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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 l.jpg

Several thousand nodes

Nodes are tens of feet of each other

Densities as high as 20 nodes/m3


Internet, Satellite, etc





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

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

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Location Finding System



Sensor ADC



Power Generator

Power Unit



Low power

Low bit rate

High density

Low cost (dispensable)





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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.

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UC Berkeley:

Smart Dust


UC Berkeley: COTS Dust

JPL: Sensor Webs

Rockwell: WINS

Examples for Sensor Nodes

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Rene Mote

Dot Mote

Mica node

weC Mote

Examples for Sensor Nodes

Sensor networks features l.jpg


    Military, Environmental, Health, Home, Space Exploration,

    Chemical Processing, Disaster Relief….


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

    Acoustic, Radar…


    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 ….

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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 l.jpg

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 l.jpg

  • 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.

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SMP: Sensor Managament Protocol

System Administrators interact with Sensors using SMP.


  • 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


  • 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

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(Query Processing)

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

User Query Types:


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.


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


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

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Sensor Query and Tasking Language (SQTL):

(C-C Shen,, “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.

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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.

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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 l.jpg
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

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Task Assignment and Data Advertisement Protocol


* 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.


* Sensor nodes advertise the available data to

the users and the users query the data which

they are interested in.

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Sensor Query and Data Dissemination Protocol

Provides user applicatons with interfaces to issue

queries, respond to queries and collect incoming


These queries are not issued to particular nodes, instead


“The locations of the nodes that sense temperature

higher than 70F”


“Temperatures read by the nodes in region A”

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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.



Sensor nodes that read >70oF temperature

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Data Aggregation (Data Fusion)

The sink asks the sensor nodes to report certain conditions.

Data coming from multiple sensor nodes are aggregated.




Sensor nodes that read >70oF temperature

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Location Awareness

(Attribute Based Naming)











Query an Attribute

of the sensor field

Region A


Region C

Region B


Temperatures read by the nodes in Region A

Important for broadcasting,

multicasting, geocasting and anycasting

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Sensor Network Management Protocol

Task Assignment and Data Advertisement Protocol

Sensor Query and Data Dissemination Protocol

Sophisticated GUI

(Graphical User Interface) Tool

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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


RMST relies on Directed Diffusion scheme


Source Node

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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

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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!!!

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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 l.jpg

Event Radius


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 l.jpg

Event Radius


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 l.jpg
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 l.jpg


a f







(1 bit)






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 operation frequency update l.jpg
ESRT OperationFrequency Update

Esrt performance l.jpg
ESRT Performance

S0 = (NC,LR)

S0 = (NC,HR)

S0 = (C,LR)

S0 = (C,HR)

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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.


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

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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


D (PA=3)



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)

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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

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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

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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

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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,


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 l.jpg








Conventional ApproachFLOODING

Broadcast data to all neighbor nodes

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Sends data to one randomly selected neighbor.


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Problems of

Flooding and Gossiping


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 l.jpg


















Data Overlap


  • Resource Blindness

    No knowledge about the available power of resources

Gossiping l.jpg

  • 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 l.jpg








The Optimum Protocol

  • “Ideal”

    • Shortest-path routes

    • Avoids overlap

    • Minimum energy

    • Need global topology information

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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

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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. ADV

2. REQ




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(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.

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Gradient Setup

Data Delivery

Interest Propagation

Directed Diffusion



Directed diffusion l.jpg



Directed Diffusion


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

Interest is named by assigning attribute-value pairs.





Interest Propagation

Gradient Setup

Data Delivery


Cannot change interest unless a new interest is broadcast.

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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.


    * 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 l.jpg

  • Optimum Number of Clusters ---????????

    - too few: nodes far from cluster heads

    • too many: many nodes send data to SINK.

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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 l.jpg

Other Protocols

5. Power Efficient Gathering in Sensor Information Systems


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.

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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

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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 l.jpg
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.

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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 !

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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!

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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


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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.

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CMAC Performance

Medium Access Delay

Packet Drop Rate

Cmac performance64 l.jpg
CMAC Performance

Avg. Energy Consumption

Conclusions l.jpg

  • 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

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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


  • 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 l.jpg

  • 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…



Need for realistic applications l.jpg
Need for Realistic Applications

  • Clear Demonstration of Testbeds and Realistic Applications

  • Not only data or audio but also video 

    Overall I  Integrated Traffic.


  • 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 l.jpg

MAC for sensor networks must have inbuilt power

management, mobility management and failure recovery


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

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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 l.jpg

  • 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


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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 l.jpg

  • 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 l.jpg
Basic Research Needs

  • An Analytical Framework for Sensor Networks

     Find a Basic Generic Architecture and Protocol

    Development which can be tailored to specific


  • More theoretical investigations of the

    Architecture and Protocol


  • Network Configuration and Planning Schemes

Further open research issues76 l.jpg

  • 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…




Some applications l.jpg
Some Applications

  • Clear Demonstration of Testbeds and Realistic Applications

  • Not only data or audio but also video as well as integrated



  • MAN  for Meteorological Observations

  • SpINet  for Mars Surface

  • Airport Security  Sensors/Actors

  • Sensor Wars

  • Wide Area Multi-campus Sensor Network

Further challenges protocol stack l.jpg

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

    Strict Layer Approach ???

  • Or Interleave the Layer functionalities???

  • Cross Layer Optimization

  • Standardization???

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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

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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.

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

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

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

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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.

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

  • 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.

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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.

  • 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.

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

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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

  • CSEM provides sensing and actuation solutions

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

  • 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

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Commercial Viability of WSN Applications

  • Melexis produces advanced integrated semiconductors, sensor ICs, and programmable sensor IC systems.

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

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

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

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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.

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InterPlaNetary Internet Architecture

  • InterPlaNetary Backbone Network

  • InterPlaNetary External Network

  • PlaNetary Network

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PlaNetary Network Architecture

  • PlaNetary Satellite Network

  • PlaNetary Surface Network

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  • 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

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Planned InterPlaNetary Internet Missions

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Proposed Consultative Committee for Space Data Systems (CCSDS) Protocol Stack

for Mars Exploration Mission Communications

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Transport Layer Issues (CCSDS) Protocol Stack

  • Extremely High Propagation Delays

  • High Link Error Rates

  • Asymmetrical Bandwidth

  • Blackouts

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Extremely Long Propagation Delays (CCSDS) Protocol Stack

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Performance of Existing TCP Protocols (CCSDS) Protocol Stack

  • 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.

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Space Communications Protocol Standards (CCSDS) Protocol Stack – 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.

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Hold (CCSDS) Protocol Stack




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


Initial State





Follow Up

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Multimedia Transport in InterPlaNetary Internet (CCSDS) Protocol Stack

Additional Challenges

* Bounded Jitter

* Minimum Bandwidth

* Smoothness

* Error Control

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OPERATIONAL State (CCSDS) Protocol Stack






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

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Transport Layer (CCSDS) Protocol StackOpen 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.

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Network Layer Issues (CCSDS) Protocol Stack

  • Naming and Addressing

    in the InterPlaNetary Internet

  • Routing

    in the InterPlaNetary Backbone Network

  • Routing

    in PlaNetary Networks

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Naming and Addressing (CCSDS) Protocol Stack

  • 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?

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Domain Name System (CCSDS) Protocol Stack(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

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Tiered Naming and Addressing (CCSDS) Protocol Stack

  • 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.

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The “Backbone” (CCSDS) Protocol Stack

Earth’s Internet

Mars’ Internet





IPN region: earth.sol

IPN region: mars.sol

IPN region: ipn.sol

An InterPlaNetary Internet: Example and Host Name Tuples

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Challenges (CCSDS) Protocol StackNetwork 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???

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Open Research Issues (CCSDS) Protocol StackNetwork 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

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Challenges (CCSDS) Protocol StackNetwork 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

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Medium Access Control (CCSDS) Protocol StackInterPlaNetary Backbone Network

  • Challenges:

    • Very Long Propagation Delays

    • Physical Design Change Constraints

    • Topological Changes

    • Power Constraints

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Medium Access Control (CCSDS) Protocol StackInterPlaNetary 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!!!)

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Error Control (CCSDS) Protocol StackInterPlaNetary 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

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Error Control (CCSDS) Protocol StackInterPlaNetary 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!!!

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Error Control (CCSDS) Protocol StackInterPlaNetary Backbone Network

  • Advance Orbiting Systems Rec. by CCSDS 

    Space Link (ARQ) Protocol (SLAP)

  • Packet Telecommand Standard of CCSDS 

    Command Operation Procedure (COP) (GoBack N)

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Open Research Issues (CCSDS) Protocol StackLink Layer

  • MAC for InterPlaNetary Backbone Network

  • MAC for PlaNetary Networks

  • Error Coding Schemes

  • Cross-layer Optimization

  • Optimum Packet Sizes

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Physical Layer Issues (CCSDS) Protocol StackInterPlaNetary 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

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Open Research Issues (CCSDS) Protocol StackPHYSICAL 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

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Challenges in Deep Space Time Synchronization (CCSDS) Protocol Stack

  • 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

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Challenges in Deep Space Time Synchronization (cont’d) (CCSDS) Protocol Stack

  • 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

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Related Work (CCSDS) Protocol Stack

  • 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

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Conclusions (CCSDS) Protocol Stack

  • 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.

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FiNAL WORDS (CCSDS) Protocol Stack


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

life beyond...


to understand and protect our home planet, to explore

the Universe and search for life, to inspire

the next generation of explorers…


to point out the research problems and inspire the

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