Advertisement

An Efficient Optimization Technique for Scheduling in Wireless Sensor Networks: A Survey

  • N. Mahendran
  • T. Mekala
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 771)

Abstract

Wireless sensor networks (WSNs) use a large number of tiny sensor devices for monitoring, gathering and processing data with low hardware complexity, low energy consumption, high network lifetime, scalability, and real-time support. Sensor node deployment, coverage, task allocation, and energy efficiency are the main constraints in WSNs that impact the node lifetime. Scheduling allows the platform to improve the performance of WSN. Scheduling the sensor nodes and category of sensor data minimizes the energy consumption and increases the lifetime of sensor nodes. This chapter describes the concept of optimization techniques in WSNs to extend performance. We surveyed four metaheuristic optimization approaches to enhance the scheduling performance because these approaches help to find optimal solutions quickly. Optimizing the sensor node placement through scheduling of sensor and sensor data allows a better quality of service (QoS) in WSN. In this chapter, we survey such optimization techniques as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC) for scheduling methods.

Keywords

WSNs Optimization techniques Energy consumption Network lifetime 

References

  1. 1.
    Deepika, T., and N. Mahendran. 2015. Comparitive analysis of optimization algorithms in wireless sensor networks. International Journal of Applied Engineering Research 38. ISSN 0973-4562.Google Scholar
  2. 2.
    Peng Li, Huqing Nie, Lingfeng Qiu and Ruchuan Wang. 2017. Energy optimization of ant colony algorithm in wireless sensor network. International Journal of Distributed Sensor Networks 13 (4).  https://doi.org/10.1177/1550147717704831.CrossRefGoogle Scholar
  3. 3.
    Muralitharan Krishnan, Vishnuvarthan Rajagopal, Sakthivel Rathinasamy. 2018. Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs.  https://doi.org/10.1007/s11276-016-1361-5.CrossRefGoogle Scholar
  4. 4.
    Gomathi, R., and N. Mahendran. 2015. An efficient data packet scheduling schemes in wireless sensor networks. In Proceeding 2015 IEEE international conference on electronics and communication systems (ICECS’15), 542–547. ISBN: 978-1-4799-7225-8.  https://doi.org/10.1109/ecs.2015.7124966.
  5. 5.
    Vanithamani, S., and N. Mahendran. 2014. Performance analysis of queue based scheduling schemes in wireless sensor networks. In Proceeding 2014 IEEE international conference on electronics and communication systems (ICECS’14), 1–6. ISBN: 978-1-4799-2320-5.  https://doi.org/10.1109/ecs.2014.6892593.
  6. 6.
    Rajwinder Kaur and Sandeep Sharma. 2017. A review of various scheduling techniques considering energy efficiency in WSN. International Journal of Computer Applications (0975–8887) 16 (28).Google Scholar
  7. 7.
    Raghavendra V. Kulkarni, and Ganesh Kumar Venayagamoorthy. 2011. Particle swarm optimization in wireless sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics.  https://doi.org/10.1109/tsmcc.2010.2054080.CrossRefGoogle Scholar
  8. 8.
    Akhtaruzzaman Adnan, Md., Mohammd Abdur Razzaque, Ishtiaque Ahmed and Ismail Fauzi Isnin. 2014. Bio-mimic optimization strategies in wireless sensor networks: A survey. Sensors 14: 299–345.  https://doi.org/10.3390/s140100299.CrossRefGoogle Scholar
  9. 9.
    Mahendran, N., Dr. S. Shankar and T. Deepika. 2015. A survey on swarm intelligence based optimization algorithms in wireless sensor networks. International Journal of Applied Engineering Research 10 (20). ISSN 0973-4562.Google Scholar
  10. 10.
    Kalaiselvi, P., and N. Mahendran. 2013. An efficient resource sharing and multicast scheduling for video over wireless networks. In Proceeding 2013 IEEE international conference on emerging trends in computing, communication and nanotechnology (ICECCN’13), 378–383. ISBN: 978-1-4673-5036-5.  https://doi.org/10.1109/ice-ccn.2013.6528527.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.M. Kumarasamy College of EngineeringKarurIndia

Personalised recommendations