wsn

Saurabh Singh
Saurabh SinghWriter, Book Editor & Graphic Designer @ MUYEM PUBLISHERS em The Philosopher
Presented By:-
SAURABH SINGH
OUTLINE
• Abstract
• Introduction
• AODV Overview
• Literature Review
• Problem Statement
• Proposed Solution
• Results
• Conclusion
• Future Scope
• References
ABSTRACT
• One key issue about WSN is that sensor nodes have a limited battery
capacity. For that, it is important to develop energy efficient solutions to
keep these networks functioning for the longest period of time.
• In this work, we introduce an basic initial energy model to gain higher
lifetime and increased coverage by managing the power at node level.
• Experimental results demonstrate that our algorithm can revealed the
superiority of the new methodology over existing heuristics.
INTRODUCTION
• Wireless Sensor Networks are networks that consists of
sensors which are distributed in an ad hoc manner.
• These sensors work with each other to sense some physical
phenomenon and then the information gathered is processed
to get relevant results.
• Wireless sensor networks consists of protocols and algorithms
with self-organizing capabilities.
WIRELESS SENSOR NETWORKS
ABCEFGS
APPLICATION OF WSN
Air Pollution
Forest Fire Detection
Offspring Care
Structural Health
Shipment Condition
Radiation Levels
Traffic Congestion
Water Quality
Waste Managment
Smart Parking
Water Leakages
WORKING PROCESS OF SENSOR NETWORK
Send any value… Using any protocol ..To any System
API+60 Sensors Wireless communication
modules
BASIC WSN NODE
• A sensor node, also known as a mote, is a node in a sensor network that is capable of
performing some processing, gathering sensory information and communicating with
other connected nodes in the network
It consist
MICRO CONTROLLER
SENSORS & MEMORY
ANTENNA
BATTERY
OPERATING SYSTEM
AODV OVERVIEW
• AODV is a packet routing protocol designed for use in mobile ad hoc networks .
• Intended for networks that may contain thousands of nodes
• One of a class of demand-driven protocols
• The route discovery mechanism is invoked only if a route to a destination is not known
• Source, destination and next hop are addressed using IP addressing
• Each node maintains a routing table that contains information about reaching
destination nodes.
AODV OPERATION
A
B D
F
C
G
E
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREP
RREP
RREP
Destination
Source
LITERATURE REVIEW
1.An Efficient Routing Technique That Maximizes The Lifetime And
Coverage Of WSN Ref [1]
• In this paper Author Introduced an efficient routing power management
heuristic to gain higher lifetime and increased coverage by managing
power at node level . This accomplished by dividing the node energy into
two ratio ; one of the sensor originated data (a) and the other part of data
relays from other sensor (b). This heuristic, which is called ERPMT
(efficient routing power management technique),has been applied to
OLM and C-MAX .
LITERATURE REVIEW
2. Balance Routing Protocol for Wireless Sensor Network Ref [2]
• WSN Operated on limited battery capacity imposes the use of energy-
efficient protocols. Such protocols minimize the total amount of energy
consumed by the network in forwarding a packet between any pair of
nodes. To achieve this, we have proposed a load-balanced routing
scheme which better fits in WSN , compared with on the fly routing
scheme of continuous monitoring applications.
LITERATURE REVIEW
3. Maximum Lifetime Routing In Wireless Sensor Networks Ref [3]
• In this paper Author shown that the lifetime maximization problem is NP-
hard. We also have proposed a new online heuristic–OML–for lifetime
maximization. Extensive simulations show that our new heuristic is
superior to previously published heuristics for lifetime maximization both
in terms of providing larger lifetime and in terms of sensitivity to algorithm
parameters. Additionally, our proposed heuristic provides larger network
capacity than provided by competing heuristics.
LITERATURE REVIEW
4.Online Routing Algorithms for Maximum Lifetime in
Wireless Sensor Networks Ref [4]
• In this paper Author present two online routing schemes, namely the
fuzzy maximum lifetime routing scheme (FML), and the fuzzy multi
objective routing scheme(FMO). Simulation results obtained under a
variety of network scenarios indicate the superiority of the proposed
schemes over a number of other online routing schemes in terms of the
lifetime as well as the energy consumption metric. For better scalability,
we also present a fully distributed implementation of the FML routing
scheme. The overhead analysis shows that the distributed scheme is
successful in considerably reducing the control overhead.
LITERATURE REVIEW
5.Cross-Layer Design for Energy Conservation in Wireless Sensor
Networks Ref [5]
• In this paper Author proposed balancing the traffic through the WSN. We
show that sending the traffic generated by each sensor node through
multiple paths instead of using a single path allows significant energy
conservation. On the other hand, at the MAC layer, we propose to control
the retry limit of retransmissions over each wireless link. We show that by
efficiently adjusting the retry limit for each link, further energy
conservation can be achieved, improving thus the network lifetime.
analysis shows that the distributed scheme is successful in considerably
reducing the control overhead.
PROBLEM STATEMENT
The WSN energy consumption falls into three categories: sensing, computing,
and communicating.
WLAN networks were designed to minimize delay and maximize throughput, but
they do not provide the energy efficiency demanded by wireless sensor
networks.
In WSNs with densely deployed nodes, a promising solution is to have sensors
collaboratively process information with distributed computation load among
sensors. To achieve application- independent parallel processing, energy
conservation and classification of nodes depending on remaining energy is a
problem that must be solved.
PROBLEM STATEMENT
• Author has used two matrixes for the development of the energy efficient
routing protocols for the WSN – first OLM (online Maximum lifetime) and
Capacity Maximization (CMAX))
• Author has proposed a new routing heuristics based on poison
distribution and two matrixes CMAX and OLM.
• The main problem with the authors proposed work is computational
overhead of CMAX and OLM.
PROPOSED SOLUTION
• Our approach is based on Energy Model. This is the core thing of our proposed work.
For calculating energy of node we will apply energy module of NS-3 on each node.
• To overcome the authors computational overhead we have adopted the idea of basic
initial energy model which gives the node based on their energy remain. And then route
will be chooses, the benefit of this scheme is to simplicity due to energy model THAT
REDUCES THE OVERHEAD OF TWO MATRIX COMPUTATION (CMAX and OLM) into
one.
• 1. We will have to form wireless sensor network depending on user requirement of
number of nodes.
• 2. Apply the Random 2D direction mobility model for creating topology between mobile
nodes in WSN.
PROPOSED SOLUTION
• 3. Apply the routing protocol AODV (on demand routing algorithm) for communication
with efficient routing between mobile nodes.
• 4. Calculating energy of mobile nodes based on energy mathematical model shown
below during communication calculating current energy of each node and generate
routing table for each mobile nodes using AODV protocol in WSN.
• 5. AODV protocol find shortest path between nodes and select path based on current
energy of nodes for communication between nodes.
• 6. After each node energy calculation we have got the energy variation better than CMAX
and OLM.
PROPOSED SOLUTION (CONT.)
• Energy mathematical calculations model:
• E (N, M) = ln>0(lm=n Etack + lm!=n ERack)+m>0(lm=n ETpck+ lm!=n ERpck)
E (N, M) =energy spent at node N due to node M
ETack=energy spent for transmission of one acknowledgement packet
ETpck= energy spent for transmission of one data packet
ERack= energy spent for reception of one acknowledgement packet
ERpck= energy spent for reception of one data packet
lp= p is true if value is 1
0 otherwise
EXPERIMENTAL SETUP
For implementing the above scenario following experimental setup will be made-
• Network Simulator (NS-3.20) and Linux UBUNTU 14.04
RESULT
1. Open Terminal Command -> Type Commands
• 2. Run Command -> Open Simulation Window -> Start Simulation
• 3. Run Simulation -> then Close Simulation Window -> Automatic
Generated Routing Data on Terminal -> Close Terminal
PACKET DELIVERY RATIO
THROUGHPUT
OML AND PROPOSED ALGORITHM
CONCLUSION
• In this thesis, the problem of energy conservation and full sensing
coverage in large WSNs where nodes are randomly deployed have been
addressed .
• We proposed a “basic initial energy model” which calculate energy at
node level and allowing one to extend the lifetime of WSN.
• Reduces the computational overhead due to OLM and C-MAX.
• The performance of ENERGY MODEL is compared on the basis of PDR ,
Routing Overhead, Throughput and Remaining node energy.
• In future component intra-sensor arranging count should be proposed to
handle these events and capably allocate sensor resources with respect
to energy.
REFERENCES
[1] Saleh Al Sharaeh, Reema Hasan and Imad Salah “An efficient routing
technique that maximizes the lifetime and coverage of wireless sensor
networks”, IEEE, Second International Conference on Digital
Information and Communication Technology and it's Applications
(DICTAP), 2012.
[2] S. V. Sankpal & Kamble D. V. “Balance Routing Protocol for Wireless
Sensor Network”, International Journal of Advanced Electrical and
Electronics Engineering (IJAEEE) ISSN (Print): 2278-8948, Volume-1,
Issue-2, 2012
REFERENCES
[3] Joongseok Park ,Sartaj Sahni “Maximum Lifetime Routing In Wireless
Sensor Networks”, National Science Foundation under grant ITR-
0326155, June 2, 2005.
[4] Mahmood R. Minhas, Sathish Gopalakrishnan, Victor C. M. Leung
“Online Routing Algorithms for Maximum Lifetime in Wireless Sensor
Networks”, Natural Sciences and Engineering Research Council of
Canada under grant STPGP 322208-05.
[5] Fatma Bouabdallah, Nizar Bouabdallah and Raouf Boutaba “Cross-
Layer Design for Energy Conservation in Wireless Sensor Networks”,
School of Computer Science, University of Waterloo ; 200 University
Ave. W., Waterloo, ON, Canada.
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wsn

  • 2. OUTLINE • Abstract • Introduction • AODV Overview • Literature Review • Problem Statement • Proposed Solution • Results • Conclusion • Future Scope • References
  • 3. ABSTRACT • One key issue about WSN is that sensor nodes have a limited battery capacity. For that, it is important to develop energy efficient solutions to keep these networks functioning for the longest period of time. • In this work, we introduce an basic initial energy model to gain higher lifetime and increased coverage by managing the power at node level. • Experimental results demonstrate that our algorithm can revealed the superiority of the new methodology over existing heuristics.
  • 4. INTRODUCTION • Wireless Sensor Networks are networks that consists of sensors which are distributed in an ad hoc manner. • These sensors work with each other to sense some physical phenomenon and then the information gathered is processed to get relevant results. • Wireless sensor networks consists of protocols and algorithms with self-organizing capabilities.
  • 6. APPLICATION OF WSN Air Pollution Forest Fire Detection Offspring Care Structural Health Shipment Condition Radiation Levels Traffic Congestion Water Quality Waste Managment Smart Parking Water Leakages
  • 7. WORKING PROCESS OF SENSOR NETWORK Send any value… Using any protocol ..To any System API+60 Sensors Wireless communication modules
  • 8. BASIC WSN NODE • A sensor node, also known as a mote, is a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network It consist MICRO CONTROLLER SENSORS & MEMORY ANTENNA BATTERY OPERATING SYSTEM
  • 9. AODV OVERVIEW • AODV is a packet routing protocol designed for use in mobile ad hoc networks . • Intended for networks that may contain thousands of nodes • One of a class of demand-driven protocols • The route discovery mechanism is invoked only if a route to a destination is not known • Source, destination and next hop are addressed using IP addressing • Each node maintains a routing table that contains information about reaching destination nodes.
  • 11. LITERATURE REVIEW 1.An Efficient Routing Technique That Maximizes The Lifetime And Coverage Of WSN Ref [1] • In this paper Author Introduced an efficient routing power management heuristic to gain higher lifetime and increased coverage by managing power at node level . This accomplished by dividing the node energy into two ratio ; one of the sensor originated data (a) and the other part of data relays from other sensor (b). This heuristic, which is called ERPMT (efficient routing power management technique),has been applied to OLM and C-MAX .
  • 12. LITERATURE REVIEW 2. Balance Routing Protocol for Wireless Sensor Network Ref [2] • WSN Operated on limited battery capacity imposes the use of energy- efficient protocols. Such protocols minimize the total amount of energy consumed by the network in forwarding a packet between any pair of nodes. To achieve this, we have proposed a load-balanced routing scheme which better fits in WSN , compared with on the fly routing scheme of continuous monitoring applications.
  • 13. LITERATURE REVIEW 3. Maximum Lifetime Routing In Wireless Sensor Networks Ref [3] • In this paper Author shown that the lifetime maximization problem is NP- hard. We also have proposed a new online heuristic–OML–for lifetime maximization. Extensive simulations show that our new heuristic is superior to previously published heuristics for lifetime maximization both in terms of providing larger lifetime and in terms of sensitivity to algorithm parameters. Additionally, our proposed heuristic provides larger network capacity than provided by competing heuristics.
  • 14. LITERATURE REVIEW 4.Online Routing Algorithms for Maximum Lifetime in Wireless Sensor Networks Ref [4] • In this paper Author present two online routing schemes, namely the fuzzy maximum lifetime routing scheme (FML), and the fuzzy multi objective routing scheme(FMO). Simulation results obtained under a variety of network scenarios indicate the superiority of the proposed schemes over a number of other online routing schemes in terms of the lifetime as well as the energy consumption metric. For better scalability, we also present a fully distributed implementation of the FML routing scheme. The overhead analysis shows that the distributed scheme is successful in considerably reducing the control overhead.
  • 15. LITERATURE REVIEW 5.Cross-Layer Design for Energy Conservation in Wireless Sensor Networks Ref [5] • In this paper Author proposed balancing the traffic through the WSN. We show that sending the traffic generated by each sensor node through multiple paths instead of using a single path allows significant energy conservation. On the other hand, at the MAC layer, we propose to control the retry limit of retransmissions over each wireless link. We show that by efficiently adjusting the retry limit for each link, further energy conservation can be achieved, improving thus the network lifetime. analysis shows that the distributed scheme is successful in considerably reducing the control overhead.
  • 16. PROBLEM STATEMENT The WSN energy consumption falls into three categories: sensing, computing, and communicating. WLAN networks were designed to minimize delay and maximize throughput, but they do not provide the energy efficiency demanded by wireless sensor networks. In WSNs with densely deployed nodes, a promising solution is to have sensors collaboratively process information with distributed computation load among sensors. To achieve application- independent parallel processing, energy conservation and classification of nodes depending on remaining energy is a problem that must be solved.
  • 17. PROBLEM STATEMENT • Author has used two matrixes for the development of the energy efficient routing protocols for the WSN – first OLM (online Maximum lifetime) and Capacity Maximization (CMAX)) • Author has proposed a new routing heuristics based on poison distribution and two matrixes CMAX and OLM. • The main problem with the authors proposed work is computational overhead of CMAX and OLM.
  • 18. PROPOSED SOLUTION • Our approach is based on Energy Model. This is the core thing of our proposed work. For calculating energy of node we will apply energy module of NS-3 on each node. • To overcome the authors computational overhead we have adopted the idea of basic initial energy model which gives the node based on their energy remain. And then route will be chooses, the benefit of this scheme is to simplicity due to energy model THAT REDUCES THE OVERHEAD OF TWO MATRIX COMPUTATION (CMAX and OLM) into one. • 1. We will have to form wireless sensor network depending on user requirement of number of nodes. • 2. Apply the Random 2D direction mobility model for creating topology between mobile nodes in WSN.
  • 19. PROPOSED SOLUTION • 3. Apply the routing protocol AODV (on demand routing algorithm) for communication with efficient routing between mobile nodes. • 4. Calculating energy of mobile nodes based on energy mathematical model shown below during communication calculating current energy of each node and generate routing table for each mobile nodes using AODV protocol in WSN. • 5. AODV protocol find shortest path between nodes and select path based on current energy of nodes for communication between nodes. • 6. After each node energy calculation we have got the energy variation better than CMAX and OLM.
  • 20. PROPOSED SOLUTION (CONT.) • Energy mathematical calculations model: • E (N, M) = ln>0(lm=n Etack + lm!=n ERack)+m>0(lm=n ETpck+ lm!=n ERpck) E (N, M) =energy spent at node N due to node M ETack=energy spent for transmission of one acknowledgement packet ETpck= energy spent for transmission of one data packet ERack= energy spent for reception of one acknowledgement packet ERpck= energy spent for reception of one data packet lp= p is true if value is 1 0 otherwise
  • 21. EXPERIMENTAL SETUP For implementing the above scenario following experimental setup will be made- • Network Simulator (NS-3.20) and Linux UBUNTU 14.04
  • 22. RESULT 1. Open Terminal Command -> Type Commands
  • 23. • 2. Run Command -> Open Simulation Window -> Start Simulation
  • 24. • 3. Run Simulation -> then Close Simulation Window -> Automatic Generated Routing Data on Terminal -> Close Terminal
  • 27. OML AND PROPOSED ALGORITHM
  • 28. CONCLUSION • In this thesis, the problem of energy conservation and full sensing coverage in large WSNs where nodes are randomly deployed have been addressed . • We proposed a “basic initial energy model” which calculate energy at node level and allowing one to extend the lifetime of WSN. • Reduces the computational overhead due to OLM and C-MAX. • The performance of ENERGY MODEL is compared on the basis of PDR , Routing Overhead, Throughput and Remaining node energy. • In future component intra-sensor arranging count should be proposed to handle these events and capably allocate sensor resources with respect to energy.
  • 29. REFERENCES [1] Saleh Al Sharaeh, Reema Hasan and Imad Salah “An efficient routing technique that maximizes the lifetime and coverage of wireless sensor networks”, IEEE, Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP), 2012. [2] S. V. Sankpal & Kamble D. V. “Balance Routing Protocol for Wireless Sensor Network”, International Journal of Advanced Electrical and Electronics Engineering (IJAEEE) ISSN (Print): 2278-8948, Volume-1, Issue-2, 2012
  • 30. REFERENCES [3] Joongseok Park ,Sartaj Sahni “Maximum Lifetime Routing In Wireless Sensor Networks”, National Science Foundation under grant ITR- 0326155, June 2, 2005. [4] Mahmood R. Minhas, Sathish Gopalakrishnan, Victor C. M. Leung “Online Routing Algorithms for Maximum Lifetime in Wireless Sensor Networks”, Natural Sciences and Engineering Research Council of Canada under grant STPGP 322208-05. [5] Fatma Bouabdallah, Nizar Bouabdallah and Raouf Boutaba “Cross- Layer Design for Energy Conservation in Wireless Sensor Networks”, School of Computer Science, University of Waterloo ; 200 University Ave. W., Waterloo, ON, Canada.