An ACO and Mobile Sink based Algorithm for Improvement of ML-MAC for Wsns using Compressive Sensing

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Sandeep Sharma
Sandeep Sharma
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Neha Sharma
Neha Sharma
σ Department of Computer Science

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An ACO and Mobile Sink based Algorithm for Improvement of ML-MAC for Wsns using Compressive Sensing

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Abstract

WSN is becoming key subject of research in computational basic principle because of its great deal of applications. ACO( Ant Colony Optimization) constructs the redirecting or routing tree via a method by which, for every single circular or round, Base Station (BS) chooses the root node in addition to shows the following substitute for every node. In order to prevail over the actual constraints with the sooner work a new increased method proposed in this research work. The proposed method has the capacity to prevail over the constraints of ACO routing protocol using the principle with reactivity, mobile sink and also the compressive sensing technique. In this paper we measure the main parameters that affect the wsn that are network lifetime, packets dropped, throughput, end to end delay and remaining energy for proposed algorithm and simulation results have shown that the proposed algorithm is highly effective.

References

14 Cites in Article
  1. Marco Dorigo,Mauro Birattari,Thomas Stutzle (2004). Ant Colony Optimization.
  2. Samayveer Singh,Satish Chand,Bijendra Kumar (2014). Optimum deployment of sensors in WSNs.
  3. Z Li,Q Shi (2013). An QoS Algorithm Based on ACO for Wireless Sensor Network.
  4. S Okdem,D Karaboga (2006). Routing in Wireless Sensor Networks Using Ant Colony Optimization.
  5. Dulanjalie Dhanapala,Vidarshana Bandara,Ali Pezeshki,Anura Jayasumana (2013). Phenomena discovery in WSNs: A compressive sensing based approach.
  6. Wenjie Yan,Qiang Wang,Yi Shen,Yan Wang,Qitao Han (2012). An efficient data gathering and reconstruction method in WSNs based on compressive sensing.
  7. Carlo Caione,Davide Brunelli,Luca Benini (2014). Compressive Sensing Optimization for Signal Ensembles in WSNs.
  8. Lucian Popa,Afshin Rostamizadeh,Richard Karp,Christos Papadimitriou,Ion Stoica (2007). Balancing traffic load in wireless networks with curveball routing.
  9. Jin Wang,Jiayi Cao,Bin Li,Sungyoung Lee,R Sherratt (2015). Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks.
  10. B Nazir,H Hasbullah (2010). Mobile Sink based Routing Protocol (MSRP) for Prolonging Network Lifetime in Clustered Wireless Sensor Network.
  11. N Vlajic,D Stevanovic (2009). Performance Analysis of ZigBee-Based Wireless Sensor Networks with Path-Constrained Mobile Sink(s).
  12. Y Nizhamudong,N Nakaya,Y Hagihara,Y Koui (2011). Performance evaluation of route cost for wireless sensor networks with a mobile sink.
  13. Manju Khurana,Ranjana Thalore,Vikas Raina,Manish Jha (2015). Improved time synchronization in ML-MAC for WSN using relay nodes.
  14. Tao Zheng; Gidlund,M Akerberg,J (2014). Medium access protocol design for time-critical applications in wireless sensor networks.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Sandeep Sharma. 2018. \u201cAn ACO and Mobile Sink based Algorithm for Improvement of ML-MAC for Wsns using Compressive Sensing\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 18 (GJCST Volume 18 Issue E2): .

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Issue Cover
GJCST Volume 18 Issue E2
Pg. 29- 33
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-E Classification: C.1.3, F.2.0
Version of record

v1.2

Issue date

May 2, 2018

Language
en
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WSN is becoming key subject of research in computational basic principle because of its great deal of applications. ACO( Ant Colony Optimization) constructs the redirecting or routing tree via a method by which, for every single circular or round, Base Station (BS) chooses the root node in addition to shows the following substitute for every node. In order to prevail over the actual constraints with the sooner work a new increased method proposed in this research work. The proposed method has the capacity to prevail over the constraints of ACO routing protocol using the principle with reactivity, mobile sink and also the compressive sensing technique. In this paper we measure the main parameters that affect the wsn that are network lifetime, packets dropped, throughput, end to end delay and remaining energy for proposed algorithm and simulation results have shown that the proposed algorithm is highly effective.

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An ACO and Mobile Sink based Algorithm for Improvement of ML-MAC for Wsns using Compressive Sensing

Neha Sharma
Neha Sharma Department of Computer Science
Sandeep Sharma
Sandeep Sharma Savitribai Phule Pune University

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