data preservation in intermittently connectted sensor network with data priority bin tang department...
TRANSCRIPT
DATA PRESERVATION IN INTERMITTENTLY CONNECTTED SENSOR NETWORK WITH
DATA PRIORITY
Bin Tang
Department of Computer ScienceCalifornia State University
Dominguez Hills, CA
1
Outline of My Talk
Introduction Data Preservation in Sensor Networks
• Motivation• Problem Formulation• Centralized Algorithm• Distributed Algorithm and Protocol• Simulation Analysis
Conclusions and Future Work
3
System Architecture of a Sensor Node
• Sensing transducer, micro-controller unit (MCU), wireless radio transceiver, memory, and battery
• Very limited resources and capabilities
TelosB Sensoro16- bit RISC Processoro10 KB RAMoIEEE 802.15.4 at 250 MbpsoTwo AA batteries or USB
DAWN Lab / UMBC
4
Sensor in a Nutshell
Wireless Sensor Networks
Base StationMulti-hop, many-to-one communication
Multi-hop wireless networks consisting of small sensor nodes equipped with sensing, communication and processing capabilities
6
Applications
Battle Field Surveillance
Structural Health Monitoring
7
Ecological Monitoring
Source: http://www.networkworld.com/
8
Source: http://csn.caltech.edu/
Community Seismic Network
My Current Sensor Network Research
ApplicationNetworkTransportLinkPhysical
(Hardware)
o Energy-efficient algorithms and protocols
o Tailored to application requirements and constraints
Intermittently Connected Sensor Networks
Underwater/ocean seismic sensor networks,
volcano eruption/glacial melting monitoring
Not feasible to install base station in field
Data generated and stored in the network, periodically uploaded via data mules or satellite links
Data Priority: data generated may have different importance (seismic, infrasonic, temperature) Source: http://fiji.eecs.harvard.edu/Volcano
10
Data Preservation In Intermittently Connected Sensor Networks Non-uniform data
generation and limited storage capacity
Data generators (DGs): storage-depleted
Data preservation: offload overflow data from DGs to nodes with available storage
Data from different DGs are of different importance
Data Generator
DG1DG2
DG3
Sensor node
v
priority of DG1 ≥ priority of DG2 > priority of DG3
11
Challenge in Data Preservation
Limited battery power
Data preservation consumes battery power
When not all the data can be preserved inside the network, how to ensure data preservation with maximum total priorities – data preservation with data priorities (DPP)
Data Generator
DG1DG2
DG3
Sensor node
v
priority of DG1 ≥ priority of DG2 > priority of DG3
12
Data Preservation With Priority (DPP)
13
Objective of DPP
Select a subset of data items to offload to maximize their total priorities
Fig.1. Illustration of the DPP problem.
Priority of node 1: 2Priority of node 3: 1
Total preserved priorities: 3
14
Maximum Flow ProblemGiven a flow network G with source s and sink t, find maximum amount of flow from s to t
s
v4v2
v3v1
t
16
13
4 9
12
7
14
4
2011
8
1
12
4
11
7
15
4
15
Multiple Sources and/or Sinks
s1
s
s2
s3
s4
s5
t3
t1
t2 t
s1
s2
s3
s4
s5
t3
t1
t2
16
Maximum Weighted Flow (MWF)
s t
s1
s2
s3
s4
s5
t3
t1
t2
Find a flow with maximum total weight from s to t
17
Priority-based Algorithm (PBA) for MWF
Find maximum flow (using Edmonds-Karp) in non-ascending order of source node’s priority
Optimality proof of PBA (omitted)
Maximum weighted flow is a maximum flow, but not vice versa
Time complexity: O(knm2) 18
Optimal Algorithm for DPP
Transform G into G′
PBA on G’ is an optimal algorithm for DPP on G.
G G′
Priority of node 1: 2Priority of node 3: 1
Total preserved priorities: 319
A Heuristic Algorithm of DPP
Offload data in non-ascending order of DG’s priority: for each DGwhile (It can still off a data item from it to a non-DG node)
Offload it to the closest non-DG node
Time complexity: O(km + kdn), d is average number of data items of each DG 20
Performance Evaluation Visual Performance Comparison
Grid network: 10 × 10, 20 × 20 Initial energy level: 30 units
Each DG has 30 data items to offload (50 data items in 20 × 20 grid)
Shape Coordinate
Priority
DG1 (4, 6) 8
DG2 (7, 6) 6
DG3 (4, 5) 4
DG4 (7, 5) 2
21
Data Preservation Blocked by Storage Constraint
22
Data Preservation Blocked by Energy Constraint
23
24
Push-Relabel Algorithm
“active” node - a node with “excess flow”
“Relabel”: increase the height of the “active” node to push excess flow
“Push”: send the excess flow to the neighbors
Terminates when no “active” nodes left
Distributed Data Preservation with Data Priority
1. Each DG broadcasts its priority to the network;2. for each DG in the non-ascending order of its priority3. s pushes maximum allowable data to this DG;4. while (there exists a node u with positive excess)5. Push-Relabel(u);6. end while;7. end for
25
The distributed algorithm preserves maximum total priority. It runs in O(kn2) time and uses O(n2m) messages.
Distributed Algorithm Comparison
26
ConclusionsData preservation in sensor networks by considering data priorities (DPP)
Maximum weighted flow (MWF), generalizing maximum flow problem
Distributed algorithm
27
Topics for Senior Project or M.S. Projects/ThesesDGs of low priority discard their locally generated data
General energy model
Combining data preservation and data retrieving
Data Resiliency With Data Aggregation
28
QualificationGood Algorithms Knowledge
Good Programming Skills
Motivation and hardworking
