Automated design of fuzzy rule base using ant colony optimization for improving the performance in Wireless Sensor Networks

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

doi:10.1109/fuzz-ieee.2013.6622416

Cited by 4 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With this momentum, studies about how to use the fixed energy and prolong the working time of the whole system have been one of the most significant cases which scientists are committing them to studying. Recent days, some novel approaches have been brought up [2], [5], [7], [10]- [14], [16], [20]- [21], [27], [29], [32]- [33]. Among them, scheduling devices efficiently, choosing a minimal subset of sensors to achieve the coverage mission while the rest of sensors turn to sleep state to save energy, has shown a satisfying performance [10]- [12], [34].…”

Section: Introductionmentioning

confidence: 99%

An Ant Colony Optimizing Algorithm Based on Scheduling Preference for Maximizing Working Time of WSN

Liu

Chen

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

With the proliferation of wireless sensor networks (WSN), the issues about how to schedule all the sensors in order to maximize the system's working time have been in the spotlight. Inspired by the promising performance of ant colony optimization (ACO) in solving combinational optimization problem, we attempt to apply it in prolonging the life time of WSN. In this paper, we propose an improved version of ACO algorithm to get solutions about selecting exact sensors to accomplish the covering task in a reasonable way to preserve more energy to maintain longer active time. The methodology is based on maximizing the disjoint subsets of sensors, in other words, in every time interval, choosing which sensor to sustain active state must be rational in certain extent. With the aid of pheromone and heuristic information, a better solution can be constructed in which pheromone denotes the previous scheduling experience, while heuristic information reflects the desirable device assignment. Orderly sensor selection is designed to construct an advisable subset for coverage task. The proposed method has been successfully applied in solving limited energy assignment problem no matter in homogenous or heterogeneous WSNs. Simulation experiments have shown it has a good performance in addressing relevant issues.

show abstract

Section: Introductionmentioning

confidence: 99%

An Ant Colony Optimizing Algorithm Based on Scheduling Preference for Maximizing Working Time of WSN

Liu

Chen

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Similarly, research in [36] also proposes an ACO-based technique for developing an energy-efficient solution for WSNs lifetime maximization and packet loss minimization. In [37], the authors have incorporated fuzzy logic in ACO-based approach for developing a rule-base for route classification VOLUME 8, 2020…”

mentioning

confidence: 99%

Optimal Sink Node Placement in Large Scale Wireless Sensor Networks Based on Harris’ Hawk Optimization Algorithm

Saad²,

et al. 2020

View full text Add to dashboard Cite

Large-scale wireless sensor network (LSWSN) is composed of a huge number of sensor nodes that are distributed in some region of interest (ROI), to sense and measure the environmental conditions like pressure, temperature, pollution levels, humidity, wind, and so on. The objective is to collect data for real-time monitoring so that appropriate actions can be taken promptly. One of the sensor nodes used in an LSWSN is called the sink node, which is responsible for processing and analyzing the collected information. It works as a station between the network sensor nodes and the administrator. Also, it is responsible for controlling the whole network. Determining the sink node location in an LSWSN is a challenging task, as it is crucial to the network lifetime, for keeping the network activity to the most possible extent. In this paper, the Harris' hawks optimization (HHO) algorithm is employed to solve this problem and subsequently the Prim's shortest path algorithm is used to reconstruct the network by making minimum transmission paths from the sink node to the rest of the sensor nodes. The performance of HHO is compared with other well-known algorithms such as particle swarm optimization (PSO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA). The simulation results of different network sizes, with single and multiple sink nodes, show the superiority of the employed approach in terms of energy consumption and localization error, and ultimately prolonging the lifetime of the network in an efficacious way.INDEX TERMS Large-scale wireless sensor network, Harris' hawks optimization, topology control, sink node placement.

show abstract

Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challenges

Houssein,

Saad,

Djenouri

et al. 2024

Cluster Comput

View full text Add to dashboard Cite

Metaheuristic algorithms have wide applicability, particularly in wireless sensor networks (WSNs), due to their superior skill in solving and optimizing many issues in different domains. However, WSNs suffer from several issues, such as deployment, localization, sink node placement, energy efficiency, and clustering. Unfortunately, these issues negatively affect the already limited energy of the WSNs; therefore, the need to employ metaheuristic algorithms is inevitable to alleviate the harm imposed by these issues on the lifespan and performance of the network. Some associated issues regarding WSNs are modelled as single and multi-objective optimization issues. Single-objective issues have one optimal solution, and the other has multiple desirable solutions that compete, the so-called non-dominated solutions. Several optimization strategies based on metaheuristic algorithms are available to address various types of optimization concerns relating to WSN deployment, localization, sink node placement, energy efficiency, and clustering. This review reports and discusses the literature research on single and multi-objective metaheuristics and their evaluation criteria, WSN architectures and definitions, and applications of metaheuristics in WSN deployment, localization, sink node placement, energy efficiency, and clustering. It also proposes definitions for these terms and reports on some ongoing difficulties linked to these topics. Furthermore, this review outlines the open issues, challenge paths, and future trends that can be applied to metaheuristic algorithms (single and multi-objective) and WSN difficulties, as well as the significant efforts that are necessary to improve WSN efficiency.

show abstract

Automated design of fuzzy rule base using ant colony optimization for improving the performance in Wireless Sensor Networks

Cited by 4 publications

References 28 publications

An Ant Colony Optimizing Algorithm Based on Scheduling Preference for Maximizing Working Time of WSN

An Ant Colony Optimizing Algorithm Based on Scheduling Preference for Maximizing Working Time of WSN

Optimal Sink Node Placement in Large Scale Wireless Sensor Networks Based on Harris’ Hawk Optimization Algorithm

Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challenges

Contact Info

Product

Resources

About