A survey on intelligent routing protocols in wireless sensor networks

Guo, Wenjing; Zhang, Wei

doi:10.1016/j.jnca.2013.04.001

Cited by 129 publications

(54 citation statements)

References 24 publications

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“…One of the most popular routing algorithm is clustering routing algorithm. The algorithm divides the network nodes into several clusters according to certain rules, such that each cluster contains a cluster head and a plurality of member nodes [4]. The overall closeness between cluster head and member nodes serves as the hallmark feature of the corresponding cluster [5].…”

Section: Introductionmentioning

confidence: 99%

Routing Algorithm of Wireless Sensor Network and Robustness Analysis Based on Fuzzy Mathematics

et al. 2017

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Abstract-In order to reduce the energy consumption and enhance the robustness of wireless sensor network (WSN), this paper proposes a hierarchical clustering routing algorithm based on fuzzy mathematics (HCRAFM). To make a comprehensive analysis of WSN, it is also necessary to detect the robustness of the network. Facing the multiple random variables, the traditional robustness detection models assume that all nodes have the same weight, making it impossible to quantify the analysis indices or obtain accurate results. Thus, the fuzzy mathematics theory was introduced to the WSN robustness detection, forming a fuzzy comprehensive evaluation method. The simulation results show that the HCRAFM strikes a load balance between WSN nodes, extends the life cycle of each node, and prolongs the service life of the network. In addition, the proposed algorithm is proved to have sound robustness and strong applicability. Keywords-Routing Algorithms; Robustness Analysis; Wireless Sensor Network (WSN); Fuzzy Mathematics IntroductionIn wireless sensor network (WSN), the routing algorithm is responsible for setting up the path and mechanism of data transmission, realizing the dynamical update of network topology, and maintaining the information of network routing [1]. Therefore, the algorithm must be able to improve the energy efficiency of WSN nodes, and provide high quality services [2]. The traditional routing algorithms cannot satisfy the demand of WSN, which carries numerous different features from those of traditional networks. This calls for the design of a routing algorithm targeted at the demand of WSN [3]. The structure of a typical WSN is presented in Figure 1.

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Section: Introductionmentioning

confidence: 99%

Routing Algorithm of Wireless Sensor Network and Robustness Analysis Based on Fuzzy Mathematics

et al. 2017

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“…It should be the first choice when looking for a flexible and low-cost routing approach. [3] In this paper we discuss some representative computational intelligence based routing algorithms. There have been several surveys done on the routing protocols for WSNs which majorly focuses on traditional routing protocols and research on ACO based routing protocols.…”

Section: Introductionmentioning

confidence: 99%

Survey on Computational Intelligence Based Routing Protocols in Wireless Sensor Network

Kulkarni¹,

Munot²,

Malathi³

2016

IJWMCIS

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“…Particle Swarm Optimization (PSO) algorithm simulates the swarm behavior of birds and fish [3]. PSO has many advantages over other alternative optimization techniques like GA which has very high processing demands [4].So PSO and its improved ones are popular choices for clustering [5,6]. Firefly Algorithm (FA) simulates the bioluminescent communication behavior of fireflies [7].…”

Section: Introductionmentioning

confidence: 99%

A novel bio-inspired algorithm based on plant root growth model for data clustering

Zhu

Zhang

et al. 2016

2016 35th Chinese Control Conference (CCC)

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Inspired by the behaviors of plant root growth, an Artificial Root M ass (ARM ) optimization algorithm based on an artificial root model is proposed. This ARM algorithm simulates the plant's root growth strategies including proliferation and 'intelligent' decisions about growth directions. Ten well-known benchmark functions are employed to validate its optimization effect. ARM is compared with other existing algorithms, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The experimental results show that ARM seems much superior to other algorithms on the selected benchmark functions in multidimensional cases. ARM algorithm is used for data clustering on several benchmark datasets. The performance of the ARM algorithm is compared with GA, PSO and DE on clustering problems. The simulation results show that the proposed ARM outperforms the other three algorithms in terms of accuracy and robustness on most of selected datasets. The proposed algorithm ARM provides a new reference for solving data clustering problems.

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A survey on intelligent routing protocols in wireless sensor networks

Cited by 129 publications

References 24 publications

Routing Algorithm of Wireless Sensor Network and Robustness Analysis Based on Fuzzy Mathematics

Routing Algorithm of Wireless Sensor Network and Robustness Analysis Based on Fuzzy Mathematics

Survey on Computational Intelligence Based Routing Protocols in Wireless Sensor Network

A novel bio-inspired algorithm based on plant root growth model for data clustering

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