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17
Bandwidth-constrained distributed estimation for wireless sensor networks -- Part I: Gaussian Case
- IEEE TRANS. SIGNAL PROCESS
, 2006
"... We study deterministic mean-location parameter estimation when only quantized versions of the original observations are available, due to bandwidth constraints. When the dynamic range of the parameter is small or comparable with the noise variance, we introduce a class of maximum-likelihood estimat ..."
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Cited by 105 (5 self)
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We study deterministic mean-location parameter estimation when only quantized versions of the original observations are available, due to bandwidth constraints. When the dynamic range of the parameter is small or comparable with the noise variance, we introduce a class of maximum-likelihood estimators that require transmitting just one bit per sensor to achieve an estimation variance close to that of the (clairvoyant) sample mean estimator. When the dynamic range is comparable or larger than the noise standard deviation, we show that an optimum quantization step exists to achieve the best possible variance for a given bandwidth constraint. We will also establish that in certain cases the sample mean estimator formed by quantized observations is preferable for complexity reasons. We finally touch upon algorithm implementation issues and guarantee that all the numerical maximizations required by the proposed estimators are concave.
Channel aware distributed detection in wireless sensor networks
- IEEE Signal Processing Mag
, 2006
"... [The integration of wireless channel conditions in algorithm design] In a distributed detection (DD) system, multiple sensors/detectors work collaboratively to distinguish between two or more hypotheses, e.g., the absence or presence of a target. While DD can be traced back to the advent of democrac ..."
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Cited by 56 (9 self)
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[The integration of wireless channel conditions in algorithm design] In a distributed detection (DD) system, multiple sensors/detectors work collaboratively to distinguish between two or more hypotheses, e.g., the absence or presence of a target. While DD can be traced back to the advent of democracy and associated voting schemes, one of its earliest formal treatments can be found in the work of Radner in the early 1960s, [1] who considered the problem of decision making by a team of multiple persons. Each person has access to different information and independently makes his/her decision. The coupling (i.e., the concept of a “team”) lies in the fact that the payoff function of the decision-making process depends on all the decisions and the state of situation in an inseparable way.
Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes
"... algorithm to locate sensors (with unknown locations) in 1, with respect to a minimal number of +1anchors with known locations. The sensors and anchors, nodes in the network, exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there a centralized fu ..."
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Cited by 39 (6 self)
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algorithm to locate sensors (with unknown locations) in 1, with respect to a minimal number of +1anchors with known locations. The sensors and anchors, nodes in the network, exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there a centralized fusion center to compute the sensors ’ locations. DILOC uses the barycentric coordinates of a node with respect to its neighbors; these coordinates are computed using the Cayley–Menger determinants, i.e., the determinants of matrices of internode distances. We show convergence of DILOC by associating with it an absorbing Markov chain whose absorbing states are the states of the anchors. We introduce a stochastic approximation version extending DILOC to random environments, i.e., when the communications among nodes is noisy, the communication links among neighbors may fail at random times, and the internodes distances are subject to errors. We show a.s. convergence of the modified DILOC and characterize the error between the true values of the sensors ’ locations and their final estimates given by DILOC. Numerical studies illustrate DILOC under a variety of deterministic and random operating conditions. Index Terms—Absorbing Markov chain, anchor, barycentric coordinates, Cayley–Menger determinant, distributed iterative
Local Vote Decision Fusion for Target Detection
- in Wireless Sensor Networks,” in Joint Research Conf. on Statistics in Quality Industry and Tech
, 2006
"... This study examines the problem of target detection by a wireless sensor network. Sensors acquire measurements emitted from the target that are corrupted by noise and initially make individual decisions about the presence/absence of the target. We propose the Local Vote Decision Fusion algorithm, in ..."
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Cited by 30 (3 self)
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This study examines the problem of target detection by a wireless sensor network. Sensors acquire measurements emitted from the target that are corrupted by noise and initially make individual decisions about the presence/absence of the target. We propose the Local Vote Decision Fusion algorithm, in which sensors first correct their decisions using decisions of neighboring sensors, and then make a collective decision as a network. An explicit formula that approximates the system’s decision threshold for a given false alarm rate is derived using limit theorems for random fields, which provides a theoretical performance guarantee for the algorithm. We examine both distance- and nearest neighbor-based versions of the local vote algorithm for grid and random sensor deployments and show that, for a fixed system false alarm, the local vote correction achieves significantly higher target detection rate than decision fusion based on uncorrected decisions. The local vote decision fusion framework is extended to the sequential case, where information becomes available over time.
Sensitivity and Coding of Opportunistic ALOHA in Sensor Networks with Mobile Access
- JOURNAL OF VLSI SIGNAL PROCESSING
, 2005
"... We consider a distributed medium access protocol, Opportunistic ALOHA, for reachback in sensor networks with mobile access points (AP). We briefly discuss some properties of the protocol, like throughput and transmission control for an orthogonal CDMA physical layer. We then consider the incorporati ..."
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Cited by 2 (2 self)
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We consider a distributed medium access protocol, Opportunistic ALOHA, for reachback in sensor networks with mobile access points (AP). We briefly discuss some properties of the protocol, like throughput and transmission control for an orthogonal CDMA physical layer. We then consider the incorporation of necessary side information like location into the transmission control and numerically demonstrate the loss in throughput in the absence of such information. Through simulations, we discuss the robustness and sensitivity of the protocol under various modeling errors and propose strategies to allow for errors in estimation of some parameters without reduction in the throughput. For networks, where the sensors are allowed to collaborate, we consider three coding schemes for reliable transmission: spreading code independent, spreading code dependent transmission and coding across sensors. These schemes are compared in terms of achievable rates and random coding error exponents. The coding across sensors scheme has comparable achievable rates to the spreading code dependent scheme, but requires the additional transmission of sensor ID. However, the scheme does not require the mobile AP to send data through the beacon unlike the other two schemes. The use of these coding schemes to overcome sensitivity is demonstrated through simulations.
STATISTICAL PROBLEMS IN WIRELESS SENSOR NETWORKS
, 2009
"... owe a debt of gratitude to many people who contributed to my success in completing this dissertation. First of all, I have been privileged to have the direction and guidance of two excellent advisers, my co-chairs Professors Elizaveta Levina and George Michailidis. From the first day of my graduate ..."
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owe a debt of gratitude to many people who contributed to my success in completing this dissertation. First of all, I have been privileged to have the direction and guidance of two excellent advisers, my co-chairs Professors Elizaveta Levina and George Michailidis. From the first day of my graduate studies in Michigan, Elizaveta has been very generous with her time and has provided very helpful advice on everything from the course schedule, the choice of my dissertation topic, the methodological design, the writing process to the choice of my future career. Her support and her ability to bring me back on track at difficult times have been a godsend. George likewise made invaluable contributions to the development of my ideas and the organization of my research. He has always motivated me to do my best work, provided timely feedback on each part of my dissertation and keen insight into the significance of my research. Both Liza and George introduced me to the joys of academic research not only by teaching me about the issues at hand, but also by encouraging my trips to a number of workshops and conferences and by sharing with
DIMENSIONALITY REDUCTION, COMPRESSION AND QUANTIZATION FOR DISTRIBUTED ESTIMATION WITH WIRELESS SENSOR NETWORKS ∗
"... Abstract. The distributed nature of observations collected by inexpensive wireless sensors necessitates transmission of the individual sensor data under stringent bandwidth and power constraints. These constraints motivate: i) a means of reducing the dimensionality of local sensor observations; ii) ..."
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Abstract. The distributed nature of observations collected by inexpensive wireless sensors necessitates transmission of the individual sensor data under stringent bandwidth and power constraints. These constraints motivate: i) a means of reducing the dimensionality of local sensor observations; ii) quantization of sensor observations prior to digital transmission; and iii) estimators based on the quantized digital messages. These three problems are addressed in the present paper. We start deriving linear estimators of stationary random signals based on reduced-dimensionality observations. For uncorrelated sensor data, we develop mean-square error (MSE) optimal estimators in closed-form; while for correlated sensor data, we derive sub-optimal iterative estimators which guarantee convergence at least to a stationary point. We then determine lower and upper bounds for the Distortion-Rate (D-R) function and a novel alternating scheme that numerically determines an achievable upper bound of the D-R function for general distributed estimation using multiple sensors. We finally derive distributed estimators based on binary observations along with their fundamental error-variance limits for pragmatic signal models including: i) known univariate but generally non-Gaussian noise probability density functions (pdfs); ii) known noise pdfs with a finite number of unknown parameters; and iii) practical generalizations to multivariate and possibly correlated pdfs. Estimators utilizing either independent or colored binary observations are developed, analyzed and tested with numerical examples.
1 Distributed Sensor Localization in Random Environments using Minimal Number of Anchor Nodes
, 2008
"... The paper develops DILOC, a distributive, iterative algorithm that locates M sensors in R m, m ≥ 1, with respect to a minimal number of m + 1 anchors with known locations. The sensors exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there centra ..."
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The paper develops DILOC, a distributive, iterative algorithm that locates M sensors in R m, m ≥ 1, with respect to a minimal number of m + 1 anchors with known locations. The sensors exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there centralized knowledge about the sensors’ locations. DILOC uses the barycentric coordinates of a sensor with respect to its neighbors that are computed using the Cayley-Menger determinants. These are the determinants of matrices of inter-sensor distances. We show convergence of DILOC by associating with it an absorbing Markov chain whose absorbing states are the anchors. We introduce a stochastic approximation version extending DILOC to random environments when the knowledge about the intercommunications among sensors and the inter-sensor distances are noisy, and the communication links among neighbors fail at random times. We show a.s. convergence of the modified DILOC and characterize the error between the final estimates and the true values of the sensors ’ locations. Numerical studies illustrate DILOC under a variety of deterministic and random operating conditions. Keywords: Distributed iterative sensor localization; sensor networks; Cayley-Menger determinant; barycentric coordinates; absorbing Markov chain; stochastic approximation.
