The Experts below are selected from a list of 87456 Experts worldwide ranked by ideXlab platform
Krishnendu Chakrabarty - One of the best experts on this subject based on the ideXlab platform.
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on computing mobile agent routes for data fusion in Distributed Sensor networks
IEEE Transactions on Knowledge and Data Engineering, 2004Co-Authors: Nageswara S V Rao, Jacob Barhen, S S Iyenger, Vijay K Vaishnavi, Krishnendu ChakrabartyAbstract:The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a Distributed Sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the Sensor network, such as target classification or tracking. We present a simplified analytical model for a Distributed Sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and Sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.
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Sensor deployment and target localization in Distributed Sensor networks
ACM Transactions on Embedded Computing Systems, 2004Co-Authors: Yi Zou, Krishnendu ChakrabartyAbstract:The effectiveness of cluster-based Distributed Sensor networks depends to a large extent on the coverage provided by the Sensor deployment. We propose a virtual force algorithm (VFA) as a Sensor deployment strategy to enhance the coverage after an initial random placement of Sensors. For a given number of Sensors, the VFA algorithm attempts to maximize the Sensor field coverage. A judicious combination of attractive and repulsive forces is used to determine the new Sensor locations that improve the coverage. Once the effective Sensor positions are identified, a one-time movement with energy consideration incorporated is carried out, that is, the Sensors are redeployed, to these positions. We also propose a novel probabilistic target localization algorithm that is executed by the cluster head. The localization results are used by the cluster head to query only a few Sensors (out of those that report the presence of a target) for more detailed information. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
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Sensor placement for effective coverage and surveillance in Distributed Sensor networks
Wireless Communications and Networking Conference, 2003Co-Authors: S S Dhillon, Krishnendu ChakrabartyAbstract:We present two algorithms for the efficient placement of Sensors in a Sensor field. The proposed approach is aimed at optimizing the number of Sensors and determining their placement to support Distributed Sensor networks. The optimization framework is inherently probabilistic due to the uncertainty associated with Sensor detections. The proposed algorithms address coverage optimization under the constraints of imprecise detections and terrain properties. These algorithms are targeted at average coverage as well as at maximizing the coverage of the most vulnerable grid points. The issue of preferential coverage of grid points (based on relative measures of security and tactical importance) is also modeled. Experimental results for an example Sensor field with obstacles demonstrate the application of our approach.
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grid coverage for surveillance and target location in Distributed Sensor networks
IEEE Transactions on Computers, 2002Co-Authors: Krishnendu Chakrabarty, S. S. Iyengar, Eungchun ChoAbstract:We present novel grid coverage strategies for effective surveillance and target location in Distributed Sensor networks. We represent the Sensor field as a grid (two or three-dimensional) of points (coordinates) and use the term target location to refer to the problem of locating a target at a grid point at any instant in time. We first present an integer linear programming (ILP) solution for minimizing the cost of Sensors for complete coverage of the Sensor field. We solve the ILP model using a representative public-domain solver and present a divide-and-conquer approach for solving large problem instances. We then use the framework of identifying codes to determine Sensor placement for unique target location, We provide coding-theoretic bounds on the number of Sensors and present methods for determining their placement in the Sensor field. We also show that grid-based Sensor placement for single targets provides asymptotically complete (unambiguous) location of multiple targets in the grid.
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multiresolution data integration using mobile agents in Distributed Sensor networks
Systems Man and Cybernetics, 2001Co-Authors: S. S. Iyengar, Krishnendu ChakrabartyAbstract:We describe the use of the mobile agent paradigm to design an improved infrastructure for data integration in a Distributed Sensor network (DSN). We use the acronym MADSN to denote the proposed mobile-agent-based DSN. Instead of moving data to processing elements for data integration, as is typical of a client/server paradigm, MADSN moves the processing code to the data locations. This saves network bandwidth and provides an effective means for overcoming network latency, since large data transfers are avoided. Our major contributions are the use of mobile agent in DSN for Distributed data integration and the evaluation of performance between DSN and MADSN approaches. We develop an enhanced multiresolution integration (MRI) algorithm where multiresolution analysis is applied at a local node before accumulating the overlap function by mobile agent. Compared to the MRI implementation in DSN, the enhanced integration algorithm saves up to 90% of the data transfer time. We develop objective functions to evaluate the performance between DSN and MADSN approaches. For a given set of network parameters, we analyze the conditions under which MADSN performs better than DSN and determine the condition under which MADSN reaches its optimum performance level.
F. Lorenzelli - One of the best experts on this subject based on the ideXlab platform.
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blind beamforming on a randomly Distributed Sensor array system
IEEE Journal on Selected Areas in Communications, 1998Co-Authors: Kung Yao, Daching Chen, R.e. Hudson, C.w. Reed, F. LorenzelliAbstract:We consider a digital signal processing Sensor array system, based on randomly Distributed Sensor nodes, for surveillance and source localization applications. In most array processing the Sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the Sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured Sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical Sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering.
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Beamforming performance of a randomly Distributed Sensor array system
1997 IEEE Workshop on Signal Processing Systems. SiPS 97 Design and Implementation formerly VLSI Signal Processing, 1Co-Authors: Kung Yao, R.e. Hudson, C.w. Reed, F. LorenzelliAbstract:We consider a digital signal processing Sensor array system based on randomly Distributed Sensor nodes for intrusion and surveillance applications. The nodes having acoustical, seismic, and other Sensors are self organized into a synchronized network using low powered spread spectrum transceivers. Beamforming array techniques for enhanced detection and estimation and performances under various ideal and practical conditions are presented.
Christos G. Panayiotou - One of the best experts on this subject based on the ideXlab platform.
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Distributed Sensor Fault Diagnosis for a Network of Interconnected Cyberphysical Systems
IEEE Transactions on Control of Network Systems, 2015Co-Authors: Vasso Reppa, Marios M. Polycarpou, Christos G. PanayiotouAbstract:This paper proposes a Distributed methodology for detecting and isolating multiple Sensor faults in interconnected cyber-physical systems. The Distributed Sensor fault detection and isolation process is conducted in the cyber superstratum, in two levels. The first-level diagnosis is based on the design of monitoring agents, where every agent is dedicated to a corresponding interconnected subsystem. The monitoring agent is designed to isolate multiple Sensor faults occurring in the Sensor set of the physical part, while it is allowed to exchange information with its neighboring monitoring agents. The secondlevel diagnosis is realized by applying a global decision logic designed to isolate multiple Sensor faults that may propagate in the cyber superstratum through the exchange of information between monitoring agents. The decision making process, executed in both levels of diagnosis, relies on a multiple Sensor fault combinatorial logic and diagnostic reasoning. The performance of the proposed methodology is analyzed with respect to the Sensor fault propagation effects and the Distributed Sensor fault detectability.
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Distributed Sensor Fault Diagnosis for a Network of Interconnected Cyberphysical Systems
IEEE Transactions on Control of Network Systems, 2015Co-Authors: Vasso Reppa, Marios M. Polycarpou, Christos G. PanayiotouAbstract:International audienceThis paper proposes a Distributed methodology for detecting and isolating multiple Sensor faults in interconnected cyber-physical systems. The Distributed Sensor fault detection and isolation process is conducted in the cyber superstratum, in two levels. The first-level diagnosis is based on the design of monitoring agents, where every agent is dedicated to a corresponding interconnected subsystem. The monitoring agent is designed to isolate multiple Sensor faults occurring in the Sensor set of the physical part, while it is allowed to exchange information with its neighboring monitoring agents. The secondlevel diagnosis is realized by applying a global decision logic designed to isolate multiple Sensor faults that may propagate in the cyber superstratum through the exchange of information between monitoring agents. The decision making process, executed in both levels of diagnosis, relies on a multiple Sensor fault combinatorial logic and diagnostic reasoning. The performance of the proposed methodology is analyzed with respect to the Sensor fault propagation effects and the Distributed Sensor fault detectability
Kung Yao - One of the best experts on this subject based on the ideXlab platform.
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blind beamforming on a randomly Distributed Sensor array system
IEEE Journal on Selected Areas in Communications, 1998Co-Authors: Kung Yao, Daching Chen, R.e. Hudson, C.w. Reed, F. LorenzelliAbstract:We consider a digital signal processing Sensor array system, based on randomly Distributed Sensor nodes, for surveillance and source localization applications. In most array processing the Sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the Sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured Sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical Sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering.
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Beamforming performance of a randomly Distributed Sensor array system
1997 IEEE Workshop on Signal Processing Systems. SiPS 97 Design and Implementation formerly VLSI Signal Processing, 1Co-Authors: Kung Yao, R.e. Hudson, C.w. Reed, F. LorenzelliAbstract:We consider a digital signal processing Sensor array system based on randomly Distributed Sensor nodes for intrusion and surveillance applications. The nodes having acoustical, seismic, and other Sensors are self organized into a synchronized network using low powered spread spectrum transceivers. Beamforming array techniques for enhanced detection and estimation and performances under various ideal and practical conditions are presented.
Xiaoling Wang - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Acoustic target classification using Distributed Sensor arrays
IEEE International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Xiaoling WangAbstract:Target classification using Distributed Sensor arrays remains a challenging problem due to the non-stationarity of target signatures, large geographical area coverage of Sensor arrays, and the requirements of time-critical and reliable information delivery. In this paper, we develop an algorithm to derive effective and stable features from both the frequency and the time-frequency domains of the acoustic signals. A modified data fusion algorithm for Distributed Sensor arrays is also developed in order to integrate the classification results from different Sensors and provide fault-tolerance. By using data fusion, the accuracy of the classification can be increased by as many as 50%.