Transcript Wireless Sensor networks

University of Tehran
Dept. Electrical and Computer Engineering
Wireless Sensor networks
survey and research challenges
Presented by
Hosein Sabaghian-Bidgoli
[email protected]
January 11, 2009
Wireless Ad hoc Network (Fall 2008)
WSN survey and research challenges
Outlines
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Main references
Introduction
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Definition
Communication Architecture
Protocol stack
WSN Characteristics
WSN Design factors
WSANs
WSN Structures
WSN Constraints
WSN Applications
WSN types
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WSN survey and research challenges
Outlines (cont.)
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Task classification
 Internal sensor system
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Standard
Storage
Testbed
Diagnostic and debugging support
 Network services
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Localization
Synchronization
Coverage
Compression and aggregation
Security
 Communication protocol
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Transport
Network
Data link
Physical
Cross-layer
Conclusion
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WSN survey and research challenges
Main references
1.
2.
3.
Ian F. Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal
Cayirci, A Survey on Sensor Networks, IEEE Communications
Magazine, August 2002
Ian F. Akyildiz, Ismail H. Kasimoglu, Wireless sensor and actor
networks research challenges, Elsevier Ad Hoc Networks 2 (2004)
351–367
Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal, Wireless sensor
network survey, Elsevier Computer Networks 52 (2008) 2292–2330
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WSN survey and research challenges
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WSN survey and research challenges
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WSN survey and research challenges
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WSN survey and research challenges
Introduction
WSN Definition
A sensor network is composed of a large
number of sensor nodes that are densely
deployed inside or very close to the
phenomenon
 random deployment
 self-organizing capabilities
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WSN survey and research challenges
Introduction
WSN communication Architecture
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WSN survey and research challenges
Introduction
Components of Sensor Node
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WSN survey and research challenges
Introduction
Protocol Stack
 Protocols
should be
 Power aware
 Location aware
 Application aware
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WSN survey and research challenges
Introduction
WSN Characteristics
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Major differences between sensor and ad-hoc
network
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Number of nodes is higher
Densely deployment
Sensor nodes are prone to failure.
Frequent topology changes
Broadcast communication paradigm
Limited processing and power capabilities.
Possible absence of unique global ID
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WSN survey and research challenges
Introduction
WSN Design Factors
 Fault
Tolerance
 Scalability
 Production Costs
 Hardware Constraints
 Sensor Network Topology
 Environment
 Transmission Media
 Power Consumption
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WSN survey and research challenges
WSN Design Factors
Fault Tolerance
 Each
Nodes are prone to unexpected
failure (more than other network)
 Fault tolerance is the ability to sustain
sensor network functionalities without
any interruption due to sensor node
failures.
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WSN survey and research challenges
WSN Design Factors
Scalability
 Size:
Number of node (100 ~1000)
 Density : μ(R)=(N R2)/A
 Protocol should
 be able to scale to such high degree
 take advantage of the high density of such
networks
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WSN survey and research challenges
WSN Design Factors
Production Costs
 The
cost of a single node must be low
given the amount of functionalities
 Much less than $1
[1]
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WSN survey and research challenges
WSN Design Factors
Hardware Constraints
 All
these units combined together must
 Extremely low power
 Extremely small volume
[1]
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WSN Design Factors
Topology
 Must
be maintained specially in very
high densities
 Pre-deployment and deployment phase
 Post-deployment phase
 Re-deployment of additional nodes phase
[1]
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WSN survey and research challenges
WSN Design Factors
Environment
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May be inaccessible
 either because of hostile environment
 or because they are embedded in a structure
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Impact of environment condition
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Temperature
Humidity
Movement
Underwater
Underground
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WSN survey and research challenges
WSN Design Factors
Transmission Media
 RF
 Infrared
 Optical
 Acoustic
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WSN Design Factors
Power Consumption
 Power
conservation
 Sensing
 Communication
 Data processing
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wireless sensor and actor
networks (WSANs)
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WSAN Capabilities
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Observing the physical world
Processing the data
Making decisions
Performing appropriate actions
WSAN applications:
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battlefield surveillance
microclimate control in buildings
nuclear, biological and chemical attack detection
Home automation
environmental monitoring
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WSN survey and research challenges
WSANs unique characteristics
 Real-time
requirement
 Coordination:
 Sensor-Actor Coordination
 Actor-Actor Coordination
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WSN survey and research challenges
WSN structure
 A WSN
typically has little or no
infrastructure
 There are two types of WSNs
 Structured model
 Unstructured model
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WSN survey and research challenges
Unstructured model
 Densely
deployed (many node)
 Randomly Deployed
 Can have uncovered regions
 Left unattended to perform the task
 Maintenance is difficult
 managing connectivity
 detecting failures
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WSN survey and research challenges
Structured model
 Deployed
in a pre-planned manner
 Fewer nodes
 Lower network maintenance
 Lower cost
 No uncovered regions
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WSN survey and research challenges
WSN constraints
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Resource constraints
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limited energy
short communication range
low bandwidth
limited processing
limited storage
Design constraints
 application dependent
 environment dependent
– size of the network / number of node
– deployment scheme
– network topology (obstacle)
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WSN survey and research challenges
Available sensors in the market
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Generic nodes (take measurements)
 Light, Temperature, Humidity, barometric pressure,
velocity, Acceleration, Acoustics, magnetic field
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Gateway (bridge) node
 gather data from generic sensors and relay them
to the base station
 higher processing capability
 higher battery power
 higher transmission (radio) range
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Types of sensor network
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Depending on the environment
1. terrestrial WSN
– Ad Hoc (unstructured)
– Preplanned (structured)
2. underground WSN
– Preplanned
– more expensive equipment, deployment, maintenance
3. underwater WSN
– fewer sensor nodes( sparse deployment)
– more expensive than terrestrial
– acoustic wave communication
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Limited bandwidth
long propagation delay
signal fading
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Types of sensor network (cont.)
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Depending on the environment
4. multi-media WSN
– sensor nodes equipped with cameras and microphones
– pre-planned to guarantee coverage
– High bandwidth/low energy, QoS, filtering, data processing
and compressing techniques
5. mobile WSN
– ability to reposition and organize itself in the network
– Start with Initial deployment and spread out to gather
information
– deployment, localization, self-organization, navigation and
control, coverage, energy, maintenance, data process
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WSN applications
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WSN applications (Open research issues)
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application-specific characteristics and requirements of
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environmental monitoring
health monitoring
industrial monitoring
Military tracking
Coupled with today’s technology
Lead to different hardware platforms and software
development
more experimental work is necessary to make these
applications more reliable and robust in the real world
Applying sensor technology to industrial applications
will improve business
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WSN survey and research challenges
Tasks Classification
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Systems
 Each sensor node is an individual system
 Development of new platforms, operating
systems, and storage schemes
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Communication protocols
 Between sensors
 In different layer(app, trspt, net, DLink, phy)
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services
 which are developed
– to enhance the application
– to improve system performance
– and network efficiency
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Internal sensor system
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sensor platform
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radio components
processors
Storage
sensors (multiple)
OS
 OS must support these sensor platforms
researches:
 Designing platforms that support
 automatic management
 optimizing network longevity,
 distributed programming
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Platform Sample 1
(Bluetooth-based sensor networks)
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WSN typically uses single freq (Share channel)
BTnodes use spread-spectrum transmission
A special version of TinyOS is used
Two radio communication
 Master (up to 7 connection)
 Slave
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Note:
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Bluetooth is connection oriented
New node enables its slave radio
Topology: connected tree
high throughput, high energy consumption
[3]
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Platform Sample 2:VigilNet
(Detection-and-classification system)
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detection and classification
 vehicles
 persons
 persons carrying ferrous objects
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200 sensor nodes with
 Magnetometer
 motion sensor,
 and a microphone
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deployed in a preplanned manner
four tiers hierarchical architecture
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sensor-level,
node-level,
group-level,
and base-level
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WSN survey and research challenges
Internal sensor system
Standards
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IEEE 802.15.4:
 standard for low rate wireless personal area
networks (LR-WPAN)
 low cost deployment
 low complexity
 low power consumption
 topology :star and peer-to-peer
 physical layer: 868/915 MHz ~2.4 GHz
 MAC layer: CSMA-CA mechanism
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WSN survey and research challenges
Internal sensor system
Standards
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ZigBee
 higher layer communication protocols built on the
IEEE 802.15.4 standards for LR-PANs.
 simple, low cost, and low power
 embedded applications
 can form mesh networks connecting hundreds to
thousands of devices together.
 types of ZigBee devices:
– ZigBee coordinator: stores information, bridge
– ZigBee router: link groups of devices
– ZigBee end device: sensors, actuators communicate only
to routers
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WSN survey and research challenges
Internal sensor system
Standards
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IEEE 802.15.3:
 physical and MAC layer standard high data rate
WPAN.
 support real-time multi-media streaming
 data rates (11 Mbps to 55 Mbps)
 time division multiple access (TDMA) =>QoS
 synchronous and asynchronous data transfer
 wireless speakers, portable video, wireless
connectivity for gaming, cordless phones, printers,
and televisions
[3]
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WSN survey and research challenges
Internal sensor system
Standards
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WirelessHART (released in September 2007)
 Process measurement and control applications
 based on IEEE 802.15.4
 supports channel hopping, and time-synchronized
messaging
 Security with encryption, verification,
authentication and key management
 support mesh, star, and combined network
topologies
 manages the routing and network traffic
[3]
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Internal sensor system
Standards
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ISA100.11a
 defines the specifications for the OSI layer, security,
and system management
 low energy consumption, scalability, infrastructure,
robustness
 interoperability with other wireless devices
 use only 2.4 GHz radio and channel hopping to
minimize interference
 provides simple, flexible, and scaleable security
functionality.
[3]
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Internal sensor system
Standards
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6LoWPAN
 IPv6-based Low power Wireless Personal Area Networks
 over an IEEE 802.15.4 based network.
 Low power device can communicate directly with IP devices
using IPbased protocols
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Wibree
 designed for low power consumption, short-range
communication, and low cost devices
 is designed to work with Bluetooth
 operates on 2.4 GHz
 data rate of 1 Mbps
 linking distance is 5–10 m.
 was released publicly in October 2006.
[3]
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WSN survey and research challenges
Internal sensor system
Storage
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problems
 storage space is limited
 Communication is expensive
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Solutions
 Aggregation and compression
 query-and-collect (selective gathering)
 a storage model to satisfy storage constraints and query requirements
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GEM: Graph Embedding
 provides an infrastructure for routing and data-centric storage
1. choosing a labeled guest graph
2. embed the guest graph onto the actual sensor topology
 Each node has a label encoded with its position
 each data item has a name that can be mapped to a label
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TSAR: Two-tier sensor storage architecture
Multi-resolution storage: provides storage and long-term querying of the
data for data-intensive applications
[3]
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WSN survey and research challenges
Internal sensor system
Testbeds
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Provides researchers a way to test their protocols, algorithms,
network issues and applications in real world setting
Controlled environment to deploy, configure, run, and monitoring of
sensor remotely
Some testbeds:
 ORBIT: Open access research testbed for next generation
wireless networks
– 64 nodes, 1 GHZ
 MoteLab: web-based WSN testbed
– central server handles scheduling, reprogramming and data
logging of the nodes
 Emulab: remotely accessible mobile and wireless sensor (such
as a robot)
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Internal sensor system
Diagnostics and debugging support
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Measure and monitor the sensor node performance of the
overall network
to guarantee the success of the sensor network in the real
environment
Sympathy:
 is a diagnosis tool for detecting and debugging failures in sensor
networks
 designed for data-collection applications
 detects failures in a system by selecting metrics such as
– Connectivity
– data flow
– node’s neighbor
 can identify three types of failures: self, path and sink
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Analysis of data packet delivery:
 packet delivery performance at the physical and MAC layers
[3]
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Internal sensor system
Open research issues
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optimization of (HW, SW, HW/SW) to make a WSN
efficient
more practical platform solution for problems in new
applications
data structure
 Performance
 energy-efficient storage
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Performance
 communication throughput when network size increases
 Scalability issues can degrade system performance
 Optimizing protocols at different layers
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services to handle node before and after failures
[3]
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WSN survey and research challenges
Network services
Localization
 Synchronization
 Coverage
 Compression and aggregation
 Security
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[3]
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Network services
Localization
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Problem:
 determining the node’s location (position)
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Solutions:
 global positioning system (GPS)
– Simple
– Expensive
– outdoor
 beacon (or anchor) nodes
– does not scale well in large networks
– problems may arise due to environmental conditions
 proximity-based
– Make use of neighbor nodes to determine their position
– then act as beacons for other nodes
[3]
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WSN survey and research challenges
Network services
Localization
 Other
solutions:
 Moore’s algorithm:
– distributed algorithm for location estimation
without the use of GPS or fixed beacon
(anchor) nodes
– algorithm has three phases:
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cluster localization phase
cluster optimization phase
cluster transformation phase
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WSN survey and research challenges
Network services
Localization
 Other
solutions:
 RIPS: Radio Interferometric Positioning
System
– Two radio transmitters create an interference
signal at slightly different frequencies
– At least two receivers are needed to measure
relative phase of two signal
– The relative phase offset is a function of the
relative positions
[3]
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WSN survey and research challenges
Network services
Localization
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Other solutions:
 Secure localization:
– goal is to prevent malicious beacon nodes from providing
false location to sensors
– Sensors must only accept information from authenticated
beacon nodes
– Sensors should be able to request location information at
anytime
– Upon a location request, information exchange must take
place immediately and not at a later time.
– SeRloc, Beacon Suite, DRBTS, SPINE, ROPE
[3]
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WSN survey and research challenges
Network services
Localization
 Other
solutions:
 MAL: Mobile-assisted localization
– Mobile node collects distance information
between itself and static sensor nodes for node
localization
– given a graph with measured distance edges
[3]
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WSN survey and research challenges
Network services
Synchronization
 Time
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synchronization is important for
routing
power conservation
Lifetime
Cooperation
Scheduling
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WSN survey and research challenges
Network services
Synchronization
Uncertainty-driven approach
 Lucarelli’s algorithm
 Reachback firefly algorithm (RFA)
 Timing-sync protocol for sensor network
(TPSN)
 CSMNS
 Time synchronization (TSync)
 Global synchronization
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[3]
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WSN survey and research challenges
Network services
Synchronization
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Synchronization protocol classification:
 application-dependent features approaches
– single-hop vs. multi-hop networks
– stationary vs. mobile networks
– MAC layer-based vs. standard-based
 synchronization issues
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adjusting their local clocks to a common time scale
master–slave synchronization
peer-to-peer synchronization
clock correction
untethered clocks
internal synchronization,
external synchronization,
Probabilistic synchronization,
deterministic synchronization,
sender to receiver synchronization,
and receiver-to-receiver synchronization.
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Network services
Coverage
 Is
important in evaluating effectiveness
 Degree of coverage is application
dependent
 Impacts on energy conservation
 Techniques:
 selecting minimal set of active nodes to be
awake to maintain coverage
 sensor deployment strategies
[3]
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WSN survey and research challenges
Network services
Compression and aggregation
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Both of them
 reduce communication cost
 increase reliability of data transfer
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Data-compression
 compressing data before transmission to base
 Decompression occurs at the base station
 no information should be lost
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data aggregation
 data is collected from multiple sensors
 combined together to transmit to base station
 Is used in cluster base architectures
[3]
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Network services
Security
 Constraints
in incorporating security into
a WSN
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limitations in storage
limitations in communication
limitations in computation
limitations in processing capabilities
[3]
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Network services
Open research issues
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localization
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efficient algorithms
minimum energy
minimum cost
minimum localization errors
Coverage: optimizing for better energy conservation
time synchronization: minimizing uncertainty errors over long periods of
time and dealing with precision
compression and aggregation: Development of various scheme
 event-based data collection
 continuous data collection
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Secure monitoring: protocols have to monitor, detect, and respond to
attacks
 It has done for network and data-link layer (can be improved)
 Should be done for different layers of the protocol stack
 Cross-layer secure monitoring is another research area
[3]
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WSN survey and research challenges
Communication protocol
 Transport
layer
 Network layer
 Data-link layer
 Physical layer
[3]
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WSN survey and research challenges
Communication protocol
Transport layer
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Packet loss
 may be due to
–
–
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–
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bad radio communication,
congestion,
packet collision,
memory full,
node failures
 Detection and recovering
– Improve throughput
– Energy expenditure
[3]
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WSN survey and research challenges
Communication protocol
Transport layer
 Congestion
control/packet recovery
 hop-by-hop
– intermediate cache
– more energy efficient (shorter retransmission)
– higher reliability
 end-to-end
– source caches the packet
– Variable reliability
[3]
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WSN survey and research challenges
Communication protocol
Transport layer
Sensor transmission control protocol (STCP)
 Price-oriented reliable transport protocol
(PORT)
 GARUDA
 Delay sensitive transport (DST)
 Pump slowly, fetch quickly (PSFQ)
 Event-to-sink reliable transport (ESRT)
 Congestion detection and avoidance (CODA):
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[3]
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WSN survey and research challenges
Communication protocol
Transport layer (Open research issues)
 cross-layer
optimization
 selecting better paths for retransmission
 getting error reports from the link layer
 Fairness
 assign packets with priority
 frequently-changing topology
 Congestion
control with active queue
management
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WSN survey and research challenges
Communication protocol
Network layer
 Important:
 energy efficiency
 traffic flows
 Routing
protocols
 location-based: considers node location to
route data
 cluster-based: employs cluster heads to do
data aggregation and relay to base station
[3]
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WSN survey and research challenges
Communication protocol
Network layer (Open research issues)
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Future research issues should address
 Security
– Experimental studies regarding security applied to
different routing protocols in WSNs should be examined
 QoS
– guarantees end-to-end delay and energy efficient routing
 node mobility
– handle frequent topology changes and reliable delivery
[3]
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WSN survey and research challenges
Communication protocol
Data-link layer (Open research issues)
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system performance optimization
Cross-layer optimization
 Cross-layer interaction can
– reduce packet overhead on each layer
– reduce energy consumption
 Interaction with the MAC layer provide
– congestion control information
– enhance route selection
 Comparing performance of existing protocols of static
network in a mobile network
 improve communication reliability and energy efficiency
[3]
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WSN survey and research challenges
Communication protocol
Physical layer
 Bandwidth
choices
 Radio architecture
 Modulation schemes
[3]
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WSN survey and research challenges
Communication protocol
Physical layer (Open research issues)
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Minimizing the energy consumption
 Optimizing of circuitry energy
– reduction of wakeup and startup times
 Optimizing of transmission energy
– Modulation schemes

Future work
 new innovations in low power radio design with emerging
technologies
 exploring ultra-wideband techniques as an alternative for
communication
 creating simple modulation schemes to reduce
synchronization and transmission power
 building more energy-efficient protocols and algorithms
[3]
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Communication protocol
Cross-layer interactions (Open research issues)
 Collaboration
between all the layers to
achieve higher
 energy saving
 network performance
 network lifetime
[3]
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Conclusion




Large number of application is exist regarding to
WSN
Large number of work has done on WSN
There are still many open issue research in WSN
Open research area:







Application-specific characteristic
Power efficient algorithm
Cross-layer optimization
more experimental work to reach more reliability
Improvement of existing protocol
Security
Error reduction in localization
[3]
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Main references
1.
2.
3.
Ian F. Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal
Cayirci, A Survey on Sensor Networks, IEEE Communications
Magazine, August 2002
Ian F. Akyildiz, Ismail H. Kasimoglu, Wireless sensor and actor
networks research challenges, Elsevier Ad Hoc Networks 2 (2004)
351–367
Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal, Wireless sensor
network survey, Elsevier Computer Networks 52 (2008) 2292–2330
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