An approach based on fuzzy inference system and ant colony optimization for improving the performance of routing protocols in Wireless Sensor Networks

Rabelo, Ricardo A. L.; Sobral, José V. V.; Araujo, Harilton S.; Baluz, Rodrigo Augusto Rocha Souza; Filho, Raimir Holanda

doi:10.1109/cec.2013.6557967

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Ant colony optimization (ACO) based algorithms emphasize the design of routing protocols that are robust, adaptable and scalable [9]. The coordination of ants depends on the ability of self-organization that ant colony optimization based swarm intelligence techniques possess [10]. The probabilistic approach of ACO is used to determine the routes, and the pheromone update formulation is used for further updating of the pheromone trail [11].…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

Sharmin¹,

Anwar²,

Motakabber³

2020

Adv. sci. technol. eng. syst. j.

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IoT integrates and connects intelligent devices or objects with varied architectures and resources. The number of IoT devices is growing exponentially. Due to the massive wave of IoT objects, their diversity and heterogeneity among their architectures, the existing communication protocols for wireless networks become ineffective in the context of IoT. Wireless Sensor Network (WSN) has the potential to be integrated to the internet of things (IoT). The issues of the routing of WSNs impose nearly similar prerequisites for IoT routing technique. Most of the traditional routing protocols are not appropriate for WSNs and IoT because of resource constraints, computational overhead and environmental interference and do not take into account the different factors affecting energy parameter and do not accommodate node mobility. Routing algorithms must ensure the data transmission in an efficient way, having proper knowledge of the IoT system. For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network's dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. The proposed routing algorithm is simulated using MATLAB for performance evaluations. The evaluation results have recorded an improvement in conservation of energy, of almost 50% less consumed energy even with an increase in the number of nodes, by comparing with an existing routing technique based on ant system, a current routing protocol for IoT and the conventional ACO algorithm.

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Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

Sharmin¹,

Anwar²,

Motakabber³

2020

Adv. sci. technol. eng. syst. j.

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“…So how to plan the WSN route calculation algorithm which can spare node vitality and enhance the nature of system correspondence is the key point for WSN. The information transmission is performed in multiple hop strategy in WSN and the gathered information is exchanged with the user via the sink [1]. A sink is the entity where information is required and the sink might be available far away from the data bearing hub.…”

Section: Introductionmentioning

confidence: 99%

A novel bio-inspired routing algorithm based on ACO for WSNs

2019

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The methods to achieve efficient routing in energy constrained wireless sensor networks (WSNs) is a fundamental issue in networking research. A novel approach of ant colony optimization (ACO) algorithm for discovering the optimum route for information transmission in the WSNs is proposed here for optimization and enhancement. The issue of path selection to reach the nodes and vital correspondence parameters, for example, the versatility of nodes, their constrained vitality, the node residual energy and route length are considered since the communications parameters and imperatives must be taken into account by the imperative systems that mediate in the correspondence procedure, and the focal points of the subterranean insect framework have been utilized furthermore. Utilizing the novel technique and considering both the node mobility and the existing energy of the nodes, an optimal route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified using MATLAB and the simulation results demonstrate that new ACO based algorithm achieved improved performance, about 30% improvement compared with the traditional ACO algorithm, and faster convergence to determine the best cost route, and recorded an improvement in the energy consumption of the nodes per transmission.

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“…In recent years, many scholars at home and abroad have extensively researched this issue. In [6], Rabelo et al proposed an approach based on a fuzzy inference system and ant-colony optimization for improving the performance of routing protocols in wireless sensor networks. This algorithm considers energy consumption and proposes a method for estimating route quality by using fuzzy systems to assist the directed diffusion routing protocol, hence increasing the energy efficiency of the network.…”

Section: Introductionmentioning

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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An approach based on fuzzy inference system and ant colony optimization for improving the performance of routing protocols in Wireless Sensor Networks

Cited by 9 publications

References 16 publications

Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT

A novel bio-inspired routing algorithm based on ACO for WSNs

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

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