A spectrum-based adaptive sampling algorithm for smart sensing

The ever-increasing diffusion of smart pervasive systems has brought to attention the need to optimally manage available energy as well as the request for more general intelligent functionalities. In this direction the paper presents a novel spectrum-based change detection test (CDT) designed to be executed on low-power embedded devices. By exploiting the energy-based features extracted from spectral sub-bands the CDT detects changes in time variance in the incoming signal (reactions to the change might follow) as well as disambiguates between changes and aliasing phenomena. When aliasing is detected an adaptation mechanism is activated to adapt the sampling frequency to the real needs of the signal under inspection (so as to reduce energy consumption). This algorithm enhances the state-of-the-art of adaptive sampling by offering an efficient alternative to complete spectral investigation. The effectiveness of the proposed solution has been assessed on synthetic and real datasets.

[1]  Cesare Alippi,et al.  An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Edward Y. Chang,et al.  Adaptive sampling for sensor networks , 2004, DMSN '04.

[3]  T. Wietsma.  Adaptive sampling for multiscale environmental sensor networks , 2012 .

[4]  Christian B Allen,et al.  Autonomous spatially adaptive sampling in experiments based on curvature, statistical error and sample spacing with applications in LDA measurements , 2015 .

[5]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[6]  Bibin Varghese,et al.  Frequency domain adaptive sampling technique for aerospace instrumentation and telemetry applications , 2011, 2011 International Conference on Electronic Devices, Systems and Applications (ICEDSA).

[7]  Gary W. Meyer,et al.  A perceptually based adaptive sampling algorithm , 1998, SIGGRAPH.

[8]  Sotiris Nikoletseas.  Distributed Computing in Sensor Systems, 4th IEEE International Conference, DCOSS 2008, Santorini Island, Greece, June 11-14, 2008, Proceedings , 2008, DCOSS.

[9]  Jun-Dong Cho,et al.  Adaptive Sampling for ECG Detection Based on Compression Dictionary , 2013 .

[10]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[11]  Cesare Alippi,et al.  A hierarchical, nonparametric, sequential change-detection test , 2011, The 2011 International Joint Conference on Neural Networks.

[12]  Jing Zhou,et al.  FloodNet: Coupling Adaptive Sampling with Energy Aware Routing in a Flood Warning System , 2007, Journal of Computer Science and Technology.

[13]  Jie Wu,et al.  Energy and bandwidth-efficient Wireless Sensor Networks for monitoring high-frequency events , 2013, 2013 IEEE International Conference on Sensing, Communications and Networking (SECON).

[14]  Rodolfo E. Haber,et al.  Self-adaptive systems: A survey of current approaches, research challenges and applications , 2013, Expert Syst. Appl..

[15]  Kang G. Shin,et al.  Real-time dynamic voltage scaling for low-power embedded operating systems , 2001, SOSP.

[16]  Martin Vetterli,et al.  DASS: Distributed Adaptive Sparse Sensing , 2013, IEEE Transactions on Wireless Communications.

[17]  D.A. Rauth,et al.  Analog-to-digital conversion. part 5 , 2005, IEEE Instrumentation & Measurement Magazine.

[18]  Kipton Barros,et al.  Spatial adaptive sampling in multiscale simulation , 2014, Comput. Phys. Commun..

[19]  Shuang-Hua Yang,et al.  Adaptive Sampling for Wireless Household Water Consumption Monitoring , 2015 .

[20]  Cesare Alippi,et al.  The (Not) Far-Away Path to Smart Cyber-Physical Systems: An Information-Centric Framework , 2017, Computer.

[21]  Ove Christiansen,et al.  An adaptive density-guided approach for the generation of potential energy surfaces of polyatomic molecules , 2009 .

[22]  Sebastian VanSyckel,et al.  A survey on engineering approaches for self-adaptive systems , 2015, Pervasive Mob. Comput..

[23]  Marimuthu Palaniswami,et al.  Energy-efficient data acquisition by adaptive sampling for wireless sensor networks , 2008, IWCMC.

[24]  Giuseppe Anastasi,et al.  Energy management in wireless sensor networks with energy-hungry sensors , 2009 .

[25]  Muriel Medard,et al.  Energy-efficient Time-Stampless Adaptive Nonuniform Sampling , 2011, 2011 IEEE SENSORS Proceedings.

[26]  Giuseppe Anastasi,et al.  Adaptive Sampling for Energy Conservation in Wireless Sensor Networks for Snow Monitoring Applications , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.