Rendezvous Planning in Mobility-assisted Wireless Sensor Networks

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 Robomote @ USC [Dantu05robomote] XYZ @ Yale http://www.eng.yale.edu/enalab/XYZ/ Networked Infomechanical Systems (NIMS) @ CENS, UCLA

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. Rendezvous-based Data Collection mobile node The field is 500 × 500 m2 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. rendezvous point source node

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 Rendezvous points Non-source nodes a branch node lies on two or more source-to-root routes Source nodes Branch nodes approximated data route real data route Source nodes

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 thanL=vD The problem is NP-hard Source nodes branch nodes Rendezvous points data route

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. Illustration # of sources in the descendents • 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 3 3 2 1 1 1 1

15. A Heuristic under Unlimited Mobility • Add virtual nodes s.t. each edge is no longer than L0 • In each iteration • Choose the RP candidate x with the max utilitydefined 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 the decreased length of data routes c(x) = the increased length of the mobile tour obtained by running a Traveling Salesman Problem solver

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

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 Non-source nodes Source nodes Rendezvous points approximated data route real data route 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 45o • 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