cps summer school the internet of things energy

CPS summer schoolThe Internet of Things Energy

Consumption Issues

Bernard Tourancheau

UJF, Grenoble-Alpes Université, UMR LIG Drakkar,

1

Why is Energy important (from IEA)

Oil reserves

2014

Cheap&easy energy is not even a possibility !→ bounded energy systems ...

Internet of Things (IoT)

● SoC● Computing● IP Networking● Sensor(s)● Actuator

→ for any object !

NB : related to WSN, M2M, ...

IoT nodes constraints

Low Power Networks Very limited resources

Energy Processing Memory

4

UbiMob 2013 – B. Tourancheau – Watteco – LIG, France.

But VAX11=TelosB ! ● 1978, computer room, DEC VAX 11/780 mainframe, 8kB RAM, 5MHz 32-bit

processor + Ethernet+main power● 2004, matchbox, Telos Berkeley mote, 10kB RAM, 8 MHz 16b MCU + radio

+ AA battery● 2010, watch, TI ez430 chrono, 8kB RAM, 32kB Flash, 16 MHz 16b-Bit

MCU + radio + LCD +button battery● 2012, thick credit card, STM Greennet, 32kB RAM, 256kB Flash, 32 MHz

32b ARM + radio + PV power supply.

Internet of Things : Privacy

(→ The end of intimacy ?) → IoT everywhere ?

IoT growth

> 50 Billion smart devices connected to the Internet by 2020 ! (Ericson)

Energy issue in the IoT

●SoC lifetime● Battery● Scavenging● mW

● Probe power● uW to 10W

● IoT power scale● mWx10^12=GW

9

IoT's powerBattery

Capacitor

Scavenging: PV, Peltier, ...

Mains (cf CPL)

However Billions of IoT devices → Billions of Watts ! There is No Moore’s law for battery technologyNiCd efficiency in J per volume improved by only x2 over the last 30 years…

Chemical ions breakthrough against electrons lithography ...

This is a major pollution source :-((

IoT Energy trends ?

CPS summer schoolThe Internet of Things Energy

Consumption Issues

Bernard Tourancheau

UJF, Grenoble-Alpes Université, UMR LIG Drakkar,

10

11

Architecture

→ Sinks placement

→ Reduced routing

Hardware Energy Consumptions

→ MCU

→ Transceiver

→ Probes

Software Impacts

→ Routing, # of packets

→ OS Optimizations

→ Address compression, coding

Energy in IoT Outline● Cross-layer routing protoco ls

→ energy savings

→ performance● Security

→ the real cost

→ OSCAR● Conclusion & Perspectives

IoT Stub Network Architecture

See L. Ben Saad PhD thesis in references [1].

13

Energy Hole Problem with batteries

Sink

Sink Sensor

14

Energy Hole Problem

SinkSensor

15

Energy Hole Problem

SinkSensor

Nonunifrom node distributionTransmission power control

Clustering approachesStatic sinks

Nodes mobility...

Sinks mobility

Solutions

16

Sinks placement optimization

Moving sinks (really/virtually) Updating the routing accordingly

Instance X

Sensor

Sink

How to choose Sinks positions to improve network lifetime ?

→ ILP optimization

17

Sink placement optimization

We compute for leaf nodes in the routing DODAG

Number of hops between node and the DAGROOT at position k

Residual energy of node Number of neighbors of node Normalization parameters Sinks move towards the leaf node with highest

→ ILP and heuristics solutions iw

iikiii

kii behbehfw )(

kih

ie

ib

,

18

ILP Formulation

Z ( s )

m..kkkl 21

Variables

Parameters

Wireless Network : G(V,E) S the set of IoT nodes F the set of feasible sites E represents the set of wireless links.

FSV E ⊆V × V

m Number of sinks

T Minimum duration of sinks sojourn time in s

e0

Initial energy of each sensor in J

eT

Energy consumption coefficient for transmitting one bit in J/bit

eR

Energy consumption coefficient for receiving one bit in j/bit

gr

Data packets generation rate in bit/s

rk

ijData transmission rate from node i to node j where the nearest sink stays at node k in bit/s

Nk

iSet of i’s neighbors whose their nearest sink is at node k.

Pk1k2...kn

iPower consumed in sending and receiving data by sensor node i when the first sink is located at node k1, the second sink is located at node k2 etc. and the m-th sink is located at node km in J/s

Pk

iPower consumed in sending and receiving data by sensor node i when the nearest sink is located at node k in J/s

Introduction Related Works Network Model Sinks positioning Simulation Results Discussion Conclusion

Network lifetime in s

Integer variables

19

, , integer, is ,0

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mm

m

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Integer Linear Program

(1)

(2)

(3)

FkFkFkFkSi

ikkkknearestSinkpp

m

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,..,,,

),( ,

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(4)

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20

10x10 sensors and 3 sinks, minhop = 9

Example of Sinks locations pattern

t = 0 t = T

t = 2T t = 3 T

Sink location

SensorId

Id

Id : node identifier


21

Static: S inks p laced in optima l locations Periphery: Sinks moving in the network periphery Random: Sinks moving randomly Hop: Sinks moving according to Hop heuristic OPT: Sinks moving according to ILP solution

Comparative sink placement study


22

Residual Energy per nodeAt the end of network Lifetime, 10x10 sensors and 3 sinks :

Static Periphery Random OptHop

1 Static Periphery Random Hop Opt

Unused energy 71% 45% 31% 17% 3%

Nb sensors with more 50% e0

80 60 20 4 4


23

10x10 sensors T=30 days

Lifetime against # of sinks

0

0,5

1

1,5

2

2,5

Static

Periphery

Random

Hop

Opt

Number of sinks

Lifetime

(E+10

seconds)


Best number of sinks

Simulations with RF-CPL parameters on monitoring app. 25 nodes on batteries 1-10 sinks optimaly positionned

App : node send 46B payload packet / minute to sink(s)

Lifetime x7

With 10 sinks

27

Architecture Autonomous battery powered RF “leaf” nodes Main powered RF-PLC router nodes

Networking and Stack Architecture 802.15.4 RF&CPL + 6LoWPAN IPv6 adaptation RPL routing + CoAP + ZCL RPL → DODAG Tree-base convergecast

Avoiding battery powered routing

Always ON Link

Energy constrained Link

Router Node (Always ON)

Leaf Node (Energy constrained)

Router Node (Always ON + Manage data delivery to leaves)

28

PLC nodes

• Standard Homeplug:– AV: OFDM / 20 - 200Mbps– GP: OFDM / 4 - 10 MbpsBUT :– Cost: a dozen $ – Power consumption : a few Watts– Size: Big

• The WPC™ Watteco Transceiver :– Specific Transceiver – 50Hz Pulses : 10 kbpsBUT :– <1$– 10 mW – 25 mm²

29

Architecture Wrap-up

Optim ized : – S inks p lacement– Sinks number

Routing tree with :– Battery powered

leaf nodes– Sinks routers

Energy efficiency with :

30

IoT Nodes Hardware

See C. Chauvenet PhD. Thesis in references [2].

Recal RF nodes

Low Power Wireless Networks Very limited resources

Energy Processing Memory

32

33

MCU Low power SoC → PC of the early 80's A few kB RAM, 100kB flash A few MHz

Transceiver Low Power RF Dozens kb/s High PER

Power source Probe(s) and ADC logic

Typical wireless nodes

WeC - 1999 Tmote SKY – 2004 [1] STM Greennet - 2012

IoT RF node schematic

34

Typical testbed node

38

2 MCU considered:- TI MSP430 (16 bit RISC – 16k RAM – 256k Flash)- SIM3CIxx (32 bit Cortex M3 – 32k RAM – 256k Flash ) :

4 different RF Transceivers considered: - 2.4 GHz : ATRF230- 868 MHz : ATRF212 / SI4461 / CC1120

6 Probes considered: • CO2 / Temperature / Temperature-Humidity / Presence / Door

Opening / Light

Nodes studied

→ Seeking for the lowest power consumption configuration

39

Bidirectional frame exchange power profile (NS/NA pkt) :

Typical Node Power Consumption

The RF Transceiver is clearly the Highest Power consumer.

But what about its Energy Budget when integrated over time ?

1 2 3 4 5 6 7 8 1 : Sleep mode2 : MCU Wakes Up3 : Transceiver Wakes Up4 : Radio Tx (NS packet)5 : Waiting for Ack6 : Radio Rx (NA packet)7 : Radio Tx (ACK)8 : Back to Sleep Mode

40

Measured MCU Wake up power through a 10.1 Ohms Load over 3V :

Energy is Lower at 16 MHz for our application, compared to 8 MHz Activity time divided by 2 Power multiplied by less than 2

MCU Energy Consumption

41

Power consumptions extracted from datasheets → Radio Links considered ideal Propagation Model Based on the Friis formula :

Prcv

= 22dB + 20 ∗ log(d/λ)

Transceivers Energy Consumption

ATRF230 (2.4 GHz) is much better for short range SI4461 (868 MHz) is the best for long range

For a given d radius, λ has a great impact on power consumption (and collisions and PER and router reachability and throughput ...).

Hardware wrap-up

For the target monitoring application :● Transceiver power > MCU power but may be used less

● MCU better at high frequency● Transceiver choice depends on inter-nodes distances and context

→ application dependant compromises

Software

See Ben Saad and Chauvenet PhD. Thesis in references [1] and [2].

44

• Contiki OS Based • IPv6 Stack following IETF and industry “standards” :

• 802.15.4-2006 (IEEE) • 6LoWPAN (IETF)• IPv6 (IETF)• RPL (IETF)• ZCL (Zigbee alliance)

→ The aim of the operating software is to turn the node componants in « sleep mode » as long as possible:

Low MCU activity through such duty cycling

Typical IoT software stack

45

• Resources are constrained thus : Low Radio activity through duty cycling MAC Protocol

→ a lot of ongoing research on that subject with beacons, slots, ... Reduce number of packet transmitted for network control Reduce Frame size:

→ Compression (6LoWPAN – CoAP)→ Aggregation→ Adresse Coding

Typical IoT networking stack

47

Routing is ensured on main powered nodes RPL proactive routing storing mod.

→ high values for periodic timers: trickle, DTSN, ... NS/NA periodic exchanges

→ trickle doubling delay timer with high max value Upward data anytime thanks to routers' availability Downward data packets triggered by piggybacked flag on NA Typical numbers of packets for a day in a monitoring app. :

Reduced # of control packets

48

SIM3C1 MCU better than MSP430 4xx→ test bed is MSP430+ATRF212 → but best seems: CortexM3 MCU + SI4461 RF

Must avoid LDO (DC/DC) energy cost Largest relative part of active energy in Radio TX and MCU Wake Up Years of theoritical lifetime

AAA battery lifetime

49

Increase periods of (or suppress) tasks that are not or seldom used Wake Up period set to 1 Hz / Fast wake-up

→ estimated lifetime rised up form 7,16 to 12,55 years for 1 000 mAh AAA Low Power mode + LP Internal Oscillator → estimated lifetime reaches 20,23 years for 1 000 mAh AAA

OS Optimizations

50

Total Bytes Sent/Received (over a day) → 28892B / 17128B Radio Activity ratio : only 0,021% of time

Platform Consumption Overview

After All Optimizations

Lifetime → >20 yearsBattery leakage 1 % / year → >16 years

51

Depends on precision Depends on internal sampling period May also affect battery depletion profile.

Probes Consumption Impact

Software optimization wrap-upThanks to arch itecture choice and application driven optim izations

→ >10 years lifetime on battery seems possible

→ Mandatory software duty cycling technics in network and OS

Nb: No RDC overhead with our architecture → research space in this domain is active

Address compression

Address compression

See L. Ben Saad PhD thesis in references [1].

Control information is big

Typical MTU Packet 127 bytes in IEEE 802.15.4 low power wireless IPv6 Header : 40B with 2x16B addresses

Header Payload

Packet

56

Motivation

Minimize energy consumption of sensors by reducing the amount of transfered control bits

Reducing header size by reducing addresses' size Exploiting RF overhearing and addresses correlation

57

Overhearing

Sinky

s

zyA

58

S overhears → Slepian-Wolf source coding S overhears → Slepian-Wolf source coding

yA

Transmission range

Example

Sinky

s

zyy AB

FAAB syy 7)(

59

Example

Linear Code [128,120,4] (8 bits)

Sinky

sz

yy AB

FAAB syy 7)(

1),( syH AAd

407:1::8000:92

406:1::8000:92

DDA

DDA

s

y

(128 bits)

(128 bits)

60

3),( syH AAd

Adresses allocation

Allocation scheme :- Assign unique address of X bits for 1-hop neighbors- m bits of X are reserved- (X-m) bits remaining used to allocate

addresses to children of 1-hop neighbors- Hamming distance increases with the depth of the tree

Allocation scheme :- Assign unique address of X bits for 1-hop neighbors- m bits of X are reserved- (X-m) bits remaining used to allocate

addresses to children of 1-hop neighbors- Hamming distance increases with the depth of the tree

61

Multi-hop networks

Cluster based overearing

Cluster purpose Optimize sinks' placement Exploit overhearing and addresses correlation Reduce the size of transmitted addresses of battery-powered nodes

62

Sink Battery-poweredsensor

Line-powered sensor

Cluster

Optimization

Max-Min problemMax-Min problem

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63

Addresses compression wrapup

Overhearing and addresses Set-up during network initialisation Reduction of addresses size by applying Slepian-Wolf coding Assumes a communication schedule May provides very small adresses

Overhearing and addresses Set-up during network initialisation Reduction of addresses size by applying Slepian-Wolf coding Assumes a communication schedule May provides very small adresses

65

72

→ Several research domains make IoT possible→ Application dependencies for optimizations→ Protocols optimizations necessary→ Very long lifetime seems possible

Still, the way to power the 10^12 devices of the future IoT may be a difficult path.

Next Steps: - Use scavenging nodes with µW average power ! - Expand software stack improvements to upper layer protocols such as CoAP and the IPSO Application Framework- Include object security architecture in the IoT

IoT Conclusion & Perspectives

73

Some bibliography Bibliography[1] Leila Ben Saad, Stratégies pour améliorer la durée de vie des réseaux de capteurs sans fil,

Thèse de doctorat, ENS-Lyon, 2011.

[2] Cédric Chauvenet, Protocoles de support IPv6 pour réseaux de capteurs sur courant porteur en

ligne, Thèse de doctorat, Grenoble Université, 2013.

[3] Malisa Vucinic, Protocoles pour la sécurité des réseaux de capteurs ; Thèse de doctorat, Grenoble

Université, 2016.

Vucinic et al., "OSCAR: Object Security Architecture for the Internet of Things", WoWMoM, IEEE, 2014. [4] Vucinic et al., "OSCAR: Object Security Architecture for the Internet of Things", WoWMoM, IEEE, 2014.

74

Thanks ! Questions ?

cps summer school the internet of things energy

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