Rakeness-Based Compressed Sensing and Hub Spreading to Administer Short/Long-Range Communication Tradeoff in IoT Settings

In common distributed sensing scenarios, a number of local wireless sensor networks perform sets of acquisitions that must be sent to a central collector which may be far from the measurement fields. Hence, readings from individual nodes may reach their destination by exploiting both local and long-range transmission capabilities. The compressed sensing (CS) paradigm may help finding a convenient mix of the two options, especially if it follows the rakeness-based design flow that has been recently introduced. CS is exploited by identifying local hubs that aggregate many sensor readings in a smaller number of quantities that are then transmitted to the central collector. We here show that, depending on the relative cost of local versus long-range transmission, carefully administering the choice of the hubs, the breadth of the neighborhood from which they collect readings, as well as the coefficients with which those readings a linearly aggregated, one may significantly reduce the energy needed to sample the field. Simulations indicate that savings may be over 50% for values of the parameters modeling nowadays local and long-range transmission technologies.

[1]  David J. Lamb,et al.  Position-Relative Identities in the Internet of Things: An Evolutionary GHT Approach , 2014, IEEE Internet of Things Journal.

[2]  Riccardo Rovatti,et al.  Rakeness-Based Design of Low-Complexity Compressed Sensing , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[4]  Luca Benini,et al.  Distributed Compressive Sampling for Lifetime Optimization in Dense Wireless Sensor Networks , 2012, IEEE Transactions on Industrial Informatics.

[5]  Umberto Spagnolini,et al.  Wireless Cloud Networks for the Factory of Things: Connectivity Modeling and Layout Design , 2014, IEEE Internet of Things Journal.

[6]  Dongqing Xie,et al.  Energy-Balanced Data Gathering and Aggregating in WSNs: A Compressed Sensing Scheme , 2015, Int. J. Distributed Sens. Networks.

[7]  Jelena Kovacevic,et al.  Signal recovery on graphs: Random versus experimentally designed sampling , 2015, 2015 International Conference on Sampling Theory and Applications (SampTA).

[8]  Vladimir Stojanovic,et al.  Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors , 2012, IEEE Journal of Solid-State Circuits.

[9]  Jun Sun,et al.  Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering , 2010, IEEE Transactions on Wireless Communications.

[10]  Xiaohua Jia,et al.  Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[11]  Riccardo Rovatti,et al.  A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing? , 2015, IEEE Signal Processing Letters.

[12]  Vincent Yan Fu Tan,et al.  Wireless Compressive Sensing for Energy Harvesting Sensor Nodes , 2013, IEEE Trans. Signal Process..

[13]  José M. F. Moura,et al.  Discrete Signal Processing on Graphs: Frequency Analysis , 2013, IEEE Transactions on Signal Processing.

[14]  R. Nowak,et al.  Compressed Sensing for Networked Data , 2008, IEEE Signal Processing Magazine.

[15]  Andrea Montanari,et al.  Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.

[16]  Jelena Kovacevic,et al.  Discrete Signal Processing on Graphs: Sampling Theory , 2015, IEEE Transactions on Signal Processing.

[17]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[18]  Nazanin Rahnavard,et al.  CCS: Energy-efficient data collection in clustered wireless sensor networks utilizing block-wise compressive sensing , 2016, Comput. Networks.

[19]  Luca Benini,et al.  Zeroing for HW-efficient compressed sensing architectures targeting data compression in wireless sensor networks , 2017, Microprocess. Microsystems.

[20]  C. Karakus,et al.  Analysis of Energy Efficiency of Compressive Sensing in Wireless Sensor Networks , 2013, IEEE Sensors Journal.

[21]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[22]  Michael B. Wakin,et al.  Modal Analysis With Compressive Measurements , 2014, IEEE Transactions on Signal Processing.

[23]  Lida Xu,et al.  A Continuous Biomedical Signal Acquisition System Based on Compressed Sensing in Body Sensor Networks , 2013, IEEE Transactions on Industrial Informatics.

[24]  Milica Stojanovic,et al.  Random Access Compressed Sensing for Energy-Efficient Underwater Sensor Networks , 2011, IEEE Journal on Selected Areas in Communications.

[25]  Qiang Ye,et al.  STCDG: An Efficient Data Gathering Algorithm Based on Matrix Completion for Wireless Sensor Networks , 2013, IEEE Transactions on Wireless Communications.

[26]  Pramod K. Varshney,et al.  Data-aggregation techniques in sensor networks: a survey , 2006, IEEE Communications Surveys & Tutorials.

[27]  Riccardo Rovatti,et al.  Hardware-Algorithms Co-Design and Implementation of an Analog-to-Information Converter for Biosignals Based on Compressed Sensing , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[28]  Riccardo Rovatti,et al.  A Pragmatic Look at Some Compressive Sensing Architectures With Saturation and Quantization , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[29]  José M. F. Moura,et al.  Discrete signal processing on graphs: Graph fourier transform , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[30]  Luca Benini,et al.  Rakeness-based compressed sensing on ultra-low power multi-core biomedicai processors , 2014, Proceedings of the 2014 Conference on Design and Architectures for Signal and Image Processing.

[31]  Seung Jun Baek,et al.  Joint routing and scheduling for data collection with compressive sensing to achieve order-optimal latency , 2017, Int. J. Distributed Sens. Networks.

[32]  Chadi Assi,et al.  On the Interaction Between Scheduling and Compressive Data Gathering in Wireless Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[33]  Jelena Kovacevic,et al.  Signal Recovery on Graphs: Fundamental Limits of Sampling Strategies , 2015, IEEE Transactions on Signal and Information Processing over Networks.

[34]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .

[35]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[36]  Riccardo Rovatti,et al.  A rakeness-based design flow for Analog-to-Information conversion by Compressive Sensing , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[37]  Riccardo Rovatti,et al.  Rakeness in the Design of Analog-to-Information Conversion of Sparse and Localized Signals , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[38]  Jan Van der Spiegel,et al.  A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface , 2016, IEEE Transactions on Biomedical Circuits and Systems.