Open Access Open Access  Restricted Access Subscription Access

A Survey on Fuzzy Based Sensor Network and Its Applications


Affiliations
1 School of CSA, REVA University, Rukmini Knowledge Park Yelahanka, Kattigenahalli, Bengaluru, Karnataka 560064, India
 

Wireless Sensor Networks (WSNs) have been broadly applied in many fields such as industry, agriculture, event detection & monitoring, time critical applications and research to facilitate the gathering and distribution of information. The WSNs consist of many low cost sensor nodes. Each sensor node consists of a microprocessors and radio transceivers and can only be equipped with limited resources like power, bandwidth etc. Fuzzy logic is a recent approach to tackle few of the important decision making aspects of WSNs. Fuzzy sets provides a robust mathematical solutions for dealing with real-world problems and non-statistical uncertainty. The paper reviews few fuzzy set based solutions for WSNs applications.

Keywords

Wireless Sensor Networks (WSNs), Fuzzy Sets, Fuzzy Types, WSN Applications.
User
Notifications
Font Size

  • Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of network and computer applications, 60, 192-219.
  • Iyengar, S. S., & Brooks, R. R. (Eds.). (2016). Distributed Sensor Networks: Sensor Networking and Applications (Volume Two). CRC press.
  • Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys & Tutorials, 19(2), 828-854.
  • Khari, M. (2018). Wireless Sensor Networks: A Technical Survey. In Handbook of Research on Network Forensics and Analysis Techniques (pp. 1-18). IGI Global.
  • Tripathi, A., Gupta, H. P., Dutta, T., Mishra, R., Shukla, K. K., & Jit, S. (2018). Coverage and Connectivity in WSNs: A Survey, Research Issues and Challenges. IEEE Access, 6, 26971-26992.
  • Yang, X., Wang, L., Xie, J., & Zhang, Z. (2018). Energy Efficiency TDMA/CSMA Hybrid Protocol with Power Control for WSN. Wireless Communications and Mobile Computing, 2018.
  • Mostafaei, H., Montieri, A., Persico, V., & PescapĂ©, A. (2017). A sleep scheduling approach based on learning automata for WSN partialcoverage. Journal of Network and Computer Applications, 80, 67-78.
  • Selvi, M., Logambigai, R., Ganapathy, S., Ramesh, L. S., Nehemiah, H. K., & Arputharaj, K. (2016, August). Fuzzy temporal approach for energy efficient routing in WSN. In Proceedings of the international conference on informatics and analytics (p. 117). ACM.
  • Abdelgawad, A., & Bayoumi, M. (2012). Data fusion in WSN. In Resource-aware data fusion algorithms for wireless sensor networks (pp. 17-35). Springer, Boston, MA.
  • Pham, H. N., Pediaditakis, D., & Boulis, A. (2007, June). From simulation to real deployments in WSN and back. In 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (pp. 1-6). IEEE.
  • Quer, G., Masiero, R., Pillonetto, G., Rossi, M., & Zorzi, M. (2012). Sensing, compression, and recovery for WSNs: Sparse signal modeling and monitoring framework. IEEE Transactions on Wireless Communications, 11(10), 3447-3461.
  • Tavakoli, R., Nabi, M., Basten, T., & Goossens, K. (2019). Topology management and tsch scheduling for low-latency convergecast in in-vehicle wsns. IEEE Transactions on Industrial Informatics, 15(2), 1082-1093.
  • Farhan, L., Alzubaidi, L., Abdulsalam, M., Abboud, A. J., Hammoudeh, M., & Kharel, R. (2018, January). An efficient data packet scheduling scheme for Internet of Things networks. In 2018 1st International Scientific Conference of Engineering Sciences-3rd Scientific Conference of Engineering Science (ISCES) (pp. 1-6). IEEE.
  • Priya, I. L., Lalitha, S., & Paul, P. V. (2018). Energy Efficient Routing Models In Wireless Sensor Networks-A Recent Trend Survey. International Journal of Pure and Applied Mathematics, 118(16), 443-458.
  • Hamdani, M., Qamar, U., Butt, W. H., Khalique, F., & Rehman, S. (2018, November). A Comparison of Modern Localization Techniques in Wireless Sensor Networks (WSNs). In Proceedings of the Future Technologies Conference (pp. 535-548). Springer, Cham.
  • AlHajri, M., Goian, A., Darweesh, M., AlMemari, R., Shubair, R., Weruaga, L., & AlTunaiji, A. (2018). Accurate and robust localization techniques for wireless sensor networks. arXiv preprint arXiv:1806.05765.
  • Chen, Q., Gao, H., Cai, Z., Cheng, L., & Li, J. (2018, April). Energy-collision aware data aggregation scheduling for energy harvesting sensor networks. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications (pp. 117-125). IEEE.
  • Pathan, A. S. K. (Ed.). (2016). Security of selforganizing networks: MANET, WSN, WMN, VANET. CRC press.
  • Ranjan, R., & Varma, S. (2016). Challenges and implementation on cross layer design for wireless sensor networks. Wireless personal communications, 86(2), 1037-1060.
  • EkbataniFard, G. H., Monsefi, R., Akbarzadeh-T, M. R., & Yaghmaee, M. H. (2010, May). A multiobjective genetic algorithm based approach for energy efficient QoS-routing in two-tiered wireless sensor networks. In IEEE 5th International Symposium on Wireless Pervasive Computing 2010 (pp. 80-85). IEEE.
  • Singh, S., Chand, S., & Kumar, B. (2016). Energy efficient clustering protocol using fuzzy logic for heterogeneous WSNs. Wireless Personal Communications, 86(2), 451-475.
  • Collotta, M., Bello, L. L., & Pau, G. (2015). A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple fuzzy logic controllers. Expert Systems with Applications, 42(13), 5403-5415.
  • Viani, F., Robol, F., Bertolli, M., Polo, A., Massa, A., Ahmadi, H., & Boualleague, R. (2016, June). A wireless monitoring system for phytosanitary treatment in smart farming applications. In 2016 IEEE International Symposium on Antennas and Propagation (APSURSI) (pp. 2001-2002). IEEE.
  • Ghosh, S., Mondal, S., & Biswas, U. (2016, August). Efficient data gathering in WSN using fuzzy C means and ant colony optimization. In 2016 International Conference on Information Science (ICIS) (pp. 258-265). IEEE.
  • Jacob, R. M., & Sravan, M. S. (2017, July). A novel method based on fuzzy logic to set the arbitration threshold in WirArb for time critical applications in wireless sensor network. In 2017 International Conference on Networks & Advances in Computational Technologies (NetACT) (pp. 196-202). IEEE.
  • Selvi, M., Logambigai, R., Ganapathy, S., Ramesh, L. S., Nehemiah, H. K., & Arputharaj, K. (2016, August). Fuzzy temporal approach for energy efficient routing in WSN. In Proceedings of the international conference on informatics and analytics (p. 117). ACM.
  • Maksimovic, M., Vujovic, V., Perisic, B., & Milosevic, V. (2015). Developing a fuzzy logic based system for monitoring and early detection of residential fire based on thermistor sensors. Comput. Sci. Inf. Syst., 12(1), 63-89.
  • Zhang, Z., Hao, Z., Zeadally, S., Zhang, J., Han, B., & Chao, H. C. (2017). Multiple attributes decision fusion for wireless sensor networks based on intuitionistic fuzzy set. IEEE Access, 5, 12798-12809.
  • Kapitanova, K., Son, S. H., & Kang, K. D. (2010, August). Event Detection in Wireless Sensor Networks–Can Fuzzy Values Be Accurate?. In International Conference on Ad Hoc Networks (pp.168-184). Springer, Berlin, Heidelberg.
  • Ko, J., & Chang, M. (2015). Momoro: Providing mobility support for low-power wireless applications. IEEE Systems Journal, 9(2), 585-594.
  • Saleh, A. E., Moustafa, M. S., Abo-Al-Ez, K. M., & Abdullah, A. A. (2016). A hybrid neuro-fuzzy power prediction system for wind energy generation. International Journal of Electrical Power & Energy Systems, 74, 384-395.
  • Shah, B., Iqbal, F., Abbas, A., & Kim, K. I. (2015). Fuzzy logic-based guaranteed lifetime protocol for real-time wireless sensor networks. Sensors, 15(8), 20373-20391.
  • Chiang, S. Y., Kan, Y. C., Chen, Y. S., Tu, Y. C., & Lin, H. C. (2016). Fuzzy computing model of activity recognition on WSN movement data for ubiquitous healthcare measurement. Sensors, 16(12), 2053.
  • Sharma, G., & Kumar, A. (2018). Fuzzy logic based 3D localization in wireless sensor networks using invasive weed and bacterial foraging optimization. Telecommunication Systems, 67(2), 149-162.
  • Patil, P., Kulkarni, U., Desai, B. L., Benagi, V. I., & Naragund, V. B. (2012). Fuzzy logic based irrigation control system using wireless sensor network for precision agriculture. Agro-Informatics and Precision Agriculture (AIPA).

Abstract Views: 202

PDF Views: 0




  • A Survey on Fuzzy Based Sensor Network and Its Applications

Abstract Views: 202  |  PDF Views: 0

Authors

Rajeev Ranjan
School of CSA, REVA University, Rukmini Knowledge Park Yelahanka, Kattigenahalli, Bengaluru, Karnataka 560064, India
K. M. Sinduja
School of CSA, REVA University, Rukmini Knowledge Park Yelahanka, Kattigenahalli, Bengaluru, Karnataka 560064, India
G. Vinay
School of CSA, REVA University, Rukmini Knowledge Park Yelahanka, Kattigenahalli, Bengaluru, Karnataka 560064, India

Abstract


Wireless Sensor Networks (WSNs) have been broadly applied in many fields such as industry, agriculture, event detection & monitoring, time critical applications and research to facilitate the gathering and distribution of information. The WSNs consist of many low cost sensor nodes. Each sensor node consists of a microprocessors and radio transceivers and can only be equipped with limited resources like power, bandwidth etc. Fuzzy logic is a recent approach to tackle few of the important decision making aspects of WSNs. Fuzzy sets provides a robust mathematical solutions for dealing with real-world problems and non-statistical uncertainty. The paper reviews few fuzzy set based solutions for WSNs applications.

Keywords


Wireless Sensor Networks (WSNs), Fuzzy Sets, Fuzzy Types, WSN Applications.

References