Shahram Mohrehkesh

PhD Dissertation

Advisor: Dr. Michele C. Weigle

May 2015

Department of Computer Science

Old Dominion University

Energy Harvesting-aware Design for

Wireless Nanonetworks

1

• Introduction

• Nanonode, Nanonetworks, …

• Applications of nanonetworks

• Communication channels

• Energy harvesting in nanonetworks

• Optimizing energy consumption

• Energy harvesting-aware MAC protocol

• Future of nanonetworks

Outline

2

Nanotechnology

3

Enables the manipulation of matter at atomic and molecular scale (1 to 100 nm)

Molecules -100 nm Atom -1 nm

• “Mechanical device that performs a useful function

using components of nanometer-scale and defined

molecular structure.” (Eric Drexler, 1991)

Nanoscale Machine

4

• Functions

Computing

Data storing

Sensing

Actuation

Communication

Nano-capacitor

Nano-actuators

Nano-EM-transreceiver

Nano-sensors

Nano-processorNano-memory

Nano-antenna

6 μm

1 μm

2 μmenergy harvester

Nanonode

Integration of several nano-machines into a single

functional entity

5

I. F. Akyildiz and J. M. Jornet, “Electromagnetic Wireless Nanosensor Networks,” Nano Communication

Networks (Elsevier) Journal, vol.1, no.1, pp. 3-19, Mar. 2010.

Nanonetworks

• Interconnections of nanonodes

• To execute more complex tasks in a distributed fashion

• To overcome individual limitations (e.g., in size, energy,

computation)

6

Applications - Adv. Health Monitoring

7

Nanosensors can be used for:• Monitoring glucose, sodium,

cholesterol

• Detection of viruses

• Localization of cancerous cellsInterface with

External Networks

Nanonodes

Nanosensors to

measure glucose

“Can you imagine if you lost a sock? You could send out a search and

sock No. 3117 would respond that it’s under the couch in the living

room.” (Vint Cerf, 2013)

Applications - Internet of Things

8

Simpler to

integrate

nanonode

with every

thing

9

Nanoantenna

• Made from

– Carbon Nanotube (CNT)

– Graphene Nanoribbon (GNR)

• 1 μm-long nano-antenna resonates in the Terahertz Band (0.1-10 THz)

• Femtosecond-long pulse emitters and detectors

1-50 nm

CNT

G. W. Hanson, “Fundamental transmitting properties of carbon nanotube antennas,” IEEE

Transactions on Antennas and Propagation, vol. 53, no. 11, pp. 3426–3435, Nov. 2005.

J. M. Jornet and I. F. Akyildiz, “Graphene-based Plasmonic Nano-antennas for Terahertz Band

Communication in Nanonetworks,” IEEE JSAC, Dec. 2013.

10

Path Loss in THz

Path Loss [dB] (10% water vapor molecules)Almost no

propagation

above a few

meters

Almost 10

THz wide

transmission

window for

distances

much below

one meter

Several

windows tens

of GHz wide

each at around

one meter

Molecular absorption is the main cause of the path loss

J. M. Jornet and I. F. Akyildiz, “Channel Modeling and Capacity Analysis of EM Wireless Nanonetworks in

the Terahertz Band,” IEEE Tran. on Wireless Comm., 2011.

11

Time Spread On-Off Keying (TS-OOK)

“1” “1” “1” “1”“0” “0” “0”

TS TP

… …

A logical “0” is transmitted as

silence

• Ideally no energy is consumed for

transmission

A logical “1” is transmitted as

a pulse

• Pulse length, Tp = 100 femtoseconds

Molecular absorption noise would affect only pulses

J .M. Jornet and I. F. Akyildiz, “Information Capacity of Pulse-based Wireless Nanosensor Networks,” in

Proc. of the 8th Annual IEEE SECON, June 2011.

PHY layer- models exist, ongoing research on impl.

Frequency Band (Atakan

2010, Jornet 2010)

Channel Models (Akkas2012, Jornet 2010, Jornet

2011)

Information

Modulation (Jornet 2011)

Data link layer- early research

Upper layers (>=3)- early research

MAC

Protocols (Jornet 2012)

Throughput

Evaluation(Wang 2013)

Potential applications

(industry, medical, …) (Akyildiz 2010)

Cross Layer

Issues:

- Energy

Harvesting

(Jornet 2012)

- Security &

Privacy (Dressler 2012)

Energy-aware

Application

Requirements (Balasubramaniam2013)

13

Research Objectives

1. Model for energy harvesting and consumption

2. Design optimum packet

1. Packet size, code weight, repetition

2. Multi-objective optimization problem

3. Optimal energy consumption policy to

maximize the energy utilization

4. Energy harvesting-aware MAC protocol for

nanonodes

• Introduction

– Nanonode, Nanonetworks, …

– Applications of nanonetworks

– Communication channels

• Energy harvesting in nanonetworks

– Optimizing energy consumption

– Energy harvesting-aware MAC protocol

• Future of nanonetworks

Outline

14

15

Energy Harvesting Sources

• Energy arrivals are stochastic

In Nanonetworks:

1. Energy storage is nonlinear.

2. Energy storage is small.

3. Consumption rate is much faster

than harvesting rate.

Mohrehkesh, Weigle, and Das, “Energy Harvesting in Nanonetworks”, book chapter to appear in "Modeling,

Methodologies and Tools for Molecular and Nano-scale Communications”

16

Two Major Challenges in NanoCommunication

• Optimum energy consumption

– Stochastic properties of energy harvesting

– Limited and non-linear energy storage

• Data link layer

– Access to the channel

– Energy status

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How much energy consumption?

• Time is slotted into unit length

• A set of energy consumption actions is defined

• Several policies – Aggressive: consume as much as possible

– Conservative: consume a minimum amount , i.e., one transmission, one reception

– Consume-Harvest (C-H)

– Mean

– Random

Agg Cons C-H Mean Rand

Chance of Being in Out of Energy State H L L M M

Chance of Being in Full Energy State L H H M M

Energy Utilization H L M M M

High, Low, Medium

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How much energy consumption?

• Solution can be stored as a look-up table

• Heuristic methods are required – Slow Beginning Fast Ending (SBFE): Combine aggressive and

conservative

– Adaptive: Proportional to the amount of available energy

Markov Decision Process (MDP)- Diagram does not show all states and transitions

Mohrehkesh and Weigle, “Optimizing Communication Energy Consumption in Perpetual Wireless

Nanosensor Networks”, in IEEE GLOBECOM 2013

• 𝐸𝐸 = log(𝐸𝑛𝑒𝑟𝑔𝑦 𝑈𝑡𝑖𝑙

𝑝𝑒∗𝑝𝑓)

19

Energy Efficiency (EE) for Various Policies

Mohrehkesh and Weigle, “Optimizing Energy Consumption in Terahertz Band Nanonetworks” In IEEE JSAC,

Molecular, Biological, and Multi-Scale Communications Series , 2014.

Aggressive

Conservative

Consume-Harvest

Mean

Random

Optimal

SBFE

Adaptive

20

EE for Nonlinear Storage

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Let's build communication

• We have...

– Characteristics of THz communication

– A simple pulse based modulation (TS-OOK)

– Energy harvesting and consumption model and

policy

• We don't have ...

– Medium Access Control

• Maximize the utilization of harvested energy

• Coordination between nanonodes, i.e., Energy harvesting-

aware

• Scalable, light-weight, and distributed

Nano-Controller

Possible Topologies

Centralized Distributed

22

23

RIH-MAC: Receiver Initiated Harvesting-aware

• Receiver-initiated ...

– Facilitate the development of the energy harvesting-

aware solution

Mohrehkesh and Weigle, “RIH-MAC: Receiver-Initiated Harvesting-aware MAC for NanoNetworks”, in

ACM NANOCOM 2014

R

T

R

DATA

DATA

R

T

R

R

T

R

R

T

RReceiver

Transmitter

R

T

R

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Centralized RIH-MAC

• Nanocontroller transmits RTR

• What is the probability of

participation p for a nanonode ?

• Assume that a nanonode

– Has energy with probability of q

– Has a DATA packet to transmit

with probability r

• The expected number of concurrent DATA

packets, X, by n nanonodes: 𝐸 𝑋 = 𝑝𝑞𝑟𝑛

• For E[X] =1 => 𝑝 =1

𝑞𝑟𝑛

Nano-Controller

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Probability of Collisions

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Distributed RIH-MAC

• Nanonodes take turns for communication with

each neighbor based on the link color

D

A B

C

G

E

F

27

Edge Coloring Algorithm

• Most edge coloring solutions are centralized

• Distributed coloring with (1 + ε) Δ colors

– 0 <ε<1, Δ: degree of graph

– High probability (>99%) of successful coloring

– Coloring process occurs in rounds

– Start with an initial palette of colors for each link

D. A. Grable and A. Panconesi. Nearly optimal distributed edge colouring in O(log log n) rounds. In

Proceedings of the Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 278- 285,

Philadelphia, PA, USA, 1997.

28

Number of Rounds to Color Edges

• Duration of one round = 2(Δ + 1) slots, with no RTR failure

• Depending on energy harvesting rate, takes several nanoseconds to several seconds

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Distributed RIH-MAC

• Direction of communication is indicated based on the

node ID

• After coloring, no collision in

communication for a nanonode

with its neighbors

Cycle

30

Energy Consumption Scheduling

• Receiver-Initiated protocol works perfectly for

the not energy constrained scenario

• In energy constrained scenario ...

– Many RTR packets would be sent with no DATA

packet

– Transmitters may listen for RTR packets but receive

no RTR packets

31

Coordinated Energy Consumption Schedule (CECS)

• Nanonodes have an optimal action set for the amount of energy consumption (number of transmissions and receptions) for various level of energy

• Rotation after each cycle

• Prediction of CECS of neighbors

Policy Pattern (5

neighbor)

1 2 3 4 5

0 0 0 0 0 0

1 1 0 0 0 0

2 0 0 1 0 1

3 1 0 1 0 1

4 0 1 1 1 1

>=5 1 1 1 1 1

2 0 0 1 0 1

32

CECS Example- Reception by Node B

Policy Pattern

A C E

0 0 0 0

1 1 0 0

2 1 1 0

>= 3 1 1 1

Policy Pattern

A C E

0 0 0 0

1 1 0 0

2 1 1 0

>= 3 1 1 1

Policy Pattern

A C E

0 0 0 0

1 0 1 0

2 1 1 0

>= 3 1 1 1

Policy Pattern

A C E

0 0 0 0

1 0 1 0

2 1 1 0

>= 3 1 1 1

Successful prediction

Unsuccessful prediction

33

Simulation Setup

Energy Storage Capacity 100 pJ

Energy per pulse (transmission) 1 fJ

Energy per pulse (reception) 0.1 fJ

RTR packet size 25 B

DATA packet size 250 B

Pulse duration 100 fs

Number of Nanonodes 100

• Nano-sim module of NS-3

G. Piro, L. A. Grieco, G. Boggia, and P. Camarda. “Nano-sim: simulating electromagnetic-based

nanonetworks in the network simulator 3”. In Proc. Of Workshop on NS- 3, Cannes, France, Mar. 2013.

34

RTR Success Percentage

Exponential energy model

Mohrehkesh, Weigle, and Das “DRIH-MAC: A Distributed Receiver-Initiated Harvesting-aware MAC for

NanoNetworks”, to appear in IEEE Trans. on Molecular, Biological, and Multi-scale Communications.

35

Energy Utilization

Exponential energy model

36

Summary

• Optimum schedule for energy consumption

• Receiver-initiated and energy harvesting-aware

MAC protocol

37

Contributions

• A model to design the optimal packet– (IEEE Globecom 2013)

• Optimum energy consumption policy – (IEEE JSAC 2014)

• Receiver-initiated and energy harvesting-aware

MAC protocol – (ACM Nanocom 2014, IEEE Trans. on MBMC 2015)

• Models for energy harvesting and consumption – (Book Chapter, IEEE Comm. Magazine 2015)

38

Future of Nanonetworks

• Near Term

– Improve heuristic policies of energy consumption

– Evaluation of RIH-MAC in other application, e.g., Internet of Things

• Long Term

– Challenges in various layers as well as Cross layer issues

– New applications:

• Nano-robots

• Medical applications, e.g., drug delivery system

• Wireless Network-on-Chip (WNoC)