Evaluating the Effect of Path Diversity over QoS and QoE in a High Speed Indoor Mesh Backbone

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1 Evaluating the Effect of Path Diversity over QoS and QoE in a High Speed Indoor Mesh Backbone Sandip Chakraborty 12, Sukumar Nandi Department of Computer Science and Engineering Indian Institute of Technology Guwahati, Guwahati INDIA 08 January, This work is supported by TATA Consultancy Services (TCS), INDIA through TCS Research Fellowship program 2 Supported by COMSNETS 2014 Travel Grant Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

2 Preface: QoS and QoE in the Network Quality of Service (QoS): Performance guarantee from network perspective, - Throughput (available bandwidth) - End-to-end delay - Jitter (variation in per packet delay) Quality of Experience (QoE): Performance for user s perspective, - Mean Opinion Score (MOS) for voice traffic - Peak Signal to Noise Ratio (PSNR) for video traffic - Structural Similarity Index Measurement (SSIM) for video traffic How path diversity affect QoS and QoE in a multi-hop IEEE n mesh network in an indoor scenario? Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

3 Diversity in a Mesh Network Path Diversity: Multiple paths from a source to a destination Traffic Diversity: Diversity in Path Quality: Path quality significantly varies with respect to time. Data Rate Diversity: Varies from 2 Mbps to 600 Mbps with IEEE n Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

4 Forwarding in a Mesh Network (IEEE s) Hybrid Wireless Mesh Protocol (Combination of proactive and reactive routing) Routing metric: Airtime Link Metric [ C = O ca +O p + B ] t 1 r 1 e f Where, O ca and O p are the constants, named as the channel access overhead and the protocol overhead, B t is the test frame size. The input parameters r and e f are the bit rate in Mbps and the frame error rate for the test frame size B t. Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

5 Testbed Environment N3 N 2 N 1 50 m G RS Lab 2 RS Lab 1 N4 N9 N10 N 8 Security Lab N5 N 6 N 7 Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

6 Testbed Setup Router: RaLink RT-3352 RoC: 2T2R MAC/BBP/PA/RF, 400MHz MIPS24KEc CPU, 64MB of SDRAM and 32MB of Flash IEEE n: 300 Mbps, channel bonding Open80211s: Linux Kernel TCP (FTP) and UDP (TFTP) using iperf ( Tx Power 16dBm, Rx Sensitivity 0 dbm (45-55 mt in indoor) Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

7 Effect of Diversity over Airtime Link Metric SINR (dbm) Load ALM (ms) Time (Hrs) Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

8 Selective Greedy Forwarding (SGF) 3 Selection of a set of potential forwarders (Proactive approach) Selection of the next hop from the set of potential forwarders (Greedy approach) - Effect of the variation in link information over the path information S 3 Chakraborty, S.; Chakraborty, S.; Nandi, S., Beyond conventional routing protocols: Opportunistic path selection for IEEE s mesh networks, in proc. of IEEE 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), pp , 8-11 Sept Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

9 Route Flapping for QoS/QoE High Route Flapping: Network inconsistency Low Route Flapping: Network fails to adopt with channel variation Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

10 SGF: Route Flapping SINR Variation (dbm) Route Flapping Low Load Reactive HWMP Proactive HWMP SGF Route Flapping High Load Reactive HWMP Proactive HWMP SGF Router Number Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

11 Forwarder Percentage (δ) Percentage of neighbors selected as the set of potential forwarders. Route Flapping Low Load Route Flapping High Load δ = 60% δ = 40% δ = 20% δ = 60% δ = 40% δ = 20% Router Number Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

12 QoS Metrics: MAC Throughput Average Per User MAC Throughput (Mbps) HWMP: Proactive HWMP: Reactive SGF: δ=30% SGF: δ=40% SGF: δ=50% Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

13 QoS Metrics: Forwarding Delay Average End-to-End Delay (ms) HWMP: Proactive HWMP: Reactive SGF: δ=30% SGF: δ=40% SGF: δ=50% Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

14 QoS Metrics: Average Jitter Average Jitter (ms) HWMP: Proactive HWMP: Reactive SGF: δ=30% SGF: δ=40% SGF: δ=50% Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

15 QoE Metrics: MOS (Voice) HWMP: Proactive HWMP: Reactive SGF: δ=30% SGF: δ=40% SGF: δ=50% 3.8 MOS Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

16 QoE Metrics: PSNR (Video) HWMP: Proactive HWMP: Reactive SGF: δ=30% SGF: δ=40% SGF: δ=50% 25 PSNR (db) Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

17 QoE Metrics: SSIM (Video) HWMP: Proactive HWMP: Reactive SGF: δ=30% SGF: δ=40% SGF: δ=50% SSIM Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

18 Adopting δ with Traffic Load Variation At the beginning of every DTIM interval, mesh routers compute traffic load in terms of number of associated users (Q r (t)). δ min 2, δ max 0.5N r Model the network as a birth-death process; pi 1 i δ +1 min δ min δ i 1 δ i δ max pi i 1 Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

19 Average Forwarding Delay Average End-to-End Delay (ms) HWMP: Proactive HWMP: Reactive SGF + Adaptive SPF Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

20 Average Jitter HWMP: Proactive HWMP: Reactive SGF + Adaptive SPF 28 Average Jitter (ms) Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

21 Voice: MOS HWMP: Proactive HWMP: Reactive SGF + Adaptive SPF 3.8 MOS Average Number of Users Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

22 Channel Fluctuation vs MOS SINR Variation (dbm) Load MOS HWMP Proactive HWMP Reactive SGF Time (Hrs) Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

23 Conclusion Explores the effects of path diversity over QoS and QoE in a high speed mesh network Evaluated Selective Greedy Forwarding : A new routing paradigm through testbed results, - Channel fluctuation and traffic load affects the performance of SGF - Use large δ at low traffic load, and small δ at high traffic load, to avoid both high and low route flapping - Adopt forwarding paths based on network conditions Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

24 Conclusion Explores the effects of path diversity over QoS and QoE in a high speed mesh network Thank You Evaluated Selective Greedy Forwarding : A new routing paradigm through testbed results, - Channel fluctuation and traffic load affects the performance of SGF - Use large δ at low traffic load, and small δ at high traffic load, to avoid both high and low route flapping - Adopt forwarding paths based on network conditions Sandip Chakraborty (CSE, IITG) COMSNETS January, / 23

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