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.
Manuscript profile
Wireless Sensor Networks (WSNs) are consist of independent distributed sensors with storing, processing, sensing and communication capabilities to monitor physical or environmental conditions. There are number of challenges in WSNs because of limitation of battery power More
Wireless Sensor Networks (WSNs) are consist of independent distributed sensors with storing, processing, sensing and communication capabilities to monitor physical or environmental conditions. There are number of challenges in WSNs because of limitation of battery power, communications, computation and storage space. In the recent years, computational intelligence approaches such as evolutionary algorithms and swarm intelligence are applied successfully to solve many problems in WSNs. Most important of these problems are data aggregation, energy-aware routing, duty cycle scheduling, security and localization. These problem are in form of distributed so distributed approaches are required to solve them. Reinforcement learning is one of the most widely used and most effective methods of computational intelligence. In this paper, we used the reinforcement learning to solve multicast Quality of Service (QoS) routing. The simulation results showed that reinforcement learning is a suitable approach to solve this problem. The algorithm is implemented easy, it has the great flexibility in topology changes and it leads to optimized results. Distributed reinforcement learning provides compatibility mechanisms that show the intelligence behavior in complicate and dynamic environment such as WSNs. Using reinforcement learning, sensors behave autonomously, independently and flexibly during topology and scenario changes.
Manuscript profile
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.
Manuscript profile
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.
Manuscript profile
Sanad
Sanad is a platform for managing Azad University publications
