Enjeux et défis scientifiques pour un déploiement durable de l’IoT : état de l’art de solutions d’optimisation énergétique

Journée Scientifique DS4H - Concilier numérique et environnement ?Sophia Antipolis - 2 Décembre 2019

Alain Pegatoquet – Maître de Conférences, LEAT

▪ Internet of Things is a buzzy phrase…

▪ IoT is not easy to define…

▪ Internet of Things definitions

▪ ”A network of items each embedded with sensors which are connected to

the Internet.” IEEE Institute. March 2014

▪ ”Cyber physical systems (CPS) involve connecting smart devices and

systems in diverse sectors like transportation, energy, manufacturing and

healthcare in fundamentally new ways.” NIST

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What is Internet of Things (IoT)?

▪ IoT Human + Physical Objects (sensors, controllers, actuators, devices,

computing, storages) + Internet

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Internet of Things Elements [Farhan 2018]

P2P People to People

M2P Machine to People

M2M Machine to Machine

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Internet of Things Architecture [Farhan 2018]

▪ IoT Sensors/Actuators (Application dependent) + Gateway (Access Point) +

Internet + Services

▪ Wireless Sensors Networks (WSNs) is one of the key underlying enabling

technology for IoT

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Wearable Devices

Current Trends

▪ More functionality per area

▪ Greater HW/SW requirements

▪ Longer Battery lifetime

Biggest Challenges

▪ Energy storage (capacity)

▪ Energy consumption

▪ Energy harvesting

▪ Smart devices

▪ Miniaturization

[Eggimann 2019]

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Smart Connected Glasses

Drowsiness Detection Fall Detection Activity Tracking

▪ A typical IoT device

▪ Different application use cases

Constraints :

▪ Computations

▪ Memory

▪ Battery

[Arcaya 2019]

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IoT Challenges

▪ Business : Silos vs. Horizontal, Application Domains, The revenue

(per device, connectivity, Data), New business model…

▪ Societal : Privacy, Data Ownership, Security, Easiness of Use, Social Cooperation

▪ Technical : complexity, communication, Data, Software platforms, energy

consumption…

[Farhan 2018]

▪ Size of the IoT market worldwide from 2017 to 2025 (in billion U.S. dollars)

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IoT Market

[Source: Statista 2019]

▪ The global IoT market is expected to grow to $212 billion by the end of 2019.

▪ The technology reached 100 billion dollars in market revenue for the first time in

2017, and forecasts suggest that this figure will grow to around 1.6 trillion by

2025.

▪ IoT: 75 billions of connected objects in 2025…

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What about the Overall Energy Consumption?

Sources : [Lucero 2016] [Rioual 2019]

Optimize Energy Everywhere !

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▪ IoT Devices Power Management (Node level)

▪ Optimize Components power consumption (DPM, Power/Clock gating, DFVS, etc.)[Ben Ameur 2018] [Mbarek 2013]

▪ Optimize communications (RF activities)[Rault 2014] [Castagnetti 2014] [Le 2016] [Maitra 2016] [Kim 2015] [Huang 2013] [Sentieys 2013] [Ait Aoudia 2017]

▪ Optimize activities (task scheduling)[Arcaya 2019]

▪ Autonomous IoT Devices

▪ Energy harvesting [Vullers 2010]

▪ Power management[Kansal 2007] [Castagnetti 2012]

▪ Network-level Optimizations

▪ IA-based approach

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Optimizing Energy Everywhere !

▪ Routing protocols (multi-hop)

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Network Level Optimizations

▪ Cooperative networks

▪ The nodes work together to improve the communication or the energy consumption of the

overall network (e.g. cluster head) [Liang 2010] [Chen 2016]

▪ Edge Computing…

▪ Process data as close as possible from the nodes to minimize the transmission, reduce the

latency and optimize the bandwidth utilization…

[Messous 2018]

Edge Computing &

IA based approach

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▪ IA has already been widely used to optimize performance/energy for embedded

systems

▪ All types of learning approaches

▪ Supervised, unsupervised, reinforcement

▪ Bio-inspired

▪ For all kinds of problematic

▪ Communications (MAC, Routing, Self-learning radios, Cooperative networks, Rate control…)

▪ Tasks scheduling

▪ Power management

▪ Energy Harvesting

▪ A new paradigm for embedded architectures

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IA for Power Management

Neuroscience

▪ Spiking Neural Networks (SNN) motivations

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Hardware Spiking Neurons for Embedded Artificial Intelligence

➔ Accuracy

➔ Hardware implementation

◆ Powerful computer (CPU/GPU)

◆ Embedded systems (FPGA/ASIC)

Neuroscience

Spiking Neural Networks (SNN)

Machine

Learning

Formal Neural Networks

[Abderrahmane 2020]

▪ Spiking versus Formal Neural Networks

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Hardware Spiking Neurons for Embedded Artificial Intelligence

Integrate-and-FirePerceptron

SNN neuron:

▪ Addition

▪ No more Multiplication !

▪ Comparison

FNN neuron

▪ Multiplication + Addition

▪ Non-linear activation function

[Abderrahmane 2020]

▪ Results with MNIST classification

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Hardware Spiking Neurons for Embedded Artificial Intelligence

SNN vs. FNN hardware cost and accuracy

ANN Model FNN SNN SNN Gain (%)

Accuracy (%) 95.73 95.37 -0.38

Total power (mW) 52.22 28.46 45.37

Total area (mm²) 1.888 0.869 53.98

Place and route synthesis on 65nm technology

▪ For almost the same accuracy, SNN is around ~50% more efficient in terms

of power / area

[Abderrahmane 2020]

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[Abderrahmane 2020] Nassim Abderrahmane, E. Lemaire and Benoit Miramond, Design Space Exploration of Hardware Spiking Neurons for Embedded

Artificial Intelligence, to appear in Neural Networks Journal, Vol. 121, pp. 366-386, 2020.

[Ait Aoudia 2017] Faycal Ait Aoudia, “Energy Harvesting Wireless Sensor Networks Leveraging Wake-up Receivers: Energy Managers and MAC

Protocols”, PhD Thesis, 28th Sept. 2017, Université de Rennes 1, Lannion, France.

[Arcaya 2019] A. Arcaya Jordan, A. Pegatoquet and A. Castagnetti, Smart Connected Glasses for Drowsiness Detection: a System-Level Modeling

Approach, IEEE International Sensor Applications Symposium (SAS), Sophia Antipolis, France, March 11-13, 2019

[Ben Ameur 2018] Amal Ben Ameur, Michel Auguin, François Verdier, Valerio Frascolla, Mobile Terminals System-Level Memory Exploratio for Power

and Performance Optimization, 28th International Symposium on Power and Timing Modeling, Optimization and Simulation

(PATMOS), pp. 23-28, Costa Brava, Spain, July 2-4th, 2018

[Castagnetti 2012] A. Castagnetti, A. Pegatoquet, C. Belleudy and M. Auguin, An Efficient State of Charge Prediction Model for Solar Harvesting WSN

Platforms,19th IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), Vienna, Austria, 11-13 April,

2012.

[Castagnetti 2014] Andrea Castagnetti, Alain Pegatoquet, Trong-Nhan Le and Michel Auguin, A Joint Duty-Cycle and Transmission Power

Management for Energy Harvesting WSN, IEEE Transactions on Industrial Informatics Journal, Special section on “Industrial

Wireless Sensor Networks”, Volume 10, Issue 2, pp. 928-936, May 2014.

[Chen 2016] Hongbin Chen, Xueyan Li et Feng Zhao. A reinforcement learning-based sleep scheduling algorithm for desired area coverage in

solar-powered wireless sensor networks. In IEEE Sensors Journal 16.8 (2016), p. 2763-2774.

[Eggimann 2019] Manuel Eggimann, Stefan Mach, Michele Magno and Luca Benini, A RISC-V Based Open Hardware Platform for Always-On

Wearable Smart Sensing, IEEE 8th International Workshop on Advances in Sensors and Interfaces (IWASI), 2019

[Farhan 2018] Laith Farhan et. al, A Concise Review on Internet of Things (IoT) - Problems, Challenges and Opportunities. 11th International

Symposium on Communication Systems, Networks, and Digital Signal Processing (CSNDSP 2018), Budapest, Hungary, 2018

[Huang 2013] P. Huang, L. Xiao, S. Soltani, M. W. Mutka, and N. Xi, The Evolution of MAC Protocols in Wireless Sensor Networks: A Survey, IEEE

Communications Surveys Tutorials, vol. 15, no. 1, pp. 101-120, January 2013.

[Kansal 2007] A. Kansal et al., Power management in energy harvesting sensor networks, ACM Transactions on Embedded Computing Systems (TECS), vol.6,

2007

References

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[Kim 2015] T. Kim, I. H. Kim, Y. Sun, et Z. Jin, « Physical Layer and Medium Access Control Design in Energy Efficient Sensor Networks: An

Overview », IEEE Transactions on Industrial Informatics, vol. 11, no 1, p. 2-15, févr. 2015.

[Le 2016] T-N. Le, A. Pegatoquet, Trinh Le Huy, Leonardo Lizzi and Fabien Ferrero, Improving Energy Efficiency of Mobile WSN Using

Reconfigurable Directional Antennas, IEEE Communications Letters, Vol. 20, Issue 6, pp. 1243-1246, April 2016.

[Liang 2010] Xuedong Liang, Min Chen, Yang Xiao et al. MRL-CC : a novel cooperative communication protocol for QoS provisioning in wireless

sensor networks. In International Journal of Sensor Networks 8(2):98-108 · August 2010.

[Lucero 2016] Sam Lucero et al. IoT Platforms : Enabling the Internet of Things. IHS market, White paper, 2016.

[Maitra 2016] Tanmoy Maitra and Sarbani Roy, A comparative study on popular MAC protocols for mixed Wireless Sensor Networks: From

implementation viewpoint, Computer Science Review, Elsevier, Volume 22, Pages 107-134, November 2016

[Mbarek 2013] O. Mbarek, A. Pegatoquet and M. Auguin, Power Domain Management Interface: Flexible Protocol Interface for Transaction-Level

Power Domain Management, IET Computers & Digital Techniques Journal, Volume 7, Issue 4, pp. 155-166, July 2013.

[Messous 2018] S. Messous, N. Liouane, A. Pegatoquet and M. Auguin, An improved energy-efficient routing protocol based on Minimum Spanning

Tree for Wireless Sensor Network, International Conference on Sensors, Systems, Signals and advanced technologies (SSS 2018),

Hammamet, Tunisia, May 10-12, 2018

[Rault 2014] Tifenn Rault, Abdelmadjid Bouabdallah and Yacine Challal, Energy efficiency in wireless sensor networks: A top-down survey,

Computer Networks, Volume 67, Pages 104-122, 4 July 2014

[Rioual 2019] Yohann Rioual, RL-based Energy Management for Autonomous Cyber Physical Systems, PhD thesis, University of Bretagne Sud,

Dec 2019

[Sentieys 2013] Olivier Sentieys, Protocol-Level Power Optimization of Wireless Sensor Networks, EcoFac2012, May 21-25 2012, La Colle-sur-Loup,

France

[Statista 2019] Online - https://www.statista.com/statistics/976313/global-iot-market-size/

[Vullers 2010] R. J. M. Vullers, R. v. Schaijk, H. J. Visser, J. Penders, and C. V. Hoof, Energy Harvesting for Autonomous Wireless Sensor Networks,

IEEE Solid-State Circuits Magazine, vol. 2, no. 2, pp. 2938, Spring 2010.

References

Journée Scientifique DS4H - Concilier numérique et environnement ?Sophia Antipolis - 2 Décembre 2019

Thank you for your attention !