RSSI and Flower Pollination Algorithm Based Location Estimation for Wireless Sensor Networks

Year 2016, Volume: 4 Issue: Special Issue-1, 13 - 17, 25.12.2016

Erhan Sesli , Gökçe Hacıoğlu

https://doi.org/10.18201/ijisae.265424

Cited By: 2

Abstract

Abstract:
Wireless Sensor Networks
(WSN’s) have been finding to itself new applications continuously. Many of
these applications need location information of nodes. The localization of
nodes can be made by range based or range free localization methods conventionally.
Angle-of-Arrival (AoA), Time-Difference-of-Arrival (TDoA), Received Signal
Strength Indicator (RSSI), Time-of-Arrival (ToA) are well known range based
methods. Therefore AoA, ToA and TDoA have some hardware and software
difficulties for nodes which have limited processing and power sources. However
RSSI based localization doesn’t cost high processing resources or complex
hardware modifications. Most of the WSN nodes already have RSSI measurement
capability. However RSSI measurements is vulnerable to noise and environmental
effects. Therefore error of RSSI based localization can be over to an
acceptable level.

Centroid,
APIT, DV-Hop and Amorphous are some of the range free localization methods.
Range free methods can only give location information approximately but they
don’t need any extra hardware or high processing capability.

In this study
WSN nodes are assumed randomly or regularly distributed on a certain area. Some
of the nodes are beacon nodes. The beacon nodes are assumed as having higher
power resources and GPS receivers. The locations of nodes are assumed as fixed.
The beacon nodes send their location information sequentially. Localization of
nodes are made through RSSI and location information of beacon nodes. The mean
of RSSI is calculated to reduce effect of noise on it. A rough location
estimation made by weighted centroid. A probabilistic based location estimation
and flower pollination algorithm (FPA) are used separately to make final
decision about the location. Rough estimates are used to limit search area of
flower pollination algorithm in order to reduce convergence time.

Keywords

RSSI, FPA, WSN, optimization, probabilistic

References

• [1] D. Puccinelli and M. Haenggi (2005). Wireless sensor networks: applications and challenges of ubiquitous sensing. IEEE Circuits and Systems Magazine. Vol. 5(3). Pages 19-31.
• [2] M. Guoqiang, B. Fidan, and BDO Anderson (2007). Wireless sensor network localization techniques. Computer Networks. Vol. 51(10). Pages 2529-2553
• [3] J. Bachrach and C. Taylor (2005). Handbook of sensor networks: Algorithms and Architectures 1, I. Stojmenovic, New Jersey, John Wiley & Sons, Inc.
• [4] F. Liu, et al. (2008). Wireless Sensor Networks and Applications, Y. Li, M.T. Thai, W. Wu, US, Springer. Pages 175-193.
• [5] S.P Singh and S. C. Sharma (2015). Range free localization techniques in wireless sensor networks: A review. Procedia Computer Science. Vol. 57. Pages 7-16.
• [6] R. Stoleru, T. He, and J.A. Stankovic (2007). Secure Localization and Time Synchronization for Wireless Sensor and Ad Hoc Networks. R. Poovendran, C. Wang and S. Roy, US, Springer. Pages 3-31.
• [7] T. He et al. (2003). Range-free localization schemes for large scale sensor networks. Proceedings of the 9th annual international conference on Mobile computing and networking. Pages 81-95.
• [8] J. Zheng et al. (2011). An Improved RSSI Measurement in Wireless Sensor Networks. Procedia engineering. Vol. 15. Pages 876-880.
• [9] M. Botta, and M. Simek (2013). Adaptive Distance Estimation Based on RSSI in 802.15. 4 Network. Radioengineering. Vol. 22(4). Pages 1162-1168.
• [10] H. Wang, J. Wan and R. Liu (2011). A Novel Ranging Method Based on RSSI. Energy Procedia. Vol. 12. Pages 230-235.
• [11] Z. Wu, et al. (2016). Improved Particle Filter Based on WLAN RSSI Fingerprinting and Smart Sensors for Indoor Localization. Computer Communications. Vol. 83. Pages 64-71.
• [12] J. Svečko, M. Malajner and D. Gleich (2015). Distance Estimation Using RSSI and Particle Filter. ISA Transactions. Vol. 55. Pages 275-285.
• [13] M. Chen and H. Liu (2012). Enhance Performance of Centroid Algorithm in Wireless Sensor Networks. Fourth International Conference on Computational and Information Sciences. Pages 1066-1068.
• [14] L. Tan, F. Luo and K. Liu (2011). Weighted Centroid Location Algorithm in Wireless Sensor Network. Wireless Mobile and Computing (CCWMC). Pages 414-418.
• [15] “CC2538 data sheet”, Texas Intruments, Texas, US.
• [16] J. Zhao et al. (2013). An improved Weighted Centroid Localization algorithm based on difference of estimated distances for Wireless Sensor Networks. Telecommunication Systems. Vol. 53. Pages 25-31, 2013.
• [17] R. Peng, and M. L. Sichitiu (2005). Robust, probabilistic, constraint-based localization for wireless sensor networks. SECON. Pages 541-550.
• [18] X. S. Yang (2012). In Unconventional computation and natural computation (Flower pollination algorithm for global optimization). J. Durand-Lose, N. Jonoska (Eds.), Springer Berlin Heidelberg.
• [19] I. Pavlyukevich (2007). Lévy flights, non-local search and simulated annealing. Journal of Computational Physics, Vol. 226(2). Pages 1830-1844.