Efficient Energy Management in Energy Harvesting Wireless Sensor Networks: An Approach Based on Distributed Compressive Sensing

Lead Research Organisation: University College London
Department Name: Electronic and Electrical Engineering

Abstract

Future deployments of wireless sensor network (WSN) infrastructures for environmental, industrial or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal or photovoltaic) in order to substantially increase their autonomy and lifetime.

However, it is also widely recognized that the existing gap between the sensors' energy availability and the sensors' energy consumption requirements is not likely to close in the near future due to limitations in current energy harvesting (EH) technology, together with the surge in demand for more data-intensive applications. Hence, perpetually operating WSNs are currently impossible to realize for data-intensive applications, as significant (and costly) human intervention is required to replace batteries.

With the continuous improvement of energy efficiency representing a major drive in WSN research, the major objective of this research project is to develop transformative sensing mechanisms, which can be used in conjunction with current or upcoming EH capabilities, in order to enable the deployment of energy neutral or nearly energy neutral WSNs with practical network lifetime and data gathering rates up to two orders of magnitude higher than the current state-of-the-art.

The theoretical foundations of the proposed research are the emerging paradigms of compressive sensing (CS) and distributed compressive sensing (DCS) as well as energy- and information-optimal data acquisition and transmission protocols. These elements offer the means to tightly couple the energy consumption process to the random nature of the energy harvesting process in a WSN in order to achieve the breakthroughs in network lifetime and data gathering rates.

The proposed project brings together a team of theoreticians and experimentalists working in areas of the EPSRC ICT portfolio that have been identified for expansion. This team is well placed to be able to develop, implement and evaluate the novel WSN technology. The consortium also comprises a number of established and early stage companies that clearly view the project as one that will impact their medium and long term product developments and also strengthen their strategic links with world class academic institutions. We anticipate that a successful demonstration of the novel WSN technology will generate significant interest in the machine-to-machine (M2M) and Internet of Things (IoT) industries both in the UK and abroad.

Planned Impact

The research work will offer a range of new sensing/monitoring capabilities that will have an impact in various industry sectors as well as the wider society. In particular, the ability to perform continuous and ubiquitous energy neutral or nearly energy neutral sensing/monitoring of the environment, infrastructure and aspects of future cities will transform the economy and society.

Of particular relevance, and in addition to the academic impact due to the ground-breaking nature of the research work, we also anticipate to be able to contribute to the domains:

1. The smart infrastructure and construction industry: We expect to make an impact on the smart infrastructure and construction industry within the UK and worldwide. In particular, we envisage that the Cambridge Centre for Smart Infrastructure and Construction (CSIC) together with its partners will be able to leverage the proposed technology to effect radical changes in the management of civil infrastructure and construction.

2. The digital economy industry: We also expect to make a significant impact on the growing Machine to Machine (M2M) and Internet of Things (IoT) industries within the UK and worldwide. In particular we anticipate that the research will have a significant economic impact on our industrial partners as they expand their presence in the M2M and IoT space. STMicroelectronics, Thales, Fujitsu and AquaMW will be in a good position to translate the research ideas into products in order to secure a competitive advantage in their respective markets.

3. The utilities and other industries: The ability to conduct continuous and widespread monitoring of key infrastructure will also yield large cost savings for infrastructure owners and operators. This will improve the competitiveness of UK industry owing to reduced disruption to and lower prices for utilities such as water and energy. Our collaboration with the EPSRC and TSB funded CSIC will also be key to exposing our research to these important sectors of the economy.

4. The wider society: The ability to conduct ubiquitous monitoring of critical infrastructure and pollution in cities will also yield significant benefits for society, e.g. lower utility prices and improved health and well-being.

In fact, the ability to carry out perpetual or nearly perpetual sensing/monitoring also translates into immediate benefits in various other domains, which include intelligent transport and healthcare.

A key objective of the research work is also to showcase a world first demonstration of the new wireless sensor network (WSN) technology not only in the state-of-the-art equipped laboratories at UCL and Cambridge University but also in key system deployments made available by CSIC. This will significantly raise the profile of the UK's ICT research community acting as a reference framework for the design of energy neutral WSN applications.

Publications

10 25 50

publication icon
Besbes H (2013) Analytic Conditions for Energy Neutrality in Uniformly-Formed Wireless Sensor Networks in IEEE Transactions on Wireless Communications

publication icon
Buranapanichkit D (2015) Convergence of Desynchronization Primitives in Wireless Sensor Networks: A Stochastic Modeling Approach in IEEE Transactions on Signal Processing

publication icon
Chen W (2015) Dictionary Design for Distributed Compressive Sensing in IEEE Signal Processing Letters

publication icon
Deligiannis N (2014) The No-Rate-Loss Property of Wyner-Ziv Coding in the Z-Channel Correlation Case in IEEE Communications Letters

publication icon
Deligiannis N (2017) Heterogeneous Networked Data Recovery From Compressive Measurements Using a Copula Prior in IEEE Transactions on Communications

publication icon
Deligiannis N (2015) Fast Desynchronization for Decentralized Multichannel Medium Access Control in IEEE Transactions on Communications

publication icon
Deligiannis N (2017) Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation. in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

publication icon
Mota J (2017) Compressed Sensing With Prior Information: Strategies, Geometry, and Bounds in IEEE Transactions on Information Theory

publication icon
Reboredo H (2016) Bounds on the Number of Measurements for Reliable Compressive Classification in IEEE Transactions on Signal Processing

publication icon
Redondi A (2014) Wireless Sensor Networks

publication icon
Redondi A (2014) Energy Consumption of Visual Sensor Networks: Impact of Spatio-Temporal Coverage in IEEE Transactions on Circuits and Systems for Video Technology

 
Description Our work has led to entirely new suite of data acquisition, communication, and processing algorithms relevant to a number of applications including the Internet of Things. In particular, our key findings/contributions include:

1. New algorithms for compressive data acquisition and processing. These algorithms exhibit superior performance in relation to the state-of-the-art.

2. New energy-constrained processing and transmission in wireless sensor networks (WSNs).

3. New communications protocols for wireless sensor networks (WSNs).

4. Deployment of a multi transducer platform for photovoltaic and piezoelectric energy harvesting as well as collection of raw data for the harvested power in commonly-encountered outdoor and indoor scenarios.
Exploitation Route Our findings might be of interest to multiple sectors:

(1) Our contribution to data acquisition and processing algorithms are of interest to multiple sectors engaged with data such as healthcare.

(2) Our contributions to wireless sensing networks data acquisition, transmission and protocols can be of interest to the digital/communication/information technologies.

(3) Our contributions to profiling harvested power capability of various energy harvesting devices in commonly-encountered outdoor and indoor scenarios can be of interest to the electronics and energy sector.
Sectors Digital/Communication/Information Technologies (including Software),Electronics,Energy,Healthcare,Culture, Heritage, Museums and Collections

 
Description Our research work has developed a suite of data processing algorithms capable of ingesting multiple data modalities in order to perform a variety of tasks. Our algorithms have been applied to a number of challenges arising in diverse sectors such as healthcare, art investigation, and art conservation & preservation. Of particular relevance, building upon the fundamental research work pioneered during the course of this research project, we recently developed additional algorithms that have led to state-of-the-art results in a variety of art investigation, conservation and preservation tasks, leading to high-profile publications, media coverage, and interest from galleries and museums worldwide.
First Year Of Impact 2019
Sector Digital/Communication/Information Technologies (including Software),Culture, Heritage, Museums and Collections
Impact Types Cultural

 
Description ARTICT | Art Through the ICT Lens: Big Data Processing Tools to Support the Technical Study, Preservation and Conservation of Old Master Paintings
Amount £759,281 (GBP)
Funding ID EP/R032785/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 09/2018 
End 09/2021
 
Description Multi-Modal Signal Processing for Art Investigation
Amount £90,750 (GBP)
Funding ID NIF\R1\192656 
Organisation The Royal Society 
Sector Charity/Non Profit
Country United Kingdom
Start 03/2019 
End 02/2021
 
Description Royal Society International Exchange Scheme
Amount £12,000 (GBP)
Organisation The Royal Society 
Sector Charity/Non Profit
Country United Kingdom
Start 09/2016 
End 08/2018
 
Description UCL / Duke University 
Organisation Duke University
Department Department of Mathematics
Country United States 
Sector Academic/University 
PI Contribution This research collaboration involved the development of new multi-modal data processing schemes. My research team contributed with theory and algorithms.
Collaborator Contribution This research collaboration involved the development of new multi-modal data processing schemes. The partner's research team contributed with applications.
Impact This collaboration has resulted in the publications: N. Deligiannis et al. Multi-Modal Dictionary Learning for Image Separation With Application In Art Investigation, IEEE Transactions on Image Processing, 2016. N. Deligiannis, J. Mota, B. Cornelis, M. R. D. Rodrigues, and I. Daubechies. X-ray image separation via coupled dictionary learning. IEEE International Conference on Image Processing, Phoenix, Arizona, USA, September 2016.
Start Year 2015
 
Description UCL / Technion: Israel Institute of Technology 
Organisation Technion - Israel Institute of Technology
Department Faculty of Electrical Engineering
Country Israel 
Sector Academic/University 
PI Contribution This collaboration relates to the development of new data processing schemes aided by side information. My research team has contributed with new theory and algorithms.
Collaborator Contribution This collaboration relates to the development of new data processing schemes aided by side information. The partner's research team has contributed with applications.
Impact This collaboration has resulted in the publications: J. Mota, L. Weizman, N. Deligiannis, Y. Eldar and M. R. D. Rodrigues. Reference-based compressed sensing: A sample complexity approach. Proceedings of the IEEE ICASSP, 2016. P. Song, Y. C. Eldar, G. Mazor, M. R. D. Rodrigues. Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network. IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, 2019. P. Song, L. Weizmann, J. M. C. Mota, Y. Eldar, and M. R. D. Rodrigues. Coupled Dictionary Learning for Multi-contrast MRI Reconstruction. IEEE Transactions on Medical Imaging, vol. 39, no. 3, pp. 621-633, March 2020. P. Song, G. Mazor, Y. C. Eldar, and M. R. D. Rodrigues. HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting. Medical Physics, vol. 46, no. 11, pp. 4951-4969, November 2019.
Start Year 2015