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Walking down the STAIRS: Efficient Collision Resolution with Constructive Interference Xiaoyu Ji Xiaoyu Ji, Yuan He, Jiliang Wang, Wei Dong, Xiaopei Wu.

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Presentation on theme: "Walking down the STAIRS: Efficient Collision Resolution with Constructive Interference Xiaoyu Ji Xiaoyu Ji, Yuan He, Jiliang Wang, Wei Dong, Xiaopei Wu."— Presentation transcript:

1 Walking down the STAIRS: Efficient Collision Resolution with Constructive Interference Xiaoyu Ji Xiaoyu Ji, Yuan He, Jiliang Wang, Wei Dong, Xiaopei Wu and Yunhao Liu INFOCOM, 2014, Toronto Hong Kong University of Science and Technology

2 Motivation 2 Wireless Sensor Networks (WSNs) – Event-driven mode – Low duty cycle operating – Large number of nodes CSMA-like protocols – Limitations – Backoff...

3 The Recent Art- COMA COMA- Contend before data transmission – Contention packets reserve channel for real data packets – The drawback: dedicated contention packets in each round 3 Can we resolve the collision in just one round! Can we resolve the collision in just one round! Ref: F. Osterlind, et. al, Strawman: resolving collisions in bursty low-power wireless networks,” in IEEE/ACM IPSN, 2012

4 One-round Collision Resolution The problem: – Count, identify and schedule – And of course in one round! Approach – Active contention – Virtual ID – Fast identification 4

5 Our weapon: RSSI Stair Pattern 5 The observation – Signals can constructively collide – Requirements of Constructive Interference (CI) 0.5 μs Identical signal waveform

6 The Principle 6 Proposition: Given the superposed signal CI(k) under CI, let A 1 = A 2 = … = A be the amplitude and τ 1 = τ 2 = … = B denoting the phase offset with respect to the first signal generated by transmitter i = 1. Consider one IEEE standard based communication system, RSSI CI(k) is equal to: Where ω c is a constant and τ 1 =0

7 Design of STAIRS Overview 7 Through intentional contention, senders can be identified from the stair-like pattern of RSSI in one round.

8 Design Challenges Challenge 1: Synchronization – Requirement of CI: Δ≤0.5μs Challenge 2: Falling edge detection – CP packets with the same length – External interference, e.g., WiFi signals 8 (1) False negatives False falling edges (2) False positives

9 Alignment for CP packets Receiver-initiated (CR) – Triggering transmissions of CP packets – Serving as ACK/NACK – Coping with hidden terminals Parallelizing receiving and reading 10

10 S-CUSUM Edge Detection Discrete lengths of CP packets – Total sender number N, maximum packet size L, increase step ΔL, length of CP is: A paradox- how to find a good ΔL? 10 Less false edges Larger CP space

11 11 Finding the optimal ΔL – p=1/m: choose any of the m lengths – α: the probability of false positives Three cases for a schedule:

12 Implementation 12 STAIRS – A plug-in between APP and MAC layer – Invoked when collision happens – Three main components

13 Evaluation Micro-benchmark – Synchronization – Edge detection Multi-hop testbed – Completion time – Efficiency – Duty cycle Large-scale simulation 13

14 Micro-benchmark 14 Offset among arriving packets less than 0.25 μs! S-CUSUM increases detection efficiency. Average detection accuracy is > 85%.

15 Testbed Settings 15 Multi-flow-multi-hop environment. ΔL is set to 10 bytes. 20 TelosB sensor nodes Flow number

16 Compared with Strawman 16 STAIRS beats Strawman, especially with large number of senders. Contention overhead of STAIRS is amortized.

17 Duty Cycle Evaluation 17 Both sender and receiver duty cycles are improved, as contention time is reduced. Energy efficiency is therefore improved.

18 Simulation Settings – Up to 50 senders – Linear backoff (CSMA-L) – Exponential backoff (CSMA-E) 18

19 Results 19 No degraded performance CSMA-L beats CSMA-E after the threshold Backoff time dominates! Pkt_size = 50 bytes Pkt_size = 100 bytes

20 Summary Collision resolution with active contention Observing the RSSI stair-like pattern, we then look into its principle Design STAIRS based on the stair pattern and solve challenges like synchronization and finding optimal ΔL Evaluation in both real testbed and large-scale simulation 20

21 Thank you ! 21


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