Lifetime enhancement has always been a crucial issue as most of the wireless sensor networks (WSNs) operate in unattended environment where human access and monitoring are practically infeasible. Clustering is one of the most powerful techniques that can arrange the system operation in associated manner to attend the network scalability, minimize energy consumption, and achieve prolonged network lifetime. To conquer this issue, current researchers have triggered the proposition of many numerous clustering algorithms. However, most of the

proposed algorithms overburden the cluster head (CH) during cluster formation. To overcome this problem, many researchers have come up with the idea of fuzzy logic (FL), which is

applied in WSN for decision making. These algorithms focus on the efficiency of CH, which could be adoptive, flexible, and intelligent enough to distribute the load among the sensor nodes that can enhance the network lifetime.


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A routing chain in WSN is an ordered sequence of all the nodes in the network forming a chain like structure to deliver the message to the BS. As discussed in beginning, Clustering approach can greatly contribute to overall system scalability, energy efficiency and network lifetime. It improves the power control and helps to reuse the bandwidth for better resource allocation. On the other hand, single hop communication overburdens the gateway with the increase in sensor density. The aim of this algorithm is to minimize the energy consumption at the cluster level by forming an optimal data gathering chain.




  • The power of fuzzy sets changes with different types of fuzzy models such as type-1 to type-n, since they are intended to cope with varying levels of uncertainty. In this work, T2FL model is used in view of improving the routing technique by efficiently electing a cluster head.
  • T2FL model can handle the uncertainty environment more accurately than T1FL model because the membership degrees of T2FL are themselves fuzzy sets. In general, random uncertainties are related to probabilistic theory and Linguistic randomness is related to fuzzy sets.




A sensor network is divided into number of levels and at each level, efficient Cluster Head is elected based on T2FL Model. Three fuzzy descriptors such as remaining battery power, distance to base station, and concentration have been considered. Each Cluster Head sends the data to the next level (starting from the first level to the last level) till it reaches at the base station. The novelty of the protocol utilizes the concept of Type 2 Fuzzy Logic justifying that fuzzy logic model handles real time problems more accurately than any other probabilistic model.




  • The proposed algorithm builds on the top of the principle of LEACH. Fuzzy Logic is capable of taking real time decisions with imprecise and incomplete information. It is very simple and flexible to take real time decisions under uncertain environment.
  • Multi-hop communication protocol provides a wider scope for larger application. It is concluded from simulation results that T2FL model provides better scalability, better lifetime compared to T1FL, LEACH single hop and LEACH multi-hop protocol
  • Homogeneous networks have been considered such that all the sensor nodes have initial equal energy.





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Fuzzy Logic

1)Let N sensor nodes distributed randomly over M×M region where k clusters are assumed

2) N sensor nodes are divided into different levels.

3) Level should be numbered according to the distance

from the base station.

4) Elect the CH at each level based on T2FL Model




[1] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energyefficient communication protocol for wireless microsensor networks,” in Proc. 33rd Hawaii Int. Conf. Syst. Sci. (HICSS), Washington, DC, USA, Jan. 2000, pp. 1–10.

[2] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660–670, Oct. 2002.

[3] S. Lindsey and C. S. Raghabendra, “PEGASIS: Power efficient gathering in sensor information systems,” in Proc. IEEE Aerosp. Conf., Mar. 2002, pp. 3-1125–3-1130.

[4] I. Gupta, D. Riordan, and S. Sampalli, “Cluster-head election using fuzzy logic for wireless sensor networks,” in Proc. Commun. Netw. Services Res. Conf., May 2005, pp. 255–260.

[5] J.-M. Kim, S.-H. Park, Y.-J. Han, and T. Chung, “CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks,” in Proc. ICACT, Feb. 2008, pp. 654–659.

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