1. More Security and Programming Language Work on SmartPhones Karthik Dantu and Steve Ko

2. WiSee: Intro Pu et al., “Whole-Home Gesture Recognition Using Wireless Signals” from MobiCom’13 An in-air gesture recognition system Think Xbox Kinect without any sensor Uses Wi-Fi signals and their Doppler shifts Works in line-of-sight, non-line-of-sight, and through-the-wall conditions (94% best-case accuracy)

3. Gestures

4. Two Questions How to detect gestures using Wi-Fi? Short answer: Doppler shifts & pattern matching How to deal with other humans in the environment? Short answer: Repeated gesture to detect the user using MIMO

5. Gesture Detection Doppler shifts Frequency change between two objects when moving E.g., Train coming towards you (higher observed frequency) & moving away from you (lower observed frequency) Wi-Fi Doppler shifts Humans reflect Wi-Fi signals, thus can be treated as signal sources Different gestures exhibit different patterns.

6. Gesture Detection Doppler shifts with Wi-Fi and human gestures Problem: the frequency variation is too little. With 5 GHz transmission, 17 Hz Doppler shift (but Wi-Fi channel is typically 20 Mhz). Question: how to amplify this frequency variation? Short answer: combine variation from multiple, identical OFDM symbols to amplify

7. Gesture Detection A bit of background OFDM is the modulation scheme for Wi-Fi. It divides a Wi-Fi channel into multiple sub- channels. One OFDM symbol (i.e., bits to send) gets sent over the channel by sending one bit for each sub- channel. If the sender sends the same OFDM symbols multiple times, we can amplify the Doppler shift.

8. Gesture Detection

9. One more issue: Wi-Fi signals are not coming from exact same data repeatedly sent. Solution Data (bits) don’t matter, we’re only interested in Doppler shifts. Data equalizer: the receiver re-generates OFDM symbols with the same data from the “first” OFDM symbol.

10. Patterns for Gestures

12. Evaluation Software radio receiver Scenarios Office building Two-bed apt Many conditions Line-of-sight, non-line-of-sight, through-the-wall, through-the-corridor, through-the-room

13. Scenarios

14. Evaluation

16. ACE: Intro Nath, “ACE: Exploiting Correlation for Energy- Efficient and Continuous Context Sensing” from MobiSys’12 Context sensing Can a phone detect what a user’s context is? Walking, driving, at home, in a meeting, etc. ACE is a system that provides this in an energy- efficient way.

17. Questions for Context Detection How accurate can we be when detecting a context? Not a focus for ACE Much work has been done Various sensors are used. How can context detection shared by multiple apps? Focus of ACE ACE does it in an energy-efficient way.

18. Key Insight Context inference is possible. Examples: If walking, then not driving. If at home, then not at office. If an app wants to know one attribute, it can infer other attributes when a different app wants to know other things.

19. Using the Key Insight Contexters: context detection modules Rule miner: keeps the history of contexts and discovers relationships between rules. Context cache: keeps recently discovered contexts and other inferred contexts (using the rule miner). Sensing planner: given a context to discover, find the cheapest sensors to get the context.

20. Architecture

21. Workflow

22. Contexters

23. Rule Miner Mines per-user rules using Apriori association rule mining algorithm

24. Inference Cache Each entry has an attribute and expiration time. E.g., IsDriving, 5 minutes Inference cache uses relationship rules to return inferred attributes as well.

25. Sensing Planner Due to the relationship rules, some attributed do not need to discovered directly. Optimize energy by using less energy consuming sensors

26. Evaluation Sensing planner

27. Evaluation Inference cache

28. Evaluation Per user power consumption

29. Summary WiSee: a gesture detection using Wi-Fi ACE: a energy-efficient context detection system