Mathematical Modelling of Wireless Sensor Network

 

Wireless Sensor Network Security Analytics – presentation

find link of my presentation by searching on google itself
google : ” Wireless Sensor Network Security Analytics ”

TopSearchWirelessSensorSecurityAnalytics2

 

Link below
https://sandyclassic.wordpress.com/2014/03/07/wireless-security-analytics-approach/

2 New Routing algorithm for ad-hoc routing wireless sensor network, mathematical modelling for wireless sensor network 4 models for over all system and 2 models for energy measurement of wireless sensor network
Project Goal:

  • 1. 10 PROJECT GOALS 1. Routing algorithm: SPIN,CTP. 2. measure energy consumed 3. Validate PPECEM Model 4. Improve in existing model for efficiency, reliability, availability.
  • 2. 10 PROJECT GOALS 5. New Model: ERAECEM Efficiency Reliability Availability Energy consumption Estimation Model. 6. ERAQP BASED on ERAECEM Model for WSN a new energy aware routing algorithm (ERAQP)
  • 3. 10 PROJECT GOALS 7. Configurable Routing Algorithm Approach Proposed on WSN motes utilizing user defined QoS parameters 8. Model for WSN: Leader-Follower Model, Directed Diffusion Model
  • 4. 10 PROJECT GOALS 9. Fuzzy routing Algorithm 10. Fuzzy Information Neural Network representation of Wireless Sensor Network.
  • 5. MOTIVATION
  • 6. 1.1 SPIN
  • 7. 1.2 CTP  Collection tree protocol
  • 8. 2 ENERGY MEASUREMENT  Agilent 33522B Waveform Generator was used to measure the Current and voltage graph .  The Graph measurement were then converted to numerical power Power= Voltage X current = V X I. The Power consumed during motes routing on SPIN and CTP then taken into is added up to give power consumption and values are applied to PPECEM.
  • 9. 1.3 WSN SECURITY
  • 10. 3.1COST OF SECURITY  Cost of security In WSN can only be estimated by looking at extra burden of secure algorithm and security of Energy Consumption as the Energy is key driver or critical resource in design of WSN. As design is completely dominated by size of battery supplying power to mote.
  • 11. 3.2 PPECEM  QCPU = PCPU * TCPU = PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) Eq.2)
  • 12. 4 ERA  Efficiency = Ptr X Prc X Pcry … (Eq.2)  Reliability = Rnode1 = FtrX FrcX Fcy  Availability= TFNode1 = Ftr+ Frc+Fcry
  • 13. 5. IMPROVE EXISTING  . ERA = fed  Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
  • 14. ERAECEM  Etotal = Average(Eff + R +A)= (E+R+A)/3  Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
  • 15. 6 ERAQP  Efficiency ,Reliability, Availability QoS prioritized routing Algorithm  ERA ranked and routing based Ranking Cost on Dijesktra to find most suitable path
  • 16. 7.CONFIG. ROUTING  q1, q2, q3 as QoS parameter algorithm rank Motes/nodes based on combined score of these parameters. Based on this we rank we apply Dijesktra algorithm to arrive at least path or elect Cluster head to node. Thus q1, q2, q3 can be added, deleted.
  • 17. 8 MATHEMATICAL MODEL  Leader Follower EACH node share defined diffusion rate given by slider control on UI which tells quantity it is diffusing with its neighbors.Since it’s a directed graph so Node B gives data towards Node A while traffic from A towards B may be non-existent  Directed Diffusion Mathematical model represent diffusion of quantity towards a directed network. Helps to understand topology, density and stability of network and a starting point for designing complex , realistic Network Model.
  • 18. 9 FUZZY ROUTING  Fuzzy set A {MoteA, p(A))  Where, p(A) is probability Of Data Usage Or Percentage Load in Fraction Compared With Global Load
  • 19. 10 FUZZY TOPOLOGY  Based on this Utilization p(A) nodes can be ranked in ascending order to find most data dwarfed node at the top. Then We can apply Dijkstra’s algorithm on the network to find best route based on weight on each node represented by Rank.
    2. WSN and BPEL and Internet Of Things (IoT)
    https://sandyclassic.wordpress.com/2013/10/06/bpm-bpel-and-internet-of-things/3. Internet Of Things (IoT) and effects on other device ecosystem.
    The Changing Landscape:
    https://sandyclassic.wordpress.com/2013/10/01/internet-of-things/

    4. How application development changes with IoT, Bigdata, parallel computing, HPC High performance computing.
    https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/

    5. Landslide detection and mpact reduction using wireless sensor network.
    https://sandyclassic.wordpress.com/2013/06/23/landslide-detection-impact-reduction-using-wireless-sensor-network

    6. Mathematical modelling Energy Wireless sensor Network.
    https://sandyclassic.wordpress.com/2014/02/04/mathematical-modelling-energy-security-of-wireless-sensor-network/

    Topic Topics Wireless sensor network Security Analytic, Wireless Security Analytics,Security QA metrics

Wireless Security Analytics- Approach

How To model wireless security mathematically. (its topmost search in Google Type(Wireless Sensor network Security Analytics) Result:
TopMostSearchWirelessSensorNetworkSecurityAnalyticsRead:

1. Go through the Slides about Modelling the Wireless sensor Network and Internet of Things

  • 10 PROJECT GOALS 1. Routing algorithm: SPIN,CTP. 2. measure energy consumed 3. Validate PPECEM Model 4. Improve in existing model for efficiency, reliability, availability.
  • 2. 10 PROJECT GOALS 5. New Model: ERAECEM Efficiency Reliability Availability Energy consumption Estimation Model. 6. ERAQP BASED on ERAECEM Model for WSN a new energy aware routing algorithm (ERAQP)
  • 3. 10 PROJECT GOALS 7. Configurable Routing Algorithm Approach Proposed on WSN motes utilizing user defined QoS parameters 8. Model for WSN: Leader-Follower Model, Directed Diffusion Model
  • 4. 10 PROJECT GOALS 9. Fuzzy routing Algorithm 10. Fuzzy Information Neural Network representation of Wireless Sensor Network.
  • 5. MOTIVATION
  • 6. 1.1 SPIN
  • 7. 1.2 CTP  Collection tree protocol
  • 8. 2 ENERGY MEASUREMENT  Agilent 33522B Waveform Generator was used to measure the Current and voltage graph .  The Graph measurement were then converted to numerical power Power= Voltage X current = V X I. The Power consumed during motes routing on SPIN and CTP then taken into is added up to give power consumption and values are applied to PPECEM.
  • 9. 1.3 WSN SECURITY
  • 10. 3.1COST OF SECURITY  Cost of security In WSN can only be estimated by looking at extra burden of secure algorithm and security of Energy Consumption as the Energy is key driver or critical resource in design of WSN. As design is completely dominated by size of battery supplying power to mote.
  • 11. 3.2 PPECEM  QCPU = PCPU * TCPU = PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) Eq.2)
  • 12. 4 ERA  Efficiency = Ptr X Prc X Pcry … (Eq.2)  Reliability = Rnode1 = FtrX FrcX Fcy  Availability= TFNode1 = Ftr+ Frc+Fcry
  • 13. 5. IMPROVE EXISTING  . ERA = fed  Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
  • 14. ERAECEM  Etotal = Average(Eff + R +A)= (E+R+A)/3  Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
  • 15. 6 ERAQP  Efficiency ,Reliability, Availability QoS prioritized routing Algorithm  ERA ranked and routing based Ranking Cost on Dijesktra to find most suitable path
  • 16. 7.CONFIG. ROUTING  q1, q2, q3 as QoS parameter algorithm rank Motes/nodes based on combined score of these parameters. Based on this we rank we apply Dijesktra algorithm to arrive at least path or elect Cluster head to node. Thus q1, q2, q3 can be added, deleted.
  • 17. 8 MATHEMATICAL MODEL  Leader Follower EACH node share defined diffusion rate given by slider control on UI which tells quantity it is diffusing with its neighbors.Since it’s a directed graph so Node B gives data towards Node A while traffic from A towards B may be non-existent  Directed Diffusion Mathematical model represent diffusion of quantity towards a directed network. Helps to understand topology, density and stability of network and a starting point for designing complex , realistic Network Model.
  • 18. 9 FUZZY ROUTING  Fuzzy set A {MoteA, p(A))  Where, p(A) is probability Of Data Usage Or Percentage Load in Fraction Compared With Global Load
  • 19. 10 FUZZY TOPOLOGY  Based on this Utilization p(A) nodes can be ranked in ascending order to find most data dwarfed node at the top. Then We can apply Dijkstra’s algorithm on the network to find best route based on weight on each node represented by Rank.

2. WSN and BPEL and Internet Of Things (IoT)
https://sandyclassic.wordpress.com/2013/10/06/bpm-bpel-and-internet-of-things/

3. Internet Of Things (IoT) and effects on other device ecosystem.
The Changing Landscape:
https://sandyclassic.wordpress.com/2013/10/01/internet-of-things/

4. How application development changes with IoT, Bigdata, parallel computing, HPC High performance computing.
https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/

5. Landslide detection and mpact reduction using wireless sensor network.
https://sandyclassic.wordpress.com/2013/06/23/landslide-detection-impact-reduction-using-wireless-sensor-network

6. Mathematical modelling Energy Wireless sensor Network.
https://sandyclassic.wordpress.com/2014/02/04/mathematical-modelling-energy-security-of-wireless-sensor-network/

Mathematical Modelling Energy Security Wireless sensor network- part 2

1. Go through the Slides about Modelling the Wireless sensor Network and Internet of Things

  • 10 PROJECT GOALS 1. Routing algorithm: SPIN,CTP. 2. measure energy consumed 3. Validate PPECEM Model 4. Improve in existing model for efficiency, reliability, availability.
  • 2. 10 PROJECT GOALS 5. New Model: ERAECEM Efficiency Reliability Availability Energy consumption Estimation Model. 6. ERAQP BASED on ERAECEM Model for WSN a new energy aware routing algorithm (ERAQP)
  • 3. 10 PROJECT GOALS 7. Configurable Routing Algorithm Approach Proposed on WSN motes utilizing user defined QoS parameters 8. Model for WSN: Leader-Follower Model, Directed Diffusion Model
  • 4. 10 PROJECT GOALS 9. Fuzzy routing Algorithm 10. Fuzzy Information Neural Network representation of Wireless Sensor Network.
  • 5. MOTIVATION
  • 6. 1.1 SPIN
  • 7. 1.2 CTP  Collection tree protocol
  • 8. 2 ENERGY MEASUREMENT  Agilent 33522B Waveform Generator was used to measure the Current and voltage graph .  The Graph measurement were then converted to numerical power Power= Voltage X current = V X I. The Power consumed during motes routing on SPIN and CTP then taken into is added up to give power consumption and values are applied to PPECEM.
  • 9. 1.3 WSN SECURITY
  • 10. 3.1COST OF SECURITY  Cost of security In WSN can only be estimated by looking at extra burden of secure algorithm and security of Energy Consumption as the Energy is key driver or critical resource in design of WSN. As design is completely dominated by size of battery supplying power to mote.
  • 11. 3.2 PPECEM  QCPU = PCPU * TCPU = PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) Eq.2)
  • 12. 4 ERA  Efficiency = Ptr X Prc X Pcry … (Eq.2)  Reliability = Rnode1 = FtrX FrcX Fcy  Availability= TFNode1 = Ftr+ Frc+Fcry
  • 13. 5. IMPROVE EXISTING  . ERA = fed  Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
  • 14. ERAECEM  Etotal = Average(Eff + R +A)= (E+R+A)/3  Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
  • 15. 6 ERAQP  Efficiency ,Reliability, Availability QoS prioritized routing Algorithm  ERA ranked and routing based Ranking Cost on Dijesktra to find most suitable path
  • 16. 7.CONFIG. ROUTING  q1, q2, q3 as QoS parameter algorithm rank Motes/nodes based on combined score of these parameters. Based on this we rank we apply Dijesktra algorithm to arrive at least path or elect Cluster head to node. Thus q1, q2, q3 can be added, deleted.
  • 17. 8 MATHEMATICAL MODEL  Leader Follower EACH node share defined diffusion rate given by slider control on UI which tells quantity it is diffusing with its neighbors.Since it’s a directed graph so Node B gives data towards Node A while traffic from A towards B may be non-existent  Directed Diffusion Mathematical model represent diffusion of quantity towards a directed network. Helps to understand topology, density and stability of network and a starting point for designing complex , realistic Network Model.
  • 18. 9 FUZZY ROUTING  Fuzzy set A {MoteA, p(A))  Where, p(A) is probability Of Data Usage Or Percentage Load in Fraction Compared With Global Load
  • 19. 10 FUZZY TOPOLOGY  Based on this Utilization p(A) nodes can be ranked in ascending order to find most data dwarfed node at the top. Then We can apply Dijkstra’s algorithm on the network to find best route based on weight on each node represented by Rank.

2. WSN and BPEL and Internet Of Things (IoT)
https://sandyclassic.wordpress.com/2013/10/06/bpm-bpel-and-internet-of-things/

3. Internet Of Things (IoT) and effects on other device ecosystem.
The Changing Landscape:
https://sandyclassic.wordpress.com/2013/10/01/internet-of-things/

4. How application development changes with IoT, Bigdata, parallel computing, HPC High performance computing.
https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/

5. Landslide detection and mpact reduction using wireless sensor network.
https://sandyclassic.wordpress.com/2013/06/23/landslide-detection-impact-reduction-using-wireless-sensor-network

6. Mathematical modelling Energy Wireless sensor Network.
https://sandyclassic.wordpress.com/2014/02/04/mathematical-modelling-energy-security-of-wireless-sensor-network/


Mathematical Modelling Energy Security Of Wireless Sensor Network

Mathematical Modelling Energy Security Of wireless Sensor Network.
A quick fix thesis submitted in October 2013 after operational issue at lab.

Mathematical Modelling the Sensor Network

 

Modelling Wireless sensor network

Modelling Wireless sensor network

1. Go through the Slides about Modelling the Wireless sensor Network and Internet of Things

  • 10 PROJECT GOALS 1. Routing algorithm: SPIN,CTP. 2. measure energy consumed 3. Validate PPECEM Model 4. Improve in existing model for efficiency, reliability, availability.
  • 2. 10 PROJECT GOALS 5. New Model: ERAECEM Efficiency Reliability Availability Energy consumption Estimation Model. 6. ERAQP BASED on ERAECEM Model for WSN a new energy aware routing algorithm (ERAQP)
  • 3. 10 PROJECT GOALS 7. Configurable Routing Algorithm Approach Proposed on WSN motes utilizing user defined QoS parameters 8. Model for WSN: Leader-Follower Model, Directed Diffusion Model
  • 4. 10 PROJECT GOALS 9. Fuzzy routing Algorithm 10. Fuzzy Information Neural Network representation of Wireless Sensor Network.
  • 5. MOTIVATION
  • 6. 1.1 SPIN
  • 7. 1.2 CTP  Collection tree protocol
  • 8. 2 ENERGY MEASUREMENT  Agilent 33522B Waveform Generator was used to measure the Current and voltage graph .  The Graph measurement were then converted to numerical power Power= Voltage X current = V X I. The Power consumed during motes routing on SPIN and CTP then taken into is added up to give power consumption and values are applied to PPECEM.
  • 9. 1.3 WSN SECURITY
  • 10. 3.1COST OF SECURITY  Cost of security In WSN can only be estimated by looking at extra burden of secure algorithm and security of Energy Consumption as the Energy is key driver or critical resource in design of WSN. As design is completely dominated by size of battery supplying power to mote.
  • 11. 3.2 PPECEM  QCPU = PCPU * TCPU = PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) Eq.2)
  • 12. 4 ERA  Efficiency = Ptr X Prc X Pcry … (Eq.2)  Reliability = Rnode1 = FtrX FrcX Fcy  Availability= TFNode1 = Ftr+ Frc+Fcry
  • 13. 5. IMPROVE EXISTING  . ERA = fed  Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
  • 14. ERAECEM  Etotal = Average(Eff + R +A)= (E+R+A)/3  Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
  • 15. 6 ERAQP  Efficiency ,Reliability, Availability QoS prioritized routing Algorithm  ERA ranked and routing based Ranking Cost on Dijesktra to find most suitable path
  • 16. 7.CONFIG. ROUTING  q1, q2, q3 as QoS parameter algorithm rank Motes/nodes based on combined score of these parameters. Based on this we rank we apply Dijesktra algorithm to arrive at least path or elect Cluster head to node. Thus q1, q2, q3 can be added, deleted.
  • 17. 8 MATHEMATICAL MODEL  Leader Follower EACH node share defined diffusion rate given by slider control on UI which tells quantity it is diffusing with its neighbors.Since it’s a directed graph so Node B gives data towards Node A while traffic from A towards B may be non-existent  Directed Diffusion Mathematical model represent diffusion of quantity towards a directed network. Helps to understand topology, density and stability of network and a starting point for designing complex , realistic Network Model.
  • 18. 9 FUZZY ROUTING  Fuzzy set A {MoteA, p(A))  Where, p(A) is probability Of Data Usage Or Percentage Load in Fraction Compared With Global Load
  • 19. 10 FUZZY TOPOLOGY  Based on this Utilization p(A) nodes can be ranked in ascending order to find most data dwarfed node at the top. Then We can apply Dijkstra’s algorithm on the network to find best route based on weight on each node represented by Rank.

2. WSN and BPEL and Internet Of Things (IoT)
https://sandyclassic.wordpress.com/2013/10/06/bpm-bpel-and-internet-of-things/

3. Internet Of Things (IoT) and effects on other device ecosystem.
The Changing Landscape:
https://sandyclassic.wordpress.com/2013/10/01/internet-of-things/

4. How application development changes with IoT, Bigdata, parallel computing, HPC High performance computing.
https://sandyclassic.wordpress.com/2013/09/18/new-breed-of-app-development-is-here/

 

TinyOS on Ubuntu 12.04 virtual box latest

Following Image in help site should be updated.

http://tinyos.stanford.edu/tinyos-wiki/index.php/Installing_XubunTOS_(with_TinyOS_from_tp-freeforall/prod_repository)_in_VirtualBox.
its deficient in following packages and still after that also not list not working
following packages missing
after comparing packages installed in working tinyos image and this image.
tinyos-2.1.0                         2.1.0-20090326
ii  tinyos-base                          2.1-20080806
ii  tinyos-required-all                  2.1-20090326
ii  tinyos-required-avr                  2.1-20090326
ii  tinyos-required-msp430
——————————————————————————————-

Follow these step as many packages are missing:
check package list $dpkg –list | grep tiny
We need to update all packages of Distribution to latest for this:
Step l : change the repository to point to latest.  edit using any editor gedit or nano               $/etc/apt/sources.list
add these lines after few steps you can follow as desribed in blog below:
#tinyOS

deb http://tinyos.stanford.edu/tinyos/dists/ubuntu lucid main

deb http://tinyos.stanford.edu/tinyos/dists/ubuntu maverick main

deb http://tinyos.stanford.edu/tinyos/dists/ubuntu natty main
Step 2: Update all packages of tinyos
$sudo apt-get update
step 3: install tinyos package
$sudo apt-get install tinyos
Step 4: $sudo apt-get install tinyos-2.1.2
You can use these instruction for fresh installation but since image is deficient in the packages you can use these you can follow these steps

http://norbertobarrocablog.wordpress.com/2012/12/19/how-to-install-tinyos-2-1-2-on-ubuntu-12-04/

Additionally here if motelist is failing due to some malware or virus trying to umount your devices list you can follow these instruction. or there may be other reason as well.
https://sandyclassic.wordpress.com/2013/10/25/on-tinyos-if-motelist-is-not-working/

On TinyOS if Motelist is not working

first diagnose using.

=========================================================
diagnose using command
$ dmesg -s
you can see messages what is causing problem.

—————————————————————————–

For motelist not working download motelist source code.  

googlecode+ tinyos+motelist
http://tinyos-main.googlecode.com/svn/trunk/tools/platforms/msp430/motelist/motelist-linux.in

Check the mount structure for
As from previous version 8 of last image to Ubuntu 12.4 the directory structure is changed little bit  hence last version directory required should be copied to new directories
check $mount all mount structure
then if there is duplicate unmount using $umount
$sudo mount –bind /dev/bus /proc/bus
dev structure should be copied to /proc

then download Google(motelist code perl)
open editor copy paste code in editor
$nano or gedit (program name ).pl

Execute the perl code $ perl
$ perl (program name).pl

BPM, BPEL and Internet of things

Most initial components of Internet of Things comes from wireless sensor networks.
Internet of Things using TinyOS platform : http://onlinelibrary.wiley.com/doi/10.1002/dac.2444/abstract
Then it has to Ubiquitous
Read for more detail:
Internet of things: https://sandyclassic.wordpress.com/2013/10/01/internet-of-things/
more details search on blog https://sandyclassic.wordpress.com
Ubiquitous computing and BI
https://sandyclassic.wordpress.com/2012/10/28/ubiquity-the-most-crucial-challenge-in-business-intelligence/
Ubiquitous computing  and ERP:
https://sandyclassic.wordpress.com/2013/09/16/new-age-enterprise-resource-planning-systems/
——————————————————————————————-
BPM and BPEL helps in aligning changing business process requirements. Any creating orchestrating business processes. creating workflow , business Rules and process Engine.
typical BPM components architecture looks like this:
BPM_Workflow_Service_Patternmore details: http://en.wikipedia.org/wiki/Business_process_management
When multiple devices interact the processes between them have to be orchestrated , easiest and best way to created interaction between devices and create rules is by creating workflow. Also workflow is What you see is What you Get (WYSIWYG). A Naive user can also diagrammatically drag and drop workflow components available in the panel and set rules. Which are quite similar to Business Rules. So Even BPM will become pervasive with internet of things.
Also programming multiple device interaction for complex task. the complexity can be reduced for programmer by using BPM using notation available in BPMN.
Read: http://en.wikipedia.org/wiki/Business_Process_Model_and_Notation
BPMN-CollectVotesA typical workflow of voting process depicted above:
Now similarly device workflow are captured and programmed using BPMN notation.
Recently IBM released Node.js pattern JavaScript for Internet of things over wireless sensor networks.
node-red-screenshotRead more detail: http://gigaom.com/2013/09/27/meet-node-red-an-ibm-project-that-fulfills-the-internet-of-things-missing-link/
you can clearly see device workflow above . Also this is how we set the filter rules to analyse filter data from the web using yahoo pipes http://pipes.yahoo.com/pipes/
More details later sometime again: Happy reading 🙂