SENSOR NETWORKS

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(No Transcript)
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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

• I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E.
  Cayirci,
• Wireless Sensor Networks A Survey, Computer
  Networks (Elsevier) Journal, March 2002.

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SENSOR NODE HARDWARE

• Small
• Low power
• Low bit rate
• High density
• Low cost (dispensable)
• Autonomous
• Adaptive

SENSING UNIT
PROCESSING UNIT
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MICA MotesBWN Lab _at_ GaTech
Processor/Radio Board MPR300CB
Speed 4 MHz
Flash 128K bytes
SRAM 4K bytes
EEPROM 4K bytes
Radio Frequency 916MHz or 433MHz (ISM Bands)
Data Rate 40 Kbits/Sec (Max)
Power 0.75 mW
Radio Range 100 feet (prog.)
Power 2 x AA batteries
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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Examples for Sensor Nodes
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Examples for Sensor Nodes
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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 .

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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
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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.

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WHY CANT 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.

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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

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APPLICATON LAYER (Query Processing)
Users can request data from the network-gt
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.
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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.

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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
  keywords receive, 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 ltgt PA
location in not in RECT
tmin lt time lt tmax task t
amplitude ltgt a group by
task based on time limit lt packet limit
lp resolution r region
xy
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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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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.

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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

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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.

Sink
Query Sensor nodes that read gt70oF 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.

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Query Sensor nodes that read gt70oF temperature
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Location Awareness (Attribute Based Naming)

• 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

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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

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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 SNPA03, 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
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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 WSNA02, 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 !
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Event-to-Sink Reliability
O. B. Akan, I. F. Akyildiz, and Y.
Sankarasubramaniam, ESRTEvent-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

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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 reliability D ,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

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Network States
State Description Condition
(NC,LR) (No Congestion, Low reliability) f lt fmax and ? lt 1 - ?
(NC,HR) (No Congestion, High reliability) f ? fmax and ? gt 1 ?
(C,HR) (Congestion, High reliability) f gt fmax and ? gt 1
(C,LR) (Congestion, Low reliability) f gt fmax and ? ? 1
OOR Optimal Operating Region f lt fmax and ? ? 1- ?, 1 ?
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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 reliability Di at the
  end of decision interval i
• Performs congestion decision based on the
  feedback from
• the sources nodes (to determine f gtlt fmax).
• Updates f based on ?i D/ Di and f gtlt fmax
  (congestion) to achieve desired event reliability
  D

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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

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ESRT OperationFrequency Update
State Frequency Update Comments
(NC,LR) fi1 fi / ?i Multiplicative increase, achieve desired reliability asap
(NC,HR) fi1 fi (?i 1) / 2?i Conservative decrease, no compromise on reliability
(C,HR) fi1 fi / ?i Aggressive decrease to state (NC,HR)
(C,LR) fi1 fi ?i Exponential decrease, relieve congestion asap
OOR fi1 fi Unchanged
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ESRT Performance
S0 (NC,LR)
S0 (NC,HR)
S0 (C,HR)
S0 (C,LR)
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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) -gt Bellman/Ford Algorithm OSPF (Open
Shortest Path Algorithm) ? Dikstra Algorithm

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Routing Algorithms Constraints Regarding Power
Efficiency (Energy Efficient Routing)
E (PA1)
F (PA4)

• 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 (PA3)
T
Sink
A (PA2)
B (PA2)
C (PA2)
Route 1 Sink-A-B-T (PA4) Route 2 Sink-A-B-C-T
(PA6) Route 3 Sink-D-T (PA3) Route 4
Sink-E-F-T (PA5)
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Why cant 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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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

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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, 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
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Conventional ApproachFLOODING
Broadcast data to all neighbor nodes
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ROUTING ALGORITHMS Gossiping
GOSSIPING Sends data to one randomly selected
neighbor. Example
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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
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Problems
Data Overlap
r

• Resource Blindness
• No knowledge about the available power of
  resources

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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

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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 MobiCom99, 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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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.
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SPIN
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ROUTING ALGORITHM (DIRECTED DIFFUSION)

• (C. Intanagonwiwat, R. Gowindan and D. Estrin,
  Directed Diffusion A Scalable and Robust
• Communication Paradigm for Sensor Networks,
  Proc. ACM MobiCom00, 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 save Energy.
• Also Robustness and Scaling requirements need
  to be considered.

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Directed Diffusion

Source
Sink
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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.
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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.

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LEACH

• 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 WCNC02, Orlando,
March 2002. 2. Rumor Routing D. Braginsky,
D. Estrin, Rumor Routing Algorithm for Sensor
Networks, ACM WSNA02, 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 WCNC02, 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.
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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
MobiCom01, 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
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Related Work

• TRAMA3
• 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
• (N-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
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CMAC Performance
Avg. Energy Consumption
65
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

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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
• 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

67
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

68
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 _at_ GaTech
• MAN ? for Meteorological Observations
• SpINet ? for Mars Surface
• Airport Security ? Sensors/Actors
• Sensor Wars
• Wide Area Multi-Campus Sensor Network

69
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

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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.

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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

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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) (gt3)
Payload (lt73)
Header (2)
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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

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FINAL REMARKS
75
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

76
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

77
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 _at_ GaTech
• MAN ? for Meteorological Observations
• SpINet ? for Mars Surface
• Airport Security ? Sensors/Actors
• Sensor Wars
• Wide Area Multi-campus Sensor Network

78
FURTHER CHALLENGESProtocol Stack

• 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

80
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

81
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-wirel
  essxyz.htm

82
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.a
  sp?iid314

83
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

84
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

85
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.

86
InterPlaNetary Internet Architecture

• InterPlaNetary Backbone Network
• InterPlaNetary External Network
• PlaNetary Network

87
PlaNetary Network Architecture

• PlaNetary Satellite Network
• PlaNetary Surface Network

88
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

89
Planned InterPlaNetary Internet Missions
Mission Name Schedule Description/Objective
Galaxy Evolution Explorer 2003 To measure star formation 11 billion years ago with UV wavelengths.
Rosetta February 2004 Comet orbiter and lander to gather scientific data.
Messenger March 2004 To study the characteristics of Mercury, and to search for water ice and other frozen volatiles.
Deep Impact December 2004 To investigate the interior of the comet, the crater formation process, the resulting crater, and any outgassing from the nucleus.
Mars Reconnaissance Orbiter July 2005 To study Mars from orbit, perform high-resolution measurements and serve as communications relay for later Mars landers until 2010.
Venus Express November 2005 To study the atmosphere and plasma environment of Venus.
New Horizons January 2006 To fly by Pluto and its moon Charon and return scientific data/images.
Dawn May 2006 To study two of the largest asteroids, Ceres and Vesta.
Kepler October 2006 Search for terrestrial planets, i.e., similar to Earth.
Europa Orbiter 2008 To study the Jupiters Moon Europas icy surface.
LISA 2007 To probe the gravity waves emitted by dwarf stars and other objects sucked into black holes.
Mars 2007 Late 2007 To launch a remote sensing orbiter and four small Netlanders to Mars.
Mars 2009 Late 2009 Smart Lander, Long Range Rover and Communication Satellite.
BepiColombo January 2011 To study Mercurys form, interior structure, geology, composition, etc.
90
Proposed Consultative Committee for Space Data
Systems (CCSDS) Protocol Stack
for Mars Exploration Mission Communications
91
Proposed Delay Tolerant Networking (DTN) Protocol
Stack
92
Transport Layer Issues

• Extremely High Propagation Delays
• High Link Error Rates
• Asymmetrical Bandwidth
• Blackouts

93
Extremely Long Propagation Delays
Planet RTTmin RTTmax
Mercury 1.1 30.2
Venus 5.6 35.8
Mars 9 55
Jupiter 81.6 133.3
Saturn 165.3 228.4
Uranus 356.9 435.6
Neptune 594.9 646.7
Pluto 593.3 1044.4
94
Performance of Existing TCP Protocols

• Window-Based TCPs are not suitable!!!
• For RTT 40 min ? 20B/s throughput on 1Mb/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.
95
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.
96
TP-PlanetO. 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
t2RTT
Initial State
tRTT
Immediate Start
FollowUP
Follow Up

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

97
Multimedia Transport in InterPlaNetary Internet

• Additional Challenges
• Bounded Jitter
• Minimum Bandwidth
• Smoothness
• Error Control

98
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

99
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.

100
Network Layer Issues

• Naming and Addressing
• in the InterPlaNetary Internet
• Routing
• in the InterPlaNetary Backbone Network
• Routing
• in PlaNetary Networks

101
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?

102
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 systems growth

103
Tiered Naming and Addressing

• Name Tuple region ID, entity ID
• Region ID identifies the entitys region and is
  known by all regions in the InterPlaNetary
  Internet
• Entity ID is a name local to its entitys 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.

104
An InterPlaNetary Internet Example and Host Name
Tuples
Host IPN regions Host name tuples
SRC earth.sol earth.sol, src.jpl.nasa.gov6769
GW1 earth.sol ipn.sol earth.sol, ipngw1.jpl.nasa.gov6769 ipn.sol, ipngw1.jpl.nasa.gov6769
GW2 ipn.sol mars.sol ipn.sol, ipngw2.jpl.nasa.gov6769 mars.sol, ipngw2.jpl.nasa.gov6769
DST mars.sol mars.sol, dst.jpl.nasa.gov6769
105
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???

106
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

107
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

108
Medium Access Control InterPlaNetary Backbone
Network

• Challenges
• Very Long Propagation Delays
• Physical Design Change Constraints
• Topological Changes
• Power Constraints

109
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!!!)

110
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

111
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!!!

112
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)

113
Open Research IssuesLink Layer

• MAC for InterPlaNetary Backbone Network
• MAC for PlaNetary Networks
• Error Coding Schemes
• Cross-layer Optimization
• Optimum Packet Sizes

114
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

115
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

116
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

117
Challenges in Deep Space Time Synchronization
(contd)

• 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

118
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

119
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.

120
FiNAL WORDS

• NASAs VISION
• to improve life here, to extend life to there,
  to find
• life beyond...
• NASAs 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!!!!!!!!!