jahangirsharif'edu, mizaniance'sharif'edu

1
????? ????? ?? ???? ??? ???? ?? ??? ? ??? ?????
????? ? ?????? ??

• ???? ???? ???? ???????
• ?????? ????????
• (jahangir_at_sharif.edu, mizanian_at_ce.sharif.edu)
• 24 ??? 1387

2

• INTRODUCTION APPLICATIONS

3
SENSOR NETWORKS ARCHITECTURE
Sink
Internet, Satellite, UAV
Sink
Task Manager

• I.F. Akyildiz, W. Su, Y. Sankarasubramaniam,
  E. Cayirci,
• Wireless Sensor Networks A Survey, Computer
  Networks (Elsevier) Journal, March 2002.
• I.F. Akyildiz, M.C. Vuran, O. B. Akan,
• Wireless Sensor Networks A Survey REVISITED
  Computer Networks (Elsevier) Journal, 2006.

4
CHARACTERISTICS OF WSNs

• Very large number of nodes, often in the order of
  thousands
• Nodes need to be close to each other
• Densities as high as 20 nodes/m3
• Asymmetric flow of information, from sensor nodes
  to sink
• Communications are triggered by queries or events
• Limited amount of energy (in many applications
  it is impossible to
• replace or recharge)
• Mostly static topology
• Low cost, size, and weight per node
• Prone to failures
• More use of broadcast communications instead of
  point-to-point
• Nodes do not have a global ID such as an IP
  address
• The security, both on physical and communication
  level, is more limited than in classical wireless
  networks

5
DIFFERENCES FROM AD-HOC NETWORKS

• 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 may change
  frequently due to node failure and node mobility
• Sensor nodes are limited in power, computational
  capacities, and memory
• May not have global ID like IP address
• Need tight integration with sensing tasks

6
SENSOR NODE HARDWARE

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

SENSING UNIT
PROCESSING UNIT
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SENSOR NETWORK TESTBED
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SENSOR NETWORK TESTBED (ZIGBEE)
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SENSOR NODE FEATURES
10
Examples for Sensor Nodes
Smart Dust
UC Berkeley Dust
UCLA WINS
Rockwell WINS
JPL Sensor Webs
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Examples for Sensor Nodes
Dot Mote
Rene Mote
MICA Mote
weC Mote
12
Berkeley Motes (Details)
13
Telos by MOTEIV.com

• Single board philosophy
• Robustness, Ease of use, Lower Cost
• Integrated Humidity Temperature sensor
• First platform to use 802.15.4
• CC2420 radio, 2.4 GHz, 250 kbps
• 3x RX power consumption of CC1000
• Same TX power as CC1000
• Motorola HCS08 processor
• Lower power consumption, 1.8V operation,faster
  wakeup time
• 40 MHz CPU clock, 10K RAM 48K Flash
• 50m indoor 125m outdoor ranges
• Package
• Integrated onboard antenna
• Everything USB Ethernet based
• 2 AA batteries
• Weatherproof packaging

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Zylogs eZ80

• Provides a way to Internet-enabled process
  control and monitoring applications.
• Temperature sensor, water leak detector and many
  more applications
• Metro IPWorks software stack embedded
• Enables users to access Webserver data and files
  from anywhere in the world.

15
SENSOR NETWORKS FEATURES

• APPLICATIONS
• Military, Environmental, Health, Home,
  Space Exploration, Chemical Processing,
  Volcanoes, Mining, 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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• Military Applications
• Command, Control, Communications, Computing,
  Intelligence, Surveillance, Reconnaissance,
  Targeting (C4ISRT)

• Monitoring friendly forces, equipment and
  ammunition
• Battlefield surveillance
• Reconnaissance of opposing forces and terrain
• Targeting
• Battle damage assessment
• Nuclear, Biological and Chemical (NBC) attack
• detection and reconnaissance

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Further Military Applications

• Intrusion detection (mine fields)
• Detection of firing gun (small arms) location
• Chemical (biological) attack detection
• Targeting and target tracking systems
• Enhanced navigation systems
• Battle damage assessment system
• Enhanced logistics systems

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Environmental Applications

• Tracking the movements of birds, small animals,
  and insects
• Monitoring environmental conditions that affect
  crops and livestock
• Irrigation
• Earth monitoring and planetary exploration
• Chemical/biological detection
• Biological, Earth, and environmental monitoring
  in marine,
• soil, and atmospheric contexts
• Meteorological or geophysical research
• Pollution study
• Precision agriculture
• Biocomplexity mapping of the environment
• Flood detection, and Forest fire detection.

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Habitat Monitoringhttp//www.greatduckisland.net
Great Duck Island in Maine.
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Habitat Monitoring

• Approx. 200 nodes including MICA, MICA2, burrow
  nodes (with IR) and weather station nodes
• Motes detect light, barometric pressure, relative
  humidity and temperature conditions.
• An infrared heat sensor detects whether the nest
  is occupied by a seabird, and whether the bird
  has company.
• Motes within the burrows send readings out to a
  single gateway sensor above ground, which then
  wirelessly relays collected information to a
  laptop computer at a lighthouse (350 feet).
• The laptop, also powered by photovoltaic cells,
  connects to the Internet via satellite.
• Computer at base-station logs data and maintains
  database

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Ecosystems, Biocomplexity

• Ecosystems infused with chemical, physical,
  acoustic, image sensors to track global change
  parameters

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Huntington Botanical Gardens - Sensor Web 3.1
http//sensorwebs.jpl.nasa.gov/

• Each pod measures light levels, air temperature
  and humidity, with optional measurements of soil
  temperature and soil moisture
• E.g., correlating soil conditions with local
  light and temperature, it is possible to deduce
  the effects of rain in the specific area

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Huntington Botanical Gardens

• Dry conditions detected by a Sensor Web could
  automatically turn on sprinklers.
• If pods used sensors that measure barometric
  pressure, the web could analyze light and
  barometric pressure levels to predict that rain
  was imminent, deciding not to use the sprinklers
  after all.
• Two plants of the same kind and age needed
  different amounts of water because of soil
  conditions.

Sensor Web pod 15 at Huntington Botanical Gardens
is covered in mud from nearby watering and has
had an antenna chewed on by a small animal.
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Cane Toad Monitoringhttp//www.cse.unsw.edu.au/s
ensar/research/projects/cane-toads/

• University of New South Wales, Sydney, Australia
• Monitoring cane toads in Kakadu National Park,
  Northern Territory, Australia
• Cane toads (Bufo marinus) - introduced to control
  sugar pests in Australia about 70
• years ago

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Cane Toad Monitoring

• Wireless, acoustic sensor network application
• Goal is to use automatic recognition of animal
  vocals to detect the existence of cane toads.
• Challenging application as it requires high
  frequency acoustic sampling, complex signal
  processing and wide area sensing coverage.
• Requirements
• high frequency acoustic sampling
• complex signal processing
• wide area sensing coverage

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Forest Fire Detection Firebughttp//firebug.sour
ceforge.net/

• Design and Construction of a Wildfire
  Instrumentation System using Networked Sensors
• Network of GPS-enabled, wireless thermal sensors
• FireBug network self-organizes into edge-hub
  configurations
• Hub motes act are base stations

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Firebug

• Firebug - mote/fireboard pair
• Mote - Crossbow MICA board
• Fireboard - Crossbow MTS420CA
• Temperature and humidity sensor.
• Barometric pressure sensor.
• GPS unit.
• Accelerometer
• Light Intensity Sensor

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Health Applications

• Providing interfaces for the disabled
• Integrated patient monitoring
• Diagnostics
• Telemonitoring of human physiological data
• Tracking and monitoring doctors and
• patients inside a hospital, and
• Drug administration in hospitals

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CodeBlue WSNs for Medical Care
http//www.eecs.harvard.edu/mdw/proj/codeblue/

• NSF, NIH, U.S. Army, Sun Microsystems and
  Microsoft Corporation
• Motivation - Vital sign data poorly integrated
  with pre-hospital and hospital-based patient care
  records

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CodeBlue WSNs for Medical Care

• Hardware
• Small wearable sensors
• Wireless pulse oximeter / 2-lead EKG
• Based on the Mica2, MicaZ, and Telos sensor node
  platforms
• Custom sensor board with pulse oximeter or EKG
  circuitry
• Pluto mote
• scaled-down version of the Telos
• rechargeable Li-ion battery
• small USB connector
• 3-axis accelerometer

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CodeBlue WSNs for Medical Care

• CodeBlue - scalable software infrastructure for
  wireless medical devices
• Routing, Naming, Discovery, and Security
• MoteTrack - tracking the location of individual
• patient devices indoors and outdoors
• Heart rate (HR), oxygen saturation (SpO2),
• EKG data monitored
• Relayed over a short-range (100m)
• Receiving devices - PDAs, laptops, or
  ambulance-based terminals
• Data can be displayed in real time and integrated
  into the developing pre-hospital patient care
  record
• Can be programmed to process the vital sign data
  (and provide alerts)

32
CodeBlue WSNs for Medical Care

• Research focuses on the following areas
• Integration of medical sensors with low-power
  wireless networks
• Wireless ad-hoc routing protocols for critical
  care security, robustness, prioritization
• Hardware architectures for ultra-low-power
  sensing, computation, and communication
• Interoperation with hospital information systems
  privacy and reliability issues
• 3D location tracking using radio signal
  information
• Adaptive resource management, congestion control,
  and bandwidth allocation in wireless networks

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Further Applications

• Monitoring product quality
• Factory Floor Automation
• Constructing smart homes
• Constructing office spaces
• Interactive toys
• Monitor disaster areas
• Smart spaces
• Machine diagnosis
• Interactive museums
• Managing inventory control
• Environmental control in office buildings

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• MAC PROTOCOLS

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Objectives of MAC Protocols

• Collision Avoidance
• Energy Efficiency
• Scalability
• Latency
• Fairness
• Throughput
• Bandwidth Utilization

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POWER CONSUMPTION
RADIO
SENSOR
CPU
TX
RX
IDLE
SLEEP
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Major Sources of Energy Waste

• Idle Listening
• - Long idle time when no sensing event happens
• - Collisions
• - Control Overhead
• - Overhearing
• Transmitter
• Receiver
• OBJECTIVE Reduce energy consumption !!

Common to all wireless networks
38
Challenges for MAC in WSNs

• 1. WSN Architecture
• High density of nodes
• Increased collision probability
• Signaling overhead should be minimized to prevent
  further collisions
• Sophisticated and simple collision avoidance
  protocols required

39
Challenges for MAC in WSNs

• 2. Limited Energy Resources
• Connectivity and the performance of the network
  is affected as nodes die
• Transmitting and receiving consumes almost same
  energy
• Frequent power up/down eats up energy
• Need very low power MAC protocols
• Minimize signaling overhead
• Avoid idle listening
• Prevent frequent radio state changes
  (activelt-gtsleep)

40
Challenges for MAC in WSNs

• 3. Limited Processing and Memory Capabilities
• Complex algorithms cannot be implemented
• Conventional layered architecture may not be
  appropriate
• Centralized or local management is limited
• Simple scheduling algorithms required
• Cross-layer optimization required
• Self-configurable, distributed protocols required

41
Challenges for MAC in WSNs

• 4. Limited Packet Size
• Unique node ID is not practical
• Limited header space
• Local IDs should be used for inter-node
  communication
• MAC protocol overhead should be minimized
• 5. Cheap Encoder/Decoders
• Cheap node requirement prevents sophisticated
• encoders/decoders to be implemented
• Simple FEC codes required for error control
• Channel state dependent MAC can be used to
  decrease
• error rate

42
Challenges for MAC in WSNs

• 6. Inaccurate Clock Crystals
• Cheap node requirement prevents expensive
  crystals
• to be implemented
• Synchronization problems
• TDMA-based schemes are not practical
• 7. Event-based Networking
• Observed data depends on physical phenomenon
• Spatial and temporal correlation in the physical
• phenomenon should be exploited

BOTTOMLINE Existing MAC protocols cannot be used
for WSNs!!!
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Overview of MAC Protocols for WSNs

• 1. Contention (RANDOM)-Based MAC Protocols
• Sleep-MAC, T-MAC, CCMAC
• 2. Reservation-Based (TDMA BASED) MAC Protocols
• TRAMA, FLAMA

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Contention (Random)-Based MAC Protocols

• Each node tries to access the channel based on
  carrier sense mechanism.
• These MAC protocols provide robustness and
  scalability to the network.
• The collision probability increases with
  increasing node density.
• They can support variable and highly correlated
  traffic.

45
IEEE 802.11
IEEE 802.11, Wireless LAN medium access control
(MAC) and physical layer (PHY) specifications,
1999

• Originally developed for WLANs

46
BASIC CSMA/CA (FLOWCHART)
47
BASIC CSMA/CA
Station senses the channel and it is idle
Slot Time
Direct access if medium is free ? IFS
48
CSMA/CA Algorithm

• If Collisions (Control or Data)
• ? Binary exponential increase (doubling) of
  CW
• Length of backoff time is exponentially
  increased as the station goes through successive
  retransmissions.

49
Inter-frame Spaces (IFS)
DIFS
DIFS
PIFS
Medium Busy
SIFS
Next Frame
Contention Window
t
Direct access if medium is free ? DIFS
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DFWMAC-DCF CSMA/CA
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Inter-frame Spaces (IFS)

• Priorities are defined through different inter
  frame spaces
• SIFS (Short Inter Frame Spacing)
• Highest priority packets such as ACK, CTS,
  polling response
• Used for immediate response actions
• PIFS (PCF IFS) - Point Coordination Function
  Inter-Frame spacing
• Medium priority, for real time service using PCF
• SIFS One slot time
• Used by centralized controller in PCF scheme
  when using polls
• DIFS (DCF, Distributed Coordination Function IFS)
• Lowest priority, for asynchronous data service
• SFIS Two slot times
• Used as minimum delay of asynchronous frames
  contending for access

52
DFWMAC-DCF CSMA/CA with ACK

• Station has to wait for DIFS before sending data
• Receiver ACKs immediately (after waiting for
• SIFS lt DIFS) if the packet was received
  correctly (CRC))
• Receiver transmits ACK without sensing the
  medium.
• If ACK is lost, retransmission done.
• Automatic retransmission of data packets in
• case of transmission errors

53
DFWMAC-DCF CSMA/CA with ACK
DIFS
Data
Sender
SIFS
ACK
Receiver
DIFS
Data
Other Stations
t
Waiting Time
Contention Window
54
DFWMAC-DCF CSMA/CA with RTS/CTS

• Transmitter sends an RTS (Request To Send) after
  medium has been idle for time interval more than
  DIFS.
• Receiver responds with CTS (Clear To Send) after
  medium has been idle for SIFS.
• Then data is transmitted.
• RTS/CTS is used for reserving channel for data
  transmission so that the collision can only occur
  in control message.

55
DFWMAC-DCF CSMA/CA with RTS/CTS

• Use short signaling packets for Collision
  Avoidance
• RTS (Request To Send) Packet (20 Bytes)
• A sender requests the right to send from a
  receiver with a short RTS packet before it sends
  a data packet
• CTS (Clear To Send) Packet (16 Bytes)
• The receiver grants the right to send as soon
  as it is ready to receive
• They contain (Sender Address Receiver Address
  Packet Size)

56
DFWMAC-DCF CSMA with RTS/CTS
SIFS
DIFS
Time
Data
RTS
Source
SIFS
SIFS
CTS
ACK
Destination
Contention Window
DIFS
Next Frame
Other
Defer Access
Backoff After Defer
57
Hidden Terminal Problem

• A sends RTS
• B sends CTS
• C overhears CTS
• C inhibits its own transmitter
• A successfully sends DATA to B

58
Hidden Terminal Problem

• How does C know how long to wait before it can
  attempt a
• transmission?
• A includes length of DATA that it wants to send
  in the RTS
• packet
• B includes this information in the CTS packet
• C, when it overhears the CTS packet, retrieves
  the length
• information and uses it to set the inhibition
  time

59
Exposed Terminal Problem

• B sends RTS to A (overheard by C)
• A sends CTS to B
• C cannot hear As CTS
• C assumes A is either down or out of range
• C does not inhibit its transmissions to D

60
Collisions

• Still possible RTS packets can collide!
• Binary exponential backoff performed by stations
  that
• experience RTS collisions
• RTS collisions not as bad as data collisions in
  CSMA
• (since RTS packets are typically much smaller
  than
• DATA packets)

61
DFWMAC-DCF CSMA/CA with RTS/CTS (Network
Allocation Vector (NAV))

• Both Physical Carrier Sensing and Virtual
  Carrier Sensing
• used in 802.11
• If either function indicates that the medium is
  busy, 802.11 treats the channel to be busy
• Virtual Carrier Sensing is provided by the
• NAV (Network Allocation Vector)

62
DFWMAC-DCF CSMA/CA with RTS/CTS (Network
Allocation Vector (NAV))

• Most 802.11 frames carry a duration field which
  is used to reserve the medium for a fixed time
  period
• Tx sets the NAV to the time for which it expects
  to use the medium
• Other stations start counting down from NAV to 0
• As long as NAV gt 0, the medium is busy

63
DFWMAC-DCF CSMA/CA with RTS/CTS (Network
Allocation Vector (NAV))

• CHANNEL VIRTUALLY BUSY ? a NAV SIGNAL is turned
  on!
• The transmission will be delayed until the NAV
  signal has disappeared.
• When the channel is virtually available, then
  MAC checks for PHY condition of the channel.

64
Illustration
Sender
RTS
DATA
CTS
ACK
Receiver
NAV
RTS
CTS
65
CSMA/CA with RTS/CTS (NAV)
DIFS
data
RTS
Sender
SIFS
SIFS
SIFS
ACK
CTS
Receiver
DIFS
NAV (RTS)
RTS
Other Stations
NAV (CTS)
t
defer access
Contention Window
66
Introduction Wireless Sensor Networks

• Design goals
• Reducing delay
• Increasing delivery ratio
• Reducing protocol overhead
• Reducing energy consumption
• Increasing throughput (spatial reuse)
• Increasing scalability
• Reducing production cost

67
Introduction REAL-TIME SYSTEM

• Real-time System
• In a real-time system the correctness of outputs
  depends on both the correctness of its
  computation logic and its response time
• Explicit timing constraints (soft, firm, hard)
• Real-time Application
• The performance-critical applications that
  require bounded delay latency are referred to as
  real-time applications

68
Introduction REAL-TIME WSN

• Real-time Wireless Sensor Networks Those wireless
  sensor networks that are capable of providing
  bounded delay guarantees on packet delivery are
  referred to as real-time wireless sensor networks
• Real-time capacity
• Real-time capacity describes how much
  real-time data the network can transfer by their
  deadlines

69
Introduction Topology control

• Topology Control
• Topology control is the art of coordinating
  nodes decisions regarding their transmitting
  ranges, in order to generate a network with the
  desired properties (e.g. connectivity) while
  reducing node energy consumption and/or
  increasing network capacity.

Sharif University of Technology
70
Real-time Behavior in WSN

• A new field of study
• A vast majority of WSN applications are real-time

Sharif University of Technology
71
Real-time Behavior in WSN

• Input
• Current state (view) update
• Tasks to be performed by real-time systems
• Output
• Actions to change real world situation
• Information to be used to support decision-making

72
Real-time Behavior in WSN

• Wireless Sensor and Actuator Networks
• WSANs are composed of heterogeneous nodes
  referred to as sensors and actuators.

73
Real-time Behavior in WSN

• Data centric
• Sensor networks are largely data centric with
  the objective of delivering data collected in a
  timely fashion to the required destination.
• Application oriented
• While traditional wired and wireless networks
  are expected to cater to a variety of user
  applications a sensor network is usually deployed
  to perform specific tasks.

74
Real-time Behavior in WSNs

• Need to support multi-dimensional requirements
• Real-time, location-dependence, power, mobility,
  wireless, size, cost, fault-tolerance, security
  and privacy
• Conflicting resource requirements and system
  architecture
• Operate in unpredictable environments
• Embedded and interacting with physical world

75
Problem Statement

• General Problems
• The general challenges for real-time
  communication and coordination in sensor networks
  arise primarily due to the large number of
  constraints, that must be simultaneously
  satisfied
• Networking Problems
• How to effectively coordinate and control
  sensors in real-time over an unreliable wireless
  ad-hoc network

76
Problem Statement-General Problems

• Paradigm shift
• Resource constraints
• Unpredictability
• High density/scale
• Real-time
• Security

77
Problem Statement-Networking Problems

• MAC Layer
• Network Layer
• Transport Layer
• Multi-Layers

78
Problem Statement-Networking Problems

• MAC Layer
• Scheduling Based MAC Protocols
• Contention-Based MAC Protocols
• Collision-Free Real-Time MAC

79
Problem Statement-Networking Problems

• Network Layer
• Ad Hoc Routing Protocols
• Proactive (DSDV, PFA, WRP)
• Reactive (DSR, TORA, LAR, AODV, ABR, SBR)
• Geographic (GPSR)
• Hierarchical (LEACH, K-cluster, min ID, max
  Degree)
• Multicast and Anycast
• Routing Protocols with Real-Time Requirements
  (SPEED)

80
Problem Statement-Networking Problems

• Transport Layer
• Fairness of the underlying MAC protocol
• Link failure due to mobility
• Coupling effects of the forward and reverse paths

81
Problem Statement-Networking Problems

• Multi-Layers
• Power Management (GAF, SPAN)
• Topology/Power Control (CBTC, COMPOW, LMST)
• Real-Time Communication Architecture (RAP)

82
Goals of this research

• Design and analysis of real-time WSN are the
  focus of this dissertation
• For example, for a system engineer to actually
  deploy a WSN for a Real-time application record
  video for detected events within area A before
  the object has moved out of the area, the system
  engineer needs an understanding of how these
  requirements impact specific WSN components

83
Goals of this research

• Previous work within Real-time WSN has been
  isolated and specific either on certain
  functional layers or application scenarios.

84
Goals of this research

• For global approach to Real-time WSN
• Identify and standardize Real-time WSN
  requirements
• We will propose a Real-time WSN framework
• Analyze how these standardized requirements
  impact WSN component functionalities
• Analyzing how the defined Real-time WSN
  requirements impact each other within this
  Real-time framework

85
Goals of this research

• The objectives of this research is
• To propose a Real-time WSN framework by first
  defining Real-time WSN requirements within WSN
  reference architecture
• To propose a Real-time WSN model that analyzes
  how Real-time WSN requirements impact each other
• To develop new communication protocols to support
  real-time and reliable event data delivery with
  minimum energy consumption in WSNs

86
Real-time WSN framework
WSN Reference Architecture
87
Real-time WSN framework

• Real-time requirements for Real-time WSN
  applications
• System Lifetime
• Response Time
• Data Freshness
• Detection Probability
• Data Fidelity
• Data Resolution

88
Real-time WSN framework
Data freshness vs. Detection probability
89
Real-time WSN framework
Energy consumption vs. Detection probability
90
A Quantitative Real-time Model

• Definition
• Real-time degree describes the percentage of
  real-time data that the network can deliver on
  time from any source to its destination
• Deadline miss ratio 1 - Real-time degree

91
A Quantitative Real-time Model
92
A Quantitative Real-time Model

• End-to-end delay under different network load

End-to-end delay under different network load
93
Current Real-Time WSN Research Projects

• QoS management in real-time data services NSF
• Event services for emergency response in WSN
  NSF
• Expendable Local Area Sensors in a Tactically
  Interconnected Cluster (ELASTIC) DARPA
• Undersea sensor systems ONR/Migma Systems
• Real-time resource management for wireless sensor
  networks University of Kaiserslautern
• CodeBlue, WSNs for Medical Care Harvard
  University with NSF, NIH, U.S. Army, Sun
  Microsystems and Microsoft Corporation
• Collaboration J. Stankovic, Brogan, S. H. Son,
  Shu (Taiwan), Hansson (CMU), Andler (Sweden),
  Park (Korea), Hur (Korea), Lam (Hong Kong), Lee
  (Hong Kong), T. Abdelzaher

94
Current Real-Time WSN Research Projects

• Service middleware for sensor networks
• event detection query management services
• formal event description language
• data aggregation/dissemination
• undersea surveillance
• Real-Time and Reliable Communication in Wireless
  Sensor and Actor Networks Georgia Institute of
  Technology
• Real-Time and Embedded Systems Laboratory
  University of Virginia

95
Current Real-Time WSN Research Projects

• Real-timeliness analysis and evaluation according
  to topology control in wireless sensor networks
  NSL Lab. in sharif university
• Survey and analysis of mutual effects of security
  and Real-timeliness in wireless sensor networks
  NSL Lab. in sharif university
• Evaluation of mutual effect of Real-timeliness on
  power consumption in wireless sensor networks
  NSL Lab. in sharif university
• Modeling and evaluation of reliability in
  wireless sensor networks NSL Lab. In sharif
  university

96
Publication

• K. Mizanian, A. H. Jahangir, A Quantitative
  Real-time Model for Multihop Wireless Sensor
  Networks, in Proc. of IEEE ISSNIP 2007, Third
  International Conference on Intelligent Sensors,
  Sensor Networks and Information Processing,
  Melbourne, Australia, Dec. 2007

97
Conclusions

• This is a new field of study
• A vast majority of WSN applications are real-time
• There are several underway research projects in
  this field
• We have proposed a Quantitative Real-time Model
• We have to
• Identify and standardize Real-time WSN
  requirements
• Analyze how these standardized requirements
  impact WSN component functionalities
• Develop new communication protocols to support
  real-time and reliable event data delivery with
  minimum energy consumption in WSNs.

98

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