Cluster heads optimum choice and route discovery by using fuzzy logic in wireless sensor networks

Optimum energy consumption in wireless sensitive networks plays an important role on network management. In the proposed model of this paper, first all the nods send the energy, station distance and density parameters to their fuzzy modules. According to each nod's fuzzy module outputs, a timer is activated for all nodes, which start reverse-counting from obtained value from fuzzy module. Timer of better nod comes to zero sooner and two best nods are selected in each zone (with the distance of r). One of them is introduced as superior cluster head and the other nods are connected to the closest cluster head. In addition, the cluster head not introduced as superior cluster head first collects data from neighbor's nods and then sends it to the superior cluster head after classifying data as package. The performance of the proposed model of this paper is compared with other methods and the preliminary results indicate that the proposed algorithm has increased first nod death time compared with other methods in the literature

A Reliable Energy-Efficient Multi-Level Routing Algorithm for Wireless Sensor Networks Using Fuzzy Petri Nets

A reliable energy-efficient multi-level routing algorithm in wireless sensor networks is proposed. The proposed algorithm considers the residual energy, number of the neighbors and centrality of each node for cluster formation, which is critical for well-balanced energy dissipation of the network. In the algorithm, a knowledge-based inference approach using fuzzy Petri nets is employed to select cluster heads, and then the fuzzy reasoning mechanism is used to compute the degree of reliability in the route sprouting tree from cluster heads to the base station. Finally, the most reliable route among the cluster heads can be constructed. The algorithm not only balances the energy load of each node but also provides global reliability for the whole network. Simulation results demonstrate that the proposed algorithm effectively prolongs the network lifetime and reduces the energy consumption

Choose the Appropriate Cluster Head for Decrease Energy Consume in Wireless Sensor Networks Based on Gravitational Emulation Local Search Algorithm

New Wireless Sensor Networks (WSN), is the new generation of real-time embedded systems with limited computation, energy and storage which have variety usage, especially when that is not possible using traditional networks. Given that, in this networks energy problem is important major challenge, using Clustering model can be considered as a solution to overcome this problem. In this instruction, sensor nodes grouped in a set of cluster and pick out a central node for Cluster Head (CH) node. Choose the appropriate cluster, reduce energy consumption in these networks, as a result increase networks lifetime. Hence, in this study, unlike previous studies, used Gravitational Emulation Local Search Algorithm (GELS), for clustering and select appropriate CH. This method is based on three descriptors of energy, dispersion and centrality of nodes, and simulations indicate, where the CH only selected based on local data set, significantly increase network lifetime