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

2. 2 Problems with static basestations 1.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 2.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. 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. 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. 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. 6 Outline of this talk Tracking the MB Data Salmon algorithm for relocating the MB Proof of optimality Simulation results Extensions

7. 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. 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. 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. 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. 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. 12 Outline of this talk Tracking the MB Data Salmon algorithm for relocating the MB Proof of optimality Simulation results Extensions

13. 13 MB relocation problem Minimize energy consumed for multihop relaying  d(i,j): hop-distance from node i to node j  w i : 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 ε i  Total data rate at WSN:

14. 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)+ ε a - ε b ε=εa+εbε=εa+εb

15. 15 Data Salmon algorithm ??? 12 1 7

16. 16 Data Salmon algorithm 12 1 7

17. 17 Data Salmon algorithm 12 1 7

18. 18 Data Salmon algorithm 4 2 3

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

20. 20 Proof of optimality Let v 0 be optimal position, v k be any node in tree We show that the path to v 0 has decreasing cost Theorem 2: Path v k,v k-1,…,v 0 satisfies M(v 0 )≤ M(v 1 )≤ …≤ M(v k ) v0v0 v1v1 v2v2 vkvk A B1B1 B2B2 BkBk

21. 21 Proof of optimality When MB moves from v 0 to v 1  hop distance for all nodes in A increases by 1  hop distance for all nodes in B decreases by 1 ≥0; since v 0 is optimal!! v0v0 v1v1 v2v2 vkvk A B1B1 B2B2 BkBk

22. 22 When MB moves from v 1 to v 2  hop distance for all nodes in AUB 1 increases by 1  hop distance for all nodes in B-B 1 decreases by 1 ≥0 Proof of optimality v0v0 v1v1 v2v2 vkvk A B1B1 B2B2 BkBk

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

24. 24 Energy consumption for SB vs MB

25. 25 Point difference between SB & MB

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

27. 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. 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. 29 Other extensions Use of more general cost functions Investigation of buffering at the nodes for buffering/latency trade-off

30. 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

31. 31 Comparison of work on MB Low energy cons. Low latency Multihop relaying Online adaptation Nw controlled Data Mules Predictable MB MES Partition Sched. Data Salmon

32. 32 Effects of MB speed