Sub-Sampling framework comparison for Low-Power data gathering: A comparative analysis

Sensors (Switzerland)

Volumn 15, Issue 3, 2015, Pages 5058-5080

  Milosevic, Bojan ^a,b   Caione, Carlo ^a   Farella, Elisabetta ^a,b   Brunelli, Davide ^c   Benini, Luca ^a,d  

^a UNIVERSITY OF BOLOGNA   (Italy)

^b FONDAZIONE BRUNO KESSLER   (Italy)

^c UNIVERSITY OF TRENTO   (Italy)

^d ETH ZURICH   (Switzerland)

Author keywords

Compressive sensing; Data reconstruction; Latent variables; Low power design; Wireless sensor networks

Indexed keywords

COMPRESSED SENSING; ELECTRIC POWER SUPPLIES TO APPARATUS; ENERGY EFFICIENCY; ENERGY UTILIZATION; EQUIVALENCE CLASSES; LOW POWER ELECTRONICS; SAMPLING; SENSOR NODES; SIGNAL RECONSTRUCTION;

COMPRESSIVE SENSING; DATA RECONSTRUCTION; FRAMEWORK COMPARISONS; HETEROGENEOUS SENSOR NETWORKS; LATENT VARIABLE; LONG TERM MONITORING; LOW-POWER DESIGN; RECONSTRUCTION ACCURACY;

WIRELESS SENSOR NETWORKS;

EID: 84928651614     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150305058     Document Type: Article

References (46)

1. Li, X.Y.; Wang, Y.; Wang, Y. Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks. IEEE Trans. Comput. 2011, 60, 386–399.
2. Ammari, H.M. Investigating the Energy Sink-Hole Problem in Connected k-Covered Wireless Sensor Networks. IEEE Trans. Comput. 2013, 63, 2729–2742.
3. Li, M.; Li, Z.; Vasilakos, A.V. A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. IEEE Proc. 2013, 101, 2538–2557.
4. Moser, C.; Thiele, L.; Brunelli, D.; Benini, L. Adaptive Power Management for Environmentally Powered Systems. IEEE Trans. Comput. 2010, 59, 478–491.
5. Zappi, P.; Lombriser, C.; Stiefmeier, T.; Farella, E.; Roggen, D.; Benini, L.; TrÃȕster, G. Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection. In Proceedings of the European Conference on Wireless Sensor Networks, Bologna, Italy, 30 January–1 February 2008; pp. 17–33.
6. Veeravalli, V.V.; Varshney, P.K. Distributed inference in wireless sensor networks. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2012, 370, 100–117.
7. Zou, Z.; Hu, C.; Zhang, F.; Zhao, H.; Shen, S. WSNs data acquisition by combining hierarchical routing method and compressive sensing. Sensors 2014, 14, 16766–16784.
8. Aquino, A.L.; Junior, O.S.; Frery, A.C.; de Albuquerque, E.L.; Mini, R.A. MuSA: Multivariate sampling algorithm for wireless sensor networks. IEEE Trans. Comput. 2014, 63, 968–978.
9. AbdelSalam, H.S.; Olariu, S. Toward adaptive sleep schedules for balancing energy consumption in wireless sensor networks. IEEE Trans. Comput. 2012, 61, 1443–1458.
10. Candes, E.; Wakin, M. An introduction to compressive sampling. IEEE Signal Process. Mag. 2008, 25, 21–30.
11. Duarte, M.; Eldar, Y. Structured compressed sensing: From theory to applications. IEEE Trans. Signal Process. 2011, 59, 4053–4085.
12. Verma, N.; Zappi, P.; Rosing, T. Latent variables based data estimation for sensing applications. In Proceedings of the IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Adelaide, Australia, 6–9 December 2011; pp. 335–340.
13. Milosevic, B.; Yang, J.; Vrema, N.; Tilak, S.; Zappi, P.; Farella, E.; Rosing, T.S.; Benini, L. Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization. In Proceedings of the ACM/IEEE International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MsWiM), Barcelona, Spain, 3–8 November 2013; pp. 247–254.
14. Caione, C.; Brunelli, D.; Benini, L. Distributed Compressive Sampling for Lifetime Optimization in Dense Wireless Sensor Networks. IEEE Trans. Ind. Inform. 2012, 8, 30–40.
15. Mamaghanian, H.; Khaled, N.; Atienza, D.; Vandergheynst, P. Design and Exploration of Low-Power Analog to Information Conversion Based on Compressed Sensing. IEEE J. Emerg. Sel. Top. Circuits Syst. 2012, 2, 493–501.
16. Koren, Y.; Bell, R.; Volinsky, C. Matrix Factorization Techniques for Recommender Systems. Computer 2009, 42, 30–37.
17. Kolda, T.G.; Bader, B.W. Tensor Decompositions and Applications. SIAM Rev. 2009, 51, 455–500.
18. Xiang, L.; Luo, J.; Vasilakos, A. Compressed data aggregation for energy efficient wireless sensor networks. In Proceedings of the Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Salt Lake City, UT, USA, 27–30 June 2011; pp. 46–54.
19. Yao, Y.; Cao, Q.; Vasilakos, A. EDAL: An Energy-Efficient, Delay-Aware, and Lifetime-Balancing Data Collection Protocol for Wireless Sensor Networks. In Proceedings of the 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), Hangzhou, China, 14–16 October 2013; pp. 182–190.
20. Baron, D.; Duarte, M.F.; Wakin, M.B.; Sarvotham, S.; Baraniuk, R.G. Distributed Compressive Sensing. Available online: arxiv.org/pdf/0901.3403 (accessed on 28 February 2015).
21. Lee, K.; Bresler, Y.; Junge, M. Subspace Methods for Joint Sparse Recovery. IEEE Trans. Inf. Theory 2012, 58, 3613–3641.
22. Duarte, M.; Baraniuk, R. Kronecker Compressive Sensing. IEEE Trans. Image Process. 2012, 21, 494 –504.
23. Deng, W.; Yin, W.; Zhang, Y. Group Sparse Optimization by Alternating Direction Method; Rice CAAM Report TR11-06. Available online: http://www.caam.rice.edu/optimization/L1/ GroupSparsity/group110419.pdf (accessed on 28 February 2015).
24. Su, X.; Khoshgoftaar, T.M. A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, doi:10.1155/2009/421425.
25. Yoshii, K.; Goto, M.; Komatani, K.; Ogata, T.; Okuno, H. An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model. IEEE Trans. Audio Speech Lang. Process. 2008, 16, 435–447.
26. Koren, Y. Collaborative filtering with temporal dynamics. Commun. ACM 2010, 53, 89–97.
27. Karatzoglou, A.; Amatriain, X.; Baltrunas, L.; Oliver, N. Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain, 26–30 September 2010; pp. 79–86.
28. Luo, C.; Wu, F.; Sun, J.; Chen, C.W. Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering. IEEE Trans. Wirel. Commun. 2010, 9, 3728–3738.
29. Mahmudimanesh, M.; Khelil, A.; Suri, N. Reordering for Better Compressibility: Efficient Spatial Sampling in Wireless Sensor Networks. In Proceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Newport Beach, CA, USA, 7–9 June 2010; pp. 50–57.
30. Mamaghanian, H.; Khaled, N.; Atienza, D.; Vandergheynst, P. Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes. IEEE Trans. Biomed. Eng. 2011, 58, 2456–2466.
31. Makhzani, A.; Valaee, S. Reconstruction of a Generalized Joint Sparsity Model Using Principal Component Analysis. In Proceedings of the 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), San Juan, Puerto Rico, 13–16 December 2011; pp. 269–272.
32. Murray, T.S.; Pouliquen, P.O.; Andreou, A.G. Design of a Parallel Sampling Encoder for Analog to Information (A2I) Converters: Theory, Architecture and CMOS Implementation. Electronics 2013, 2, 57–79.
33. Yu, Z.; Hoyos, S. Digitally Assisted Analog Compressive Sensing. In Proceedings of the 2009 IEEE Dallas Circuits and SystemsWorkshop, Richardson, TX, USA, 4–5 October 2009; pp. 1–4.
34. Baheti, P.; Garudadri, H. An Ultra Low Power Pulse Oximeter Sensor Based on Compressed Sensing. In Proceedings of the Sixth InternationalWorkshop onWearable and Implantable Body Sensor Networks, Berkeley, CA, USA, 3–5 June 2009; pp. 144–148.
35. Garudadri, H.; Baheti, P.; Majumdar, S.; Lauer, C.; Massé, F.; van de Molengraft, J.; Penders, J. Artifacts mitigation in ambulatory ECG telemetry. In Proceedings of the 2010 12th IEEE International Conference on e-Health Networking Applications and Services (Healthcom), Lyon, France, 1–3 July 2010; pp. 338–344.
36. Prada, M.A.; Toivola, J.; Kullaa, J.; Hollmen, J. Three-way analysis of structural health monitoring data. Neurocomputing 2012, 80, 119–128.
37. Chen, W.; Wassell, I. Energy efficient signal acquisition via compressive sensing in wireless sensor networks. In Proceedings of the 2011 6th International Symposium on Wireless and Pervasive Computing (ISWPC), Hong Kong, China, 23–25 February 2011; pp. 1–6.
38. Needell, D.; Vershynin, R. Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit. Found. Comput. Math. 2009, 9, 317–334.
39. Chen, W.; Rodrigues, M.; Wassell, I. Distributed compressive sensing reconstruction via common support discovery. In Proceedings of the 2011 IEEE International Conference on Communications (ICC), Kyoto, Japan, 5–9 June 2011; pp. 1–5.
40. Acar, E.; Yener, B. Unsupervised Multiway Data Analysis: A Literature Survey. IEEE Trans. Knowl. Data Eng. 2009, 21, 6–20.
41. Aderohunmu, F.A.; Balsamo, D.; Paci, G.; Brunelli, D. Long Term WSN Monitoring for Energy Efficiency in EU Cultural Heritage Buildings. Lect. Notes Electr. Eng. 2014, 281, 253–261.
42. Keshavarzian, A.; Lee, H.; Venkatraman, L. Wakeup scheduling in wireless sensor networks. In Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Florence, Italy, 22–25 May 2006; pp. 322–333.
43. Ferrari, F.; Zimmerling, M.; Thiele, L.; Saukh, O. Efficient network flooding and time synchronization with Glossy. In Proceedings of the 2011 10th International Conference on Information Processing in Sensor Networks (IPSN), Chicago, IL, USA, 12–14 April 2011; pp. 73–84.
44. Hohlt, B.; Doherty, L.; Brewer, E. Flexible power scheduling for sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, Berkeley, CA, USA, 26–27 April 2004; pp. 205–214.
45. Caione, C.; Brunelli, D.; Benini, L. Compressive Sensing Optimization for Signal Ensembles in WSNs. IEEE Trans. Ind. Inform. 2014, 10, 382–392.
46. Caione, C.; Brunelli, D.; Benini, L. Compressive sensing optimization over ZigBee networks. In Proceedings of the 2010 International Symposium on Industrial Embedded Systems (SIES), Trento, Italy, 7–9 July 2010; pp. 36–44.