Tag Archives: Mathematical Modelling

Mathematical Modelling and Analysing data from Wireless Sensor Network: part 2

  • Describe the Evaluation ,implementation wireless sensor network protocols. Comparison and Evaluation of Energy Estimation Model Evaluation Implementation And Comparison Of Energy Dissipation of Routing protocols for Wireless Sensor Network M Engg Project Sandeep Sharma
  • Evaluation Implementation And Comparison Of Energy Dissipation of Routing protocols for Wireless Sensor Network Evaluation Implementation And Comparison Of Energy Dissipation of Routing protocols for Wireless Sensor Network Name : Sandeep Sharma Enrollment No: 11092297 Supervisor’s Name: Reiner Dojen P a g e |1 Student: Sandeep Sharma [Type text]
  • Evaluation Implementation And Comparison Of Energy Dissipation of Routing protocols for Wireless Sensor Network Acknowledgement Page I would like to thank my supervisor, Reiner Dojen, for her help in the completion of my dissertation and also for her helpful actions when personal obstacles arose. Thanks go to the people of Limerick City for making this an enjoyable year, and also to all lecturer for thoroughly and attentively proofreading my thesis. Finally I would like to thank Mr Anish, both a gentleman and a scholar; I am surprised this man gets any work done with the amount of time he gives to others. P a g e |2 Student: Sandeep Sharma [Type text]
  • Evaluation Implementation And Comparison Of Energy Dissipation of Routing protocols for Wireless Sensor Network Index Page Contents Motivation
  • 4 Abstract
    5 SECTION 1: BACKGROUND ………………………………………………………………………………………………………………………5 Chapter #1: Introduction to Wireless Sensor Networks …………………………………………………………………………………6
    Chapter #2 :Protocol and Technology …………………………………………………………………………………………………………9
    Chapter #3 : Applications and Usage of WSN example ……………………………………………………………………………….12
    Chapter #4 :Security Needs of WSN and estimating cost of security ……………………………………………………………..15
    Chapter #5 :(Routing) Protocols for WSN ………………………………………………………………………………………………….17
    Chapter #6 : Introduction to the estimation model PPECEM [FaZ12] and Comparison with other model of Energy Estimation for (Routing) Protocols for WSN. ……………………………………………………………………………………………..22
    Chapter #7: Evaluation and implementation of PPECEM [FaZ12] the estimation model ………………………………….24
    Chapter #8 : Comparison of protocols efficiency ………………………………………………………………………………………..26
    Chapter #9 :Resources/Equipment Used during experiment and procedure for project extension of project to security algorithms (if time permits): ……………………………………………………………………………………………………….27
    SECTION 2 :IMPLEMENTATION WITH PRIOR EXISTED TECHNIQUES …………………………………………………………….29
    Chapter 10: SPIN Protocol implementation: ………………………………………………………………………………………………29
    Chapter 11: CTP Protocol implementation ………………………………………………………………………………………………..33
    Chapter 12 : Cryptography Code AES …………………………………………………………………………………………………36
    SECTION 3 : OWN ORIGINAL WORK ………………………………………………………………………………………………………..37
    Chapter 13: Energy Measurement Cost of Routing and security Challenges ………………………………………37
    Chapter 14: Enhancement/Improvement in Existing model:
    …………………………………………………………………………………………38
    ERA: Efficiency, Reliability,Availability…………………………………….41
    Chapter 15 : ERAECEM …………………………………………45
    Proposed New Energy aware Routing Algorithm ERAQP ……..47
    Chapter 16 Mathematical Model to study behavior of WSN ……49
    Chapter 17: Incorporating Data bias: ………………….54
    Fuzzy Measurement Model ………………………….54
    17.3 Routing Algorithm 2 proposed: Fuzzy Rank Routing …………….54
    17.4 Configurable Routing Algorithm ……………………..55
    Data bias based on Fuzziness ……………….56
    WSN Fuzzy Information Fuzzy Neural network………….56
    Chapter 16: Future Direction: : sensor Cloud, Sensor and BPM, Sensor ERP, sensor security …………..57
    Conclusion:

Mathematical Modelling and Analysing data from Wireless Sensor Network: part 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.
  • 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)
  • 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
  • 10 PROJECT GOALS 9. Fuzzy routing Algorithm Motes represent 10. Fuzzy Information Neural Network representation of Wireless Sensor Network.
  • MOTIVATION
  • 1.1 SPIN
  • 1.2 CTP  Collection tree protocol
  • 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.
  • 1.3 WSN SECURITY
  • 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.
  • 3.2 PPECEM  QCPU = PCPU * TCPU = PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) Eq.2)
  • 4 ERA  Efficiency = Ptr X Prc X Pcry … (Eq.2)  Reliability = Rnode1 = FtrX FrcX Fcy  Availability= TFNode1 = Ftr+ Frc+Fcry
  • 5. IMPROVE EXISTING  . ERA = fed  Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
  • ERAECEM  Etotal = Average(Eff + R +A)= (E+R+A)/3  Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
  • 6 ERAQP  Efficiency ,Reliability, Availability QoS prioritized routing Algorithm  ERA ranked and routing based Ranking Cost on Dijesktra to find most suitable path
  • 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.
  • 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.
  • 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
  • 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.