Distributed Compressed Sensing-Based Data Fusion in Sensor Networks

Kang, Jian; Tang, Liwei; Zuo, Xianzhang; Li, Aihua; Li, Hao

doi:10.1109/pcspa.2010.266

Cited by 5 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, reference [39] presented a new distributed algorithm based on sparse random projections that dose not need global coordination. In addition, reference [40] presented a DCS and data-fusion based data aggregation model and algorithm to improve the sensing precision and reduce the number of transmissions.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

et al. 2017

View full text Add to dashboard Cite

Because sensing nodes typically have limited power resources, it is extremely important for signals to be acquired with high efficiency and low power consumption, especially in large-scale wireless sensor networks (WSNs) applications. An emerging signal acquisition and compression method called compressed sensing (CS) is a notable alternative to traditional signal processing methods and is a feasible solution for WSNs. In our previous work, we studied several data recovery algorithms and network models that use CS for compressive sampling and signal recovery. The results were validated on large data sets from actual environmental monitoring WSNs. In this paper, we focus on the hardware solution for signal acquisition and processing on separate end nodes. We propose the paradigm of an analog-to-information converter (AIC) based on CS theory. The system model consists of a modulation module, filtering module, and sampling module, and was simulated and analyzed in a MATLAB/Simulink 7.0 environment. Further, the hardware design and implementation of an improved digital AIC system is presented. We also study the performances of three different greedy data recovery algorithms and analyze the system power consumption. The experimental results show that, for normal environmental signals, the new system overcomes the Nyquist limit and exhibits good recovery performance with a low sampling frequency, which is suitable for environmental monitoring based on WSNs.

show abstract

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

et al. 2017

View full text Add to dashboard Cite

show abstract

“…As DCS reduces the redundancy among signal groups from both inter-and intrasignal correlations, a lower quantity of data must be transmitted, reducing energy costs. References [18][19][20][21] proposed different DCS-based data gathering strategies for wireless sensor networks. Reference [18] presented a DCSbased data aggregation model and algorithm to reduce the number of transmissions in sensor networks and improve the precision of sensing.…”

Section: Related Workmentioning

confidence: 99%

“…References [18][19][20][21] proposed different DCS-based data gathering strategies for wireless sensor networks. Reference [18] presented a DCSbased data aggregation model and algorithm to reduce the number of transmissions in sensor networks and improve the precision of sensing. To implement this algorithm, the variance of each recover sensing sequence of sensors was estimated using the wavelet transform, and the optimum weighing factor for each sensing was obtained accordingly.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks

Wang

et al. 2016

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Energy-efficient data aggregation is important for underwater acoustic sensor networks due to its energy constrained character. In this paper, we propose a kind of energy-efficient data aggregation scheme to reduce communication cost and to prolong network lifetime based on distributed compressed sensing theory. First, we introduce a distributed compressed sensing model for a clusterbased underwater acoustic sensor network in which spatial and temporal correlations are both considered. Second, two schemes, namely, BUTM-DCS (block upper triangular matrix DCS) and BDM-DCS (block diagonal matrix DCS), are proposed based on the design of observation matrix with strictly restricted isometric property. Both schemes take multihop underwater acoustic communication cost into account. Finally, a distributed compressed sensing reconstruction algorithm, DCS-SOMP (Simultaneous Orthogonal Matching Pursuit for DCS), is adopted to recover raw sensor readings at the fusion center. We performed simulation experiments on both the synthesized and real sensor readings. The results demonstrate that the new data aggregation schemes can reduce energy cost by more than 95 percent compared with conventional data aggregation schemes when the cluster number is 20.

show abstract

“…Online collecting is the main stream, namely CDR in the device and platform is directly collected through the network in real time in the billing system. Then it will be analyzed, rated and charged based on customer information [2,3,4]. CDR collecting promptly dramatically affects charging timeliness, user experience and perception.…”

Section: Introductionmentioning

confidence: 99%

The Optimized Scheme of CDR Collecting and Abnormity Monitoring

Wen

2014

AMM

View full text Add to dashboard Cite

On the basis of current Charge Detail Record (CDR) collecting process in one communication operator, the paper analyzed processing bottleneck of this module. It easily caused system untimely collecting CDR and was unable to response promptly to fault. This paper added a Disaster Recovery System with collecting procedures to ensure collecting CDR promptly. And it analyzed and monitored abnormal situation such as CDR collecting with delay or processing with abnormity in the billing system, CDR generating with delay or abnormity in the device or platform and so on. Then it would alarm by sending alarm short messages and displaying alarm hints. The results show that this scheme can effectively collects CDR and discovers abnormity promptly in the billing system, device and platform. And it is much better than the old one in processing timeliness, system safety and stability.

show abstract

Distributed Compressed Sensing-Based Data Fusion in Sensor Networks

Cited by 5 publications

References 10 publications

Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks

The Optimized Scheme of CDR Collecting and Abnormity Monitoring

Contact Info

Product

Resources

About