ASDP: An Action-Based Service Discovery Protocol Using Ant Colony Algorithm in Wireless Sensor Networks

Huo, Hongwei; Gao, Deyun; Niu, Yanchao; Gao, Shuai

doi:10.1007/978-3-540-77024-4_32

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…WSN are very effective in many fields such as intrusion detection, weather monitoring, security and tactical surveillance, distributed computing, detecting ambient conditions such as temperature, movement, sound, light, or the presence of certain objects, inventory control, and disaster management [8]. Nowadays, these sensor nodes are equipped with a small processor, constrained memory and constrained wireless communication capabilities.…”

Section: Introductionmentioning

confidence: 99%

Routing Algorithms for Wireless Sensor Networks Using Ant Colony Optimization

Domínguez-Medina

Cruz-Cortés

2010

Advances in Soft Computing

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Abstract. Wireless Sensor Networks have become an active research topic in the last years. The routing problem is a very important part in this kind of networks that need to be considered in order to maximize the network life time. As the size of the network increases, routing becomes more complex due the amount of sensor nodes in the network. Sensor nodes in Wireless Sensor Networks are very constrained in memory capabilities, processing power and batteries. Ant Colony Optimization based routing algorithms have been proposed to solve the routing problem trying to deal with these constrains. We present a comparison of two Ant Colony-based routing algorithms, taking into account current amounts of energy consumption under different scenarios and reporting the usual metrics for routing in wireless sensor networks.

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Section: Introductionmentioning

confidence: 99%

Routing Algorithms for Wireless Sensor Networks Using Ant Colony Optimization

Domínguez-Medina

Cruz-Cortés

2010

Advances in Soft Computing

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Garra Rufa‐inspired optimization technique

Jaber¹,

Abdulbari

Shalash³

et al. 2020

Int J Intell Syst

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Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well‐ developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular “fish massage session.” A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

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