Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

QoS Improvement Using Enhanced Manhattan Mobility Model on Proposed Ant Colony Optimization Technique in MANETs


Affiliations
1 Deptt. of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar 143 005, India
2 Deptt. of ECE, Sant Longowal Institute of Engineering & Technology, Longowal 148 106, Sangrur, India
3 Deptt. of Engineering and Technology, Guru Nanak Dev University Regional Campus, Jalandhar 144 009, India
 

In the current Mobile Ad-hoc Network (MANET) route discovery procedure, traffic overflow and overhead may pose a major challenge for a path finding between communicating nodes. Swarm intelligence technique has been applied for various routing problems. We suggest an ant-based bottleneck routing method for MANET to identify weak links in the chosen route for routing overhead and delay issues. During data exchange, it selects the route using a swarm based technique called Ant Colony Optimization (ACO). Initially, we have created node movements by Enhanced Manhattan Mobility Model (EMMM) and then a bottleneck value based ant colony optimization technique is applied. The simulation results show the improvement over existing ACO technique in terms of mobility and pause time. The Quality of Service (QoS) performance metrics showed improvement of 66% in drop rate, 141% in the packet delivery ratio, 42% in packet overhead, 171% in throughput, and 34% in the average end-to-end delay for mobility experiment. There is an improvement of 82% in drop rate, 108% in the packet delivery ratio, 45% in packet overhead, 171% in throughput, and 49% in the average end-to-end delay for a pause time experiment.

Keywords

Bonnmotion-3.0.1, Mobility Models, NS-2.35, Optimization, Routing Protocols.
User
Notifications
Font Size

  • Eissa T, Razak S A, Khokhar R H & Samian N, Trust based routing mechanism in MANET: design and implementation, Mob Netws Appl, 18 (2013) 666–677.

  • Das S R, Castañeda R & Yan J, Simulation-based performance evaluation of routing protocols for mobile ad hoc networks, Mob Netw Appl, 5 (2000) 179–189.

  • Zhang Y & Ren K, On address privacy in mobile Ad Hoc networks, Mob Netw Appl, 14 (2009) 188–197.

  • Pentikousis K, Agüero R, Sargento S & Aguiar R L, Mobility and network management in virtualized networks, Mob Netw Appl, 17 (2012) 431–434.

  • Yu Z, Yu Z & Chen Y, Multi-hop mobility prediction, Mob Netw Appl, 21 (2016) 367–371.

  • Kaur S & Ubhi J S, A simplified approach to analyze routing protocols in dynamic MANET environment, Int J Soft Comput Eng, 5 (2015) 19–23.

  • Selvi P F A & Manikandan M S K, Ant based multipath backbone routing for load balancing in MANET, IET Commun, 11 (2017) 136–141.

  • Nallusamy C & Sabari A, Particle swarm based resource optimized geographic routing for improved network lifetime in MANET, Mob Netw Appl, (2017) 1–11.

  • Selvi P F A & Manikandan M S K, Latency and energy aware backbone routing protocol for MANET, J Theor Appl Inf Technol, 64 (2014) 63–73.

  • Kour S & Ubhi J S, A novel approach to predict mobility pattern of mobile nodes in mobile Ad-hoc networks, J Sci Ind Res, 77 (2018) 629–632.

  • Roy R R, Group mobility extending individual mobility models-handbook of mobile Ad hoc networks for mobility models (Springer, Boston, MA) 2011.

  • Martyna J, Simulation study of the mobility models for the wireless mobile Ad hoc and sensor networks, in Computer Networks: 19th Int Conf (Springer Berlin Heidelberg) 2012, 324–333

  • Kour S & Ubhi J S, Performance Analysis of mobile nodes in mobile ad-hoc networks using enhanced manhattan mobility model, J Sci Ind Res, 78 (2019) 69–72.

  • Sangwan S, Goel N & Jnagra A, AELB: Adaptive and efficient load balancing schemes to achieve fair routing in Mobile Ad hoc Networks (MANETs), Int J Comput Sci Techol, 2 (2011) 11–15.

  • Kaur R, Dhillon R S & Sohal H S, Load balancing of ant based algorithm in MANET, Int J Comput Sci Techol, 1 (2010) 173–178.

  • Souihli O, Frikha M & Hamouda M B, Load-balancing in MANET shortest-path routing protocols, Ad Hoc Netw, 7(2) (2009) 431–442.

  • Tekaya M, Tabbane N & Tabbane S, Multipath routing with load balancing and QoS in Ad hoc network, Int J Comput Sci Netw Secur, 10 (2010) 280–286.

  • Lin N & Shao Z, Improved ant colony algorithm for multipath routing algorithm research, Int Symp Intel Info Proces Trusted Comput (IEEE) 2010, 651–655.

  • Krishna P V, Saritha V & Vedha G, Quality-of-service-enabled ant colony-based multipath routing for mobile ad hoc networks, IET Commun, 6 (2010) 76–83.

  • Yang F, Ling S & Xu H, Network coding-based AOMDV routing in MANET, IEEE Int Conf Info Sci Technol (Wuhan, Hubei, China) 2012, 337–340.

  • Zhoujie D & Huaikou M, An optimization method based on Be-ACO algorithm in service composition context, Comput Intell Neurosci, (2022) 1–10. https://doi.org/10.1155/2022/5231262.

  • Ping D & Yong A I, Research on an improved ant colony optimization algorithm and its application, Int J Hybrid Inf Technol, 9 (2016) 223–234. http://dx.doi.org/10.14257/ijhit.2016.9.4.20

  • Karimpour R, Khayyambashi M R & Movahhedinia N, Applying ant colony optimization for load balancing on grid, J Chin Inst Eng, 39 (2015) 49–56.

  • Aschenbruck, N, Ernst R, Gerhards-Padilla E & Schwamborn M, BonnMotion - A mobility scenario generation and analysis tool. SIMUTools, in 3rd International ICST Conference on Simulation Tools and Techniques 2010, 16 May. 10.4108/ICST.SIMU TOOLS2010.8684.

  • https://www.fortinet.com/resources/cyberglossary/qos-quality-of-service (Accessed, July 2022)

  • Johnson D & Lysko A, Comparison of MANET routing protocols using a scaled indoor wireless grid, Mob Net Appl, 13 (2008) 82–96.


Abstract Views: 66

PDF Views: 55




  • QoS Improvement Using Enhanced Manhattan Mobility Model on Proposed Ant Colony Optimization Technique in MANETs

Abstract Views: 66  |  PDF Views: 55

Authors

Satveer Kour
Deptt. of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar 143 005, India
Jagpal Singh Ubhi
Deptt. of ECE, Sant Longowal Institute of Engineering & Technology, Longowal 148 106, Sangrur, India
Manjit Singh
Deptt. of Engineering and Technology, Guru Nanak Dev University Regional Campus, Jalandhar 144 009, India

Abstract


In the current Mobile Ad-hoc Network (MANET) route discovery procedure, traffic overflow and overhead may pose a major challenge for a path finding between communicating nodes. Swarm intelligence technique has been applied for various routing problems. We suggest an ant-based bottleneck routing method for MANET to identify weak links in the chosen route for routing overhead and delay issues. During data exchange, it selects the route using a swarm based technique called Ant Colony Optimization (ACO). Initially, we have created node movements by Enhanced Manhattan Mobility Model (EMMM) and then a bottleneck value based ant colony optimization technique is applied. The simulation results show the improvement over existing ACO technique in terms of mobility and pause time. The Quality of Service (QoS) performance metrics showed improvement of 66% in drop rate, 141% in the packet delivery ratio, 42% in packet overhead, 171% in throughput, and 34% in the average end-to-end delay for mobility experiment. There is an improvement of 82% in drop rate, 108% in the packet delivery ratio, 45% in packet overhead, 171% in throughput, and 49% in the average end-to-end delay for a pause time experiment.

Keywords


Bonnmotion-3.0.1, Mobility Models, NS-2.35, Optimization, Routing Protocols.

References