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August 16, 2014 Modeling the Performance of Wireless Sensor Networks Carla Fabiana Chiasserini Michele Garetto Telecommunication Networks Group Politecnico.

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Presentation on theme: "August 16, 2014 Modeling the Performance of Wireless Sensor Networks Carla Fabiana Chiasserini Michele Garetto Telecommunication Networks Group Politecnico."— Presentation transcript:

1 August 16, 2014 Modeling the Performance of Wireless Sensor Networks Carla Fabiana Chiasserini Michele Garetto Telecommunication Networks Group Politecnico di Torino, Italy INFOCOM 2004 – Hong Kong

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

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

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

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

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… A Example: M = 3

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

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

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

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 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: 0.24 mJ/slot mJ/slot

18 data delivery delay (slots) distance from sink sim - load = 0.4 mod - load = 0.4 sim - load = 0.9 mod - load = 0.9 Average transfer delay for 40 different sensors ( p = q = 0.1 )

19 pdf data delivery delay (slots) sim - load = 0.4 mod - load = 0.4 sim - load = 0.9 mod - load = 0.9 Transfer delay distribution for the farthest sensor ( p = q = 0.1 )

20 q/p energy cons. [mJ] sim mod delay [slot] sim mod qp SLEEP ACTIVE Energy / delay trade-off (1) (load = 0.4)

21 p ( = q ) delay [slot] sim mod sim mod Energy / delay trade-off (2) energy cons. [mJ] (load = 0.9)

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 generation rate energy consumption distance from sink generation rate energy consumption Sensors unfairness

25 mod sim Average Buffer Occupancy y = x mod sim Sensor Throughput y = x mod sim Average Generation Rate y = x mod sim Probability of Phase N y = x Sensor model validation

26 mod sim y = x sensor throughput Network model validation

27 β distance from sink sim mod Interference model validation

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

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

30 sim mod β distance from sink Interference model validation

31  distance from sink sim Interference model validation


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