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Modeling the Performance of Wireless Sensor Networks

This paper presents an analytical framework for modeling the performance of wireless sensor networks (WSNs) equipped with self-organizing micro-sensors. It addresses crucial aspects such as energy efficiency, data transfer delay, energy consumption, and network capacity. By combining sensor dynamics, interference models, and traffic routing, the proposed framework enables a comprehensive understanding of fundamental trade-offs in WSN design. Numerical results showcase the impact of various parameters on network performance, aiding future research and design improvements in energy-limited networks.

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Modeling the Performance of Wireless Sensor Networks

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  1. INFOCOM 2004 – Hong Kong Modeling the Performance of Wireless Sensor Networks Carla Fabiana Chiasserini Michele Garetto Telecommunication Networks Group Politecnico di Torino, Italy

  2. Outline • Network Scenario • Our contribution • Modelling approach • Sensor model • Network model • Interference model • Numerical results • Conclusions and future work

  3. Network scenario • Large number of self organizing, unattended micro-sensors • Short radio range  multi-hop wireless communications towards a common gateway • Energy-limited (battery operated) • Sleep/active dynamics • Energy efficiency is the crucial design criterion

  4. Our contribution • Analytical model to predict the performance of a wireless sensor network • Responsiveness (data transfer delay) • Energy consumption • Network capacity • Our model combines together • Sleep / active sensor dynamics • Channel contention and interference • Traffic routing • An analytical approch to understand fundamental trade-offs and evaluate different design solutions

  5. Modelling approach • Sensed information is organized into data units of fixed length • Time is slotted • slot = time needed to transfer a data unit between two nodes (including channel access overhead) • discrete time model • Data units are generated by each sensor at a given rate (during active period) • Data units can be buffered at intermediate nodes (infinite buffers)

  6. Reference scenario sensor gateway N = 400 sensors randomly placed (uniformly) in the disk of unit radius

  7. System solution Model decomposition    SENSOR MODEL NETWORK MODEL INTERFERENCE MODEL  Iterate with a Fixed Point Solution

  8. Sensor model: assumptions ACTIVE Generation of new data units Transmission of data units Reception of data units R S SLEEP N Transmission of data units ~ geom(p) ~ geom(q) R S R N S S TIME SLOTS Buffer Buffer not empty empty

  9. Sensor model • Unknown parameters: • : probability to receive a data unit in a time slot • : probability to transmit a data unit in a time slot • Computes: • Probabilities of phases R,S,N • Average data generation rate • Sensor throughput • Average buffer occupancy

  10. System solution Model decomposition    SENSOR MODEL NETWORK MODEL INTERFERENCE MODEL  Iterate with a Fixed Point Solution

  11. Network model: assumptions • Each node A maintains up to M routes (according to some routing protocol) • Each route is associated to a different next-hop (a neighbor of A within radio range) • To forward a data unit, node A selects the best next-hop currently available to receive …zzz… Example: M = 3 1 A 2 3

  12. Locally generated traffic (computed by the Sensor Model) Total traffic forwarded by the sensors Routing matrix Network model • The sensor network can be modelled as an open queuing network • The routing matrix is computed according to routing policy of each sensor, and the sleep/active dynamics of its neighbors

  13. System solution Model decomposition    SENSOR MODEL NETWORK MODEL INTERFERENCE MODEL  Iterate with a Fixed Point Solution

  14. Wireless channel : assumptions • Common maximum radio range r • Ideal CSMA/CA protocol with handshaking (RTS-CTS) • No collisions • No wasted slots • Error-free channel • At each time slot, all feasible transmissions occur successfully • The only constraint is interference (channel contention)

  15. Probability that A can transmit a data unit in a time slot (parameter of the sensor model) Interference model Total interferer Partial interferer G F B C A E I H D

  16. Analysis of data transfer delay • A separate markov chain is build to compute the transfer delay distribution for each sensor node • The state represents the location of a data unit while moving towards the gateway

  17. 0.24 mJ/slot 0.24 mJ/slot 0.057 mJ/slot Numerical results N = 400 sensors Radio range r = 0.25 Number of routes M = 6 • Energy consumption: • active mode : 0.24 mJ/slot • sleep mode : 300 nJ/slot • sleep  active transition : 0.48 mJ • transmission/reception of data units:

  18. Average transfer delayfor 40 different sensors (p = q = 0.1) 18 sim - load = 0.9 16 mod - load = 0.9 14 12 10 data delivery delay (slots) 8 6 4 sim - load = 0.4 2 mod - load = 0.4 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 distance from sink

  19. Transfer delay distribution for the farthest sensor (p = q = 0.1) sim - load = 0.4 mod - load = 0.4 sim - load = 0.9 0.1 mod - load = 0.9 pdf 0.01 0.001 0 10 20 30 40 50 60 data delivery delay (slots)

  20. sim mod delay [slot] Energy / delay trade-off (1) 0.3 (load = 0.4) sim 100 mod 0.25 energy cons. [mJ] 0.2 0.15 10 0.1 SLEEP 0.05 1 0 p q 0.1 1 10 q/p ACTIVE

  21. sim mod delay [slot] Energy / delay trade-off (2) 30 0.24 (load = 0.9) sim mod 0.22 energy cons. [mJ] 25 0.2 0.18 20 0.16 0.14 15 0.12 10 0.1 0.025 0.05 0.1 0.2 0.4 p ( = q )

  22. Conclusions and future work • We have developed an analytical model of a wireless sensor network, capable of predicting the fundamental performance metric and trade-offs • Many possible extensions: • Introduction of hierarchy (clusters) • Finite buffers and channel errors • Congestion control mechanisms • More details at the MAC level • Impact of node failures  network lifetime

  23. References • Carla Fabiana Chiasserini, Michele Garetto, “Modeling the Performance of Wireless Sensor Networks”, IEEE INFOCOM, Hong Kong, March 7-11, 2004

  24. Sensors unfairness 0.011 0.3 energy consumption 0.01 0.25 0.009 0.008 0.2 0.007 0.006 0.15 energy consumption generation rate 0.005 0.004 0.1 0.003 0.002 0.05 generation rate 0.001 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 distance from sink

  25. Sensor model validation Average Generation Rate Sensor Throughput 0.003 y = x y = x 0.05 0.0025 0.04 0.03 0.002 mod mod 0.02 0.0015 0.01 0.001 0 sim sim Average Buffer Occupancy Probability of Phase N 1.4 y = x y = x 0.4 1.2 1 0.3 0.8 mod mod 0.2 0.6 0.4 0.1 0.2 0 0 sim sim

  26. Network model validation 1 y = x sensor throughput 0.1 mod 0.01 0.001 0.001 0.01 0.1 1 sim

  27. Interference model validation 1 0.9 0.8 0.7 0.6 β 0.5 0.4 sim 0.3 mod 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 distance from sink

  28. Assumptions - CSMA/CA (RTS/CTS) D …zzz… E F …zzz… B CTS C A …zzz… G RTS

  29. Modern Sensor Nodes UC Berkeley: COTS Dust UC Berkeley: Smart Dust UC Berkeley: COTS Dust Rockwell: WINS UCLA: WINS JPL: Sensor Webs

  30. Interference model validation 1 0.9 0.8 0.7 0.6 β 0.5 0.4 sim 0.3 mod 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 distance from sink

  31. Interference model validation 0.08 sim 0.07 0.06 0.05  0.04 0.03 0.02 0.01 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 distance from sink

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