Energy Efficient GPS Acquisition with Sparse-GPS+

Misra, Prasant; Kumar, Ashwani; Chandra, M. Girish; Balamurali, P.

doi:10.1145/2994551.2996696

Cited by 5 publications

(6 citation statements)

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“…Our work in this paper is guided by the current hardware limitations of low-cost and low-power sensor platforms. We believe that the key observations and principles derived here will find their application [39], [40] in location sensing systems that have constrained hardware resources to handle the bulk of data processing.…”

Section: Conclusion and Discussionmentioning

confidence: 89%

“…Towards this end, we argue that it is a reasonable trade-off for achieving the performance of cross-correlation on moteclass devices. We envision that, besides having an impact on current location sensing systems, it would create a new drive for WSN applications [39] where the requirement for reliable location information on constrained network embedded sensing elements hold more importance than centralized computation.…”

Section: Cross-correlationmentioning

confidence: 99%

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Sparsity Based Efficient Cross-Correlation Techniques in Sensor Networks

Misra

Yang

et al. 2017

IEEE Trans. on Mobile Comput.

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Cross-correlation is a popular signal processing technique used in numerous location tracking systems for obtaining reliable range information. However, its efficient design and practical implementation has not yet been achieved on mote platforms that are typical in wireless sensor network due to resource constrains. In this paper, we propose SparseS-XCorr : cross-correlation via structured sparse representation, a new computing framework for ranging based on 1 -minimization [1] and structured sparsity. The key idea is to compress the ranging signal samples on the mote by efficient random projections and transfer them to a central device; where a convex optimization process estimates the range by exploiting the sparse signal structure in the proposed correlation dictionary. Through theoretical validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework can achieve up to two orders of magnitude better performance compared to other approaches such as working on DCT domain and downsampling. Compared to the standard cross-correlation, it is able to obtain range estimates with a bias of 2-6 cm with 30% and approximately 100 cm with 5% compressed measurements. Its structured sparsity model is able to improve the ranging accuracy by 40% under challenging recovery conditions (such as high compression factor and low signal-to-noise ratio) by overcoming limitations due to dictionary coherence.

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Section: Conclusion and Discussionmentioning

confidence: 89%

Section: Cross-correlationmentioning

confidence: 99%

Sparsity Based Efficient Cross-Correlation Techniques in Sensor Networks

Misra

Yang

et al. 2017

IEEE Trans. on Mobile Comput.

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“…Future work will investigate how the algorithms proposed here may be used in other scenarios and also if they may be extended to multiple parameter estimation problems, such as in radar and GPS localization where both a frequency and a delay must be estimated [38], [39].…”

Section: Discussionmentioning

confidence: 99%

Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation

Fyhn

Duarte

Jensen

2015

IEEE Trans. Signal Process.

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We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non-negative amplitude parameters to arbitrary complex ones, and (ii) we allow for mismatch between the manifold described by the parameters and its polar approximation. To quantify the improvements afforded by the proposed extensions, we evaluate six algorithms for estimation of parameters in sparse translation-invariant signals, exemplified with the time delay estimation problem. The evaluation is based on three performance metrics: estimator precision, sampling rate and computational complexity. We use compressive sensing with all the algorithms to lower the necessary sampling rate and show that it is still possible to attain good estimation precision and keep the computational complexity low. Our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency-estimation problem followed by a super-resolution algorithm. The algorithms studied here provide various tradeoffs between computational complexity, estimation precision, and necessary sampling rate. The work shows that compressive sensing for the class of sparse translation-invariant signals allows for a decrease in sampling rate and that the use of polar interpolation increases the estimation precision.

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“…Obviously, when offloading computations to the cloud, the energy consumption attributed to data transmission will increase. To reduce this effect, a framework for pre-transmission compression of GNSS signals was presented in [157].…”

Section: E Mobile Cloud Computingmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

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Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

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Energy Efficient GPS Acquisition with Sparse-GPS+

Cited by 5 publications

References 1 publication

Sparsity Based Efficient Cross-Correlation Techniques in Sensor Networks

Sparsity Based Efficient Cross-Correlation Techniques in Sensor Networks

Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

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