Energy Efficient Routing in Wireless Sensor Networks Based on Fuzzy Ant Colony Optimization

Amiri, Ehsan; Keshavarz, Hassan; Alizadeh, Mojtaba; Zamani, Mazdak; Khodadadi, Touraj

doi:10.1155/2014/768936

Cited by 65 publications

(36 citation statements)

References 36 publications

(54 reference statements)

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“…SACA, Fuzzy Ant Colony Algorithm(FACA) [21] and GA2C2A were tested by using 3 datasets in Table 1 (description of testing datasets). These datasets have classification tables that can be used in final performance evaluation.…”

Section: Simulation Experimental Resultsmentioning

confidence: 99%

An Optimized Clustering Algorithm based on Integration of Genetic Algorithm and Ant Colony Algorithm

Xiao¹,

Chen²,

Qiu³

et al. 2017

IJSIA

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Section: Simulation Experimental Resultsmentioning

confidence: 99%

An Optimized Clustering Algorithm based on Integration of Genetic Algorithm and Ant Colony Algorithm

Xiao¹,

Chen²,

Qiu³

et al. 2017

IJSIA

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“…NS-2 centered imitation outcomes specify that the grid's function of FBTCM enhances in contrast to the current EDTM reliance prototypical convention. Amiri et al, [15] brought out an optimum directing convention for WSN enthused by the searching manners of ants. The ants attempt to locate current pathways between the origin and the base station.…”

Section: Literature Surveymentioning

confidence: 99%

An Fuzzy Trust Based Energy Aware Multipath Secure Data Collection in WSN

Babu¹,

Anuradha²,

Murty³

2018

IJET

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A Wireless Sensor Network (WSN) is an array of radar nodules that energetically organize itself into a cable-less grid without utilizing any prevailing arrangement. One of the significant issues in WSNs is the drive's intake, whereby the grid's lifespan is reliant on this aspect. Security is another significant issues in cable-less radar grids, because of absence of wire/cable besides resource constraint's nature. Reliance model shows a key role in fortifying the radar grids by recognizing the egotistical, malevolent and give -in nodules and separating them from message grid. Directed Random Propagation (DRP) operates two-hop vicinity data to enhance the circulation competence, leading to a reduced pack capture possibility. In this study, Shuffled Frog Leaping Algorithm (SFLA) is applied to augment ill-defined faith centered drive conscious multiple route locked data group in WSN.

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“…The specific formula for the synthetic pheromone is given as Equation (18) in Section 4.4.1. Based on this expression, the formula for calculating the state transition rule [29,30,31] is shown in Equation (1).

i and j are nodes in the network.

P ij ( t ) is the integrated pheromone concentration on the path from i to j at time t .

allowed k represents the set of valid next-hop nodes that have not yet been visited.

α and β are two constants.

…”

Section: Trusted Network Secure Routing Algorithmmentioning

confidence: 99%

A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

Zhang

Yin

Xiong

et al. 2017

Sensors

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With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.

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Energy Efficient Routing in Wireless Sensor Networks Based on Fuzzy Ant Colony Optimization

Cited by 65 publications

References 36 publications

An Optimized Clustering Algorithm based on Integration of Genetic Algorithm and Ant Colony Algorithm

An Optimized Clustering Algorithm based on Integration of Genetic Algorithm and Ant Colony Algorithm

An Fuzzy Trust Based Energy Aware Multipath Secure Data Collection in WSN

A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

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