Non-Preference Bi-Objective Compound Event Barrier Coverage Algorithm in 3-D Sensor Networks

Zhuang, Yaoming; Wu, Chengdong; Zhang, Yunzhou

doi:10.1109/access.2018.2849995

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Dorigo M. found that the famous traveling salesman problem (TSP) is quite similar to the ant foraging behavior. After efforts, the ACO algorithm was successfully used to solve the TSP problem [27,28]. It uses the characteristics of ant colony foraging, such as discrete optimization which can be solved by the EIACO algorithm.…”

Section: Eiaco-based Task Allocation For Maximizing Task Execution Efmentioning

confidence: 99%

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Zhou

2020

Journal of Sensors

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The research of agricultural wireless sensor networks (AWSNs) plays an important role in the field of facility agricultural technology. The temperature and humidity nodes in AWSNs are so tiny that they are limited on computation, network management, information collection, and storage size. Under this condition, task allocation plays a key role in improving the performance of AWSNs to reduce energy consumption and computational constraints. However, the optimization of task allocation is a nonlinearly constrained optimization problem whose complexity increases when constraints such as limited computing capabilities and power are undertaken. In this paper, an elite immune ant colony optimization (EIACO) is proposed to deal with the problem of task allocation optimization, which is motivated by immune theory and elite optimization theory. The EIACO uses ant colony optimization (ACO) to combine the clone operator and elite operator together for the optimization of task allocation. The performances of EIACO with different numbers of temperature and humidity sensor nodes and tasks have been compared by both genetic algorithm (GA) and simulated annealing (SA) algorithm. Simulation results show that the proposed EIACO has a better task execution efficiency and higher convergence speed than GA and SA. Furthermore, the convergence speed of EIACO is faster than GA and SA. Therefore, the whole system efficiency can be improved by the proposed algorithm.

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Section: Eiaco-based Task Allocation For Maximizing Task Execution Efmentioning

confidence: 99%

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Zhou

2020

Journal of Sensors

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“…Owing to the importance of WMSN, researchers have conducted extensive studies in this area [7,8]. Most of the existing studies have concentrated on localization [9], barrier coverage [3], data fusion [10], and sensor deployment [11]. Among these studies, the sensor network deployment problem guarantees the coverage of the monitoring area corresponding to the sensors, and provides the foundation of all the upper-layer applications.…”

Section: Introductionmentioning

confidence: 99%

A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks

Jia

Yang³

et al. 2019

Sensors

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Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system’s accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface.

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Non-Preference Bi-Objective Compound Event Barrier Coverage Algorithm in 3-D Sensor Networks

Cited by 2 publications

References 24 publications

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks

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