The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile More
The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile sinks, which increases the life of wireless sensor networks. Using network coding, this paper presents a Mixed Integer Linear Programming Model to determine the optimal multicast routing of source sensor nodes to mobile sinks in wireless sensor networks, which determines the time and location of sinks to collect maximum coded data and reduces the delay in sink movement and energy consumption. Solving this problem in polynomial time is not possible due to the involvement of various parameters and the constrained resources of wireless sensor networks. Therefore, several exploratory and greedy and fully distributed algorithms are proposed to determine the movement of sinks and their residence location based on maximizing the value of coded data and the type of data dead time. By simulating, the optimal method and the use of coding and proposed algorithms, reduce the runtime and energy consumption and increase the value of collected data and network lifetime than non-coding methods.
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One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imp More
One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imperialist competitive algorithm is used for solving the mentioned problem. In the proposed method, a colony can immigrate from a weak empire to more powerful empire. The idea of immigration is inspired from human society in which a human can emigrate from a country to another country. The network is supposed to be a three-dimensional grid network and the sensor nodes can be only placed at cross-points of the grids while the target points can be deployed at each point of three-dimensional space. The simulation results show that the proposed method uses fewer number of sensor nodes than other similar algorithms and has the less running time.
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Internet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging info More
Internet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging information through headers is one way to improve energy consumption. This paper introduces a new clustering-based routing protocol called KHCMSBA. The proposed protocol biologically uses fast and efficient search features inspired by the Krill Herd optimization algorithm based on krill feeding behavior to cluster the sensor nodes. The proposed protocol also uses a mobile well to prevent the hot spot problem. The clustering process at the base station is performed by a centralized control algorithm that is aware of the energy levels and position of the sensor nodes. Unlike protocols in other research, KHCMSBA considers a realistic energy model in the grid that is tested in the Opnet simulator and the results are compared with AFSRP (Artifical Fish Swarm Routing ProtocolThe simulation results show better performance of the proposed method in terms of energy consumption by 12.71%, throughput rate by 14.22%, end-to-end delay by 76.07%, signal-to-noise ratio by 82.82%. 46% compared to the AFSRP protocol
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