Data acquisition and storage in Wireless Sensor Network

Data Acquisition and Storage in Wireless Sensor Network

Under the guidance ofProf. Sambhaji Sarode

ByRutvik Pensionwar

Pranav Tambat Nilesh Thite

Onkar Tummanpalli

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

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 WSN2. 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 readings1. if mod(n,m)=0 then2. let each element of { xk*} equal to n/m – 1;3. else4. if m < n/2 then5. 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. else7. let arbitrary 2m-n elements of { xk*} equal to 0, the other

n-m elements of { xk*} equal to 1;8. end if9. end if

Adaptive Rate Control Algorithm

•Input: {Pdrop}: packet-drop rate vector

{Qbuf} : buffer occupancy vector•Output: rr : updated reporting rate1. for each update period rate do 2. calculate avg({Pdrop}) and avg({Qbuf});3. if avg({Pdrop}) >= Pmax then4. rr = rr/(1+avg({Qbuf})/Lbuf)2; 5. broadcast updated rr;6. else if avg({Pdrop}) < Pmax then7. rr = rr + d;8. broadcast rr;9. end if10.end for

STG-based algorithm

1. int ROUND = 0;2. repeat3. for each state S do4. 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 for9. 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. repeat4. Apply the marking process on Gs(V*,L*)5. Apply Rules 1 and 2 or Rule K on the induced graph6. 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 dataSensor 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

Activity Diagram

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 AnalysisFunction 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, and2. Data Recovery.

Feasibility (continued)

• Technical Feasibility :

1. Coding : C++2. Interface design : HTML5, CSS3. Technology : ZigBee, Simulation (NS3)4. Microcontroller : MSP430F5437A / Gennic

Project PlanProject 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 Networkso Paper accepted in IJERT (International Journal of Engineering Research

and Technology)