Data acquisition and storage in Wireless Sensor Network
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Transcript of 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)