Energy-efficient analog sensing for large-scale, high-density persistent wireless monitoring

The research challenge of current Wireless Sensor Networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities among tiers is proposed. Tier-1 consists of dumb sensors that only sense and transmit, while the nodes in Tier-2 do all the smart processing on Tier-1 sensor data. A low-power and low-cost solution for Tier-1 sensors has been proposed using Analog Joint Source Channel Coding (AJSCC). An analog circuit that realizes the rectangular type of AJSCC has been proposed and realized on a Printed Circuit Board for feasibility analysis. A prototype consisting of three Tier-1 sensors (sensing temperature and humidity) communicating to a Tier-2 Cluster Head has been demonstrated to verify the proposed approach. Results show that our framework is indeed feasible to support large scale high density and persistent WSN deployment.

[1]  Deborah Estrin,et al.  Preprocessing in a Tiered Sensor Network for Habitat Monitoring , 2003, EURASIP J. Adv. Signal Process..

[2]  Javier Garcia-Frías,et al.  Analog Joint Source-Channel Coding for OFDM systems , 2013, 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[3]  Dario Pompili,et al.  Energy-efficient Wireless Analog Sensing for Persistent Underwater Environmental Monitoring , 2018, 2018 Fourth Underwater Communications and Networking Conference (UComms).

[4]  Dario Pompili,et al.  Argus: Smartphone-enabled human cooperation for disaster situational awareness via MARL , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[5]  Dario Pompili,et al.  Low-power all-analog circuit for rectangular-type analog joint source channel coding , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[6]  Waylon Brunette,et al.  Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks , 2003, Ad Hoc Networks.

[7]  Bo Hu,et al.  A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective , 2014, IEEE Internet of Things Journal.

[8]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[9]  Mohammad Maymandi-Nejad,et al.  An ultra low-power low-voltage track and latch comparator , 2010, 2010 17th IEEE International Conference on Electronics, Circuits and Systems.

[10]  Dario Pompili,et al.  ECO-UW IoT: Eco-friendly Reliable and Persistent Data Transmission in Underwater Internet of Things , 2019, 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[11]  B. Nazer,et al.  Structured Random Codes and Sensor Network Coding Theorems , 2008, 2008 IEEE International Zurich Seminar on Communications.

[12]  Fady Alajaji,et al.  Compressed Sensing With Nonlinear Analog Mapping in a Noisy Environment , 2012, IEEE Signal Processing Letters.

[13]  Honggang Wang,et al.  A Wireless Health Monitoring System Using Mobile Phone Accessories , 2017, IEEE Internet of Things Journal.

[14]  M. Gastpar Uncoded transmission is exactly optimal for a simple Gaussian "sensor" network , 2007 .

[15]  Yichuan Hu,et al.  Analog Joint Source-Channel Coding Using Non-Linear Curves and MMSE Decoding , 2011, IEEE Transactions on Communications.

[16]  Haiyun Luo,et al.  A two-tier data dissemination model for large-scale wireless sensor networks , 2002, MobiCom '02.

[17]  Javier Garcia-Frias,et al.  Analog Joint Source Channel Coding for Wireless Optical Communications and Image Transmission , 2014, Journal of Lightwave Technology.

[18]  P. C. Chao Energy Harvesting Electronics for Vibratory Devices in Self-Powered Sensors , 2011, IEEE Sensors Journal.

[19]  Dario Pompili,et al.  Improved Circuit Design of Analog Joint Source Channel Coding for Low-Power and Low-Complexity Wireless Sensors , 2019, IEEE Sensors Journal.

[20]  Mohamed F. Younis,et al.  A modular and power-intelligent architecture for wireless sensor nodes , 2012, 37th Annual IEEE Conference on Local Computer Networks.

[21]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[22]  Igor Bisio,et al.  Guest Editorial "Things" as Intelligent Sensors and Actuators in the Users' Context: Processing and Communications Issues , 2017, IEEE Internet Things J..

[23]  Luis Castedo,et al.  Experimental Evaluation of Analog Joint Source-Channel Coding in Indoor Environments , 2011, 2011 IEEE International Conference on Communications (ICC).

[24]  Dario Pompili,et al.  Argus: Smartphone-Enabled Human Cooperation via Multi-agent Reinforcement Learning for Disaster Situational Awareness , 2016, 2016 IEEE International Conference on Autonomic Computing (ICAC).

[25]  Richard Demo Souza,et al.  Spatial Diversity Using Analog Joint Source Channel Coding in Wireless Channels , 2013, IEEE Transactions on Communications.

[26]  Dario Pompili,et al.  Towards low-power wearable wireless sensors for molecular biomarker and physiological signal monitoring , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[27]  Dario Pompili,et al.  Towards Ultra-Low-Power Realization of Analog Joint Source-Channel Coding using MOSFETs , 2019, 2019 IEEE International Symposium on Circuits and Systems (ISCAS).

[28]  Tor A. Ramstad,et al.  Using 2:1 Shannon mapping for joint source-channel coding , 2005, Data Compression Conference.

[29]  Ali M. Niknejad,et al.  Ultra low-power transceiver SoC designs for IoT, NB-IoT applications , 2018, 2018 IEEE Custom Integrated Circuits Conference (CICC).