Linear Decision Fusion under the Control of Constrained PSO for WSNs

Jiang, Shuyun; Zhao, Zhiwen; Mou, Sheng; Wu, Zushun; Luo, Yi

doi:10.1155/2012/871596

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…The fusion center then makes an ultimate decision based on specific local decisions as well as the fusion rule, therefore a successful fusion rule decision is critical. A linear decision fusion model for WSNs is suggested in [52]. To set the parameters of the linear decision fusion model, the constrained PSO method is used, and the typical penalty function is used to tackle the constrained PSO issue.…”

Section: Ai Based Solutions To Data Collection Aggregation and Dissem...mentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

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Section: Ai Based Solutions To Data Collection Aggregation and Dissem...mentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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“…The centralized nature of PSO enables its application in the minimization of the coverage holes for near-optimal coverage in WSNs [78,183,184,185,186,187,188,189,190]. Data aggregation is a repetitive process, which makes it suitable for PSO [191,192,193,194,195]. PSO is suitable for selecting CH's with high energy in each round [196,197,198,199,200].…”

Section: Applications Of Pso In Wsnsmentioning

confidence: 99%

“…Guo et al [194] proposed a multi-source temporal data aggregation algorithm (MSTDA) for data aggregation in WSNs. Subsequently, Jiang et al [195] have used the constrained PSO with the penalty function concept, which increases the accuracy.…”

Section: For Data Aggregation Using Psomentioning

confidence: 99%

Nature-Inspired Algorithms for Wireless Sensor Networks: A Comprehensive Survey

Singh,

Sharma,

Singh

2021

Preprint

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In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery-driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGA-BACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area.

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“…Jiang et al in [ 70 ] designed a linear decision fusion rule and proposed a way of controlling the parameters of the model taking the advantage of the constrained PSO. It is obvious that the decision-making capability of each node is different due to the different signal noise ratios and some other factors, so a specific sensor's contribution to the global decision should be constrained by this sensor's decision-making capability.…”

Section: Survey Of Existing Workmentioning

confidence: 99%

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Adnan

Razzaque

Ahmed

et al. 2013

Sensors

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For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

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Linear Decision Fusion under the Control of Constrained PSO for WSNs

Cited by 10 publications

References 21 publications

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

Nature-Inspired Algorithms for Wireless Sensor Networks: A Comprehensive Survey

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

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