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

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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