Swarm Intelligence based Approaches for Efficient Routing in Wireless Sensor Networks

1. Design Consideration and Comparative
Evaluation of Swarm Intelligence based
Approaches for Efficient Routing in
Wireless Sensor Networks
PRESENTED BY :
PALAKURTHI SHASHANK

2. Presentation Outlines
 Introduction
 Routing Challenges and Design Issues in WSNs
 Comparative Evaluation Of Swarm Intelligence Based Routing Protocols
 Conclusion
 References

3. SWARM
Swarm :
 Swarm is a collection of agents
interacting locally with one another
and with their environment.
Swarm Intelligence :
 “Any attempt to design algorithms or
distributed problem-solving devices inspired
by the collective behaviour of social insect
colonies and other animal societies “
 Computer scientists are increasing interested
in swarm intelligence since it can be used to
solve many optimization problems.

4. What is Wireless
Sensor Network ?
 A Wireless Sensor Network (WSN) is a
collection of compact size low power
computational nodes capable of detecting
local environmental conditions and forward
such information to a central base station
(sink) for appropriate processing .
WSN FEATURES :
 Large Network Size
 Self-organised
 All nodes acts as routers
 No wired infrastructure

5. The Difference between WSN &Ad-hoc
 The number of nodes
 The topology of a sensor network changes very frequently
 WSN broadcast but ad hoc point-to point
 Sensor node are limited in power computation capacities and memory
 Sensor nodes may not have global identification

6. Routing Challenges and Design Issues in
WSNs
The design of routing protocols in WSNs is
influenced by many challenging factors. These
factors must be overcome before efficient
communication can be achieved in WSNs.
 Node deployment
 Data delivery model
 Node/link heterogeneity
 Scalability
 Transmission media
WSN RESOURCE CONSTRAINTS :
 Most of the wireless sensor networks are composed
of sensor motes equipped with a 8-bit
microcontroller running at 8/16MHz and having
memory comprised of 4KB of RAM, 4KB of EEPROM
and 128 KB of flash.

7. Large Scale Network & Scalability
 The routing protocol must be able to handle large and
dense networks
 It is to be noted that the expansion of the network scale
results in drastic increase in the number of possible paths
from source to sink
 Routing protocols should be designed to exhibit scalable
performance.

8. Design & Implementation Issues in WSN
Routing
Data Aggregation
 Solves implosion and overlap
problem
 Energy efficient
Traffic Patterns –
Data Delivery Models
 The case of MANETs traffic
patterns strictly depend on the
application and are address
centric.
 Common data delivery models
present in WSNs include - event-
driven, query driven, continuous
monitoring .
Self-Organization
 A routing protocol must be
resilient to such dynamic and
generally unpredictable
variations
 The failure of sensor nodes such
as blockage due to lack of power

9. Need for Bioinspired Approaches
 The network protocols and communication techniques must be designed to
provide robust, reliable and highly efficient wireless sensor networks
 It is evident that the adaptiveness, flexibility, robustness characteristics exhibited
in coordination of their behaviors have made them capable of solving real world
problems.
 Self-organization may help in distributed control and management tasks.

10. Comparative Evaluation Of Swarm
Intelligence Based Routing Protocols
 The swarm intelligence based routing protocols considered in our comparative
study are –
 IABR[3], EEABR[3], IEEABR[4], SC[5]
 ACO based Energy Aware Routing[6], Sensor Ant[7], MACS[8], SEB[9]
 ACA for Data Aggregation[10], Ant-Aggregation[11]
 Basic-DAACA[12], ES-DAACA[12]
 ACS-DAACA[12], SIBER XLP[2]
 SIBER-DELTA[13].

11. PARAMETERS USED IN
ROUTING DECISION &
PERFORMANCE
EVALUATION METRICS
USED
Routing Protocol
Parameters used
in the Routing Decision
Performance Metrics
BABR D ARE,ME,SD,EE
IABR E ARE,ME,SD,EE
EEABR D,E ARE,ME,SD,EE
IEEABR D,E EC,EE,SD,L,SR
SC D L,SR,EC,EE
ACO based Energy Aware
Routing
E EC, NL
SensorAnt E, QR EC,EE
MACS D TEC,ATD
SEB D,E NL, RSD
ACA for Data Aggregation D TEC, AEC, NL,TO,TTAD
Ant-Aggregation D OC,EE
Basic-DAACA D,E AET,EDNL,SSR
ES-DAACA E AET,EDNL,SSR
MM-DAACA E AET,EDNL,SSR
ACS-DAACA E AET,EDNL,SSR
SIBER-XLP D,E,LQ PDR,EE, L, ARE, ME, SD
SIBER-DELTA D,E,LQ,TR PDR,EE, L, ARE, ME, SD
Parameters Used Legend :
D-Distance, E- Energy, QR- Quality of
Route, LQ- Link Quality, TR-Trust
Rating
Performance Metrics Legend:
ARE- Average Minimum Energy, ME-
Minimum Energy, SD-
Standard Deviation, EE- Energy
Efficiency, L- Latency, EC-Energy
Consumption, SR-Success Rate/Ratio,
NL-Network Lifetime, TEC-Total
Energy Consumption, ATD-Average
Transmission Delay, RSD-Relative
Standard Deviation, ED-Energy
difference, AET-Average Energy cost,
PDR-Packet delivery Ratio

12. Comparison of simulation area, initial
energy and number of nodes used in the
simulation
 Number of nodes deployed in the network of given simulation area with
specified initial energy on the nodes used by different protocols
 Deploying network of node size of 10 to 2000 nodes varies from 40x50 to
2000x1000 sq mts .
 Initial energy on the nodes used by different protocols vary from minimum value
of 0.25J to maximum value of 1000J.

13. Initial energy and number of nodes used
in the simulation
Ant-
Aggregatio
n
A12,A13, A14,A15,A16 Not Specified 50
Basic-DAACA A1,A2,A3,A4,A5,A6,
A7,A8,A9,A10,A11
10J 200-2000
ES-DAACA A1,A2,A3,A4,A5,A6,
A7,A8,A9,A10,A11
10J 200-2000
MM-DAACA A1,A2,A3,A4,A5,A6,
A7,A8,A9,A10,A11
10J 200-2000
ACS-DAACA A1,A2,A3,A4,A5,A6,
A7,A8,A9,A10,A11
10J 200-2000
SIBER-XLP A14,A16,A17,A18 30J 25,49,64,
100
SIBER-DELTA A16, A18 30J 50,100
Routing Protocol Simulation Area sq mts Initial Energy Number of
BABR A12,A13, A14,A15,A16 50J(S),30J(D),
20J(M)
10-100
IABR A12,A13, A14,A15,A16 50J(S),30J(D),
20J(M)
10-100
EEABR A12,A13, A14,A15,A16 50J(S),30J(D),
20J(M)
10-100
IEEABR A11 30J(S),60J(D) 49
SC A11 3N 49
ACO based Energy
Aware Routing
A12 40J 50,100,200,
300,500
SensorAnt A12,A13, A14,A15,A16 1250mJoule 10-100
MACS A12 1000J 50,100,200,
300,500
SEB A19,A20,A21,A22 80-100units 50,100,150,
200
ACA for Data
Aggregation
A6 0.25J 300-500
Simulation Area Legend :
A1- 40 × 50, A2-60 × 60, A3-70 × 80, A5-80 × 90, A6-100 × 100,
A7- 100 × 120, A8- 100 × 140, A9- 120 × 130, A10-130 × 120,
A11-140 × 140, A12-200 X 200, A13- 300 X 300, A14-400 X 400,
A15- 500X500, A16- 600X600, A17- 700X700, A18-900X900,
A19-1000X500, A20-1000X1000, A21-500X1000, A22-2000X1000
Initial Energy Legend :
S-static, D-Dynamic, M- Mesh Networks, N- number of nodes

14. Simulation Tools and Control parameters
used
 Tools like NS-2, QualNet , Prowler ,
Matlab , J-SIM are used by researchers
to evaluate different routing protocols.
Routing Protocol Simulation Tool Simulation Control Parameters
BABR NS-2 α, β, ƿ
IABR NS-2 α, β, ƿ
EEABR NS-2 α, β, ƿ ,
IEEABR Prowler α, β, ƿ
SC Prowler
ACO based Energy Aware
routing
MATLAB
SensorAnt QualNet V5 β , 𝜔
MACS NS-2 α=0.9, β=2, q0=0.45, ƿ=0.95 ,m =30
SEB J-SIM α, β, γ
ACA for Data Aggregation NS-2 β=20, ƿ=0.3, ξ=1
Ant-Aggreagation MATLAB
Q=8, ƿ=0.8, α=3, β=6, T=0.5, H=0.4, K=0.1
Basic-DAACA
NS-2
α =2,β=2,initial pheromone=0.8, ηmin=0.5, ηmax=0.9, ƿ=0.2,
roundToUpdate=100,200,ξ=0.9, q0=0.5, rho in ACA=0.3
ES- DAACA NS-2 α=2,β α α =2,initial pheromone=0.8, ηmin=0.5, ηmax=0.9, ƿ=0.2,
roundToUpdate=100,200 ξ=0.9, q0=0.5, rho in ACA=0.3
MM-DAACA
NS-2 α=2,β=2,initial pheromone=0.8, ηmin=0.5, ηmax=0.9,
ƿ=0.2, roundToUpdate=100,200
ξ=0.9, q0=0.5, rho in ACA=0.3
ACS-DAACA NS-2 α=2,β=2,initial pheromone=0.8, ηmin=0.5, ηmax=0.9, ƿ=0.2,
roundToUpdate=100,200 ξ=0.9, q0=0.5, rho in ACA=0.3
SIBER-XLP NS-2 α=1, β =1, γ =1, ƿ=0.2
SIBER-DELTA NS-2 α=2, β =2, γ =1, δ=1, ƿ=0.2

15. Probability and Pheromone Update
Functions
 One or combinations of more parameters such as distance,
energy, link probability and trust ratings are considered by
different routing protocols to find the optimal route to the
destination from source node.
 Protocol uses its own pheromone update scheme to
increase the pheromone on the path so as to select the best
path to the destination.

16. PROBILITY FUNCTIONS USED TO SELECT NEXT
NODE

17. PHEROMONE UPDATE FUNCTIONS USED

18. Conclusion
 This paper provides the comparative study carried out to evaluate various
Swarm Intelligence based approaches reported in literature for efficient routing
in wireless sensor network.
 Main objective of this work is to perform detailed analysis of these algorithms to
determine their advantages and limitations to provide guidelines to design a
robust
 Design and implementation issues in the context of WSN deployed environment
and networking conditions based routing protocols for wireless sensor networks.

19. THANK YOU !