Introduction
• Wireless Sensor Network is evolving as a
new research field.
• WSN is a field of automation in which:
 Human involvement is greatly reduced
 Helps quick decision making

Data acquisition and storage in Wireless Sensor Network

ARCHITECTURE OF NODE APPLICATION
STRUCTURE
• Node software has 3 parts
• Operating System which performs device-specific tasks
• Sensor Driver which initializes the sensor hardware and
performs the measurements in the sensor
• Host Middleware which organizes the co- operation of
the distributed nodes in the network
• Individual nodes interact with the distributed middleware
layer to perform the functions dictated by the sensor
network application
• The general overall software architecture of the sensor
net is shown in the figure on next slide.

INTERNAL ARCHITECTURE OF A HARWARE NODE

Features of Project
• Deploying sensors in the environment.
• Sensing the temperature.
• Storing the sensed data on secondary storage.
• Sending the sensed data to base station.
• On base station visualizing the data.
• Storing the data to database.
• Plotting graphs/calculating the average of
temperature.
• Extra functionality sending the sensed data directly
to cloud.

Literature Survey
• The sensor nodes are equipped with microSD slots to provide a cost
effective way to store large amounts of data.
• Hence, FAT file system is used so that it can be easily read by the PC
(sync) w/o any software or protocols either.
• RATFAT, an efficient implementation of the flexible real time capable
FAT file system can be used in real time applications which breaks up
file system operations into multiple atomic operations.

Literature Survey
• We are concerned of two major objectives:
1. Maximizing Lifetime of WSN
2. Develop Efficient Data Reporting Strategy

Quasi-equi-interval-based Allocation
• Input:
n: the number of complete sensor readings
m:the number of reported sensor readings
• Output:
{xk*}: final intervals between reported sensor readings
1. if mod(n,m)=0 then
2. let each element of { xk*} equal to n/m – 1;
3. else
4. if m < n/2 then
5. let arbitrary m([n/m]+1) – n elements of { xk*} equal to [n/m]-1,
the other n-m[n/m] elements of { xk*} equal to [n/m];
6. else
7. let arbitrary 2m-n elements of { xk*} equal to 0, the other n-m
elements of { xk*} equal to 1;
8. end if
9. end if

Adaptive Rate Control Algorithm
• Input:
{Pdrop}: packet-drop rate vector
{Qbuf} : buffer occupancy vector
• Output:
rr : updated reporting rate
1. for each update period rate do
2. calculate avg({Pdrop}) and avg({Qbuf});
3. if avg({Pdrop}) >= Pmax then
4. rr = rr/(1+avg({Qbuf})/Lbuf)2;
5. broadcast updated rr;
6. else if avg({Pdrop}) < Pmax then
7. rr = rr + d;
8. broadcast rr;
9. end if
10.end for

STG-based algorithm
1. int ROUND = 0;
2. repeat
3. for each state S do
4. Get associated energy levels of S;
5. Cut out the resultant energy levels using the min()
function;
6. Compute and select the energy level with the maximal-
minimum energy value.
7. Set S’s energy level to the energy level with the
maximum summation among the resultant energy levels;
8. end for
9. ROUND = ROUND + 1;
10. until All the energy levels of the states in ROUND are zero;
11. Return the schedule represented by the path ending in ROUND

VBS-based algorithm
1. S = {};
2. Construct the VSG Gs(V*,L*) of G(V,L);
3. repeat
4. Apply the marking process on Gs(V*,L*)
5. Apply Rules 1 and 2 or Rule K on the induced graph
6. Construct the PMCDS C* from the resultant CDS C;
7. Remove the highest indexed virtual nodes of the
ancestors whose virtual nodes is in C* from Gs(V*,L*);
8. Find the corresponding CDS Ci of C* in G;
9. S = S U {(Ci,Ti)};
10. until Any ancestor’s virtual nodes are all eliminated from Gs(V*,L*);
11. return S.

Problem Statement
• Data Acquisition and Storage in Wireless Sensor
Network.

UML Diagram
Sensor nodes
Sense data
Send data
Deploy nodes
Store on SD card
Forward data
Sensor Node
Collect Data
Visualize data
Generate report
By ChartBy Graph
Use Case Diagram
Topology used
Fire Query
Display on UI
StarMesh
«uses»«uses»
Project Team
«uses»«uses»
System

Limitations
• Environmental factors
• Storage restrictions
• Limited resources (e.g. Power supply)

Future Scope
• Storing the sensed data on the cloud
• Visualizing the results on Android smart phones.

Function Point Analysis
Function Point Calculation:
1. External input-Sensed Data (Temperature).
2. External Output-Visualized Data.
3. External Inquiries- The system is requested for things such as node, base station, data.
4. External Interface- There’s no EIF to consider.
5. Internal Logical Files- Stored data file, receives data file.
Category Multiplier Weight
EI 1 4
EO 1 4
EQ 3 6
ILF 2 7
Function Point =1*4+1*4+6*3+2*7=40[FP]
Now to calculate how long it takes to produce 30 functions. Considering 15hrs of work in C++
language.
Then estimate for developing application would take 15*40=600[hours].

Feasibility Assessment
• Operational Feasibility :
Storing the sensed data in the locally provided secondary
memory would be beneficial in two ways :-
1. Data Aggregation, and
2. Data Recovery.

Feasibility (continued)
• Technical Feasibility :
1. Coding : C++
2. Interface design : HTML5, CSS
3. Technology : ZigBee, Simulation (NS3)
4. Microcontroller : MSP430F5437A / Gennic

Project Plan
Project Plan
Date
Description
3/7 15/7 22/7 29/8 13/8 28/9 2/9 5/9 11/9 25/9 27/10 3/11 20/11
Overview of
Project
Preliminary
Investigation
Problem
statement
evaluation
Presentation
on Problem
Statement
approval
Prepare
Synopsis
Paper
discussion
Literature
Survey
Preparation
of Partial
Project
Report
Submitting
and
acceptance
of Paper
Submission
of Partial
Project
Report

Applications
• Environmental Monitoring
• Home Automation
• Military Application
• Civil Structure Monitoring
• Security Surveillance

Conclusion
• We would be able to store the sensed data into the
locally provided secondary storage and visualize it at the
base station.
• NS3.

References
[1] On Maximizing the Lifetime of Wireless Sensor Networks Using
Virtual Backbone Scheduling by
Yaxiong Zhao, Student Member, IEEE, Jie Wu, Fellow, IEEE,
Feng Li, Member, IEEE, and Sanglu Lu, Member, IEEE
[2] Rate-constrained uniform data collection in wireless sensor
networks
H. Deng1 , B. Zhang2,4 , J. Zheng3
[3] RATFAT: ReAl-Time FAT for Cooperative Multitasking Environments
in WSNs
Sebastian Schildt, Wolf-Bastian P¨ottner, Felix B¨usching,
and Lars Wolf

Participation Details
• Achieving efficient data acquisition and storage
techniques in Wireless Sensor Networks
o Paper accepted in IJERT (International Journal of Engineering Research and
Technology)

Data acquisition and storage in Wireless Sensor Network

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