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Several thousand nodes Nodes are tens of feet of each other Densities as high as 20 nodes/m3. Sink. Internet, Satellite, etc. Sink. Task Manager. SENSOR NETWORKS ARCHITECTURE. I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci,

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sensor networks architecture
Several thousand nodes

Nodes are tens of feet of each other

Densities as high as 20 nodes/m3

Sink

Internet, Satellite, etc

Sink

Task

Manager

SENSOR NETWORKS ARCHITECTURE
  • I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci,

“Wireless Sensor Networks: A Survey”,Computer Networks (Elsevier) Journal, March 2002.

slide3

Location Finding System

Mobilizer

Transceiver

Sensor ADC

Processor

Memory

Power Generator

Power Unit

SENSOR NODE HARDWARE

Small

Low power

Low bit rate

High density

Low cost (dispensable)

Autonomous

Adaptive

SENSING UNIT

PROCESSING UNIT

mica motes bwn lab @ gatech
MICA MotesBWN Lab @ GaTech

Processor and Radio platform (MPR300CB) is based on Atmel ATmega 128L low power Microcontroller that runs TinyOs operating system from its internal flash memory.

slide5

UC Berkeley:

Smart Dust

UCLA: WINS

UC Berkeley: COTS Dust

JPL: Sensor Webs

Rockwell: WINS

Examples for Sensor Nodes

slide6

Rene Mote

Dot Mote

Mica node

weC Mote

Examples for Sensor Nodes

sensor networks features
SENSOR NETWORKS FEATURES
  • APPLICATIONS:

Military, Environmental, Health, Home, Space Exploration,

Chemical Processing, Disaster Relief….

  • SENSOR TYPES:

Seismic, Low Sampling Rate Magnetic, Thermal, Visual, Infrared,

Acoustic, Radar…

  • SENSOR TASKS:

Temperature, Humidity, Vehicular, Movement, Lightning Condition,

Pressure, Soil Makeup, Noise Levels, Presence or Absence of Certain

Types of Objects, Mechanical Stress Levels on Attached Objects,

Current Characteristics (Speed, Direction, Size) of an Object ….

factors influencing sensor network design
Factors Influencing Sensor Network Design

A. Fault Tolerance (Reliability)

B. Scalability

C. Production Costs

D. Hardware Constraints

E. Sensor Network Topology

F. Operating Environment

G. Transmission Media

H. Power Consumption

sensor networks communication architecture

Application Layer

Transport Layer

Task Management Plane

Mobility Management Plane

Network Layer

Power Management Plane

Data Link Layer

Physical Layer

Sensor Networks Communication Architecture

Used by sink and all sensor nodes

Combines power and routing awareness

Integrates data with networking protocols

Communicates power efficiently through

wireless medium and

Promotes cooperative efforts.

why can t ad hoc network protocols be used here
WHY CAN’T AD-HOC NETWORK PROTOCOLS BE USED HERE?
  • Number of sensor nodes can be several orders of magnitude higher
  • Sensor nodes are densely deployed and are prone to failures
  • The topology of a sensor network changes very frequently due to node mobility and node failure
  • Sensor nodes are limited in power, computational capacities, and memory
  • May not have global ID like IP address.
  • Need tight integration with sensing tasks.
slide11

APPLICATON LAYER

SMP: Sensor Managament Protocol

System Administrators interact with Sensors using SMP.

TASKS:

  • Moving the sensor nodes
  • Turning sensors on and off
  • Querying the sensor network configuration and the status of

nodes and re-configuring the sensor network

  • Authentication, key distribution and security in data

communication

  • Time-synchronization of the sensor nodes
  • Exchanging data related to the location finding algorithms
  • Introducing the rules related to data aggregation,

attribute-based naming and clustering to the sensor nodes

slide12

APPLICATON LAYER

(Query Processing)

Users can request data from the network-> Efficient Query Processing

User Query Types:

1. HISTORICAL QUERIES:

Used for analysis of historical data stored in a storage area (PC),

e.g., what was the temperature 2 hours back in the NW quadrant.

2. ONE TIME QUERIES:

Gives a snapshot of the network, e.g., what is the current temperature in the NW quadrant.

3. PERSISTANT QUERIES:

Used to monitor the network over a time interval with respect to some parameters, e.g., report the temperature for the next 2 hours.

slide13

APPLICATON LAYER

Sensor Query and Tasking Language (SQTL):

(C-C Shen, et.al., “Sensor Information Networking Architecture and Applications”, IEEE Personal Communications Magazine, pp. 52-59, August 2001.)

  • SQTL is a procedural scripting language.
  • It provides interfaces to access sensor hardware:

- getTemperature, turnOn

for location awareness:

- isNeighbor, getPosition

and for communication:

- tell, execute.

slide14

APPLICATON LAYER

Sensor Query and Tasking Language (SQTL):

  • By using the upon command, a programmer can create an event handling block for three types of events:

- Events generated when a message is received by a sensor node,

- Events triggered periodically,

- Events caused by the expiration of a timer.

  • These types of events are defined by SQTL keywordsreceive, every and expire, respectively.
slide15

Simple Abtract Querying Example

Select [ task, time, location, [distinct | all], amplitude,

[[avg | min |max | count | sum ] (amplitude)]]

from [any , every , aggregate m]

where [ power available [<|>] PA |

location [in | not in] RECT |

tmin < time < tmax |

task = t |

amplitude [<|==|>] a ]

group by task

based on [time limit = lt | packet limit = lp |

resolution = r | region = xy]

data centric query
Data Centric Query
  • Attribute-based naming architecture
  • Data centric protocol
  • Observer sends a query and gets the response from valid sensor node
  • No global ID
slide17

APPLICATON LAYER

Task Assignment and Data Advertisement Protocol

INTEREST DISSEMINATION

* Users send their interest to a sensor node,

a subset of the nodes or the entire network.

* This interest may be about a certain attribute

of the sensor field or a triggering event.

ADVERTISEMENT OF AVAILABLE DATA

* Sensor nodes advertise the available data to

the users and the users query the data which

they are interested in.

slide18

APPLICATON LAYER

Sensor Query and Data Dissemination Protocol

Provides user applicatons with interfaces to issue

queries, respond to queries and collect incoming

replies.

These queries are not issued to particular nodes, instead

ATTRIBUTE BASED NAMING (QUERY)

“The locations of the nodes that sense temperature

higher than 70F”

LOCATION BASED NAMING (QUERY)

“Temperatures read by the nodes in region A”

slide19

71

75

68

67

66

71

71

71

68

69

Interest Dissemination

Interest dissemination is performed to assign the sensing tasks to the sensor nodes.

Either sinks broadcast the interest or sensor nodes broadcast an advertisement for

the available data and wait for a request from the sinks.

Sink

Query:

Sensor nodes that read >70oF temperature

slide20

68

67

66

Sink

71

71

68

71

69

Data Aggregation (Data Fusion)

The sink asks the sensor nodes to report certain conditions.

Data coming from multiple sensor nodes are aggregated.

71

75

Query:

Sensor nodes that read >70oF temperature

slide21

Location Awareness

(Attribute Based Naming)

71

75

68

67

66

71

71

71

68

69

Query an Attribute

of the sensor field

Region A

Sink

Region C

Region B

Query:

Temperatures read by the nodes in Region A

Important for broadcasting,

multicasting, geocasting and anycasting

slide22

APPLICATON LAYER RESEARCH NEEDS

Sensor Network Management Protocol

Task Assignment and Data Advertisement Protocol

Sensor Query and Data Dissemination Protocol

Sophisticated GUI

(Graphical User Interface) Tool

slide23

Sink

TRANSPORT LAYERReliable Multi-Segment Transport (RMST)

F. Stann and J. Heidemann, “RMST: Reliable Data Transport in Sensor Networks,”In Proc. IEEE SNPA’03, May 2003, Anchorage, Alaska, USA

RMST provides end-to-end data-packet

transfer reliability

Each RMST node caches the packets

When a packet is not received before the

so-called WATCHDOG timer expires, a

NAK is sent backward

The first RMST node that has the required

packet along the path retransmits the

packet

RMST relies on Directed Diffusion scheme

RMST Node

Source Node

slide24

Transport Layer PSFQ - Pump Slowly Fetch QuicklyC. Y. Wan, A. T. Campbell and L. Krishnamurthy, “PSFQ: A Reliable Transport Protocol for Wireless Sensor Networks,” In Proc. ACM WSNA’02, September 2002, Atlanta, GA

  • Packets are injected slowly into the network
  • Aggressive hop-by-hop recovery in case of packet losses
  • “PUMP” performs controlled flooding and requires each intermediate node to create and maintain a data cache to be used for local loss recovery and in-sequence data delivery.
  • Applicable only to strict sensor-sensor guaranteed delivery
  • And for control and management of the end-to-end reliability for the downlink from sink to sensors
  • Does not address congestion control
related work
Related Work
  • Wireless TCP variants are NOT suitable for sensor networks
    • Different notion of end-to-end reliability
    • Huge buffering requirements
    • ACKing is energy draining
  • BOTTOMLINE: Traditional end-to-end guaranteed reliability (TCP solutions) cannot be applied here.

 New Reliability Notion is required!!!

reliable event transport in wsn
Reliable EVENT Transport in WSN
  • NEW NOTION: Reliably Detect/Estimate EVENT features from COLLECTIVE information
  • Challenges:
    • Significant energy and processing constraints, multi-hop ad hoc communication
    • Network congestion

Need to address Congestion Control

and Reliability in Sensor Networks !

event to sink reliability

Event Radius

Sink

Sensor nodes

Event-to-Sink Reliability

O. B. Akan, I. F. Akyildiz, and Y. Sankarasubramaniam, “ESRT:Event-to-Sink Reliable Transport in Wireless Sensor Networks,”in Proceedings of ACM MOBIHOC 2003,pp. 177-188, Annapolis, Maryland, USA, June 2003.

Also to appear in IEEE/ACM Transactions on Networking,2004.

  • Sensor networks are event-driven
  • Multiple correlated data flows from event to sink
  • Goal is to reliably detect/estimate event features from collective information
  • Necessitates event-to-sink collective reliability notion
event to sink reliability28

Event Radius

Sink

Sensor nodes

Event-to-Sink Reliability
  • Sink decides about event features every  time units
  • Observed event reliability Di , the DISTORTION observed in event estimation in the decision interval i at the sink
  • Desired event reliabilityD* ,the desired event estimation distortion level for reliable event detection
    • Application specific, known a priori at the sink
  • Normalized reliability i =D*/Di
  • Reporting rate f packet transmissions rate at source nodes
esrt protocol overview
ESRTProtocol Overview
  • Determine reporting frequency f to achieve desired reliability D* with minimum resource utilization
  • Source (Sensor nodes):
    • Send data with reporting frequency f
    • Monitor buffer level and notify congestion to the sink
  • Sink:
    • Measures the observed event reliabilityDiat the end of decision interval i
    • Performs congestion decision based on the feedback from

the sources nodes (to determine f >< fmax).

    • Updates f based on i=D*/Diand f >< fmax(congestion) to achieve desired event reliabilityD*
esrt congestion detection mechanism

B

a f

bk

bk-1

Db

Event

ID

CN

(1 bit)

Time

Stamp

Destination

Payload

FEC

ESRT Congestion Detection Mechanism
  • ACK/NACK not suitable
  • We use local buffer level monitoring in sensor nodes

bk : Buffer fullness level at the end of reporting interval k

Db : Buffer length increment

B : Buffer size

f : reporting frequency

  • Mark CN field in packet if congested
esrt performance
ESRT Performance

S0 = (NC,LR)

S0 = (NC,HR)

S0 = (C,LR)

S0 = (C,HR)

slide34

NETWORK LAYER

(ROUTING BASIC KNOWLEDGE)

The constraints to calculate the routes:

1. Additive Metrics: Delay, hop count, distance, assigned costs (sysadmin preference),

average queue length...2. Bottleneck Metrics: Bandwidth, residual capacity and other bandwidth related metrics.

REMARK:

All routing algorithms are based on the same principle used as in Dijkstra\'s,

which is used to find the minimum cost path from source to destination.

Dikstra and Bellman solve the SHORTEST PATH PROBLEM…

RIP (Distant Vector Algorithm) -> Bellman/Ford Algorithm

OSPF (Open Shortest Path Algorithm)  Dikstra Algorithm

slide35

Routing Algorithms Constraints Regarding

Power Efficiency (Energy Efficient Routing)

E (PA=1)

F (PA=4)

Maximum power available (PA) route

Minimum hop route

Minimum energy route

Maximum minimum PA node

route (Route along which the

minimum PA is larger than the

minimum PAs of the other routes

is preferred, e.g., Route 3 is the

most efficient; Route 1 is the

second).

D (PA=3)

T

Sink

A (PA=2)

B (PA=2)

C (PA=2)

Route 1: Sink-A-B-T (PA=4)

Route 2: Sink-A-B-C-T (PA=6)

Route 3: Sink-D-T (PA=3)

Route 4: Sink-E-F-T (PA=5)

slide36

Why can’t we use conventional

routing algorithms here?

Global (Unique) addresses, local addresses.

Unique node addresses cannot be used in many sensor networks

  • sheer number of nodes
  • energy constraints
  • data centric approach

Node addressing is needed for

  • node management
  • sensor management
  • querying
  • data aggregation and fusion
  • service discovery
  • routing
slide37

NETWORK LAYER

(ROUTING for SENSOR NETWORKS)

Important considerations:

  • Sensor networks are mostly data centric
  • An ideal sensor network has attribute based addressing and location awareness
  • Data aggregation is useful unless it does not hinder collaborative effort
  • Power efficiency is always a key factor
some concepts
Some Concepts
  • Data-Centric
    • Node doesn\'t need an identity
      • What is the temp at node #27 ?
    • Data is named by attributes
      • Where are the nodes whose temp recently exceeded 30 degrees ?
      • How many pedestrians do you observe in region X?
      • Tell me in what direction that vehicle in region Y is moving?
  • Application-Specific
    • Nodes can perform application specific data aggregation, caching and forwarding
slide39

Taxonomy of Routing Protocols

for Sensor Networks

Categorization of Routing Protocols for Wireless Sensor Networks:

(K. Akkaya, M. Younis, “A Survey on Routing Protocols for Wireless Sensor Networks,” Elsevier AdHoc Networks, 2004)

1. Data Centric Protocols

Flooding, Gossiping, SPIN,SAR(Sequential Assignment

Routing), Directed Diffusion, Rumor Routing, Gradient Based

Routing, Constrained Anisotropic Diffused Routing, COUGAR,

ACQUIRE

2. Hierarchical

LEACH, TEEN (Threshold Sensitive Energy Efficient Sensor Network Protocol),

APTEEN, PEGASIS, Energy Aware Scheme

3. Location Based

MECN, SMECN (Small Minimum Energy Com Netw), GAF

(Geographic Adaptive Fidelity), GEAR

conventional approach flooding

B

D

G

C

A

E

F

Conventional ApproachFLOODING

Broadcast data to all neighbor nodes

slide41

ROUTING ALGORITHMS

Gossiping

GOSSIPING:

Sends data to one randomly selected neighbor.

Example:

slide42

Problems of

Flooding and Gossiping

PROBLEMS:

Although these techniques are simple and reactive, they have some disadvantages including:

* Implosion

(NOTE: Gossiping avoids this by selecting only one node; but this causes delays to

propagate the data through the network)

* Overlap

* Resource Blindness

* Power (Energy) Inefficient

problems

(a)

(a)

A

Implosion

B

A

C

B

(a)

(a)

D

C

q

s

(r,s)

(q,r)

Problems

Data Overlap

r

  • Resource Blindness

No knowledge about the available power of resources

gossiping
Gossiping
  • Uses randomization to save energy

Selects a single node at random and sends the data to it

  • Avoids implosions
  • Distributes information slowly
  • Energy dissipates slowly
the optimum protocol

B

D

G

C

A

E

F

The Optimum Protocol
  • “Ideal”
    • Shortest-path routes
    • Avoids overlap
    • Minimum energy
    • Need global topology information
slide46

SPIN: Sensor Protocol for Information via Negotiation(W.R. Heinzelman, J. Kulik, and H. Balakrishan, “Adaptive Protocols for Information Dissemination in Wireless Sensor Networks”,Proc. ACM MobiCom’99, pp. 174-185, 1999 )

  • Two basic ideas:
      • Sensors communicate with each other about the data that they already have and the data they still need to obtain
          • to conserve energy and operate efficiently
          • exchanging data about sensor data may be cheap
      • Sensors must monitor and adapt to changes

in their own energy resources

slide47
SPIN

Good for disseminating information to all sensor nodes.

SPIN is based on data-centric routing where the sensors broadcast an

advertisement for the available data and wait for a request from

interested sinks

1.

1. ADV

2. REQ

3. DATA

2.

3.

slide48

ADV

DATA

REQ

ADV

REQ

DATA

SPIN

slide49

ROUTING ALGORITHM

(DIRECTED DIFFUSION)

(C. Intanagonwiwat, R. Gowindan and D. Estrin, “Directed Diffusion: A Scalable and Robust

Communication Paradigm for Sensor Networks”, Proc. ACM MobiCom’00, pp. 56-67, 2000.)

  • This is a DATA CENTRIC ROUTING scheme!!!!
  • The idea aims at diffusing data through sensor nodes by using

a naming scheme for the data.

  • The main reason behind this is to get rid off unnecessary

operation of routing schemes to saveEnergy.

Also Robustness and Scaling requirements need to be considered.

slide50

Gradient Setup

Data Delivery

Interest Propagation

Directed Diffusion

Source

Sink

directed diffusion

source

sink

Directed Diffusion

Features

Sink sends interest, i.e., task descriptor, to all sensor nodes.

Interest is named by assigning attribute-value pairs.

source

source

sink

sink

Interest Propagation

Gradient Setup

Data Delivery

Drawbacks

Cannot change interest unless a new interest is broadcast.

slide52

LEACH

Low Energy Adaptive Clustering Hierarchy (LEACH)

(W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,\'\' IEEE Proceedings of the Hawaii International Conference on System Sciences, pp. 1-10, January, 2000.)

  • * LEACH is a clustering based protocol which minimizes energy dissipation

in sensor networks.

Idea:

* Randomly select sensor nodes as cluster heads, so the high energy

dissipation in communicating with the base station is spread to all sensor

nodes in the sensor network.

* Forming clusters is based on the received signal strength.

* Cluster heads can then be used kind of routers (relays) to the sink.

leach
LEACH
  • Optimum Number of Clusters ---????????

- too few: nodes far from cluster heads

    • too many: many nodes send data to SINK.
slide54

Other Protocols

1. Energy Aware Routing

R. Shah, J. Rabaey, “Energy Aware Routing for Low Energy Ad Hoc Sensor

Networks,” IEEE WCNC’02, Orlando, March 2002.

2. Rumor Routing

D. Braginsky, D. Estrin, “Rumor Routing Algorithm for Sensor Networks,”

ACM WSNA’02, Atlanta, October 2002.

3. Threshold sensitive Energy Efficient sensor Network (TEEN)

A. Manjeshwar, D.P. Agrawal, “TEEN: A Protocol for Enhanced Efficiency in

Wireless Sensor Networks,” IEEE WCNC’02, Orlando, March 2002.

4. Constrained Anisotropic Diffusion Routing (CADR)

M. Chu, H.Hausecker, F. Zhao, “Scalable Information-Driven Sensor Querying

and Routing for Ad Hoc Heterogeneous Sensor Networks,” International Journal

of High Performance Computing Applications, Vol. 16, No. 3, August 2002.

slide55

Other Protocols

5. Power Efficient Gathering in Sensor Information Systems

(PEGASIS)

S. Lindsey, C.S. Raghavendra, “PEGASIS: Power Efficient Gathering in Sensor

Information Systems,” IEEE Aerospace Conference, Montana, March 2002.

6. Self Organizing Protocol

L. Subramanian, R.H. Katz, “An Architecture for Building Self Configurable

Systems,” IEEE/ACM Workshop on Mobile Ad Hoc Networking and

Computing, Boston, August 2000.

7. Geographic Adaptive Fidelity (GAF)

Y. Yu, J. Heideman, D. Estrin, “Geography-informed Energy Conservation for

Ad Hoc Routing,” ACM MobiCom’01, Rome, July 2001.

open research issues
Open Research Issues
  • Store and Forward Technique

that combines data fusion and aggregation.

  • Routing for Mobile Sensors

Investigate multi-hop routing techniques for

high mobility environments.

  • Priority Routing

Design routing techniques that allow different priority

of data to be aggregated, fused, and relayed.

  • 3D Routing
medium access control mac in wsn
Medium Access Control (MAC) in WSN
  • IEEE 802.11 [1]
    • Originally developed for WLANs
    • Per-node fairness
    • High energy consumption due to idle listening
  • S-MAC [2]
    • Aims to decrease energy consumption by sleep schedules with virtual clustering
    • Redundant data are still sent with increased latency due to sleep schedules

[1] IEEE 802.11, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications,” 1999

[2] W. Ye, J. Heidemann and D. Estrin, “An Energy Efficient MAC Protocol for Wireless Sensor Networks,” In Proc. ACM MOBICOM ’01, pp.221 –235, Rome, Italy 2001

related work58
Related Work
  • TRAMA[3]
    • Based on time-slotted structure
    • Information about every two-hop neighbor is used for slot selection
    • High signaling overhead for high density networks
    • High latency due to time-slotted structure

[3] V. Rajendran, K. Obraczka, and J. J. Garcia-Luna-Aceves, “Energy-Efficient, Collision-Free Medium Access Control for Wireless Sensor Networks,” in Proc. ACM SenSys 2003, Los Angeles, California, November 2003.

mac for sensor networks
MAC for Sensor Networks
  • WSN are characterized by dense deployment of sensor nodes
  • MAC Layer Challenges
    • Limited power resources
    • Need for a self-configurable, distributed protocol
    • Data centric approach rather than per-node fairness

Exploit spatial correlation to reduce transmissions in MAC layer !

collaborative mac cmac protocol
Collaborative MAC (CMAC) Protocol

M.C. Vuran, and I. F. Akyildiz, “Spatial Correlation-based Collaborative Medium Access Control in Wireless Sensor Networks,”submitted for publication, Nov. 2003.

  • If a node transmits data then all its correlation neighbors have redundant information
  • Route-thru data has higher priority over generated data

Filter out transmission of redundant data and prioritize filtered data through the network!

collaborative mac cmac protocol61
Collaborative MAC (CMAC) Protocol
  • Source function: Transmit event information
  • Router function: Forward packets from other nodes in the multi-hop path to the sink
  • Two components
    • Event MAC (E-MAC)
    • Network MAC

(N-MAC)

node selections
Node Selections
  • Choose representative nodes such that
    • They are located as close to the event source as possible
    • They are located as farther apart from each other as possible.
cmac performance
CMAC Performance

Medium Access Delay

Packet Drop Rate

cmac performance64
CMAC Performance

Avg. Energy Consumption

conclusions
Conclusions
  • Spatial correlation in sensor networks is exploited on both Transport and MAC layers
  • Redundant transmissions are suppressed
  • Number of transmissions are reduced instead of number of transmitted bits
  • Both protocols achieve low energy consumption
research needs for sensor networks
Research Needs for Sensor Networks
  • An Analytical Framework for Sensor Networks

 Find a Basic Generic Architecture and Protocol

development which can be tailored to specific

applications.

  • More theoretical investigations of the Architecture and

Protocol developments

  • Follow the TCP/IP Stack, i.e., keep the Strict Layer

Approach ???

  • Cross Layer Optimization
  • Explore both Spatial-Temporal Correlations for

Protocol development

further open research issues
FURTHER OPEN RESEARCH ISSUES
  • Research to integrate WSN domain into NGWI (Next Generation Wireless Internet)

e.g., interactions of Sensor and AdHoc Networks or Sensor and Satellite or any other combinations…

  • Explore the SENSOR/ACTOR NETWORKS
  • Explore the SENSOR-ANTISENSOR NETWORKS
need for realistic applications
Need for Realistic Applications
  • Clear Demonstration of Testbeds and Realistic Applications
  • Not only data or audio but also video 

Overall I  Integrated Traffic.

SOME OF OUR EFFORTS IN BWN LAB @ GaTech

  • MAN  for Meteorological Observations
  • SpINet  for Mars Surface
  • Airport Security  Sensors/Actors
  • Sensor Wars
  • Wide Area Multi-Campus Sensor Network
medium access control mac further research needs
MEDIUM ACCESS CONTROL (MAC) FURTHER RESEARCH NEEDS

MAC for sensor networks must have inbuilt power

management, mobility management and failure recovery

strategies

Need for a self-configurable, distributed protocols

Data centric approach rather than per-node fairness

Develop MACs which differentiate Multimedia Traffic

Exploit Spatial & Temporal Correlation

error control
Error Control

Some sensor network applications like mobile tracking

require high data precision

Coding gain is generally expressed in terms of the additional

transmit power needed to obtain the same BER without coding

FEC is preferred over ARQ

Since power consumption is crucial, we must take into

account encoding and decoding energy expenditures

Coding is profitable only if the encoding and decoding

power consumption is less than the coding gain.

error control research needs
ERROR CONTROL RESEARCH NEEDS
  • Design of suitable FEC codes with minimal encoding

and relatively higher decoding complexities

  • Feasibility of ARQ schemes in multihop sensor networks

(hop by hop ARQ instead of end-to-end). This is needed for

reliable communications (data critical)

  • Adaptive/Hybrid FEC/ARQ schemes
  • Extension to Rayleigh/Rician fading conditions with mobile

nodes

slide72

Optimal Packet Size for Wireless Sensor NetworksY. Sankarasubramaniam, I. F. Akyildiz, S. McLaughlin, ”Optimal Packet Size for Wireless Sensor Networks”, IEEE SNPA, May 2003.

  • Determining the optimal packet size for sensor networks is necessary to operate at high energy efficiencies.
  • The multihop wireless channel and energy consumption characteristics are the two most important factors that influence choice of packet size.

Trailer (FEC) (>=3)

Payload (<=73)

Header (2)

physical layer
PHYSICAL LAYER
  • New Channel Models (I/O/Underwater/Deep Space)
  • Explore Antennae Techniques

(e.g., Smart Antennaes)

  • Software Radios??
  • New Modulation Schemes
  • SYNCH Schemes
  • FEC Schemes on the Bit Level
  • New Data Encryption
  • Investigate UWB
basic research needs
Basic Research Needs
  • An Analytical Framework for Sensor Networks

 Find a Basic Generic Architecture and Protocol

Development which can be tailored to specific

applications.

  • More theoretical investigations of the

Architecture and Protocol

developments

  • Network Configuration and Planning Schemes
further open research issues76
FURTHER OPEN RESEARCH ISSUES
  • Research to integrate WSN domain into NGWI (Next Generation Wireless Internet)

e.g., interactions of Sensor and AdHoc Networks or Sensor and Satellite or any other combinations…

  • Explore the SENSOR/ACTOR NETWORKS
  • Explore the SENSOR-ANTISENSOR NETWORKS
  • SECURITY ISSUES
some applications
Some Applications
  • Clear Demonstration of Testbeds and Realistic Applications
  • Not only data or audio but also video as well as integrated

traffic.

SOME OF OUR EFFORTS IN BWN LAB @ GaTech

  • MAN  for Meteorological Observations
  • SpINet  for Mars Surface
  • Airport Security  Sensors/Actors
  • Sensor Wars
  • Wide Area Multi-campus Sensor Network
further challenges protocol stack
FURTHER CHALLENGESProtocol Stack
  • Follow the TCP/IP Stack, i.e., keep the

Strict Layer Approach ???

  • Or Interleave the Layer functionalities???
  • Cross Layer Optimization
  • Standardization???
commercial viability of wsn applications
Commercial Viability of WSN Applications
  • Within the next few years, distributed sensing and computing will be everywhere, i.e., homes, offices, factories, automobiles, shopping centers, super-markets, farms, forests, rivers and lakes.
  • Some of the immediate commercial applications of wireless sensor networks are
    • Industrial automation (process control)
    • Defense (unattended sensors, real-time monitoring)
    • Utilities (automated meter reading),
    • Weather prediction
    • Security (environment, building etc.)
    • Building automation (HVAC controllers).
    • Disaster relief operations
    • Medical and health monitoring and instrumentation
commercial viability of wsn applications80
Commercial Viability of WSN Applications
  • XSILOGY Solutions is a company which provides wireless sensor network solutions for various commercial applications such as tank inventory management, stream distribution systems, commercial buildings, environmental monitoring, homeland defense etc.

http://www.xsilogy.com/home/main/index.html

  • In-Q-Tel provides distributed data collection solutions with sensor network deployment.

http://www.in-q-tel.com/tech/dd.html

  • ENSCO Inc. invests in wireless sensor networks for meteorological applications.

http://www.ensco.com/products/homeland/msis/msis_rnd.htm

  • EMBER provides wireless sensor network solutions for industrial automation, defense, and building automation.

http://www.ember.com

commercial viability of wsn applications81
Commercial Viability of WSN Applications
  • H900 Wireless SensorNet System(TM), the first commercially available end-to-end, low-power, bi-directional, wireless mesh networking system for commercial sensors and controls is developed by the company called Sensicast Systems. The company targets wide range of commercial applications from energy to homeland security.

http://www.sensicast.com

  • The Sensor-based Perimeter Security product is introduced by a company called SOFLINX Corp. (a wireless sensor network software company)

http://www.soflinx.com

  • XYZ On A Chip: Integrated Wireless Sensor Networks for the Control of the Indoor Environment In Buildings is another commercial application project currently performed by Berkeley.

http://www.cbe.berkeley.edu/research/briefs-wirelessxyz.htm

commercial viability of wsn applications82
Commercial Viability of WSN Applications
  • The Crossbow wireless sensor products and its environmental monitoring and other related industrial applications of such as surveillance, bridges, structures, air quality/food quality, industrial automation, process control are introduced.

http://www.xbow.com

  • Japan\'s Omron Corp has two wireless sensor projects in the US that it hopes to commercialize in the near future. Omron\'s Hagoromo Wireless Web Sensor project consists of wireless nodes equipped with various sensing abilities for providing security for major cargo-shipping ports around the world.

http://www.omron.com

  • Possible business opportunity with a big home improvement store chain, Home Depot, with Intel and Berkeley using wireless sensor networks

http://www.svbizink.com/otherfeatures/spotlight.asp?iid=314

commercial viability of wsn applications83
Commercial Viability of WSN Applications
  • Millennial Net builds wireless networks combining sensor interface endpoints and routers with gateways for industrial and building automation, security, and telemetry

http://www.millennial.net

  • CSEM provides sensing and actuation solutions

http://www.csem.ch/fs/acuating.htm

  • Dust Inc. develops the next-generation hardware and software for wireless sensor networks

http://www.dust-inc.com

  • Integration Associates designs sensors used in medical, automotive, industrial, and military applications to cost-effective designs for handheld consumer appliances, barcode readers, and wireless computer input devices

http://www.integration.com

commercial viability of wsn applications84
Commercial Viability of WSN Applications
  • Melexis produces advanced integrated semiconductors, sensor ICs, and programmable sensor IC systems.

http://www.melexis.com

  • ZMD designs, manufactures and markets high performance, low power mixed signal ASIC and ASSP solutions for wireless and sensor integrated circuits.

http://www.zmd.biz

  • Chipcon produces low-cost and low-power single-chip 2.4 GHz ISM band transceiver design for sensors.

http://www.chipcon.com

  • ZigBee Alliance develops a standard for wireless low-power, low-rate devices.

http://www.zigbee.com

interplanetary internet deep space network state of the art and research challenges
InterPlanetary Internet (Deep Space Network):State-of-the-Art and Research Challenges*

* I.F. Akyildiz, O. Akan, C.Chen, J. Fang, W. Su, “InterPlanetary Internet:

State-of-the-Art and Research Challenges”, Computer Networks Journal, Oct. 2003.

interplanetary internet architecture
InterPlaNetary Internet Architecture
  • InterPlaNetary Backbone Network
  • InterPlaNetary External Network
  • PlaNetary Network
planetary network architecture
PlaNetary Network Architecture
  • PlaNetary Satellite Network
  • PlaNetary Surface Network
challenges
CHALLENGES
  • Extremely long and variable propagation delays
  • Asymmetrical forward and reverse link capacities
  • Extremely high link error rates
  • Intermittent link connectivity, e.g., Blackouts
  • Lack of fixed communication infrastructure
  • Effects of planetary distances on the signal strength and the protocol design
  • Power, mass, size, and cost constraints for communication hardware and protocol design
  • Backward compatibility requirement due to high cost involved in deployment and launching processes
proposed consultative committee for space data systems ccsds protocol stack
Proposed Consultative Committee for Space Data Systems (CCSDS) Protocol Stack

for Mars Exploration Mission Communications

transport layer issues
Transport Layer Issues
  • Extremely High Propagation Delays
  • High Link Error Rates
  • Asymmetrical Bandwidth
  • Blackouts
performance of existing tcp protocols
Performance of Existing TCP Protocols
  • Window-Based TCP’s are not suitable!!!

ForRTT = 40 min  20B/sthroughput on1Mb/s link !!

O. B. Akan, J. Fang, I. F. Akyildiz, “Performance of TCP Protocols in Deep Space Communication Networks”,

IEEE Communications Letters, Vol. 6, No. 11, pp. 478-480, November 2002.

space communications protocol standards transport protocol scps tp
Space Communications Protocol Standards – Transport Protocol (SCPS-TP)
  • Addresses link errors, asymmetry, and outages
  • SCPS-TP: Combination of existing TCP protocols:
    • Window-based
    • Slow Start
    • Retransmission timeout
    • TCP-Vegas congestion control scheme – variation of the RTT value as an indication of congestion
  • SCPS-TP Rate-Based:
    • Does not perform congestion control
    • Uses fixed transmission rate

New Transport Protocols are needed !!!

* Space Communications Protocol Specification-Transport Protocol (SCPS-TP)", Recommendation for Space Data Systems Standards, CCSDS 714.0-B-1, May 1999.

slide96

Hold

Blackout

Decrease

Increase

TP-Planet*O. B. Akan, J. Fang and I.F. Akyildiz, “TP-Planet: A Reliable Transport Protocol for InterPlaNetary Internet”, to appear in IEEE Journal of Selected Areas in Communications (JSAC), early 2004.

Steady State

  • Objective:To address challenges of InterPlaNetary Internet
  • A New Initial State Algorithm
  • A New Congestion Detection Algorithm in Steady State
  • A NewRate-Based scheme instead of Window-Based

t=2*RTT

Initial State

t=RTT

Immediate

Start

FollowUP

Follow Up

multimedia transport in interplanetary internet
Multimedia Transport in InterPlaNetary Internet

Additional Challenges

* Bounded Jitter

* Minimum Bandwidth

* Smoothness

* Error Control

slide98

OPERATIONAL State

t=RTT

Increase

BEGIN State

Blackout

Decrease

RCP-Planet: OverviewJ. Fang and I.F. Akyildiz, “RCP Planet: A Rate Control Scheme for Multimedia Traffic in InterPlaNetary Internet”, July 2003.

  • Objective:To Address the Challenges
  • Framework:

* A New Packet Level FEC

* A New Rate-Based Approach

* A New BEGIN State Algorithm

* A New Rate Control Algorithm in OPERATIONAL State

transport layer open research issues
Transport LayerOpen Research Issues
  • End-to-End Transport:
    • Feasibility of the end-to-end transport should be investigated and new end-to-end transport protocols should be devised accordingly.
  • Extreme PlaNetary Distances:
    • Deep Space links with extreme delays such as Jupiter, Pluto have intermittent connectivity even within an RTT.
  • Cross-layer Optimization:
    • The interactions between the transport layer and lower/higher layers should be maximized to increase network efficiency for scarce space link resources.
network layer issues
Network Layer Issues
  • Naming and Addressing

in the InterPlaNetary Internet

  • Routing

in the InterPlaNetary Backbone Network

  • Routing

in PlaNetary Networks

naming and addressing
Naming and Addressing
  • Purpose: To provide inter-operability between different elements in the architecture
  • Influencing Factors:
    • What objects are named?

(Typically nodes or data objects)

    • Whether a name can be directly used by a data router in order to determine the delivery path?
    • The method by which name/object binding is managed?
domain name system dns approach in internet
Domain Name System (DNS) Approach in Internet

If an application on a remote planet needs to resolve an Earth based name to an address:

  • Problems:
    • If query an Earth-resident name server:

A significant delay of a round-trip time in the commencement of communication

    • If maintain a secondary name server locally: State updates would dominate communication channel utilization
    • If maintain a static list of host names and addresses:

Not scale well with system’s growth

tiered naming and addressing
Tiered Naming and Addressing
  • Name Tuple = {region ID, entity ID}
    • Region ID identifies the entity’s region and is known by all regions in the InterPlaNetary Internet
    • Entity ID is a name local to its entity’s local region and treated as opaque data outside this region

 The opacity of entity names outside their local region

enforces Late Binding: the entity ID of a tuple is not interpreted outside its local region

which avoids a universal name-to-address binding space and preserves a significant amount of autonomy within each region.

an interplanetary internet example and host name tuples

The “Backbone”

Earth’s Internet

Mars’ Internet

SRC

DST

GW1

GW2

IPN region: earth.sol

IPN region: mars.sol

IPN region: ipn.sol

An InterPlaNetary Internet: Example and Host Name Tuples
challenges network layer
ChallengesNetwork Layer
  • Long and Variable Delays
    • Without timely distribution of topology information, routing computations fail to converge to a common solution, resulting in route inconsistency or oscillation
    • The node movement adds to the variability of delays
  • Intermittent Connectivity
    • Determine the predicted time and duration of intermittent links and the degree of uncertainity
    • Obtain knowledge of the state of pending messages
    • Schedule transmission of the pending messages when links become available

SCPS-NP  possible solution???

open research issues network layer
Open Research IssuesNetwork Layer
  • Distribution of Topology Information
    • Combination of link state and distance vector information exchange
    • Distribution of trajectory and velocity information
  • Path Calculation
    • Hop-by-hop routing is expected using incomplete topology information and probabilistic estimation
    • Adaptive algorithms are needed for rerouting and caching decisions
  • Interaction with Transport Layer Protocols
challenges network layer planet
ChallengesNetwork Layer (Planet)
  • Extreme Power Constraints
    • Space elements mainly depend on rechargeable battery using solar energy
  • Frequent Network Partitioning
    • The network can be partitioned due to harsh environmental factors
  • Adaptive Routing through Heterogeneous Networks
    • Fixed elements (e.g., landers)
    • Satellites with scheduled movement
    • Mobile elements with slow movement (e.g., rovers)
    • Mobile elements with fast movement (e.g., spacecraft)
    • Low-power sensor nodes in clusters
medium access control interplanetary backbone network
Medium Access Control InterPlaNetary Backbone Network
  • Challenges:
    • Very Long Propagation Delays
    • Physical Design Change Constraints
    • Topological Changes
    • Power Constraints
medium access control interplanetary backbone network109
Medium Access Control InterPlaNetary Backbone Network
  • Vastly unexplored research field
  • The suitability and performance evaluation of fundamental MAC schemes, i.e., TDMA, CDMA, and FDMA, should be investigated
  • Thus far, Packet Telecommand, and Packet Telemetry standards developed by CCSDS are used to address deep space link layer issues

(Virtual Channelization method!!!)

error control interplanetary backbone network
Error ControlInterPlaNetary Backbone Network
  • Deep space channel is generally modelled as Additive White Gaussian Noise (AWGN) channel
  • Scientific space missions require bit-error rate of 10-5 or better after physical link layer coding

 Error control at link layer is necessary

error control interplanetary backbone network111
Error ControlInterPlaNetary Backbone Network
  • CCSDS Telemetry Standard: (Telemetry Channel Coding):
    • For Gaussian Channels 

½ Rate Convolutional Code

    • For Bandwidth-Constrained Channels 

Punctured Convolutional Codes

    • For Further Constrained Channels 

Concatenated Codes (i.e.,Convolutional code as the inner code and the RS code as the outer code)

Own Experience  TORNADO CODES!!!

error control interplanetary backbone network112
Error ControlInterPlaNetary Backbone Network
  • Advance Orbiting Systems Rec. by CCSDS 

Space Link (ARQ) Protocol (SLAP)

  • Packet Telecommand Standard of CCSDS 

Command Operation Procedure (COP) (GoBack N)

open research issues link layer
Open Research IssuesLink Layer
  • MAC for InterPlaNetary Backbone Network
  • MAC for PlaNetary Networks
  • Error Coding Schemes
  • Cross-layer Optimization
  • Optimum Packet Sizes
physical layer issues interplanetary backbone network
Physical Layer Issues InterPlaNetary Backbone Network
  • Possible approach is to increase radiated RF signal energy:
    • Use of high power amplifiers such as travelling wave tubes (TWT) or klystrons which can produce output powers up to several thousand watts
    • This comes with an expense of increased antenna size, cost and also power problems at remote nodes
  • Current NASA DSN has several 70m antennas for deep space missions
  • DSN operates in S-Band and X-Band (2GHz and 8GHz, respectively) for spacecraft telemetry, tracking and command
    • Not adequate to reach high data rates aimed for InterPlaNetary Internet
  • New 34m antennas are being developed to operate in Ka-Band (32 GHz) which will significantly improve data rates
open research issues physical layer
Open Research IssuesPHYSICAL LAYER
  • Signal Power Loss:
    • Powerful and size-, mass-, and cost-efficient antennas and power amplifiers need to be developed
  • Channel Coding:
    • Efficient and powerful channel coding schemes should be investigated to achieve reliable and very high rate bit delivery over the long delay InterPlaNetary Backbone links
  • Optical Communications:
    • Optical communication technologies should be investigated for possible deployment in InterPlaNetary Backbone links
  • Hardware Design:
    • Low-power low-cost transceiver and antennas should be developed
  • Modulation Schemes:
    • Simple and low-power modulation schemes should be developed for PlaNetary Surface Network nodes. Ultra-wide Band (UWB) could be explored for this purpose
challenges in deep space time synchronization
Challenges in Deep Space Time Synchronization
  • Variable and long transmission delays
    • The long and variable delays may cause a fluctuating offset to the clock
  • Variable transmission speed
    • It may produce a fluctuating offset problem
  • Variable temperature
    • It may cause the clock to drift in different rate
  • Variable electromagnetic interference
    • This may cause the clock to drift or even permanent damage to the crystal if the equipment is not properly shielded
challenges in deep space time synchronization cont d
Challenges in Deep Space Time Synchronization (cont’d)
  • Intermittent connectivity
    • The situation may cause the clock offset to fluctuate and jump
  • Impractical transmissions
    • A time synchronization protocol can not depend on message retransmissions to synchronize the clocks, because the distance between deep space equipments are simply too large
  • Distributed time servers
    • Deep space equipments may require to synchronize to their local time servers, and the time servers have to synchronize among themselves
related work118
Related Work
  • Network Time Protocol
    • Can not handle mobile servers and clients (variable range and range rate with intermittent connectivity)
    • Has time offset wiggles of few milliseconds of amplitude
  • DSN Frequency and Time Subsystems
    • Uses several atomic frequency standards to synchronize the devices and provide references for the three DSN sites, i.e., Goldstone, USA; Madrid, Spain; Canberra, Australia
  • Recommendation for proximity-1 space link protocol
    • Finds the correlation between the clocks of proximity nodes. The correlation data and UTC time are used to correct the past and project the future UTC values
conclusions119
Conclusions
  • InterPlaNetary Internet will be the Internet of next generation deep space networks.
  • There exist many significant challenges for the realization of InterPlaNetary Internet.
  • Many researchers are currently engaged in developing the required technologies for this objective.
final words
FiNAL WORDS

NASA’s VISION:

to improve life here, to extend life to there, to find

life beyond...

NASA’s MISSION:

to understand and protect our home planet, to explore

the Universe and search for life, to inspire

the next generation of explorers…

OUR AIM:

to point out the research problems and inspire the

researchers worldwide to realize these objectives!!!!!!!!!

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