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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.

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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

Murat Demirbas

Onur Soysal

SUNY Buffalo

Ali Saman Tosun

U. Texas @ San Antonio

problems with static basestations
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
work on mobile basestations
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

work on mbs
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

our work data salmon
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

outline of this talk
Outline of this talk
  • Tracking the MB
  • Data Salmon algorithm for relocating the MB
  • Proof of optimality
  • Simulation results
  • Extensions
model
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
distributed arrow algorithm
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)

distributed arrow algorithm1
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)

distributed arrow algorithm2
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)

distributed arrow algorithm3
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)

outline of this talk1
Outline of this talk
  • Tracking the MB
  • Data Salmon algorithm for relocating the MB
  • Proof of optimality
  • Simulation results
  • Extensions
mb relocation problem
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:
greedy algorithm
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

outline of this talk2
Outline of this talk
  • Tracking the MB
  • Data Salmon algorithm for relocating the MB
  • Proof of optimality
  • Simulation results
  • Extensions
proof of optimality

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)
proof of optimality1

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!!

slide22

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

outline of this talk3
Outline of this talk
  • Tracking the MB
  • Data Salmon algorithm for relocating the MB
  • Proof of optimality
  • Simulation results
  • Extensions
outline of this talk4
Outline of this talk
  • Tracking the MB
  • Data Salmon algorithm for relocating the MB
  • Proof of optimality
  • Simulation results
  • Extensions
tree reconfiguration problem
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
multiple mb extension
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?
other extensions
Other extensions
  • Use of more general cost functions
  • Investigation of buffering at the nodes for buffering/latency trade-off
summary
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