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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 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 2 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 3 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 4 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 5 Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions 6 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 7 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)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 10 Demmer, Herlihy (1998) Distributed arrow algorithm • Assume initially all arrows point to the basestation • When the MB moves, just flip the direction of traversed edge 11 Demmer, Herlihy (1998) Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions 12 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 Total data rate at WSN: εi 13 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 14 Data Salmon algorithm ??? 7 1 1 2 15 Data Salmon algorithm 7 1 1 2 16 Data Salmon algorithm 7 1 1 2 17 Data Salmon algorithm 3 2 4 18 Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions 19 Proof of optimality B2 Bk vk A B1 v2 v1 v0 • 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) 20 Proof of optimality B2 Bk vk A B1 v2 v1 v0 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!! 21 Proof of optimality B2 Bk vk B1 v2 A v1 v0 • 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 22 Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions 23 Energy consumption for SB vs MB 24 Point difference between SB & MB 25 Outline of this talk • Tracking the MB • Data Salmon algorithm for relocating the MB • Proof of optimality • Simulation results • Extensions 26 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 27 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? 28 Other extensions • Use of more general cost functions • Investigation of buffering at the nodes for buffering/latency trade-off 29 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 30