Wireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very More
Wireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very important features of wireless sensor networks is their lifetime. Two important factors come to mind to increase the lifetime of networks: These factors are maintaining the coverage of the network and reducing the energy consumption of sensor nodes simultaneously with the uniform consumption of energy by all of them. Clustering, as the optimal method of data collection, is used to reduce energy consumption and maintain the coverage of the network in wireless sensor networks. In clustered networks, each node transmits acquired data to the cluster head to which it belongs. After a cluster head collects all the data from all member nodes, it transmits the data to the base station (sink). Given that fuzzy logic is a good alternative for complex mathematical systems, in this study, a fuzzy logic-based trust model uses the clustering method in wireless sensor networks. In this way, cluster-head sensors are elected from among sensors with high reliability with the help of fuzzy rules. As a result, the best and most trusted sensors will be selected as the cluster heads. The simulation results in MATLAB software show that in this way, in comparison with K-Means, FCM, subtractive clustering, and multi-objective fuzzy clustering protocols, the energy consumption in clustered nodes will decrease and the network’s lifetime will increase.
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Wireless sensor networks are a large number of sensor nodes with limited energy in a scattered geographically limited area. Due to limited resources in wireless sensor networks, increasing the lifetime of the networks by reducing energy consumption is always considered. More
Wireless sensor networks are a large number of sensor nodes with limited energy in a scattered geographically limited area. Due to limited resources in wireless sensor networks, increasing the lifetime of the networks by reducing energy consumption is always considered. More nodes to send data to the central station energy consumption. Sequential routing based on clustering, this responsibility falls on the head, and this increases the energy consumption of cluster heads. In recent years later all the energy of cluster heads, routing protocols and a lot of clustering is proposed. The purpose of this study, the combination of clustering and routing in order to extend the lifetime of this type of network. For clustering of genetic algorithm with fixed and harmony search algorithm is used for routing. Customize search algorithm for routing in harmony, three criteria neighborhood, reducing energy consumption and proper distribution of energy consumption is taken into account. Harmony algorithm is proposed to establish a proper balance between the criteria listed will generate more efficient routes. Finally change the routing cluster heads in each round will be balancing energy consumption between nodes per cluster. The results of the tests show the superiority of 2.14% proposed increase in messaging as well as 24.84% Lifetime network protocol is DEEC.
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Wireless sensor networks have a large number of limited-energy sensor nodes dispersed in a finite area. Most node energies are used to send data to the central station. Due to the energy constraints in this type of grid, increasing life expectancy has always been a conc More
Wireless sensor networks have a large number of limited-energy sensor nodes dispersed in a finite area. Most node energies are used to send data to the central station. Due to the energy constraints in this type of grid, increasing life expectancy has always been a concern with decreasing energy consumption. The aim of this study is to provide surface clustering based on genetic algorithm in order to increase the life span of these networks. In proposed surface clustering, the geographic area is divided into three levels according to the radio range and the clustering of the nodes of each level is done individually. The cluster heads use more energy than other nodes to send information, so the proposed algorithm aims to reduce the number of cluster heads in order to increase the network lifetime. Finally, by changing the clusters in each routing round, there is a greater energy balance between the nodes. The results from the experiments indicate the superiority of the proposed algorithm in transmitting messages and network lifetimes over other similar protocols.
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Visual sensor networks (VSNs) apply directional sensors that can be configured only in one direction and also can be set in one of the possible observing ranges. In this battery-resource-limited environment, battery management and network lifetime expansion are still im More
Visual sensor networks (VSNs) apply directional sensors that can be configured only in one direction and also can be set in one of the possible observing ranges. In this battery-resource-limited environment, battery management and network lifetime expansion are still important challenges. The target coverage problem in such networks, in which all of the specified targets must be continuously observed and monitored by administrators is formulated as an integer linear programming problem (ILP) that is an NP-Hard problem. Although several approaches have been presented in the literature to solve the aforementioned problem, the majority of them suffer from getting stuck in the local trap and low exploration in search space. To address the issue, a discrete cuckoo-search optimization algorithm (DCSA) is extended to solve this combinatorial problem. The discrete operator of the proposed algorithm is designed in such a way that explore search space efficiently and lead to balancing in the local and global search process. The proposed algorithm was examined in different conducted scenarios. The returned results of simulations of numerous scenarios show the dominance of the proposed algorithm in comparison with other existing approaches in terms of network lifetime maximization. In other words, the proposed DCSA has 19.75% and 13.75% improvement in terms of network average lifetime expansion against HMNLAR and GA-based approaches respectively in all scenarios.
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