Fuzzy rule-based system for energy efficiency in wireless sensor networks

The sensor nodes consume a large amount of energy to transfer the sensed information directly to the base station (BS). To reduce the energy consumption from the direct transmission, clustering and routing techniques are used. In this paper, we propose clustering and routing algorithms called energy-efficient two-phase approach using fuzzy logic (EETPF). Here, the rule-based fuzzy logic is used to associate the input values of clustering and routing algorithm. The fuzzy system makes decisions according to the residual energy of sensor nodes, distance of sensor nodes from the BS and the number of nodes in the communication range. The crisp values of the input variables are converted into different fuzzy values. The fuzzy output values are converted to crisp values using centroid defuzzification method. The cluster heads (CHs) and routers are selected with respect to the output values. The sensor nodes get allocated to respective CHs according to the load handling capacity of CHs. The routing path is generated according to the capacity of routers. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round and sustainability period of the network. It is observed that EETPF outperforms state-of-the-art algorithms under these evaluation factors.

[1]  Arputharaj Kannan,et al.  Fuzzy logic based unequal clustering for wireless sensor networks , 2016, Wirel. Networks.

[2]  Halil Yetgin,et al.  A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks , 2017, IEEE Communications Surveys & Tutorials.

[3]  Damodar Reddy Edla,et al.  Shuffled Complex Evolution Approach for Load Balancing of Gateways in Wireless Sensor Networks , 2018, Wirel. Pers. Commun..

[4]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[5]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[6]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[7]  Faisal Karim Shaikh,et al.  Energy harvesting in wireless sensor networks: A comprehensive review , 2016 .

[8]  Damodar Reddy Edla,et al.  A PSO Based Routing with Novel Fitness Function for Improving Lifetime of WSNs , 2018, Wirel. Pers. Commun..

[9]  Vinu Sundararaj,et al.  An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks , 2018, Comput. Secur..

[10]  Arun Kumar Sangaiah,et al.  Survey on clustering in heterogeneous and homogeneous wireless sensor networks , 2017, The Journal of Supercomputing.

[11]  Damodar Reddy Edla,et al.  Shuffled Particle Swarm Optimization for Energy Efficiency Using Novel Fitness Function in WSN , 2019, PReMI.

[12]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[13]  Damodar Reddy Edla,et al.  Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function , 2019, Appl. Soft Comput..

[14]  Seon-Ho Park,et al.  CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks , 2008, 2008 10th International Conference on Advanced Communication Technology.

[15]  Jasbir Kaur,et al.  Improved LEACH Protocol for Wireless Sensor Networks , 2011, 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing.

[16]  Damodar Reddy Edla,et al.  An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs , 2017, IEEE Sensors Journal.

[17]  Dirk Timmermann,et al.  Low energy adaptive clustering hierarchy with deterministic cluster-head selection , 2002, 4th International Workshop on Mobile and Wireless Communications Network.

[18]  Damodar Reddy Edla,et al.  SCE-PSO based clustering approach for load balancing of gateways in wireless sensor networks , 2019, Wirel. Networks.

[19]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[20]  Damodar Reddy Edla,et al.  Novel Fitness Function for SCE Algorithm Based Energy Efficiency in WSN , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[21]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[22]  Adnan Yazici,et al.  An energy aware fuzzy approach to unequal clustering in wireless sensor networks , 2013, Appl. Soft Comput..

[23]  Damodar Reddy Edla,et al.  GWO-GA Based Load Balanced and Energy Efficient Clustering Approach for WSN , 2020 .

[24]  S. Sitharama Iyengar,et al.  Biologically Inspired Cooperative Routing for Wireless Mobile Sensor Networks , 2007, IEEE Systems Journal.

[25]  Naixue Xiong,et al.  Energy Efficiency QoS Assurance Routing in Wireless Multimedia Sensor Networks , 2011, IEEE Systems Journal.

[26]  B. Baranidharan,et al.  DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach , 2016 .