This document proposes a self-scheduling approach to increase the lifetime of wireless sensor networks. It introduces an Energy Efficient Self Scheduling (EESSA) algorithm where sensors are only active when needed to send data, remaining idle at other times to conserve energy. This algorithm aims to use less energy than existing methods and extend the lifetime of sensor nodes. Key aspects of the approach include scheduling node activity to minimize active nodes while ensuring quality of service, and using an ad-hoc on-demand distance vector routing protocol to find optimal data transmission paths.

1. INCREASE THE LIFETIME OF
WIRELESS SENSOR NETWORK USING
SELF SCHEDULING APPROACH
DEEPAK PRABHU.B (211012205009)
DHATCHANAMOORTHI.M (211012205010)

2. In communication using wireless sensor networks,
energy is one of the most essential parameter to look at.
In many network communication using sensor, due to
regular usage of sensors the lifetime of each sensor are
reduced due to which, it requires regular replacement.
To avoid this scenario, a method is proposed in which
the sensors are operated in a self scheduled manner i.e.
the sensor uses or consumes energy only when it is
active and remains idle during rest of the time.
Here the network uses EESSA (Energy Efficient Self
Scheduling) algorithm to main high throughput and to
consume less energy by the sensor, therefore the lifetime
of sensors can be increased.
ABSTRACT

3. Network lifetime is one of the most critical issues in
Wireless sensor networks (WSNs) since most sensors
equipped with non-rechargeable batteries with limited
energy. WSNs used to monitor and detect movement,
temperature changes, precipitation etc.
The nodes are typically equipped with power-
constrained batteries, which are often difficult,
expensive and even impossible to be replaced once the
nodes are deployed.
The energy consumed by a node depends on its state.
Each node may be in the following states:Active & Idle
states
INTRODUCTION

4. It seems to be more adequate to leave the node at
the sleep state most of the time. Recent research
showed that significant energy savings can be
achieved by scheduling node’s activities in high-
density WSNs.
 The main issue here is how to minimize the number
of active nodes in order to maximize the network
lifetime and at the same time to ensure the required
quality of service (QoS) for applications.
Thus, a self scheduling algorithm is introduced.

5. Existing method uses cluster based concept in
which the data are sent using cluster and each
cluster has a cluster head.
Cluster heads formed in a round robin fashion
EXISTING METHOD

6. Here data is sent randomly which causes loss of
data and delay.
It consumes lot of power
Life time of sensor gets decreased
EXISTING DISADVANTAGES

7. Here we use Energy Efficient Self Scheduling
Algorithm in which the nodes are active only when
there is a need to send data, remaining time the
nodes will be idle.
This algorithm uses less energy and it is more
efficient.
Ad hoc On-Demand Distance vector(AODV) for
finding the optimal path
PROPOSED METHOD

8. Most efficient for large-scale sensor network
Energy dissipation-even distribution
Prolong network lifetime
PROPOSED ADVANTAGES

9. ARCHITECTURE DIAGRAM
Node placement Establishing
network
Data collection
SchedulingData
Transmission
Simulation
result

10. Node Placement
Establishing network
Data collection phase
Scheduling nodes
Data Transmission
Simulation result
MODULES

11. It should aware about location and distance from Base
Station
It should place the node at right location
Because of Environmental Changes like
calamity,weather,rain,earthquake
Node Placement

12.  RREQ – Route request
 RREP – Route reply
 RERR – Route error
ESTABLISHING NETWORK

13. A RREQ message is broadcasted when a node needs to
discover a route to a destination
As a RREQ propagates through the network,
intermediate nodes use it to update their routing tables
(in the direction of the source node).
The RREQ also contains the most recent sequence
number for the destination
RREQ

14. RREQ Message
B?
B?
B?
B?
B?
B? B?
B
A

15. When a RREQ reaches a destination node, the
destination route is made available by unicasting a
RREP back to the source route.
A node generates a RREP if:
 It is itself the destination.
 It has an active route to the destination. Ex: an intermediate
node may also respond with an RREP if it has a “fresh
enough” route to the destination.
RREP

16. RREP Message
B
A
A
A
A
A
A
A

17. This message is broadcast for broken links
Generated directly by a node or passed on when
received from another node
RERR

18. MESSAGE ROUTING
A
B D
F
C
G
E
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREQ
RREP
RREP
RREP
Source
Destination

19. In this phase when the data is sensed, the nodes verify
the routing table and choose the best path for
transmission.
 Depending upon the data sensed, based on it the best
path is chosen for transmission.
 Based on the size of the data and the updates in the
routing table the first best path is chosen.
DATA COLLECTION PHASE

20. Node create themselves at what time to transmit and
receive
Sleep schedule information is shared by the periodic
SYNC packet
SYNC packets is very short.It contains the address of
the sender and the time of its next sleep
Receivers will adjust its timer immediately after
received the SYNC packet
Nodes enter the desired state based on the residual
energy
SCHEDULING NODES

21. The residual energy can be classsified as
Case 1: Erem ≥ Ptx: Sleep and active
Case 2: Ptx ≥ Erem ≥ Prx : Sensor node only receive not
transmit
Case 3: Prx ≥ Erem: Sensor node is regarded as Dead node

22. In this the best optimal path is selected,that is AODV
In AODV, routing table selects the path and assign the
sequence number
Alternate path for collision avoidance and
retransmission
DATA TRANSMISSION

23. CREATION OF NODES

24. TRANSMISSION OF DATA
THROUGH FIRST SHORTEST
PATH

25. TURNING NODES TO SLEEP
STATE AFTER TRANSMISSION

26. By this technique of load balancing and by the S- Mac
protocol, the wireless sensor network achieves increased
throughput and reduced delay compared to the existing
systems.
Conclusion