Murat Demirbas Onur Soysal SUNY Buffalo Ali Saman Tosun U. Texas @ San Antonio

1. Data Salmon: A greedy mobile basestation protocol for efficient data collection in WSNs Murat Demirbas Onur Soysal SUNY Buffalo Ali Saman Tosun U. Texas @ San Antonio

2. Problems with static basestations • Static basestation (SB) approach ignores the spatiotemporally varying nature of data generation • Most of the time the network remains idle, with burst of data generation from a region upon event detection • SB approach leads to multihop relaying of high traffic data • Multihop relaying of high data-rate traffic consumes energy • Collisions result due to high data-rate traffic contending over multihops

3. Work on Mobile Basestations • Data Mules: • MBs move randomly and collect data opportunistically from sensors • Sensors buffer data until mobile basestation (MB) is within range • Predictable Data Collection: • Sensors are assumed to know the trajectory of MBs • Sensors buffer data until MB is within range These work address problem 2 but also introduce latency

4. Work on MBs… • Mobile Element Scheduling • MB visits sensors such that no sensor buffer overflow occurs • Problem is NP-complete, heuristic solutions provided • Partition Based Scheduling • Algorithm partitions the network into regions according to data rates • Reduced overall complexity but still NP-complete These work address problem 2, problem 1 is addressed only for static/predetermined data generation rates

5. Our work: Data Salmon • We address the spatiotemporal nature of data generation by using a network controlled MB • We achieve low-latency data collection by maintaining a path to the MB for continuous data forwarding • We reduce multihop relaying of high data-rate traffic by devising an algorithm for relocating the MB to the regions that produce higher data rates • We prove that our local greedy algorithm is optimal by showing the convexity of the cost function for our setup

6. Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions

7. Model • A static WSN • A mobile basestation • Suspended cableway mobility platform as in NIMS, SkyCam • A spanning backbone tree over WSN • MB uses the backbone tree to navigate

8. Distributed arrow algorithm • Assume initially all arrows point to the basestation • When the MB moves, just flip the direction of traversed edge Demmer, Herlihy (1998)

9. Distributed arrow algorithm • Assume initially all arrows point to the basestation • When the MB moves, just flip the direction of traversed edge Demmer, Herlihy (1998)

10. Distributed arrow algorithm • Assume initially all arrows point to the basestation • When the MB moves, just flip the direction of traversed edge Demmer, Herlihy (1998)

11. Distributed arrow algorithm • Assume initially all arrows point to the basestation • When the MB moves, just flip the direction of traversed edge Demmer, Herlihy (1998)

12. Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions

13. MB relocation problem • Minimize energy consumed for multihop relaying • d(i,j): hop-distance from node i to node j • wi: the data rate of node i • The energy spent for relaying when MB is at m : • The problem is to find optimal m* with minimum M(m*) • Notation for the algorithm • Total data rate forwarded from subtree rooted at i is εi • Total data rate at WSN:

14. Greedy algorithm • Go to a neighbor b with a lower cost function M(b) • It turns out b is unique if it exists! M(b)=M(a)+ εa - εb ε=εa+εb

15. Data Salmon algorithm ??? 7 1 1 2

16. Data Salmon algorithm 7 1 1 2

17. Data Salmon algorithm 7 1 1 2

18. Data Salmon algorithm 3 2 4

19. Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions

20. B2 A Bk B1 v0 vk v1 v2 Proof of optimality • Let v0 be optimal position, vk be any node in tree • We show that the path to v0 has decreasing cost • Theorem 2: Path vk,vk-1,…,v0 satisfies M(v0)≤ M(v1)≤ …≤ M(vk)

21. B2 A Bk B1 v0 vk v1 v2 Proof of optimality When MB moves from v0 to v1 • hop distance for all nodes in A increases by 1 • hop distance for all nodes in B decreases by 1 ≥0; since v0 is optimal!!

22. Proof of optimality B2 A Bk B1 v0 vk v1 v2 • When MB moves from v1 to v2 • hop distance for all nodes in AUB1 increases by 1 • hop distance for all nodes in B-B1 decreases by 1 ≥0 ≥0

23. Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions

24. Energy consumption for SB vs MB

25. Point difference between SB & MB

26. Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions

27. Tree reconfiguration problem • Static backbone tree leads to hotspot problem & also do not provide shortest path routing toward MB • Is it possible/worthwhile to achieve an update-efficient algorithm for dynamically reconfiguring the tree as the MB relocates? • NB: Strictly local updating leads to deformed trees soon

28. Multiple MB extension • Multiple MBs would mean multiple roots (DAG structure) • When there are multiple outgoing edges in a node the incoming traffic is equally divided among the outgoing edges • MBs calculate their movement in the same manner (local greedy) • Edge directions are maintained in the same manner • How do we achieve an optimal multiple MB algorithm?

29. Other extensions • Use of more general cost functions • Investigation of buffering at the nodes for buffering/latency trade-off

30. Summary • We address the spatiotemporal nature of data generation by using a network controlled MB • We achieve low-latency data collection by maintaining a path to MB for continuous data forwarding • We reduce multihop relaying of high data-rate traffic by devising an algorithm for relocating the MB to minimize the average weighted multihop data traffic • We prove that our local greedy algorithm is optimal by showing the convexity of the cost function for our setup