To ensure reliable Internet of Things (IoT) applications, this study provides a new intelligent deployment technique for sensor nodes. The proposed intelligent deployment technique places sensor nodes in appropriate l...
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To ensure reliable Internet of Things (IoT) applications, this study provides a new intelligent deployment technique for sensor nodes. The proposed intelligent deployment technique places sensor nodes in appropriate locations to achieve high levels of connectivity and, as a result, boost the WSN's overall reliability. To generate optimal sensor node coordinate positions, a modified version of the Expectation-Maximization method is utilized as a machine learning technique. Following that, each node's neighbours are determined, and links between them are built using the K-Nearest neighbour algorithm. Then, to assess the reliability of WSN, a novel algorithm is proposed. The proposed algorithms are all well-illustrated with appropriate examples. When the algorithms provided in this research are compared to certain existing methods in terms of node positioning accuracy (10%), network connectivity (10%), and estimated dependability values (5%), it is clear that the suggested strategy outperforms them in every way.
Nowadays, the internet of thing (IoT) is a novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications. Wireless sensor network (WSN) is an important part of IoT, and it is main...
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Nowadays, the internet of thing (IoT) is a novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications. Wireless sensor network (WSN) is an important part of IoT, and it is mainly responsible for acquiring and reporting data. As lifetime and coverage area of WSN directly determine IoT performance, how to design a method to conserve nodes energy and reduce nodes death rates become important issues. Sensor network clustering is one efficient method to solve these problems. It divides nodes into clusters and selects one to be cluster head (CH). The data transmission and communication within one cluster are managed by its CH. Many traditional strategies have been designed out, but because of network dynamic feature, machine learning methods become more attractive and many literature are working on them. Particle swarm optimization (PSO) is one evolutionary algorithm. Inspired by this algorithm, we propose a novel energy-aware bio-inspired clustering scheme (PSO-WZ). We firstly initialize CHs combination randomly and assign non-CHs based on division rules. Then, using the fitness function to guide the selection process until the maximum time is reached. Since the division rule is directly related with the network topology and node energy consumption distribution, we design it from two angles: non-CHs and the whole network, to save the energy of each node as much as possible. Meanwhile, in order to balance energy load among nodes, which contributes to lowering nodes reduction and preserving network coverage range, we introduce the Gini coefficient into the objective function. From the results obtained, we conclude that the proposed algorithm is able to keep more nodes alive over time, prolong the network life cycle, and improve the overall performance of IoT further.
This paper provides a new distributed algorithm for the wireless sensor network connectivity and coverage issues by means of analyzing the market competition of human society. In this algorithm, the sensor nodes in th...
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ISBN:
(纸本)9781424421831
This paper provides a new distributed algorithm for the wireless sensor network connectivity and coverage issues by means of analyzing the market competition of human society. In this algorithm, the sensor nodes in the network were seen as enterprises in economic activities, the interested areas were taken as resources, network configurations were seen as market competitions. Take advantage of this algorithm, the amount of computation, mobile distance and information complexity of every node can be reduced, the efficiency boost, and the project of energy saving is indirect achieved Experimental results show that the method is effective.
This paper provides a new distributed algorithm for the wireless sensor network connectivity and coverage issues by means of analyzing the market competition of human society. In this algorithm, the sensor nodes in th...
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This paper provides a new distributed algorithm for the wireless sensor network connectivity and coverage issues by means of analyzing the market competition of human society. In this algorithm, the sensor nodes in the network were seen as enterprises in economic activities, the interested areas were taken as resources, network configurations were seen as market competitions. Take advantage of this algorithm, the amount of computation, mobile distance and information complexity of every node can be reduced;the efficiency boost;and the project of energy saving is indirect achieved. Experimental results show that the method is effective.
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