1. Rendezvous Planning in Mobility- assisted Wireless Sensor Networks Guoliang Xing; Tian Wang; Zhihui Xie; Weijia Jia Department of Computer Science City University of Hong Kong

2. Agenda Motivation Problem formulation Rendezvous planning algorithms –Optimal algorithm under limited mobility –Heuristic under unlimited mobility Protocol design Performance evaluation

3. Challenges for Data-intensive Sensing Applications Many applications are data-intensive –Structural health monitoring Accelerometer@100Hz, 30 min/day, 80Gb/year –Micro-climate and habitat monitoring Acoustic & video, 10 min/day, 1Gb/year Most sensor nodes are powered by batteries A tension exists between the sheer amount of data generated and the limited power supply

4. Mobility-assisted Data Collection Mobile nodes move close to sensors and collect data via short-range communications Number of wireless relays is reduced Mobile nodes are less power-constrained –Can move to wired power sources

5. Mobile Sensor Platforms Low movement speed (0.1~2 m/s) –Increased latency of data collection –Reduced network capacity Networked Infomechanical Systems (NIMS) @ CENS, UCLA Robomote @ USC [Dantu05robomote] XYZ @ Yale http://www.eng.yale.edu/ enalab/XYZ/

6. Rendezvous-based Data Collection Some nodes serve as “rendezvous points” (RPs) –Other nodes send their data to the closest RP –Mobiles pick up data from RPs and transport to BS In-network caching + controlled mobility –Mobiles can collect a large volume of data at a time –Mobiles contact static nodes at RPs at scheduled times and disruptions to network topology are reduced

7. mobile node rendezvous point Rendezvous-based Data Collection source node The field is 500 × 500 m 2 The mobile moves at 0.5 m/s It takes ~20 minutes to visit six randomly distributed RPs It takes > 4 hours to visit 200 randomly distributed nodes.

8. Assumptions Only one mobile is available Average speed of mobile is v m/s Mobile picks up data at locations of nodes Data collection deadline is D seconds –User requirement: “report every 10 minutes and the data is sampled every 10 seconds” –Recharging period: e.g., Robomotes powered by 2 AA batteries recharge every ~30 minutes

9. Geometric Network Model Transmission energy is proportional to distance Base station, source nodes and branch nodes are connected with straight lines a multi-hop route is approximated by a straight line Source nodes approximated data route real data route Non-source nodes Branch nodes Rendezvous points a branch node lies on two or more source- to-root routes

10. The Rendezvous Planning Problem Choose RPs s.t. the data collection tour of mobile node is no longer than L=vD Total network energy of transmitting data from sources to RPs is minimized Joint optimization of positions of RPs, motion path of mobile, and routing paths of data

11. Illustration of Problem Formulation Objective: minimize length of routes from sources to RPs Constraint: mobile tour is no longer than L=vD The problem is NP-hard Source nodes Rendezvous points data route branch nodes

12. Rendezvous Planning under Limited Mobility The mobile only moves along routing tree –Simplifies motion control and improves reliability XYZ @ Yale

13. An Optimal Algorithm Sort edges in the descending order of the number of sources in descendents Choose a subset of (partial) edges from the sorted list whose length is L/2 The mobile tour is the pre-order traversal of the chosen edges Set the intersections between the tour and the routing tree as RPs

14. 2 3 Illustration All edges have a length of 50m Deadline = 500 s, v = 0.5 m/s L = 0.5 m/s x 500 s = 250 m Correctness Edges chosen are connected Optimality A tour can cover at most L/2 edges L/2 mostly 'used' edges are chosen # of sources in the descendents 1 1 1 1 3

15. A Heuristic under Unlimited Mobility Add virtual nodes s.t. each edge is no longer than L 0 In each iteration –Choose the RP candidate x with the max utility defined by c(x) –Remove RPs with zero utility Terminate if all sources become RPs or no more RPs can be chosen without violating the constraint of L c(x) = the increased length of the mobile tour the decreased length of data routes obtained by running a Traveling Salesman Problem solver

16. Illustration two RP candidates A B C E G D F

17. Agenda Motivation Problem formulation Rendezvous planning algorithms –Optimal algorithm under limited mobility –Heuristic under unlimited mobility Protocol design Performance evaluation

18. Initialization Mobile computes locations of RPs Find real nodes around the computed RPs –Find the nodes along the routing tree –Mobile travels to RPs and discover real nodes Source nodes Rendezvous points approximated data route real data route Non-source nodes

19. Handling Unexpected Delays Movement of mobile node is subject to various delays –Obstacles, mechanical failures… RPs should cache data as long as possible without violating the deadline Mobile node may adjust motion path online e.g., skips some of the RPs

20. Simulation Settings 100 sources are randomly distributed in a 300m X 300m field, base station is on the left corner Each source generates 2 bytes/second, delivery deadline is 20 minutes Implemented USC model [Zuniga et al. 04] to simulate lossy links on Mica2 motes Baseline algorithms –NET: collect data via the routing tree without using mobile nodes –Sector: mobile moves on a sector of 45 o –RP-CP: the optimal algorithm with limited mobility –RP-UG: the utility-based heuristic –RP-SRC: choose a subset of sources as RPs

21. Network Energy Consumption

22. Impact of Variance of Mobile Speed Mean mobile speed is 1m/s, with a variance + α m/s

23. Conclusions Proposed a rendezvous-based data collection approach –In-network caching + controlled mobility Developed two rendezvous planning algorithms –An optimal algorithm under limited mobility –A efficient heuristic under unlimited mobility Designed the rendezvous-based data collection protocol