Data fusion utilization for optimizing large-scale Wireless Sensor Networks

Soltani, Mohammadreza; Hempel, Michael; Sharif, Hamid

doi:10.1109/icc.2014.6883346

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…The details of the comparison are discussed in Subsection 4.2. In other words, using the same number of transmission bits c i , the state estimation algorithm based on this strategy is more accurate than those of [4][5][6][7][8]. Therefore, this transmission strategy can efficiently reduce the bandwidth consumption.…”

Section: Transmission Strategymentioning

confidence: 94%

“…According to Table 2 and [4][5][6][7][8], with the same number of quantization levels L, the quantized innovation d i k can be transmitted with less bits using transmission strategy proposed in this paper than using those in [4][5][6][7][8]. The details of the comparison are discussed in Subsection 4.2.…”

Section: Transmission Strategymentioning

confidence: 96%

“…For the ith activated sensor, a dynamic transmission strategy is put forward for data payload and it is different from those used in [4][5][6][7][8].…”

Section: Transmission Strategymentioning

confidence: 99%

“…Several data fusion methods have been proposed to conserve energy in WSNs [4][5][6]. One approach utilizes data fusion to set a node as active in the WSN, thereby resulting in reduced network resource…”

Section: Introductionmentioning

confidence: 99%

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Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

Zhang

Liu

2015

Sci. China Inf. Sci.

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Low energy consumption and limited power supply are significant factors for wireless sensor networks (WSNs); thus, distributed state estimation and data fusion with quantized innovation are explored. The universal features of practical WSNs are investigated, and a dynamic transmission strategy is introduced. Furthermore, quantization state estimation based on Bayesian theory is derived. Unlike previous algorithms suitable for processing scalar measurement, the proposed distributed data fusion algorithm is applicable to general vector measurement. Furthermore, the efficiency of the proposed dynamic transmission strategy is analyzed. It is concluded that the proposed algorithm is more efficient than previous methods, and its estimation accuracy comparable to that of the standard Kalman filtering, which is based on analog-amplitude vector measurement.

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Section: Transmission Strategymentioning

confidence: 94%

Section: Transmission Strategymentioning

confidence: 96%

“…For the ith activated sensor, a dynamic transmission strategy is put forward for data payload and it is different from those used in [4][5][6][7][8].…”

Section: Transmission Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

Zhang

Liu

2015

Sci. China Inf. Sci.

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“…Assuming the set of CM collected by the ferry is X = (3,4,3,2,6,8,9,7,2,3,1,2,10,8,7,9). The DFMW deals with the CM by different scales.…”

Section: High Frequency Coefficients Selected Algorithmmentioning

confidence: 99%

Data fusion mechanism based on wavelet for opportunistic sensor networks

Han

Guo

Shu

et al. 2015

11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

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Due to the limited energy of the nodes in opportunistic sensor networks, and connectivity parameters in the process of collecting, processing, transmission have the features of information redundancy, this paper proposed DFMW (Data Fusion Mechanism based on Wavelet for Opportunistic Sensor Networks). DFMW, compresses the parameters by HFCSA (High Frequency Coefficients Selected Algorithm), which excludes the unnecessary high frequency coefficients generated from the wavelet decomposes the discrete CM sequences. DFMW uses the MSE (Mean Square Error) to control the accuracy of reconstruction, and the result shows it can lower the error of reconstruction.

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A novel clustering method for fault recovery and routing in mobile ad‐hoc networks

Gurumoorthy¹,

Devaraj²,

Gopinath

et al. 2021

Int J Communication

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Summary Mobile ad‐hoc network (MANET) is a group of self‐organized autonomous wireless devices that serve communication in human unattended and emergency environments. The network is decentralized and uses wireless links for communication, which is vulnerable to network resource depletion rapidly. Energy and link stability are vital factors that support the prolonged operation of the network, obstructing earlier resource depletions. These depletions are overwhelmed with the help of scattered, isolated nodes; the process of augmenting them increases the control overhead. We propose a genetic algorithm‐based routing (GAR) with fault route recovery (FRR) caused due to isolated nodes. In this method, clustering is used for energy balancing for retaining the live nodes' count reliably. The FRR phase prevents cluster head flooding using the local rerouting process. The former phase of GAR‐FFR governs the network's energy optimization aiming at controlled energy consumption. The later part reduces routing overhead due to route failures, preventing backtracking to the cluster head. The proposed GAR‐FFR is analyzed using the following metrics: throughput, packet delivery ratio, live nodes count, remaining energy, and routing overhead. The proposed GAR‐FRR achieves 15.4% high throughput, 16.29% high live nodes, 8.9% high remaining energy, and 21.04% fewer control packets for different rounds, compared with the existing A‐ECOPS and REAC‐IN methods.

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Data fusion utilization for optimizing large-scale Wireless Sensor Networks

Cited by 12 publications

References 13 publications

Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

Data fusion mechanism based on wavelet for opportunistic sensor networks

A novel clustering method for fault recovery and routing in mobile ad‐hoc networks

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