Efficient data gathering using Compressed Sparse Functions

Xu, Liwen; Wang, Yuexuan; Moscibroda, Thomas

doi:10.1109/infcom.2013.6566785

Cited by 16 publications

(33 citation statements)

References 8 publications

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“…The proposed method can reduce the global data traffic and balance the energy consumption of the sensor nodes. Similarly, the so-called Compressed Sparse Function (CSF) method is proposed in [20] [23] for large-scale monitoring applications with vehicular networks. They used entropy analysis on the traffic data to show the strong correlation in the data readings of vehicles.…”

Section: A Traffic Monitoring With Vsnmentioning

confidence: 99%

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Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Du¹,

Chen²,

Yang³

et al. 2015

IEEE Trans. Veh. Technol.

141

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Abstract-Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks (VSNs). Probe Vehicles (PVs), such as taxis and buses, and Floating Cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to traffic monitoring center (TMC) for traffic estimation. In TMC, sensing reports are aggregated to form traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads for all the time, TMC needs to estimate the un-sampled data in traffic matrix. As this matrix can be approximated to be of low-rank, Matrix Completion (MC) is an effective method to estimate the un-sampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e. average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real dataset analysis, the estimation error reduces from 35% to about 10%, compared with random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.

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Section: A Traffic Monitoring With Vsnmentioning

confidence: 99%

“…We calculate the number of required FCs according to Eqn. (20), so that TMC can use MC based estimation to recover the traffic matrix within a pre-described tolerant error. The simulation result is shown in Fig.…”

Section: A Circuit Patrolmentioning

confidence: 99%

Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Du¹,

Chen²,

Yang³

et al. 2015

IEEE Trans. Veh. Technol.

141

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“…Output: the sums of each top-| | elements. The initial data set selection phase (1) If node is a member agent node Then (2) Sendsfirst positive and last negative elements in to administrator agent node; (3) Else (4) Computes partial sums for received elements according to (17); (5) ← {the largest positive sums and the smallest negative sums}; (6) Sends to all member agent nodes; The candidate data set selection phase (1) If node is a member agent node Then (2) Finds and in among all elements in the initial data set ; (3) Sends unsent elements in whose values ≥ or ≤ combined with and to the administrator agent node; (4) Else (5) Computes the partial sum for received elements according to (17); (6) ← the th highest positive element; (7) ← the th lowest negative element; (8) Computes upper bounds of whole sum for received elements according to (18); (9) Computes lower bounds of whole sum for received elements according to (19); (10) ← { | ( ) ≥ or ( ) ≤ }; (11) Sends to all member agent nodes; The top-|k| elements selection phase (1) If node is a member agent node Then (2) Sends unsent elements in to administrator agent node; (3) Else (4) Computes whole sums for received elements according to (16); (5) top-| | ← {the largest elements in magnitude};…”

Section: An Example Of the Three-phase Top-| | Query Algorithmmentioning

confidence: 99%

“…Therefore, member agent nodes 1 and 2 transmit unsent elements {(6, 2), {8, −3}} and {(9, −1)} to the administrator agent node. After receiving the elements, the administrator agent node begins to compute the partial sums as well as the upper and the lower bounds of the whole sum according to (17)- (19). The positive threshold = 17 and the negative threshold = −9 are the second highest positive element and the lowest negative element.…”

Section: An Example Of the Three-phase Top-| | Query Algorithmmentioning

confidence: 99%

“…Wang et al proposed a data collection scheme based on the adaptive compressive sampling theory [16] by taking advantage of characteristics of the spatial-temporal inconsistency of the sparsity in the sensed data and by introducing an autoregressive model into the data reconstruction process. Xu et al proposed a group of compressive sparse functions by utilizing the symmetric and orthogonal properties of the discrete cosine transform (DCT) functions so that sensed data can be reconstructed after the coefficients of the compressive sparse functions are recovered from the partial sensed data [17]. Motivated by the chaos technology and compressive sampling theory, Lu et al proposed a distributed secure and efficient data collection scheme by designing a chaotic sequence based sensing matrix generation algorithm and active node matrix algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

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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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Energy-Efficient Compressive Sensing Based Data Gathering and Scheduling in Wireless Sensor Networks

Ghosh

Banerjee

2022

Wireless Pers Commun

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Efficient data gathering using Compressed Sparse Functions

Cited by 16 publications

References 8 publications

Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Effective Urban Traffic Monitoring by Vehicular Sensor Networks

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

Energy-Efficient Compressive Sensing Based Data Gathering and Scheduling in Wireless Sensor Networks

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