time fundamentally limited--in their ability to characterize system performance.We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research.