wireless networks and advance wireless technologies...

IBSS,Mumbai,2015 Copyright 2015 1

Wireless Networks and Advance Wireless Technologies: Challenges & Opportunities

11th September 2015Prof. Saurabh Mehta

Associate Professor & HODDepartment of Electronics and Telecommunication

Vidyalankar Institute of Technology, Wadala , Mumbai


Topics

Advance Wireless Technologies Our present and past work New research directions


Advance Wireless Technologies

Wireless Sensor networks Cognitive radio based networks


Introduction

A sensor network is composed of a large number of sensor nodes, which are densely deployed either inside the phenomenon or very close to it.

Random deployment Cooperative capabilities


Introduction

Sensors Enabled by recent

advances in MEMS technology

Integrated Wireless Transceiver

Limited in Energy Computation Storage Transmission range Bandwidth

Battery

Memory

CPU

Sensing Hardware

WirelessTransceiver


Introduction


Differences with ad hoc networks

Sensor networks VS ad hoc networks: The number of nodes in a sensor network can be several orders

of magnitude higher than the nodes in an ad hoc network. Sensor nodes are densely deployed. Sensor nodes are limited in power, computational capacities and

memory. Sensor nodes are prone to failures. The topology of a sensor network changes frequently. Sensor nodes mainly use broadcast, most ad hoc networks are

based on p2p. Sensor nodes may not have global ID.


Overall Architecture of a sensor node


Example of WSN


Sink / Base Station

Task Manager Node

Internet or Satellite

Self-organizing, non-homogenous Sensor NetworkEnd User

Multi-hop wireless

Cluster-Head or Aggregator

Density of nodes μ(R) = N πR2/A

N = # of nodes in area AR is radio range

area A

Communication Topology


Biomedical / Medical

Health Monitors Glucose Heart rate Cancer detection

Chronic Diseases Artificial retina Cochlear implants

Hospital Sensors Monitor vital signs Record anomalies


Military

Remote deployment of sensors for tactical monitoringof enemy troop movements.


Industrial & Commercial

Numerous industrial and commercial applications: Agricultural Crop Conditions Inventory Tracking In-Process Parts Tracking Automated Problem Reporting RFID – Theft Deterrent and Customer Tracing Plant Equipment Maintenance Monitoring


Traffic Management & Monitoring

Future cars could use wireless sensors to: Handle Accidents Handle Thefts

Sensors embedded in the roads to:

–Monitor traffic flows–Provide real-time route updates15


Characteristics of Wireless Sensor Networks

Wireless Sensor Networks mainly consists of sensors. Sensors are - low power limited memory energy constrained due to their small size.

Wireless networks can also be deployed in extreme environmental conditions and may be prone to enemy attacks.

Although deployed in an ad hoc manner they need to be self organized and self healing and can face constant reconfiguration.


Design Challenges

Heterogeneity The devices deployed maybe of various types and need

to collaborate with each other. Distributed Processing

The algorithms need to be centralized as the processing is carried out on different nodes.

Low Bandwidth Communication The data should be transferred efficiently between

sensors


Continued..

Large Scale Coordination The sensors need to coordinate with each other to

produce required results. Utilization of Sensors

The sensors should be utilized in a ways that produce the maximum performance and use less energy.

Real Time Computation The computation should be done quickly as new data is

always being generated.


Operational Challenges of Wireless Sensor Networks

Energy Efficiency Limited storage and computation Low bandwidth and high error rates Errors are common

Wireless communication Noisy measurements Node failure are expected

Scalability to a large number of sensor nodes Survivability in harsh environments Experiments are time- and space-intensive


Enabling Technologies

Embedded Networked

Sensing

Control system w/Small form factorUntethered nodes

ExploitcollaborativeSensing, action

Tightly coupled to physical world

Embed numerous distributed devices to monitor and interact with physical world

Network devices to coordinate and perform higher-level tasks

Exploit spatially and temporally dense, in situ, sensing and actuation


Fault Tolerance

Handle loss of nodes

ScalabilityHandle high density of nodes

CostsNodes die, make them low cost

Hardware LimitationsNodes are tiny

Changing Topology

Nodes moving, new nodes, loss of nodes

Hostile Environment

Survive and maintain communication

Transmission Media

wireless: RF, optical, infrared

PowerLimited Tx, computation, and lifetime

Security ?Security ?Confidentiality, Authentication

etc

Special Constraints for Communication in Sensor Networks


5. Application Layer5. Application Layer

4. Transport Layer4. Transport Layer

3. Network Layer3. Network Layer

2. Data Link Layer2. Data Link Layer

1. Layer Physical1. Layer Physical

Power

Power

Moving

Moving

Collaboration

Collaboration

Sensor Network Manage-ment

Protocol Stack and Sensor Network Management


Cognitive radio based networks


Cognitive Radio: A new concept

• Several definitions (and variations) of Cognitive Radio exist:

•Mitola - Cognitive radio signifies a radio that employs model based reasoning to achieve a specified level of competence in radio related domains.

•FCC - A cognitive radio (CR) is a radio that can change its transmitter parameters based on interaction with the environment in which it operates.

•Kolodzy - A cognitive radio has the flexibility and the adaptability to change its operating conditions to its environment (either real or perceived)


Window of Opportunity

Time (min)

Freq

uenc

y (H

z)

Existing spectrum policy forces spectrum to behave like a fragmented disk

Bandwidth is expensive and good frequencies are taken

Unlicensed bands – biggest innovations in spectrum efficiency

Recent measurements by the FCC in the US show 70% of the allocated spectrum is not utilized

Utilization varies 15% ~ 85%

Time scale of the spectrum occupancy varies from msecs to hours


Spectrum Efficiency The limited available spectrum and the inefficiency

Necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically.

Spectrum Sharing Unlicensed bands (WiFi 802.11 a/b/g) Underlay licensed bands (UWB) Opportunistic access to the licensed bands Recycling (exploit the SINR margin of legacy systems) Spatial Multiplexing and Beamforming

Drawbacks of existing techniques: No knowledge or sense of spectrum availability Limited adaptability to spectral environment Fixed parameters: BW, Fc, packet lengths, synchronization, coding, protocols, …

New radio design philosophy: all parameters are adaptive Cognitive Radio Technology


Application ScenariosLicensed network

Secondary markets

Leased network

Third party access in licensed networks

Unlicensed network

Cellular, PCS band

Improved spectrum efficiency

Improved capacity

Public safety band

Voluntary agreements between licensees and third party

Limited QoS

TV bands (400-800 MHz)

Non-voluntary third party access

Licensee sets a protection threshold

Automatic frequency coordination

Interoperability

Co-existence

ISM, UNII, Ad-hoc


Basic Functionalities for CR Networks

Spectrum Sensing Determine which portions of the spectrum is available and detect the

presence of licensed users when a user operate in a licensed band. Without harmful interference with other users.

Spectrum Management Select the best available channel (QoS consideration)

Spectrum Sharing Coordinate access to this channel with other users Fair spectrum scheduling method

Spectrum Mobility Vacate the channel when a licensed user is detected Seamless QoS support during the transition


Two Main Characteristics

Cognitive capability The ability of the radio technology to capture or sense the information from

its radio environment. Capture the temporal and spatial variations in the radio and avoid

interference to other users. Best spectrum selection, appropriate operating parameter decision

Reconfigurability Enables the radio to be dynamically programmed according to the radio

environment. Without any modification on the hardware components

Operating frequency based on radio environment, modulation (adaptive user requirements and channel condition), transmission power to reduce interference

timer and counter values, protocol behaviors, ..


Present and Past Work


Sr# Topic %1 Deployment 9.702 Target tracking 7.273 Localization 6.064 Data gathering 6.065 Routing and aggregation 5.766 Security 5.767 MAC protocols 4.858 Querying and databases 4.249 Time synchronization 3.64

10 Applications 3.3311 Robust routing 3.3312 Lifetime optimization 3.3313 Hardware 2.7314 Transport layer 2.7315 Distributed algorithms 2.7316 Resource-aware routing 2.4217 Storage 2.4218 Middleware and task allocation 2.4219 Calibration 2.1220 Wireless radio and link characteristics 2.1221 Network monitoring 2.1222 Geographic routing 1.8223 Compression 1.8224 Taxonomy 1.5225 Capacity 1.5226 Link-layer techniques 1.2127 Topology control 1.2128 Mobile nodes 1.2129 Detection and estimation 1.2130 Diffuse phenomena 0.9131 Programming 0.9132 Power control 0.6133 Software 0.6134 Autonomic routing 0.30

Publication Statistics in WSN

Table 1. Publication Statistics


Research Topic 1Design Of

Wireless Sensor Node (Mote)


Sensors Microcontroller Actuator

Battery

RF Transceiver

Fig.6. Blocks within a Mote

Blocks within a Mote

33


Sr#

Parameters

weC[1],[2]

Rene [1],[2,[

3]

Dot [1],

[2], [4]

Mica[1], [5]

Telos[2]

Sunspot[3], [6]

TmoteSky

[7], [6], [8]

Waspmote [8]1 Image

2 Computation Technology2.1

Micro-controller

AT90LS8535

Atmega163L

Atmega163L

ATmega103

L

TI-MSP 430

family

AT91RM920T

TI-MSP 430

family

ATmega12812.

2Bus

(Bits)8 16 32 16 8

2.3

Clock Speed (MHz)

4 4 8 8

2.4

Prog. Memory

(KB)

8 16 128 48 4000 48 128

2.5

Data Memory

(KB)

32 32 512 1024 512 10 2 GB SD Card2.

6ADC

Resol.(bits)

10 12 10

3 Wireless Communication Technology3.1

Transceiver

TR1000 CC 2420

CC 24203.2

Frequency (MHz)

868 / 916 2400 868/ 900/ 24003.

3Modulati

onon-off key O-

QPSKDSSS-QPSK3.

4Data Rate

(Kbps)10 40 250 250

4 Power Source and Consumption4.1

Battery Coin Cell, 575 mAh

3V/2850 mAh

3V/2850

mAh

3.3 V -4.2V4.

2Active (mW)

24 100 414.3

Idle (µW)

60

Comparison between Commercial Motes

34

Table 2. Comparison between motes


Sr# Particulars Motes Features Limitations Remarks Proposed Node1 Microcontroller

Clock - 4 MHzRAM / Flash - 512 / 8KClock-8 MHz,RAM / Flash - 1K / 16KClock - 8 MHz Proposed

RAM / Flash - 4K / 128K

Clock - 75 MHzFlash - 256KClock - 8 MHz, ProposedFlash / EEPROM - 10K

2 External Memory2.1 EEPROM 32K WeC, Rene 1, Rene 2, Dot2.2 EEPROM 48K TelosB / Tmote Sky, eyesIFXv2, SHIMMER Proposed

2.3 EEPROM 512KMica, BT Node,Mica2, Mica2Dot, iBadge,CENS Medusa MK2, Nymph, MicaZ, AquisGrain,DSYS25, Ember RF Module, Module, Fleck

2.4 EEPROM 2M Sun Spot3 Radio Transceiver

BW (Kbps) - 10Freq (MHz) - 916.5

BW (Kbps) - 38.4 ProposedFreq (MHz) - 900

BW (Kbps) - 250 ProposedFreq (MHz) - 2400

4 Operating System

4.1 TinyOS

WeC, Rene 1, Rene 2, Dot, Mica, BT Node, Spot ON, Telos, Mica2, Mica2Dot, iMote, Spec, MicaZ, CIT Sensor Node, BSN Node, AquisGrain TelosB / Tmote Sky

Most popular among Motes

Proposed

4.2 Squawk VM (Java) Sun Spot5 Multiple Radio Chip

Not Present Proposed

6 Directional Antenna & MAC Protocol

Not Present Proposed7 On Air programming

WeC Proposed

High Data Rate Applications-Audio ,

Low data Rate

32 bit ( Suitable for Sink)

16 bit ( Lowest power consumption )

Most popular among Motes

3.3

Chipcon CC 1000

Telos, MicaZ, BSN Node, AquisGrain, Pluto, iMote2, XYZ Sensor Node, ProSpeKz II

8 bit

1.3 Atmel Atmega 128LMica, BT Node,Mica2, Mica2Dot, iBadge,CENS Medusa MK2, Nymph, MicaZ, AquisGrain,DSYS25, Ember RF Module, Module, Fleck

1.5 TI MSP430F1611

Chipcon CC 2420

BT Node, Mica2, Mica2Dot, Nymph

3.1

Sun Spot

RFM TR1000 WeC, Rene 1, Rene 2, Dot, Mica

TelosB / Tmote Sky, eyesIFXv2, SHIMMER

1.4 Atmel AT91FR40162S

3.2

1.1 Atmel AT90LS8535 WeC, Rene 1

1.2 Atmel Atmega 163 Rene 2, Dot

Commercial Motes & Proposed Node

35


CO Sensors Microcontroller (ATMEGA 128L)

Current Driver

Battery

RF Transceiver (CC 2520)

Fig.7 Block Diagram of designed Mote

CH4 Sensors

Temp. Sensor

Humidity Sensor

Block Diagram of Designed Mote


Designed node

Fig.8 Designed Node


Research Topic 2Design Of WSN for Gas Sensing

Application


WSN of designed nodes

Fig.9 WSN of designed Nodes


Design of Box for testing gas sensors

Fig.10 Acrylic Box for testing gas sensors


Test Results of Gas Sensors


Methane Gas Sensor MQ4 & Test Circuit

MQ 4

MQ4

1A2H3A 4 B

5 H6 B

0

RL20 K Ohms

Vdc5V

Test Circuit

Fig.11 Methane sensor and its driver circuit


Test Setup for Methane Gas Sensor MQ4

Fig.12 Test set up block diagram and Image


Observations for Methane Gas Sensor

44

Fig.13 Test set up block diagram and Image


Test Results for Carbon Monoxide Gas Sensor


Carbon Monoxide Gas Sensor MQ7 & Test Circuit

MQ 7

Test CircuitAssembled Board Fig.14 Driver circuit for CO sensor


Test Setup for Carbon Monoxide Gas Sensor MQ7

Gas Box (1ft X 1ft X 1.5ft)

Gas Sensor

CO Cylinder

5V Power Supply

Fig.15 Test set up for CO sensor


Observations for Carbon Monoxide Gas Sensor

Gas Concentration in PPM

O/P

in V

olts

Fig.16 Observations for CO sensor


Research Topic 3Fractal Antennas


Introduction to fractals

Any Geometrical shape can be called fractal if it exhibit following properties Self Similarity Infinite Details Non Integer Dimension

For e.g. the cauliflower

50


Introduction to Fractals

•All natural objects are fractals• Natural objects such as a fern cannot be assumed to be a triangular shape and so

we cannot apply Euclidean geometrical rules and axioms to such geometrical shapes

• With respect to Euclid this are rough surface and are more random an out of scale so called as monstrous curves

• But fractal geometry shows a deterministic way to solve this problem. Because every randomness in nature follows a deterministic path.

• For e.g. This fern is a random curve. But more detail if we zoom we can see the repetition of patterns occurs very deterministically.

• So fractal mathematics is a technique to find deterministic characteristics of random phenomenon.

51


Deterministic FractalFamous Cantor rule production geometries

52


SIMULATION CRITERIA

Criteria Geometry: Square, Sierpinski carpet No. of Iterations: 4 Size of Patch: 2.8 x 2.8 cm Simulation Tool: ADS 2009 Simulation Method: MoM Frequency Range: 0 to 10GHz Parameters Under observations:

S11,Gain,Directivity

First four Iteration of Sierpinski carpet antenna

53


S- parameters

2 4 6 80 10

-20

-15

-10

-5

-25

0

Frequency

Mag

. [dB

]

m1

S11

m1freq=dB(patchantnennaIIT_mom..S(1,1))=-21.021Min

5.500GHz

2 4 6 80 10

-10

-8

-6

-4

-2

-12

0

Frequency

Mag

. [dB

]m1

m2

m3m4m5

m6

S11

m1freq=dB(Iteration3_mom..S(1,1))=-11.083Min

8.900GHz

m2freq=dB(Iteration3_mom..S(1,1))=-10.110Valley

6.900GHz


7.600GHz


5.200GHz


4.700GHz


3.500GHz

54


Radiation Characteristics

-80

-60

-40

-20

0 20 40 60 80-100

100

-30

-20

-10

0

10

-40

20

THETA

Mag

. [dB

]

m1m2

m1THETA=10*log10(real(Gain))=9.626Max

-2.000m2THETA=10*log10(real(Directivity))=10.654Max

-2.000

-80-60-40-200 20 40 60 80-100

100

-40

-30

-20

-10

0

-50

10

THETA

Mag

. [dB

]

m1m2

m1THETA=10*log10(real(Gain))=5.513Max

61.000m2THETA=10*log10(real(Directivity))=8.451Max

61.000

55


Antenna Parameters

56


Research Topic 4Localization methods used in WSN


Basic Elements of Localization

Fig. Main components of Localization and their Recognized Techniques


Distance Estimation

Fig. Various Techniques in Distance Estimation


Position Computation

Fig. Various Techniques in Position Computation


Classification of Localization Techniques

Centralized vs Distributed

Anchor-free vs Anchor-based

Range-free vs Range-based

Mobile vs Stationary

According to the ways of Sensors implementation, the current wireless sensor network localization algorithms can be classified into several categories such as:


Localization Categories

Fig. Localization Categories


Algorithm MDS-MAP RSSI-based technique

Simulated Annealing(SA)

Semi-Definite Programming (SDP)

Principle

Computes shortest paths between all pairs of nodes in the region of consideration. Algorithm uses the law of cosines and linear algebra to reconstruct the relative position of the points based on the pair wise distances

Localizes nodes through RF attenuation in Electromagnetic waves.

Localize the sensor nodes in a centralized manner, Method is based on neighborhood information of nodes and it works well in a sensor network with medium to high node density.

Based on LMI (Linear matrix inequality)

Advantages

Does not need anchor or beacon nodes to start with. Works well in situations with low ratios of anchor nodes. High accuracy, Error propagation is low, Low node density, Beacon percentage is low

It is a practical, self- organizing scheme allows addressing any outdoor environments

This algorithm does not propagate error in localization. Gives better accuracy than the semi-definite programming localization.

Its elegance on concise problem formulation, clear model representation, and elegant mathematic solution. High accuracy, Beacon density is medium, error is low.

Limitation/

challenging issues

Requires global information of the network and centralized computation. Computation cost is high, Communication cost is high.

Scheme is power consuming.

When the node density is low, the node is flipped & still maintains the correct neighborhood; the proposed algorithm fails to

All geometric constraints cannot be expressed as LMIs. Precise range data cannot be conveniently represented. Inability to accommodate precise range data.

Summary of Centralized Localization Techniques


Algorithm

Beacon-based

distributed algorithms

Relaxation-based


Coordinate system

stitching based


Hybrid localization algorithms

Interferometric ranging

based localization

Error propagatio

n aware localization

Principle

Estimates distance to reference nodes that may be several hops away.

Nodes estimating their positions with a method such as gradient distance propagation

Localization is originated in a local group of nodes in relative coordinates. By gradually merging such local maps, it achieves entire network localization in global coordinates.

Two orthogonal techniques tailored and combined into a powerful hybrid localization algorithm (RH+).

radio waves emitted from two locations at slightly different frequencies to obtain the necessary ranging information

Integrates the path loss and distance measurement error model

Advantages

Computation & Communication cost is low.

Fully distributed & concurrent. Operate without beacons

No global resources or communications are needed. Beacon % is low.

Reduce communication & computation cost. Robustness & more accurate.

Gives precise measurements than other common techniques

Precise estimation than other localization schemes

Limitations/

challenging issues

Accuracy is low, Node, Beacon % & Error propagation is high.

Susceptible to local minima, Techniques are quite sensitive to initial starting

Convergence may take some time and that nodes with high mobility may be hard to cover. Low accuracy, High

It does not perform well when there are only few anchors.

Requires considerably larger set of measurement which limits their solution to smaller

Estimation cost is high.

Summary of Distributed Localization Techniques


Proposed scheme for node design using multiple directional antennas for Localization

Radiation pattern

Node

Patch Antenna

Energy efficiency due to directivity in radiation

Directional Microstrip Patch Antennas are made – testing is in progress

Node is designed for switching up to six directional antennas for each node

Localization algorithm is under development

Directional MAC layer to be developed


References for WSN section• I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E.Cayirci, Wireless sensor networks: a survey, Computer Networks, Elsevier, 2002.• Chee-Yee Chong, Srikanta P. Kumar, Sensor Networks:Evolution, Opportunities, and Challenges, Proceedings of the IEEE, vol. 91, No. 8,

August 2003.• Raja Bose, Sensor Networks—Motes, Smart Spaces, and beyond, Pervasive Computing, IEEE CS, July – September 2009.• Michael Healy, Thomas Newe and Elfed Lewis, Wireless sensor node hardware: a review, IEEE Sensors 2008 Conference.• Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal, Wireless sensor network survey, Elsevier Computer Networks 52 (2008), pp. 2292–2330.• Jerome P. Lynch and Kenneth J. Loh, A summary review of wireless sensors and sensor networks for structural health monitoring, The Shock

and Vibration Digest, Vol. 38, No. 2, March 2006, pp. 91–128. • Alan Mainwaring, Joseph Polastre, Robert Szewczyk, David Culler, John Anderson, Wireless sensor networks for habitat monitoring, WSNA’02,

September 28, 2002, Atlanta, Georgia, USA.• G. Werner-Allen, J. Johnson, M. Ruiz, J. Lees, and M. Welsh, “Monitoring volcanic eruptions with a wireless sensor network,” in Proceedingsof

the Second European Workshop on Wireless Sensor Networks (EWSN’05), Jan. 2005.• Puccinelli, D.; Haenggi, M, Wireless sensor networks: applications and challenges of ubiquitous sensing, Circuits and Systems Magazine,

IEEE, Volume: 5, Issue: 3, Publication Year: 2005, pp. 19 – 31.• Jason L. Hill, David E. Culler, MICA: a wireless platform for deeply embedded networks, IEEE Micro, November December 2002.• Joseph Polastre, Robert Szewczyk, and David Culler, Telos: enabling ultra-low power wireless research, Fourth International Symposium on

Information Processing in Sensor Networks, 2005.• Jason Hill, Mike Horton, Ralph Kling, and Lakshman Krishnamurthy, The platforms enabling wireless sensor networks, Communications of the

ACM – Wireless sensor networks, Vol. 47, Issue 6, June 2004, pp. 41-46.• Vini Madan and SRN Reddy, Review of wireless sensor mote platforms, VSRD International Journal of Electrical, Electronics & Comm.

Engg.,vol.2, 2012.• Jan Beutel, Metrics for sensor network platforms, REALWSN ’06 Uppsala, Sweden, ACM, 2006. [15] Ana-Beleń Garcıá-Hernando, Jose´-

Fernań Martıńez-Ortega, Juan-Manuel Lo´pez-Navarro, Aggeliki Prayati, Luis Redondo-Lo´ pez, MsC, Problem solving for wireless sensor networks, Springer.

• http://www.libelium.com/products/waspmote• Thang Vu Chien, Hung Nguyen Chan and Thanh Nguyen Huu, “A comparative study on hardware platforms for wireless sensor networks”,

International Journal on Advanced Science Engineering Information Technology, vol. 2, (2012) No. 1.• Wei Dong, Xue Liu, Providing OS support for wireless sensor, networks: challenges and approaches, IEEE communications surveys & tutorials,

vol. 12, N.. 4, fourth quarter 2010. • Muhammad Omer Farooq and Thomas Kunz “Operating systems for wireless sensor networks: a survey”, Sensors 2011.


References for Fractal Antenna Section


• Amitangshu Pal, Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges, Network Protocols and algorithms, Microthink Institute, Vol. 2, No. 1.

• Zheng Yang, Localization and Localizability in Sensor and Ad-hoc Networks, Ph.D. Thesis, Hong Kong University of Science & Technology, June 2010.

• Can Basaran. A hybrid localization algorithm for wireless sensor networks, Masters Thesis, Yeditepe University, 2007.

• Azzedine Boukerche, Horacio A. B. F. Oliveira, Eduardo F. Nakamura and FUCAPI Antonio A. F. Loureiro, Secure Localization

• Algorithms for Wireless Sensor Networks, IEEE Communications Magazine, April 2008.

• Poorya Ghafoorpoor Yazdi, ppt on “Localization in Wireless Sensor Networks”.

• Lina M. Pestana, An Analysis of Localization Problems and Solutions in Wireless Sensor Networks.

References for Localization Section


New Research Direction:WirelessNetworks and Game Theory


Motivation

Some key Issues to be addressed for a Wireless Networks. To realize spectrum sharing throughout the network. To increase the efficiency of wireless channel. To achieve the maximum energy/power efficiency. To prolong the networks lifetime. To maintain the overall balance networks. To model the networks dynamics under the various

conditions. To implemnet a intrude free network.


MotivationTwo approaches to answer key issues

Traditional Approach Optimization of an individual performance Selfish Behavior Overall Networks performance degrades Not good for cross layer design concept

Game Theoretic Approach Improves the overall networks performance Could be very useful for cross layer design concept Remove/reduce the selfish behavior Best approach to model de-centralized entity without full

information of network conditions Obtain an optimization for whole network rather than an

individual


Game Theory BasicsApplication of game theory


Game Theory BasicsWhat is Game Theory ?

Game theory provides a mathematical basis for the analysis of interactive decision-making process between the nodes (players). It provides tools for predicting what might happen when nodes (players) with conflicting interests interact.

A game is madeup of three basic components

A set of players A set of actions A set of preferences


Game Theory Basics5 basic assumptions

1. Each player has two or more well-specified moves/strategies.

2. Every player has possible combinations of moves/strategy that leads to an optimum response (End-state like win, loss or draw) in a given game.

3. Each player has a specified payoff for each optimum response.

4. Each player has perfect knowledge of the game and his/her opponent; that is, player knows the rules of the game as well as the payoffs of all other players.

5. All players are rational; that is, each player, given the two moves/strategies, will choose that one that gives him/her the better payoff.


Game Theory Basics

Game Players Strategies Payoff Matrix form Extensive form


Game Theory Basics

Nash Equilibrium: A Nash equilibrium, also called strategic equilibrium, is a set of strategies, one for each player, such that no player can unilaterally change his/her strategy and get a better payoff.

Pareto Efficiency: An outcome of a game is Pareto efficient if there is no other outcome that makes every player at least as well off and at least one player strictly better off. That is, a Pareto Optimal outcome cannot be improved upon without hurting at least one player. Often, a Nash Equilibrium is not Pareto efficient implying that the players' payoffs can all be increased.


Wireless Networks Games

Characteristics of Wireless Networks

Self-Configuration Multi-hop Networks Completely Distributed Energy Constrained Mobile Network Attack/Hacking Proof Network Highly Reliable


Wireless Networks GamesThe classification of the games according to protocol layers in a given Wireless Network



Physical Layer Game Power Control game Waveform adaptation Game

Research Challenges Mobile/Ad-hoc based networks Reduce additional signaling cost Stability analysis Efficient mechanism design, etc.


Wireless Networks Games-MAC Layer Many nodes contending for access to a shared

communication medium . Selfish nodes seek to maximizing their utility by

obtaining an unfair of access to the channel.

Where,p1 and p2 are nodes. r1 and r2 are communication range of p1 and p2, respectively. Q and T represents quite and transmit, respectively. c represents cost of transmitting a packet.

Outcomes of multiple access gameThe Multiple access game scenario


Wireless Networks Games-MAC Layer

Research Challenges at MAC layer More research needed for the imperfect

information model with feedback. Specially, scheduled access problems such as

channel or time-slot assignment have to be addressed for wireless networks


Wireless Networks Games-Network Layer The presence of selfish nodes in a network. The effects of different node behavior on routing .

Forwarder’s Dilemma:

The Forwarder's Dilemma game Outcomes of forwarder's game


Wireless Networks Games-Network Layer

Joint Packet Forwarding Game:

The Joint Packet Forwarding game Outcomes of joint packet forwarding game

Where,p1 and p2 are nodes. r1 and r2 are receivers. F and D represents forward and drop, respectively. c represents cost of transmitting a packet.


Wireless Networks Games-Network Layer Network could be collapse Trust management needed Co-operation needed among the nodes Nesh equilibrium should be reach

* *There is a good amount of literature available on routing formulation based on following mechanisms

Incentive mechanism Credit exchange Reputation mechanism Barter System

**Due to space limitation a complete list of citations is omitted, however interested reader can get it by dropping a mail to author



- reputation in social NetworksGeneral Networks

-spectrum sharing in cellular Networks - selfish behavior in CSMA/CA - reputation-based Wi-Fi development

Cellular and Wi-Fi Networks(WWANs and WLANs)

- multi-radio channel allocation-IEEE 802.22 Working Group Cognitive radio

-cooperative packet forwardingSensor Networks

-incentives for cooperationHybrid ad hoc Networks

[4]

- cooperation without incentives - incentives for cooperation: currency- incentives for cooperation: reputation system

Ad-hoc Networks

ReferencesThe Proposed work/solutionSubject

Related Works/Previous Research Results



Advantages of applying game theory to wireless networks Analysis of distributed systems Cross layer optimization Design of incentive schemes Fully exploits the available frequency bands Prolong the resource contained network’s life time Maintain the balance of overall networks Model the networks dynamics under various

conditions



Challenges in application of game theory to wireless networks Assumption of rationality Realistic scenarios require complex model Choice of utility functions Mechanism design Incomplete information scenarios Mapping variables in the game Trust management


Summary

Game Theory is a very useful tool to analyze a wireless network at different protocol layers.

Game theoretic approach to a wireless network could be a good solution to cross-layer optimizations for wireless networks.

Game theoretic approach to a wireless network is still at a nascent stage, with the bulk of the work done in the past few years.


References

1. J. Miller,Game Theory at Work, How to use Game Theory to Outthink and Out maneuver Your Competition, McGraw-Hill, 2002.

2. M. Felegyhazi and J.-P. Hubaux“Game Theory in Wireless Networks: A Tutorial,” EPFL technical report, LCA-REPORT-2006-002, February, 2006.

3. S.Mehta and K.S.Kwak, “ Game Theory and Networks,” Technical Report-3, UWB Research Center, Inha University,2007.

4. S.Mehta and K.S.Kwak, “ Game Theoretic approach to Cognitive Radio based Tactical Maneuvering Networks”, Project Proposal, UWB Research Center, Inha University, 2007/8.


Introduction

Mathematical Modeling Simulation Works Practical/test-bed Work


Motivation


System and Network Tools

Networks NS-2 GloMoSim J-SIM OMNnet++ OPNET QualNet

System MATLAB Monte Carlo based Simulation tools


Comparison


Analysis


Summary


Reference

“A Case Study of Networks Simulation Tools for Wireless Networks,” S. Mehta

et al., in proceeding of AMS 2009.Indonesia.


Thank You !

Questions [email protected]

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