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Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 1
Wireless Sensor Networks
Boleslaw [email protected]
Center for Pervasive Computing and Networking
Rensselaer Polytechnic Institute
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 2
networking distributed computing real time systems simulations multimedia
adaptive scalable run-time execution environments
mobile software engineering
scalable distributed real-time systems
distributed system security
next generation network management
crisis & catastrophe management
mobile asset management
educationassistive technologies for the disabled
secure access & protection of assets
integration, scalability and composability:
fundamental contributing fields of computer science and engineering:
applications:
RPI’s Center onPervasive Computing and Networking:
Hierarchy of Challenges
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 3
Gap between Trends Drives System Design
Changes relative cost-structures… Larger implications also for impact of IT on society
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 4
Pervasive Wireless Communications
Ever-growing communication capacity needs for broadband wireless and mobile accessing in multimedia-rich environment, everywhere and anytime
There is a worldwide recognition that traditional methods of radio resource usage reach their limit and are no longer optimal
New communications frontier need to be explored for future wireless and mobile environments
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 5
Sense-and-respond systemsSense-and-respond systemsBackground
“Sentient” networks: Computer networks composed of embedded “nodes” with onboard sensing, computational, and communication capability used for autonomous environmental monitoring
Military acousticnetworks
Air-defense radars
DARPA-lead projects[SensIT]
Past (80s-90s)Past (80s-90s)
Present (last 5 yrs.)Present (last 5 yrs.)Multi-modal devices
Ad hoc comm.
Small form factor
Industrial apps.
Future (next decade)Future (next decade)
Smart “dust”
Low cost
Disposable
Consumer apps.
Heterogeneousnetworks
Pervasive Ubiquitous
Actuators: Responsive services/ devices offering sensor or environmental control
The “Embedded Internet!”The “Embedded Internet!”
•System size•Amount of decentralization•Projected revenues!!!
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 6
SensorTemp., light, humidity,
chemicals, acoustics, vibration
Computer4 MHz Atmel ATmega 128L
(equiv. to original ’82 IBM PC)CC RRSS
Radio2.4 GHz IEEE 802.15.4,<100m TX range
Basestation
Sense-and-respond systemsSense-and-respond systemsWireless sensor networks and applications
Features Offers macroscopic observation for real-time
environmental/contextual interaction Self-organizing, self-regulating, and self-repairing systems Multi-hop or direct-connect configurations to base station(s) Current state – extremely application-oriented!!!
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 7
SensorTemp., light, humidity,
chemicals, acoustics, vibration
Computer4 MHz Atmel ATmega 128L
(equiv. to original ’82 IBM PC)
• Enemy intrusion detection• Habitat monitoring• Structural monitoring• Home automation and safety• Traffic control• Supply chain management (RFID)
Practical applicationsPractical applications
CC RRSS
Radio2.4 GHz IEEE 802.15.4,<100m TX range
Basestation
Sense-and-respond systemsSense-and-respond systemsWireless sensor networks and applications
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 8
Sense-and-respond systemsSense-and-respond systemsSalient Challenges
Constrained resources Limited CPU, battery, and storage Premium communication costs
Ad hoc routing Dynamic topology
Transient wireless links and devices Collaborative information processing
Faulty sensors produce erroneous data Tradeoffs between performance and
resource utilization Sensor and actuator interaction
Synchronization between independent and heterogeneous services
Many more (querying, tasking, security, pollution, etc.)
Crossbow® MICAZ mote
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 9
ESCORT: MotivationESCORT: Motivation
Wireless communication is a premium cost
Transient wireless links threaten application integrity Experiments show that at least 20%
of nodes exhibit at least 10% packet loss, and at least 10% of nodes exhibit more than 30% packet loss
Assuming an ARQ protocol is used, transmission cost increases as link quality worsens
Many proposed routing protocols A protocol-independent method for
enhancing energy-efficiency must be adopted
WSNs are envisioned to be highly redundant
Function/component
Operating current (mA)
Transmission (full power)
25
Reception 8Radio(sleep)
<1µA
Sensor board (full
power)
5
Sensor board (sleep)
5µA
CPU(full power)
8
CPU(sleep)
8µA
MICA2DOT series specs. [Crossbow]
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 10
ESCORT: OverviewESCORT: Overview
Blue and orange nodes form communities which act as “virtual” nodes to the network layer
Orange nodes help coordinate community operation Green nodes are shared neighbors of the community Signal quality assessment, a combination of two
separate metrics, is used to form clusters of redundant nodes
Source Sink
Communal node
Shared neighbor Communication borderCoordinator node
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 11
Lecture Hall AlgorithmLecture Hall Algorithmand the local leader election problemand the local leader election problem
Local leader election describes the problem of finding a node (leader) with the most desired property among a local group of nodesExample desired properties may include
distance-from-destination, energy, computational load, etc.
Can readily be applied to routing (selecting the next hop neighbor)
The traditional approach would require at least n messages and log(n) time
We require at most 3 messages in constant time using the “self-selection” algorithm
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 12
SSR: OverviewSSR: Overview
Forwarding tables are not used Packets are forwarded based on a gradient metric – “hop
count” Packets are freely broadcast to all neighbors and “self-
selection” is used to determine the forwarding node
X-dimY-dim
Hop
coun
t
-10-5
0 5
10 -10
-5
0
5
10
-15
-10
-5
0
5
10
15
Hop
coun
t
Basestation
Simulated WSN Simplified SSR example
Basestation
Boleslaw Szymanski, RPI, Troy, NYCenter for Pervasive Computing and Networking 13
The SSR algorithm: ConclusionThe SSR algorithm: Conclusion
Remarks Low overhead for route maintenance and repair Good performance with simulated device failures and
transient (and asynchronous) links Self-selection algorithm is well suited for application
tuningFuture work Evaluate SSR using real wireless sensor network under
various operational conditions Explicitly extend SSR to exhibit energy-efficiency (via
radio control) Interact radio behavior with link behavior or self-
selection results Reduce amount of required synchronization