TY - JOUR
T1 - An overview of recent advances on distributed and agile sensing algorithms and implementation
AU - Banavar, Mahesh K.
AU - Zhang, Jun J.
AU - Chakraborty, Bhavana
AU - Kwon, Homin
AU - Li, Ying
AU - Jiang, Huaiguang
AU - Spanias, Andreas
AU - Tepedelenlioglu, Cihan
AU - Chakrabarti, Chaitali
AU - Papandreou-Suppappola, Antonia
N1 - Funding Information:
This work was partly supported by the NSF Grant No. 0817596 ; NSF Grant No. CSR-EHS 615135 ; NSF Grant No. 0830799 ; AFOSR MURI Grant No. FA9550-05-1-0443 ; and the Sensor, Signal and Information Processing (SenSIP) Center .
Publisher Copyright:
© 2015 Elsevier Inc. All rights reserved.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation at the fusion center and wireless networks for the sensor systems for real time scenarios. We also study waveform-agile sensing, which includes methods for adapting the sensor transmit waveform to match the environment and to optimize the selected performance metric. We specifically concentrate on radar and underwater acoustic signal transmission environments. As we consider systems with potentially large number of sensors, we discuss the use of resource-agile implementation approaches based on multiple-core processors in order to efficiently implement the computationally intensive processing in configuring the sensors. These resource-agile approaches can be extended to also optimize sensing in distributed sensor networks.
AB - We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation at the fusion center and wireless networks for the sensor systems for real time scenarios. We also study waveform-agile sensing, which includes methods for adapting the sensor transmit waveform to match the environment and to optimize the selected performance metric. We specifically concentrate on radar and underwater acoustic signal transmission environments. As we consider systems with potentially large number of sensors, we discuss the use of resource-agile implementation approaches based on multiple-core processors in order to efficiently implement the computationally intensive processing in configuring the sensors. These resource-agile approaches can be extended to also optimize sensing in distributed sensor networks.
KW - Agile sensing
KW - Distributed inference
KW - Distributed sensing
KW - Resource-agile processing
KW - Sensor networks
KW - Smart grid
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U2 - 10.1016/j.dsp.2015.01.001
DO - 10.1016/j.dsp.2015.01.001
M3 - Review article
AN - SCOPUS:84924541975
SN - 1051-2004
VL - 39
SP - 1
EP - 14
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
ER -