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Murat Demirbas
Onur Soysal
SUNY Buffalo
Ali Saman Tosun
U. Texas @ San Antonio
Data Salmon: A greedy mobile basestation protocolfor efficient data collection in WSNs
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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
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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 2but also introduce latency
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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
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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
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Outline of this talk
• Tracking the MB
• Data Salmon algorithm for relocating the MB
• Proof of optimality
• Simulation results
• Extensions
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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
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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)
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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)
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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)
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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)
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Outline of this talk
• Tracking the MB
• Data Salmon algorithm for relocating the MB
• Proof of optimality
• Simulation results
• Extensions
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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:
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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
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Data Salmon algorithm
???
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Data Salmon algorithm
1 2
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Data Salmon algorithm
1 2
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Data Salmon algorithm
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Outline of this talk
• Tracking the MB
• Data Salmon algorithm for relocating the MB
• Proof of optimality
• Simulation results
• Extensions
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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)
v0v1v2vk
AB1
B2
Bk
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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!!
v0v1v2vk
AB1
B2Bk
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• 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
Proof of optimality
v0v1v2vk
AB1
B2Bk
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Outline of this talk
• Tracking the MB
• Data Salmon algorithm for relocating the MB
• Proof of optimality
• Simulation results
• Extensions
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Energy consumption for SB vs MB
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Point difference between SB & MB
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Outline of this talk
• Tracking the MB
• Data Salmon algorithm for relocating the MB
• Proof of optimality
• Simulation results
• Extensions
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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
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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?
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Other extensions
• Use of more general cost functions
• Investigation of buffering at the nodes for buffering/latency trade-off
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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
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