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.
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.
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.
Organisations
Publications
Besbes H
(2013)
Analytic Conditions for Energy Neutrality in Uniformly-Formed Wireless Sensor Networks
in IEEE Transactions on Wireless Communications
Buranapanichkit D
(2014)
On the stochastic modeling of desynchronization convergence in wireless sensor networks
Buranapanichkit D
(2015)
Convergence of Desynchronization Primitives in Wireless Sensor Networks: A Stochastic Modeling Approach
in IEEE Transactions on Signal Processing
Chen M
(2018)
Compressive Sensing With Side Information: How to Optimally Capture This Extra Information for GMM Signals?
in IEEE Transactions on Signal Processing
Chen W
(2015)
Dictionary Design for Distributed Compressive Sensing
in IEEE Signal Processing Letters
Deligiannis N
(2015)
Decentralized multichannel medium access control
Deligiannis N
(2015)
Fast Desynchronization for Decentralized Multichannel Medium Access Control
in IEEE Transactions on Communications
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
Deligiannis N
(2016)
X-ray image separation via coupled dictionary learning
Deligiannis N
(2014)
The No-Rate-Loss Property of Wyner-Ziv Coding in the Z-Channel Correlation Case
in IEEE Communications Letters
Deligiannis N
(2017)
Heterogeneous Networked Data Recovery From Compressive Measurements Using a Copula Prior
in IEEE Transactions on Communications
Deligiannis N
(2015)
Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature
in IEEE Sensors Journal
Mota J
(2016)
Adaptive-Rate Reconstruction of Time-Varying Signals With Application in Compressive Foreground Extraction
in IEEE Transactions on Signal Processing
Mota J
(2017)
Compressed Sensing With Prior Information: Strategies, Geometry, and Bounds
in IEEE Transactions on Information Theory
Reboredo H
(2016)
Bounds on the Number of Measurements for Reliable Compressive Classification
in IEEE Transactions on Signal Processing
Redondi A
(2014)
Wireless Sensor Networks
Redondi A
(2014)
Energy Consumption of Visual Sensor Networks: Impact of Spatio-Temporal Coverage
in IEEE Transactions on Circuits and Systems for Video Technology
Renna F
(2016)
Classification and Reconstruction of High-Dimensional Signals From Low-Dimensional Features in the Presence of Side Information
in IEEE Transactions on Information Theory
Renna F
(2016)
Media Query Processing for the Internet-of-Things: Coupling of Device Energy Consumption and Cloud Infrastructure Billing
in IEEE Transactions on Multimedia
Sabetsarvestani Z
(2020)
Source Separation With Side Information Based on Gaussian Mixture Models With Application in Art Investigation
in IEEE Transactions on Signal Processing
Smart G
(2016)
Decentralized Time-Synchronized Channel Swapping for Ad Hoc Wireless Networks
in IEEE Transactions on Vehicular Technology
Sokolic J
(2017)
Robust Large Margin Deep Neural Networks
in IEEE Transactions on Signal Processing
Sokolic J
(2016)
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification
in IEEE Transactions on Signal Processing
Song P
(2020)
Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction.
in IEEE transactions on medical imaging
Song P
(2019)
HYDRA: Hybrid deep magnetic resonance fingerprinting.
in Medical physics
Song P
(2020)
Multimodal Image Super-Resolution via Joint Sparse Representations Induced by Coupled Dictionaries
in IEEE Transactions on Computational Imaging
Xu J
(2014)
Non-Stationary Resource Allocation Policies for Delay-Constrained Video Streaming: Application to Video over Internet-of-Things-Enabled Networks
in IEEE Journal on Selected Areas in Communications
| 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 |