Shahram Mohrehkesh's PhD Defense Slides Department of Computer Science Old Dominion University May 6, 2015

1. 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

2. • 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

3. Nanotechnology
3
Enables the manipulation of matter at atomic and
molecular scale (1 to 100 nm)
Molecules -100 nm Atom -1 nm

4. • “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

5. 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.

6. Nanonetworks
• Interconnections of nanonodes
• To execute more complex tasks in a distributed fashion
• To overcome individual limitations (e.g., in size, energy,
computation)
6

7. 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

8. “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. 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. 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. 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.

12. 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. 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

14. • 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. 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. 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

17. 17
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

18. 18
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

19. • 𝐸𝐸 = log(
𝐸𝑛𝑒𝑟𝑔𝑦 𝑈𝑡𝑖𝑙
𝑝 𝑒∗𝑝 𝑓
)
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. 20
EE for Nonlinear Storage

21. 21
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

22. Nano-Controller
Possible Topologies
Centralized Distributed
22

23. 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

24. 24
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

25. 25
Probability of Collisions

26. 26
Distributed RIH-MAC
• Nanonodes take turns for communication with
each neighbor based on the link color
D
A B
C
G
E
F

27. 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. 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

29. 29
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. 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. 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. 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. 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. 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. 35
Energy Utilization
Exponential energy model

36. 36
Summary
• Optimum schedule for energy consumption
• Receiver-initiated and energy harvesting-aware
MAC protocol

37. 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. 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)