Utilizing ZigBee Technology for More Resource-efficient Wireless Networking

1. Utilizing ZigBee Technology for More Resource-efficient Wireless Networking Hua Qin Major professor: Dr. Wensheng Zhang Dr. DajiQiao Dr. Johnny S. Wong Dr. Yong Guan Dr. Robyn R. Lutz

2. Wireless Networks Hybrid Vehicular ad hoc network (VANET) Infrastructure-based Wireless local area network (WLAN) Access Point Internet Infrastructure-less client client client Mobile ad hoc network (MANET)

3. Challenges • Three categories • Infrastructure-based (e.g., WLAN) • Infrastructure-less (e.g., MANET) • Hybrid (e.g., VANET) • Challenges • Energy efficiency • Quality of Service(QoS) • E.g., latency, throughput, packet loss, etc. • Dynamics • Heterogeneity • E.g., WiFi, Bluetooth, etc.

4. ZigBee Technology • ZigBee (IEEE 802.15.4) • Low data rate • Typically, 250 Kbps • Low power • Low cost • Easily-embedded • Applications • Home/Building automation • Smart energy monitoring • Mobile payment • Remote control • Sensor network TazTag TPH­ONE (Android smartphone embedded with ZigBee)

5. Motivation – The Potential of Utilizing ZigBee 1. Infrastructure-based – WLAN Internet Integrated with ZigBee • Energy efficiency 2. Infrastructure-less – MANET • Energy efficiency • Bandwidth efficiency Integrated with ZigBee 3. Hybrid – VANET • Communication efficiency • Investment efficiency Use ZigBee devices as roadside stations Objective: Utilize ZigBee for more resource-efficient wireless networking.

6. Preliminary Work Utilizing ZigBee for More Resource-efficient WLAN Utilizing ZigBee for More Resource-efficient VANET Access Point Internet WLAN client client client

7. WiFi Power Consumption • WiFi consumes much energy • Up to 60% of smartphone's total energy • Power Saving Management (PSM) • Not adapt to traffic • Many other protocols • Rely on traffic prediction Access Point Clients • R. Krashinsky and H. Balakrishnan, “Minimizing Energy for Wireless Web Access with Bounded Slowdown,” MobiCom ’02. • M. Anand, E. Nightingale, and J. Flinn., “Self-Tuning Wireless Network Power Management”, MobiCom ’ 03. • P. Agrawal, A. Kumar, J. Kuri, and et al., “OPSM - Opportunistic Power Save Mode for Infrastructure IEEE 802.11 WLAN,” ICC ’10. • E. Rozner, V. Navda, and et al., “NAPman: Network-Assisted Power Management for WiFi Devices”, MobiSys ’10.

8. Standard Power Saving Management (SPSM) pkt 1 pkt 2 Buffering Beacon Interval (BI) AP Beacon frame pkt 2 pkt 1 PS-POLL Useful wakeups Client i Unnecessary wakeups • Access Point (AP) • No energy constraint • Always awake • Client • Energy-constrained • Wake up periodically • Synchronized with the AP WiFi on Ideal Wakeup Strategy (Wake up only when necessary) By default, BI = 100ms

9. ZigBee-assisted Power Saving Management (ZPSM) – Key Ideas Use the low-powerZigBee radio to dynamically wake up asleep high-powerWiFi radio for packet transmission between the AP and clients Compatible with the SPSM WiFi wakeup energy consumption in one second ≈ 20mJ • ZigBee transmits 250 or receives 310 packets of 29 bytes (default size) • ZigBee idly listening for 33.3 seconds [1] Silex Technology America, Inc., “SX-SDWAG 802.11g wireless module”, www.silexamerica.com [2] Texas Instruments Incorporated., “CC2530 RF transceiver for 2.4-GHz IEEE 802.15.4.”

10. System Model • Network interfaces • WiFi (IEEE 802.11) • ZigBee (IEEE 802.15.4) • Delay requirements • The percentage of downlink packets received with a delay lower than the delay bound should be greater than the required delay-meet ratio. • ZigBee link quality • Random packet loss • Other applications • All clients are synchronized Downlink packet AP/Client ZigBee WiFi AP delay Client Goal: minimize energy consumption of all clients while satisfy their delay requirements.

11. Key Ideas – Wakeup Strategies Two wakeup strategies • Proactive: regular wakeup • Reactive: on-demand wakeup pkt 1 pkt 2 Beacon Interval (BI) • WiFi: Listen Interval = 3 • ZigBee: Wakeup Interval = 2 WiFi pkt 1 AP Regular wakeup Wakeup Interval Beacon Frame pkt2 ZigBee On-demand wakeup Client i Wakeup Frame Turn on WiFi ZigBee WiFi Wakeup Slot Listen Interval

12. Limitations – On-demand Wakeup pkt di Beacon Interval (BI) WiFi Beacon Frame Wakeup Interval ZigBee Wakeup Frame AP Client i BI1 BI2 BI3 WiFi deadline Listen Interval ZigBee Wakeup Slot Using only on-demand wakeup may not satisfy delay requirements!

13. Limitations – Regular Wakeup pkt di Beacon Interval (BI) WiFi Beacon Frame Wakeup Interval ZigBee Wakeup Frame AP Client i BI1 BI2 BI3 WiFi deadline Listen Interval ZigBee Wakeup Slot Reduce wakeup interval of the AP Not energy-efficient!

14. Limitations – Regular Wakeup pkt di Beacon Interval (BI) WiFi Beacon Frame Wakeup Interval ZigBee Wakeup Frame AP Client i WiFi deadline Listen Interval ZigBee Wakeup Slot Decrease listen interval of the client Not energy-efficient!

15. Problem Definition • Regular wakeup: (m, yi) • Can satisfy delay requirements but consume more energy • On-demand wakeup: xi • Consume less energy but may not satisfy delay requirements Wakeup Interval (m) AP ZigBee Wakeup Frame Expected number of on-demand wakeups (xi) Beacon Frame Client i … WiFi Wakeup Schedule Z is the set of all ZPSM clients Listen Interval (yi) Problem: how to schedule the regular and on-demand wakeups?

16. Our Approaches – Roadmap I. Theoretical study Model System Formulate Problem Optimal Solution Insights II. Design III. Evaluation Minimize energy consumption Ensure delay requirements Comparison SPSM ZSPM schemes

17. Roadmap I. Theoretical study Model System Formulate Problem Optimal Solution Insights II. Design III. Evaluation Minimize energy consumption Ensure delay requirements Comparison SPSM ZSPM schemes

18. Theoretical Study • Analysis • Delay • Energy • Formulated as an optimization problem Packet arrival rate (λi) Optimization Problem Optimal wakeup schedule ZigBee link quality (pi)

19. Optimization Problem • Objective: Find wakeup schedule Ω to minimize overall energy consumption rate • s.t., WiFi energy ZigBee energy Constraint on regular wakeup Constraint on on-demand wakeup Feasible range Z is the set of all ZPSM clients Can be transformed to a linear programming problem!

20. Roadmap I. Theoretical study Model System Formulate Problem Optimal Solution Insights Schedule on-demand wakeup Configure regular wakeup II. Design III. Evaluation Minimize energy consumption Ensure delay requirements Comparison SPSM ZSPM schemes

21. Configure Regular Wakeup • The AP solves and applies optimal (m, yi) • Traffic • ZigBee link quality • Delay meet-ratio of client i Only depend on (m, yi) Apply optimal (m, yi) Delay requirements are already satisfied!

22. Schedule On-demand Wakeups – S-ZPSM pkt di Beacon Interval (BI) WiFi Beacon Frame Wakeup Interval ZigBee Wakeup Frame AP Client i BI1 BI2 BI3 WiFi deadline Listen Interval ZigBee Wakeup Slot At which BI should the client wake up in order to maximize energy efficiency? Simple ZPSM (S-ZPSM):Always wake up at the latest BI?

23. Schedule On-demand Wakeups – Insights • Two conditions to minimize energy consumption • Wakeup frequency is minimized. • Transmission workload is balanced among different BIs. • Definition: transmission workload of a BI • (# of awake clients in the BI) × (# of data packets transmitted in the BI) • S-ZSPM • Only consider Condition 1 • Ignore Condition 2 Minimize contention Reduce energy consumed on idle listening

24. Schedule On-demand Wakeups – A-ZPSM • Advanced ZPSM (A-ZPSM) • Schedule on-demand wakeup as late as possible • Balance transmission workload by adopting greedy approach • Go through clients in descending order with respect to their transmission workload. • Schedules each of them to the BI with the smallest transmission workload. 12 Transmission workload incurred by each client on a BI 11 11 11 4 largest 1 3 3 largest 9 9 6 7 5 7 6 5 smallest smallest 4 3 3 3 2 1 1 1 BI1 BI2 BI3 BI4 C1 C2 C3 C4 C5 C6 C7 C8

25. Roadmap I. Theoretical study Model System Formulate Problem Optimal Solution Insights II. Design III. Evaluation Minimize energy consumption Ensure delay requirements Comparison Propose an advanced ZSPM (A-ZPSM) S-ZPSM SPSM

26. Simulation – Setup • ns2 simulation • One AP and 20 clients • WiFi • IEEE 802.11g • Default ZigBeelink quality • Intentionally drop 20% ~ 40% packets • Performance metrics • Per-packet energy consumption (mJ/pkt) • Actual delay-meet ratio

27. Simulation – Per-packet Energy Consumption • In the default settings, A-ZPSM can save energy • 42.1% more than S-ZPSM • 85.1% more than SPSM Adapt to traffic change Adapt to delay requirements By default, packet rate = 5pkt/s, delay bound = 2s, required delay-meet ratio = 0.9, ZigBee link quality = 0.6~0.8

28. Simulation – Actual Delay-meet Ratio • SPSM ≈ 100% • ZPSM > 95%

29. Implementation – Architecture of Prototyped ZPSM System On-demand Wakeup Scheduler Regular Wakeup Configurator On-demand Wakeup BI ZPSM Listen Interval Wakeup Interval ZigBee Controller SPSM Wakeup Frame AP Data Packet ZigBee WiFi Client ZPSM Turn WiFi on if wakeup BI arrives SPSM ZigBee Controller Packet flow Control flow

30. Experiment – Testbed 9 Dell D-Series laptops One AP and 8 clients ZigBee: Crossbow telosB mote WiFi: D-link wireless adapter (802.11g)

31. Experiment – Results • Settings • Packet rate: 1 pkt/s (low), 10 pkt/s (high) • Delay bound: 1 s (short), 5 s (long) • ZigBee link quality: 0.5 (bad), 0.9 (good)

32. Preliminary Work Utilizing ZigBee for More Resource-efficient Wireless LAN Utilizing ZigBee for More Resource-efficient VANET VANET

33. Issues in VANET • Unique features • High mobility • Sparsely-deployed roadside stations • Varied traffic density • High in rush hours • Low in rural areas • CANNOT guarantee • Communication connectivity • Real-time detection and report

34. Integrate VANET with Wireless Sensor Network (WSN) • Goals • Timely detection of dangerous road conditions • Effective and efficient vehicle-sensor and sensor-sensor interactions 1 2 Detected by a vehicle Delivered by WSN Report to VANET Detected by a sensor Delivered via WSN Report to VANET 3 3 2 Roadside Sensor 1 • Y. Ding, C. Wang, and L. Xiao, “A static-node assisted adaptive routing protocol in vehicular networks,” VANET ’07. • M. Abuelela, S. Olariu, and G. Yan, “Enhancing Automatic Incident Detection Techniques Through Vehicle To Infrastructure Communication,” ITSC ’08. • J. Bohli, A. Hessler, O. Ugus, and D. Westhoff, “A secure and resilient WSN roadside architecture for intelligent transport systems,” WiSec ’08. • H. Qin, X. Lu, W. Zhang, and et al, “Heterogeneity-aware Design for Automatic Detection of Problematic Road Conditions,” MASS ’11.

35. System Model • Vehicle • WiFi (IEEE 802.11) • Communicate with other vehicles • ZigBee (IEEE 802.15.4) • Communicate with roadside sensors • Roadside sensor • ZigBee (IEEE 802.15.4) • Communicate with vehicles • Communicate with other roadside sensors WiFi Communication ZigBee Communication VANET Problem Sense WSN

36. Design Challenges • Scalability • Large-scale road system • Flexibility • Road changes • Energy efficiency • Roadside sensors • QoS • Timely delivery of safety-related information • Viability • Interference, noises and other environmental factors

37. Our Approaches: Roadmap Scalability Group-based modular design Flexibility Energy efficiency Duty cycle scheduling for message propagation QoS Viability Implement and test a prototype Extensive simulations

38. Our Approaches: Roadmap Scalability Group-based modular design Flexibility Energy efficiency Duty cycle scheduling for message propagation QoS Viability Implement and test a prototype Extensive simulations

39. Group-based Network Deployment • Types of sensor nodes • Access point (AP) sensor node • Sense and relay messages • Discover and communicate with vehicles • Managing the network • Regular sensor node • Sense and relay messages AP Regular node VANET Cluster Cluster Head Head … … WSN Group Group

40. The Interaction Between VANET and WSN • Two phases • Forward sensor activation • Backward warning message propagation VANET A cluster of vehicles Moving direction … Head Event Spot Beacon message Detect/Store Activation … WSN AP Warning message Backward direction Forward direction

41. Roadmap Scalability Group-based modular design Flexibility Energy efficiency Duty cycle scheduling for message propagation QoS Viability Implement and test a prototype Extensive simulation

42. Design Overview • Challenge • Bidirectional message propagation • Cause contentions • Large delay and high energy consumption • Duty cycle scheduling for message propagation • Intra-group scheduling • Contention-less • Inter-group scheduling • Contentions are properly handled by APs

43. Intra-group Scheduling – Overview • All nodes in a group are synchronized • Outline • One direction, one period • Two directions, multiple periods Period Period Period Time AP 1 Forward propagation 1 ... ... 2 ... ... Group 3 ... ... ... 4 ... ... 5 ... ... Backward propagation AP 3 • Slot • Awake: send or receive • Asleep: save energy

44. Intra-group Scheduling – One direction, One period Time 1 DATA ACK 2 3 4 5 Collision Free! Maximum times to retransmit = 4 – 1 = 3 Forward Direction Retransmission quota (r) Sending Slot Receiving Slot

45. Intra-group Scheduling – Two directions, Multiple periods Period of Group 1 Time AP1 1 Forward Group 1 . . . . . . . . . . . . . . . . . . ... Beacon messages AP2 3 Group 2 . . . . . . . . . . . . . . . ... Backward AP3 5 Period of Group 2 • System parameters • Forward interval (cf) • Backward interval (cb) Forward Direction

46. Inter-group Scheduling • Ferry data packets between two groups • Buffer-and-forward • Collision resolution • Yield mechanism DATA ACK Time AP Group 1 Forward ACK DATA Time AP yield Group 2 Backward

47. Roadmap Scalability Group-based modular design Flexibility Energy efficiency Duty cycle scheduling for message propagation QoS Viability Implement and test a prototype Extensive simulation

48. Prototyping & Field Tests • Prototype • Vehicle node • Laptop equipped with a ZigBeesensor mote • AP and regular node • ZigBee sensor mote • Field test • ISC parking lot • One vehicle • Two groups • 9 sensor motes Generate warning message Discover vehicle AP2 5 4 3 AP0 0 1 2 AP1 60m

49. Experiment Results • WiFiInterference • Two laptops • 10Mbps traffic • Performance metrics • Dforward: average per-hop delay for the forward activation message propagation • DBackward: average per-hop delay for the backward warning message propagation

50. Simulation – Setup • ns2 simulation • Evaluation • Theoretical • System parameters • Empirical • Empirical traffic generation