Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks

In this paper, we revisit the problem of fusing decisions transmitted over fading channels in a wireless sensor network. Previous development relies on instantaneous channel state information (CSI). However, acquiring channel information may be too costly for resource constrained sensor networks. In this paper, we propose a new likelihood ratio (LR)-based fusion rule which requires only the knowledge of channel statistics instead of instantaneous CSI. Based on the assumption that all the sensors have the same detection performance and the same channel signal-to-noise ratio (SNR), we show that when the channel SNR is low, this fusion rule reduces to a statistic in the form of an equal gain combiner (EGC), which explains why EGC is a very good choice with low or medium SNR; at high-channel SNR, it is equivalent to the Chair-Varshney fusion rule. Performance evaluation shows that the new fusion rule exhibits only slight performance degradation compared with the optimal LR-based fusion rule using instantaneous CSI.     

Scheduling Sleeping Nodes in High Density Cluster-based Sensor Networks

In order to conserve battery power in very dense sensor networks, some sensor nodes may be put into the sleep state while other sensor nodes remain active for the sensing and communication tasks. In this paper, we study the node sleep scheduling problem in the context of clustered sensor networks. We propose and analyze the Linear Distance-based Scheduling (LDS) technique for sleeping in each cluster. The LDS scheme selects a sensor node to sleep with higher probability when it is farther away from the cluster head. We analyze the energy consumption, the sensing coverage property, and the network lifetime of the proposed LDS scheme. The performance of the LDS scheme is compared with that of the conventional Randomized Scheduling (RS) scheme. It is shown that the LDS scheme yields more energy savings while maintaining a similar sensing coverage as the RS scheme for sensor clusters. Therefore, the LDS scheme results in a longer network lifetime than the RS scheme. Jing Deng received the B.E. and M.E. degrees in Electronic Engineering from Tsinghua University, Beijing, P. R. China, in 1994 and 1997, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Cornell University, Ithaca, NY, in 2002. Dr. Deng is an assistant professor in the Department of Computer Science at the University of New Orleans. From 2002 to 2004, he visited the CASE center and the Department of Electrical Engineering and Computer Science at Syracuse University, Syracuse, NY as a research assistant professor, supported by the Syracuse University Prototypical Research in Information Assurance (SUPRIA) program. He was a teaching assistant from 1998 to 1999 and a research assistant from 1999 to 2002 in the School of Electrical and Computer Engineering at Cornell University. His interests include mobile ad hoc networks, wireless sensor networks, wireless network security, energy efficient wireless networks, and information assurance. Wendi B. Heinzelman is an assistant professor in the Department of Electrical and Computer Engineering at the University of Rochester. She received a B.S. degree in Electrical Engineering from Cornell University in 1995 and M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 1997 and 2000 respectively. Her current research interests lie in the areas of wireless communications and networking, mobile computing, and multimedia communication. Dr. Heinzelman received the NSF Career award in 2005 for her work on cross-layer optimizations for wireless sensor networks, and she received the ONR Young Investigator award in 2005 for her research on balancing resource utilization in wireless sensor networks. Dr. Heinzelman was co-chair of the 1st Workshop on Broadband Advanced Sensor Networks (BaseNets '04), and she is a member of Sigma Xi, the IEEE, and the ACM. Yunghsiang S. Han was born in Taipei, Taiwan, on April 24, 1962. He received the B.S. and M.S. degrees in electrical engineering from the National Tsing Hua University, Hsinchu, Taiwan, in 1984 and 1986, respectively, and the Ph.D. degree from the School of Computer and Information Science, Syracuse University, Syracuse, NY, in 1993. From 1986 to 1988 he was a lecturer at Ming-Hsin Engineering College, Hsinchu, Taiwan. He was a teaching assistant from 1989 to 1992 and from 1992 to 1993 a research associate in the School of Computer and Information Science, Syracuse University. From 1993 to 1997 he was an Associate Professor in the Department of Electronic Engineering at Hua Fan College of Humanities and Technology, Taipei Hsien, Taiwan. From 1997 to 2004 he was with the Department of Computer Science and Information Engineering at National Chi Nan University, Nantou, Taiwan. He was promoted to Full Professor in 1998. From June to October 2001 he was a visiting scholar in the Department of Electrical Engineering at University of Hawaii at Manoa, HI, and from September 2002 to January 2004 he was the SUPRIA visiting research scholar in the Department of Electrical Engineering and Computer Science and CASE center at Syracuse University, NY. He is now with the Graduate Institute of Communication Engineering at National Taipei University, Taipei, Taiwan. His research interests are in wireless networks, security, and error-control coding. Dr. Han is a winner of 1994 Syracuse University Doctoral Prize. Pramod K. Varshney was born in Allahabad, India on July 1, 1952. He received the B.S. degree in electrical engineering and computer science (with highest honors), and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign in 1972, 1974, and 1976 respectively. Since 1976 he has been with Syracuse University, Syracuse, NY where he is currently a Professor of Electrical Engineering and Computer Science and the Research Director of the New York State Center for Advanced Technology in Computer Applications and Software Engineering. His current research interests are in distributed sensor networks and data fusion, detection and estimation theory, wireless communications, intelligent systems, signal and image processing, and remote sensing he has published extensively. He is the author of Distributed Detection and Data Fusion, published by Springer-Verlag in 1997 and has co-edited two other books. Dr. Varshney is a member of Tau Beta Pi and is the recipient of the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected to the grade of Fellow of the IEEE in 1997 for his contributions in the area of distributed detection and data fusion. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor's Citation for exceptional academic achievement at Syracuse University. He serves as a distinguished lecturer for the AES society of the IEEE. He is on the editorial board Information Fusion. He was the President of International Society of Information Fusion during 2001.     

An adaptive multimodal biometric management algorithm

This paper presents an evolutionary approach to the sensor management of a biometric security system that improves robustness. Multiple biometrics are fused at the decision level to support a system that can meet more challenging and varying accuracy requirements as well as address user needs such as ease of use and universality better than a single biometric system or static multimodal biometric system. The decision fusion rules are adapted to meet the varying system needs by particle swarm optimization, which is an evolutionary algorithm. This paper focuses on the details of this new sensor management algorithm and demonstrates its effectiveness. The evolutionary nature of adaptive, multimodal biometric management (AMBM) allows it to react in pseudoreal time to changing security needs as well as user needs. Error weights are modified to reflect the security and user needs of the system. The AMBM algorithm selects the fusion rule and sensor operating points to optimize system performance in terms of accuracy.     

An image change detection algorithm based on Markov random field models

This paper addresses the problem of image change detection (ICD) based on Markov random field (MRF) models. MRF has long been recognized as an accurate model to describe a variety of image characteristics. Here, we use the MRF to model both noiseless images obtained from the actual scene and change images (CIs), the sites of which indicate changes between a pair of observed images. The optimum ICD algorithm under the maximum a posteriori (MAP) criterion is developed under this model. Examples are presented for illustration and performance evaluation.     

Further results on distributed Bayesian signal detection

The problem of distributed Bayesian signal detection is addressed. The problem is reformulated and a new design approach is presented that allows the use of efficient optimization algorithms     

An experimental facility for determination of steady flame spread rate of materials employing the Quasi-stationary flame front technique

An experimental facility based on the Quasi-stationary Flame Front Technique for determination of steady flame spread rate of materials at discrete levels of external radiant heat flux was developed. The method employs an external radiant heat source in front of which an element of a specimen of the material is positioned at a location corresponding to the desired level of external radiant heat flux. A specimen movement assembly, which can be operated manually, was designed for moving the specimen towards the stationary external radiant heat source such that the flame front could be maintained quasi-stationary. The experimental technique employed is simple in operation yet is capable of yielding reliable flame fornt displacement–time data. In the paper the design considerations of the experimental facility, details of its components, calibration and typical experimental results obtained are presented.     

Decentralized Bayesian detection with feedback

A decentralized detection system with feedback and memory using the Bayesian formulation is investigated. The optimization of this system results in a likelihood ratio test at the local detectors for statistically independent observations. In addition, local detector thresholds and the system probability of error are shown to be a function of the fed back global decision. The issue of data transmission between local detectors and the fusion center is addressed. Two protocols are proposed and studied to reduce data transmissions. Numerical examples are also presented for illustration     

CO2 laser annealing of plasma-deposited high-T c superconducting thick films

Recent advances in the fabrication of high-T _c superconducting thick films demand processing techniques which can eliminate film/substrate interdifiusion that occurs during subsequent post-annealing heat treatment after the film is deposited, thereby limiting the application of the thick films for devices. The present study evaluates laser annealing techniques for plasma-deposited Y-Ba-Cu-O thick films using a high-energy CO₂ laser (10.6Μm) in a continuous wave mode. The results are compared with those obtained by conventional furnace annealing techniques necessary for post-heat treatment of as-deposited superconducting thick films. The high-T _c superconducting phase is recovered by cationic diffusion during subsequent post-annealing heat treatment. Crystallographic phases and microstructural characterization have been performed using XRD, SEM, and EPMA analytical techniques. The significance of the technology lies in the elimination of film/substrate interdiffusion problems, thereby resulting in high-quality superconducting thick films. The technology will significantly reduce the post-annealing times usually required by conventional furnace annealing techniques.