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SenProbe: Path Capacity Estimation in Wireless Sensor Networks Tony Sun, Ling-Jyh Chen, Guang Yang M. Y. Sanadidi, Mario Gerla Outline Motivation Background Design Consideration What do we want to measure? Effective Capacity Previous Method Proposed Approach: SenProbe Simulation Results Conclusion SenMetrics05 2 Motivation Mobile computing platforms may interact with ambient sensor environment establishing “Opportunistic wireless networks” Evaluation and measurement of wireless path capacity in sensor network is of realistic interest (i.e. Capacity planning, protocol design, performance analysis, system deployment, assess applicability of deployment) Need a tool that can monitor and measure opportunistic path capacity in wireless sensor networks SenMetrics05 3 Outline Motivation Background Design Consideration What does SenProbe actually measure? Effective Capacity Previous Method Proposed Approach: SenProbe Simulation Results Conclusion SenMetrics05 4 Design Consideration CSMA-CA and variant schemes still popularly used in sensor network for its simplicity (IEEE 802.15.4 beaconless mode, Berkeley motes, etc) Basic CSMA-CA doesn’t incorporate RTS/CTS mechanisms Send packet when an idle channel is detected Smaller packet overhead if idle channel can be detected quickly Suffers from hidden terminal problem SenMetrics05 5 What do we want to measure? (1) The effective end-to-end rate is defined as the maximum achievable data rate in the absence of any cross traffic connection. It is smaller than the raw data rate at the physical layer due to Packet Overhead Interference between multiple packets in the pipeline SenMetrics05 7 What do we want to measure? (2) In fact, path capacity in wireless net also varies with: MAC protocol and link scheduling Link interference S/N ratio; Tx power Encoding/modulation scheme Number of antennas (eg MIMO) Antenna directionality etc SenMetrics05 8 Neighborhood Example If Dr=Di=250m , nodes {3,4,5} are within the same n-hood, C’=C/3 If Dr=250m, Di=500m, nodes {2,3,4,5,6} are in n-hood, C’=C/4 Dr= effective receive range from node 4 (solid-line circle) Di = interference range caused by node 4 (dotted-line circle) Distance between nodes: 200m SenMetrics05 9 Effective Capacity of CSMA-CA Enabled Wireless Channel The effective capacity of a one-hop link can be calculated as C TPacket TPacket CP TACK TOverhead TPacket S Packet CP TACK S ACK CP For the CSMA environment in our study (if ACKs are used) C 6ms 2Mbps 1.89Mbps 6ms 0.156ms 0.188ms SenMetrics05 10 Previous Work (Morris et al) Dr=250m, Di=500m Use UDP flows to probe the maximum achievable throughput (brute force method) SenMetrics05 11 Outline Motivation Background Design Consideration What does SenProbe actually measure? Effective Capacity Previous Method Proposed Approach: SenProbe Simulation Results Conclusion SenMetrics05 12 CapProbe Concept Key insight: a packet pair that gets through with zero queueing delay yields the exact estimate Capacity SenMetrics05 13 Issues: Compression and Expansion • Queueing delay on the first packet => compression • Queueing delay on the second packet => expansion SenMetrics05 14 SenProbe Path capacity estimation tool specially designed for the multi-hop CSMA based wireless networks. One-way estimation technique, based on CapProbe concepts Aimed to simplify the path capacity estimation process A back-to-back packet train technique designed to overcome the hidden terminal effects in CSMA environment SenProbe measures end-to-end effective capacity in wireless ad hoc networks. SenProbe is simple, fast and less intrusive to comparative techniques. SenMetrics05 15 SenProbe Algorithm(1) Instead of using back-to-back packet pairs, SenProbe relies on back-to-back packet train to overcome the effect of hidden terminal in CSMA-CA The length of this back-to-back packet train depends on the interference range and the transmission range of the specific radio technology under question InterferenceRange NTRAIN 2 Tranmissio nRange SenMetrics05 16 SenProbe Algorithm(2) The receiver measures the OWD of every packet in kth packet train received as the difference between time received and time sent OWD[k , i] Trcv [k , i] Tsend [k , i], 1 i NTRAIN the minimum OWDSUM is kept for OWDSUM [k ] min OWD [k , i] OWD [k , j ] the kth packet train. The “good” 1i j N dispersion sample r (i.e. samples r arg min OWDSUM [k ] encountering no cross traffic) is the k sample with the minimum OWD sum TRAIN Dispersion of the good sample calculated, and used to estimation capacity SenMetrics05 T OWD [r , v] OWD [r , u ] P C T 17 SenProbe-Visualization 4 3 2 1 1) 4 3 2 1 4 3 1 4 1 2) 3) 4) SenMetrics05 18 Outline Motivation Background Design Consideration What does SenProbe actually measure? Effective Capacity Previous Method Proposed Approach: SenProbe Simulation Results Conclusion SenMetrics05 19 Simulation Results (1) End-to-End Capacity (Mbps) 2.5 Packet-pair technique FTP 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 Number of hops Path Capacity measured via FTP connection and Packet-Pair technique (one way CapProbe) SenMetrics05 20 Simulation Results (2) End-to-End Capacity (Mbps) 2.5 SensorProbe Theoretical 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 Number of hops Path capacity of adhoc multi-hop forwarding chain in CSMA-CA wireless environment SenMetrics05 21 Simulation Results (3) End-to-End Capacity (Mbps) 2.5 Theoretical SensorProbe 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 Number of hops End-to-end capacity estimation of multi-hop connections within the same collision domain SenMetrics05 22 Simulation Results (4) End-to-End Capacity (Mbps) 2.5 Theoretical SensorProbe 2 1.5 1 0.5 0 1 2 3 4 5 Number of hops 6 7 8 Capacity estimates along a multi-hop forwarding chain for CSMA-CA with ACK enabled wireless sensor network SenMetrics05 23 Conclusion SenProbe uses back-to-back packet trains, and relies on packet dispersion between the packet trains to measure the path capacities in a one-way fashion. SenProbe estimates e2e path capacity in CSMA enabled wireless sensor networks. SenProbe is a simple and non-intrusive technique that can accurately reflects the effective path capacity SenMetrics05 25 Thanks! SenMetrics05 26