distributed compressive sensing (DCS) usually improves the signal recovery performance of multi-signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. However, the existing DCS...
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distributed compressive sensing (DCS) usually improves the signal recovery performance of multi-signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. However, the existing DCS had proposed for a very limited ensemble of signals that has only single common information. This paper proposes a generalized DCS (GDCS) framework which can improve sparse signal detection performance given arbitrary types of common information, which are classified into full common information and partial common information after overcoming against existing limitation. Specifically, the theoretical bound on the required number of measurements under the GDCS is obtained. We also develop a practical algorithm to obtain benefits using the GDCS. At the end of this paper, it simply summarizes the potential security issues when it gets all sensing information in a sensor network. Finally, numerical results verify that the proposed algorithm reduces the required number of measurements for correlated sparse signal detection compared to the DCS algorithm. This research lays down the basis for efficient distributed signal detection so that it can improve the detection performance or it can detect the signal reliably when the number of signal observations is limited.
In this paper, we consider the problem of correlated data gathering in M2M (machine-to-machine) wireless networks with a large number of machines. Since machines communicate directly with the aggregator, the limited r...
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ISBN:
(纸本)9781479913510
In this paper, we consider the problem of correlated data gathering in M2M (machine-to-machine) wireless networks with a large number of machines. Since machines communicate directly with the aggregator, the limited radio resources at the aggregator become the bottleneck for supporting all machines. Unlike related work that employs distributed source coding for minimizing resource usage, we assume that machines only perform local sourcecoding. However, machines can leverage data overheard from transmissions of other machines for removing redundancy based on "dependent" sourcecoding. To explore the performance tradeoffs of overhearing, we formulate a joint optimization problem involving node selection, resource allocation, and transmission scheduling, and then solve the problem based on the cross entropy method. Evaluation results show that without incurring the complexity of distributed source coding, dependent sourcecoding via overhearing can achieve noticeable performance gain compared to independent sourcecoding - even if the overhearing range and time are limited due to energy consideration. The results thus motivate further investigation for leveraging overhearing opportunities in M2M networks with limited radio resources.
This paper studies the problem of correlated data gathering in wireless sensor networks. For a maximum network utility, a efficient data capture and transmission framework is proposed for correlated sources, where loc...
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ISBN:
(纸本)9781612842325
This paper studies the problem of correlated data gathering in wireless sensor networks. For a maximum network utility, a efficient data capture and transmission framework is proposed for correlated sources, where localized S-W sourcecoding, network coding based flow control and opportunistic routing are jointly optimized. To increase the throughput and guarantee the decodability simultaneously, a dynamic network coding strategy is proposed, with which the intermediate node can easily decide whether to make a combination among the incoming flows. Also, an opportunistic routing approach is presented, which adopts a new metric (minimum congestion price) for forwarding node selection and results in a maximum utility benefit for the sensor nodes. Through the Lagrange dual and gradient approach, a fully distributed algorithm is represented. And the convergence and performance are validated by the numerical results.
This paper addresses distributed finite-rate quantized compressed sensing (QCS) acquisition of correlated sparse sources in wireless sensor networks. We propose a distributed variable-rate QCS compression method with ...
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ISBN:
(纸本)9781509017508
This paper addresses distributed finite-rate quantized compressed sensing (QCS) acquisition of correlated sparse sources in wireless sensor networks. We propose a distributed variable-rate QCS compression method with complexity-constrained encoding to minimize a weighted sum of the mean square error distortion of the signal reconstruction and the average encoding rate. The variable-rate coding is realized via entropy-constrained vector quantization, whereas the restrained encoding complexity is obtained via vector pre-quantization of CS measurements. We derive necessary optimality conditions for the system blocks for two-sensor case. Numerical results show that our proposed method efficiently exploits the signal correlation, and achieves superior distortion-rate compression performance.
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