Abstract
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.
Highlights
Sensitive wireless networks normally include enormous multipurpose sensitive nods with low power and small size, which could communicate in short distances
There are some challenges associated with design of sensitive wireless networks and one of them is that energy resources are substantially more restricted than wire networks and sensors' charging or battery replacement in a network might be hard or impossible and this may create relatively high limitation in setting communication and sensor's process time among all networks
In steady state fuzzy data, transferring is used through single hop to base station, a nod is chosen as cluster head in each cluster, and cluster member is another type, which can be used
Summary
Sensitive wireless networks normally include enormous multipurpose sensitive nods with low power and small size, which could communicate in short distances. In networks with high density, which implement multi-hop frameworks, the energy of nodes near to BS are discharged with a higher speed while in single-hop methods the energy of nodes which are far from BS are dismissed earlier To overcome these problems, they implement a combination of the single and multi-hop techniques to increase the lifetime of the network. PRWSN recommends a method where each node implements a fuzzy processor for energy consumption In this technique, first, each node sends its distance to the destination as the input fuzzy processor, and it gets much of its power and reinforcing type as output (Ghorbannia Delavar et al, 2011). In FUMOR technique, there are other factors used such as distance, energy and node level as a fuzzy model for increasing of network lifetime (Tajari et al, 2011)
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