Cross-layer multirate interaction with Distributed Source Coding in Wireless Sensor Networks

Wang, Wei; Peng, Dongming; Wang, Honggang; Sharif, Hamid; Chen, Hsiao‐Hwa

doi:10.1109/twc.2009.071009

Cited by 40 publications

(27 citation statements)

References 23 publications

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“…On the wireless channel side, let L denote the loss of signal strength in wireless channel, l oh denote the packet header overhead, B w denote the frequency spectrum bandwidth resource, and N 0 denote the noise power density. Let ς denote the estimated Bit Error Rate (BER) for data transmission, which could be approximated to [21]:…”

Section: A Resource Allocation Problem Formulationmentioning

confidence: 99%

Multimedia Sensing as a Service (MSaaS): Exploring Resource Saving Potentials of at Cloud-Edge IoTs and Fogs

Wang

Sohraby

2016

IEEE Internet Things J.

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With the popularity of multimedia sensing at cloud edges and the reducing cost of the Internet of Things (IoTs) fog devices and systems, new challenges have been posed to efficiently deal with the big data multimedia traffic generated from IoT sensing units. Specifically, in this paper we introduce the concept of Multimedia Sensing as a Service (MSaaS), and propose a generalized premium prioritization-based Quality of Experience (QoE) paradigm for wireless big-volumes-of-data (B-VD) multimedia communications, with significant energy saving potentials for future multimedia IoT devices and systems. The key contribution of this new framework is its data diversity flexibility at the application layer, which could be flexibly adopted by future multimedia communication systems. Data dependencies in spatial, frequency and temporal domains are analyzed, and interaction with uplink resource allocation optimization are investigated with regards to wireless communication energy cost estimation. Extensive simulation results demonstrate that the proposed prioritization-based communication paradigm has significant energy saving potentials for BVD MSaaS wireless multimedia communications at cloud edges and fogs.

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Section: A Resource Allocation Problem Formulationmentioning

confidence: 99%

Multimedia Sensing as a Service (MSaaS): Exploring Resource Saving Potentials of at Cloud-Edge IoTs and Fogs

Wang

Sohraby

2016

IEEE Internet Things J.

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“…The reason is obvious since any increase on the data sampling rate would cause more data to be transmitted and computed while leading to better performance on data reconstruction. Figures 6,7,and 8 show the SNR of the reconstructed data, the amount of transmitted data, and the amount of computed data of the proposed scheme, respectively, in different surveillance environments. In the experiment, the number of The amount of sensor nodes Data sampling rate = 40% Data sampling rate = 50% Data sampling rate = 60% random data sources varies between 2 and 4.…”

Section: Performance Analysismentioning

confidence: 99%

A Three-Phase Top-k Query Based Distributed Data Collection Scheme in Wireless Sensor Networks

2015

International Journal of Distributed Sensor Networks

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We propose a three-phase top-| | query based distributed data collection scheme which is designed for clustered or multisink wireless sensor networks. The proposed scheme consists of a distributed iterative hard thresholding algorithm and a three-phase top-| | query algorithm. In the distributed iterative hard thresholding algorithm, the cluster heads or sink nodes reconstruct the compressed data in a distributed and cooperative manner. Meanwhile, the top-| | query operation in the above algorithm is realized by pruning unnecessary elements among cluster heads or sink nodes in the three-phase top-| | query algorithm. Simulation results show that there is no obvious difference in the performance of data reconstruction between our proposed scheme and existing compressive sensing theory based data collection schemes. However, both the number of interactions and the amount of transmitted data among cluster heads or sink nodes can be effectively reduced in the proposed scheme. The performance of the proposed scheme is analyzed in detail in this paper to support the claims.

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“…Thus, fewer bits are transmitted and energy is saved. Wang et al (2008aWang et al ( , 2009 able to calculate the best transmission rate to achieve low packet error rates.…”

Section: Application Layermentioning

confidence: 99%

A survey on cross-layer solutions for wireless sensor networks

Mendes

Rodrigues

2011

Journal of Network and Computer Applications

139

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Cross-layer multirate interaction with Distributed Source Coding in Wireless Sensor Networks

Cited by 40 publications

References 23 publications

Multimedia Sensing as a Service (MSaaS): Exploring Resource Saving Potentials of at Cloud-Edge IoTs and Fogs

Multimedia Sensing as a Service (MSaaS): Exploring Resource Saving Potentials of at Cloud-Edge IoTs and Fogs

A Three-Phase Top-k Query Based Distributed Data Collection Scheme in Wireless Sensor Networks

A survey on cross-layer solutions for wireless sensor networks

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