On Distributed Sensor Fusion in Batteryless Intermittent Networks

Distributed and collaborative computation has never been considered before in networks of batteryless sensors. This can bring many advantages for applications (e.g. longer transmission ranges, lower network costs), however introducing new research challenges. In this paper, we focus on the well-known distributed sensor fusion but in an intermittently-powered batteryless sensor network. The goal is to estimate a parameter collaboratively by considering individual sensor measurements. We show that, even though the nodes stop operation with high probability due to random power failures and they neither communicate with their neighbors nor perform computation most of the time, the simplest implementation of the fully-distributed sensor fusion based on average consensus improves the overall estimation quality of the network considerably. In the light of this, we anticipate that if harvested energy is used efficiently so that nodes have more opportunity to receive and send packets, existing fully-distributed protocols can be implemented with tiny modifications in networks of batteryless sensors.

[1]  Matthew Hicks,et al.  Intermittent Computation without Hardware Support or Programmer Intervention , 2016, OSDI.

[2]  Mohammad Rostami,et al.  Enabling Practical Backscatter Communication for On-body Sensors , 2016, SIGCOMM.

[3]  Bruce H. Krogh,et al.  Energy-efficient surveillance system using wireless sensor networks , 2004, MobiSys '04.

[4]  C.F. Chiasserini,et al.  On the concept of distributed digital signal processing in wireless sensor networks , 2002, MILCOM 2002. Proceedings.

[5]  Brandon Lucia,et al.  Adaptive Dynamic Checkpointing for Safe Efficient Intermittent Computing , 2018, OSDI.

[6]  Jacob Sorber,et al.  Timely Execution on Intermittently Powered Batteryless Sensors , 2017, SenSys.

[7]  Stephen P. Boyd,et al.  A scheme for robust distributed sensor fusion based on average consensus , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[8]  Luca Mottola,et al.  HarvOS: Efficient Code Instrumentation for Transiently-Powered Embedded Sensing , 2017, 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[9]  Sparsh Mittal,et al.  A Survey of Techniques for Approximate Computing , 2016, ACM Comput. Surv..

[10]  Brandon Lucia,et al.  Chain: tasks and channels for reliable intermittent programs , 2016, OOPSLA.

[11]  Amjad Yousef Majid,et al.  On the Synchronization of Computational RFIDs , 2019, IEEE Transactions on Mobile Computing.

[12]  Kubra Alemdar,et al.  Multiband Ambient RF Energy Harvesting Circuit Design for Enabling Batteryless Sensors and IoT , 2018, IEEE Internet of Things Journal.

[13]  Kirk Martinez,et al.  Deploying a sensor network in an extreme environment , 2006, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC'06).

[14]  Amir Rahmati,et al.  Persistent Clocks for Batteryless Sensing Devices , 2016, ACM Trans. Embed. Comput. Syst..

[15]  Alanson P. Sample,et al.  Design of an RFID-Based Battery-Free Programmable Sensing Platform , 2008, IEEE Transactions on Instrumentation and Measurement.

[16]  G. Strang.  Introduction to Linear Algebra , 1993 .

[17]  Petar M. Djuric,et al.  BARNET: Towards Activity Recognition Using Passive Backscattering Tag-to-Tag Network , 2018, MobiSys.

[18]  David Peleg,et al.  Distributed Computing: A Locality-Sensitive Approach , 1987 .

[19]  Brandon Lucia,et al.  A simpler, safer programming and execution model for intermittent systems , 2015, PLDI.

[20]  Arnab Raha,et al.  QUICKRECALL: A Low Overhead HW/SW Approach for Enabling Computations across Power Cycles in Transiently Powered Computers , 2014, 2014 27th International Conference on VLSI Design and 2014 13th International Conference on Embedded Systems.

[21]  David Wetherall,et al.  Ambient backscatter: wireless communication out of thin air , 2013, SIGCOMM.

[22]  Brandon Lucia,et al.  Alpaca: intermittent execution without checkpoints , 2017, Proc. ACM Program. Lang..

[23]  Kevin Fu,et al.  Mementos: system support for long-running computation on RFID-scale devices , 2011, ASPLOS XVI.

[24]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[25]  Jacob Sorber,et al.  Flicker: Rapid Prototyping for the Batteryless Internet-of-Things , 2017, SenSys.

[26]  Matthew Hicks,et al.  Clank: Architectural support for intermittent computation , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).

[27]  Luca Benini,et al.  Hibernus++: A Self-Calibrating and Adaptive System for Transiently-Powered Embedded Devices , 2016, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[28]  R. Wattenhofer,et al.  Dozer: Ultra-Low Power Data Gathering in Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[29]  Samir R. Das,et al.  Design and Evaluation of “BTTN”: A Backscattering Tag-to-Tag Network , 2018, IEEE Internet of Things Journal.

[30]  Aggelos Bletsas,et al.  Multistatic Scatter Radio Sensor Networks for Extended Coverage , 2017, IEEE Transactions on Wireless Communications.

[31]  Przemyslaw Pawelczak,et al.  Multi-hop Backscatter Tag-to-Tag Networks , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[32]  Luca Benini,et al.  Hibernus: Sustaining Computation During Intermittent Supply for Energy-Harvesting Systems , 2015, IEEE Embedded Systems Letters.

[33]  Przemyslaw Pawelczak,et al.  InK: Reactive Kernel for Tiny Batteryless Sensors , 2018, SenSys.

[34]  Kay Römer,et al.  The design space of wireless sensor networks , 2004, IEEE Wireless Communications.