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

Architecture sap hana vs oracle exadata competitive analysis part -2

READ part 1:
https://sandyclassic.wordpress.com/2011/11/04/architecture-and-sap-hana-vs-oracle-exadata-competitive-analysis/
This debate of SAP Vs Oracle or last 2 yrs buzz SAP HANA vs Oracle Exalytics
Every year in Enterprise Software space Competition of SAP Vs Oracle Hots up with new announcements and New technology comparisons of SAP new Tech Vs Oracle New Tech.
The Enterprise Software stack built up by No.1 and No.2  world’s top most valued companies in Enterprise Technology Space.
So SAP Vs Oracle comparison will never go out of picture only every year it will up the ante. 
In Memory Technology: SAP Vs Oracle (New tech now 2 yrs old).

Now a day In-Memory Technology is the Hottest area especially in Business Intelligence (BI). But its not limited to BI it goes into ERP, Application development of any Kind.
The processing infrastructure in form of in Memory Systems is much faster than any other form. As Cost of RAM is coming down and capacity of server is expanding So most data to be processed can be pulled inside memory at once instead of using Locality of reference to pull and process from secondary memory.
Also Server can hold lot more data to processed in memory at once.
SAP HANA Vs Oracle Exadata
So SAP Came with SAP HANA and Oracle has its own in memory systems. At same time Oracle released high performance machine Exadata which brook many performance records. So There was running comparisons of SAP Vs Oracle in Enterprise Technology space.
innovation at SAP : ABAP, BSP and BAPI 
SAP customized by using  Advanced Business Application Programming (ABAP) Language.
Using which reports are customized, forms are modified, business processes are written to reflect business Logic. ILE, BAPI, IDOC can be used to interface with external software or integration or developing Adaptor. Custom Exits and User Exits are written to customize forms and reports.
For SAP datawarehouse technology SAP BIW or SAP BI datawarehouse is created using
Extraction Legacy System Migration Workbench (LSMW) or Cost and profitability Analysis (COPA) Extrations then transformation can be applied using ABAP user exits.
Problem with ABAP is compared to mordern langauges it has not evolved much over time.
Most of ABAP constructs are similar to COBOL constructs. There is Object oriented ABAP also like JSP or ASP  it came up with BSP (Business Server Pages) to Expose ABAP code directly to Web like JSP does for Java or ASP does for Microsoft Technology.

SAP Netweaver  Vs SAP ABAP BSP
SAP began adopting Java in 2003 and came up with Netweaver product which was J2EE server for ABAP code. Now you can code using either java or ABAP in netweaver. For Application requiring functionality to exposed to Web Java was natural choice.
But Oracle having acquired Java in year 2010 Since then there was continuous Effort on part of SAP to move away from its dependence of Java.  So BSP came into picture as first step.
Benefits of In-memory in SAP HANA
next was paradigm shift using Columar database instead of Row oriented databases which consumed less storage (on account of reduction in repetitions of similar column values as well compression of data).
Also it integrates:
BI and datawarehousing system or OLAP with
Operational systems or OLTP Systems as one.
Even Analytic requiring lowest level of granularity can be queried on same server.
Effect of which was Whole data can be pulled and kept in In-memory system offering faster response time to multiple user connected at same time rather than
Regular database Query processing logic
parsing request , making parse tree,
comparing with already fetched query parse tree in cache
if not available
then
fetching data from secondary memory
when depending on the request into

Improvement in data processing in SAP HANA and column oriented database
Now since whole data can be kept in-memory So Every query can directly fetch data quicker.
Future Technology Like SAP HANA
Enhancement over this Technology are Probabilistic databases and Graph databases.
Graph databases are available commercially since long time.
Index free storage. Every element has direct pointer to adjacent element, hence no lookup needed.
Here is list:
http://en.wikipedia.org/wiki/Graph_database
And
Probabilistic databases : Are active area of research as discussed above as well.
http://en.wikipedia.org/wiki/Probabilistic_database

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/


Case Study Artificial Intelligence,ETL and Datawarehousing Examples part 1

Read : IPTV and Augmented Reality using Artificial Intelligence.
https://sandyclassic.wordpress.com/2012/06/27/future-of-flex-flash-gamification-of-erp-enterprise-software-augmented-reality-on-mobile-apps-iptv/

AI is there in many place like one area of AI Fuzzy Set there is already Fuzzy Transformation in SQL Server Integration Services since year 2010.
What it does Fuzzy logic Transformation achieve?
So when we match two records we do it by checking each alphabet using regular matches.
But when we use fuzzy logic it brings out similar sounding and combination matches although alphabet may not be same also it checks meaning is same. Even it can override spelling mistakes to get right results How?
Example Fuzzy logic in SSIS:
USA,us, united states – For country Any person can enter any of these combination.
Usually its taken up for Data cleansing.
If data is not cleaned using De-dup it may not show many of these records in result for matches.
But Fuzzy logic we use Fuzzy set from all records it creates fuzzy set of record with
Set A { ElementA, membershipOfElementA}
membershipOfElementA define in percentage terms the possibility of it being in the similarly grouped set.
{us,0.97} {united states,0.98} {usa,0.99} {united states of America,1} so we can set tolerance level to 3% then all of these matches are there in result.
code you can see at http://www.codeproject.com/Tips/528243/SSIS-Fuzzy-lookup-for-cleaning-dirty-data
SIRI:  Speech Recognition Search Which was introduced in iPhone long back takes speech.
Speech input to pressure sensor –> generate Waveform –> Then Compare wave form
That’s process but.
AI in SIRI
The Waveform may be amplitude modulated but yet same thing let suppose we say
Apple the Two Waveform compared may have boundary level aberrations which can be defined by membership function Then same result within same Tolerance limit can be deemed to be similar. This membership can be calculated each time person do a search dynamically when it says something in on Mike which repeat same process again.
There can be lots of image processing and AI search algorithm can be built to make better.
Like A* search etc.
Already if the words are linked can be understood by Neural Network. Similar way Neural Network is used to predict The  traffic congestion aggregating data paths from street light sensors in japan Tokyo.
Aggregation of words can be achieved by neural Network in not exact but similar way to some Extent. Thus completing the search.
This aggregation may be used in text, covariance matrix of images or covariance of sound score or speech search.
Using Laplace Transform’s Cross correlation. Read (http://en.wikipedia.org/wiki/Cross-correlation512px-Comparison_convolution_correlation.svg
Now TV is large platform just like difference between watching movie on laptop or TV Vs on 70 mm screen. Each of those has there own market.
Costly Miniaturisation
What effect you can provide on TV may not be provided on mobile until there is technical break through in miniaturisation. I am not saying it cannot be provided but it will require relative less technical  break through compared with miniaturized chips or may be less costly.
Second TV is like we have last mile connectivity in Telecom.
So When you have something to watch in any storage device you can just throw that on TV Ubiquitously . As TV would be there in every house so you need not carry screen to watch. Just like Last mile wireless connectivity using HotSpot.

Why Diversity important from sociology, management to healthcare

Diversity in Management: Brings differentiation in product offering.
Diversity in Social Science: Brings Stability and Wholesome view of social paradigm hence sustainability in long term.
Diversity in healthcare: Brings new comprehensive approach towards solving health issues like from pathology, physiology , to use other speciality and engineering speciality now like we see data science usage.
Diversity in Technology: In Tech domain diversity is more important than anything else like you see What brings disruptive innovations ? its complete new technology from ground up. First thing is cleaning slate of old technology with learning outcomes from old yet start fresh with new approach. Can diversity bring that ? you can answer urself.
Diversity Business process thinking implementation : Diversity approach can bring BPR Business process Re-engineering.