Regional Optimization Dynamic Algorithm for Node Placement in Wireless Sensor Networks

Zhang, Yijie; Liu, Mandan

doi:10.3390/s20154216

Cited by 6 publications

(9 citation statements)

References 24 publications

(21 reference statements)

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“…The goal is to ensure adequate coverage even in the event of outages. In the case of a fail, relocating nodes can provide an excellent solution to hold the uniformity of 2D coverage [ 40 ]. Energy supply is also an important aspect of border surveillance.…”

Section: Related Literaturementioning

confidence: 99%

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Kovács

Bolemányi

Botzheim

2022

Sensors

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This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.

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Section: Related Literaturementioning

confidence: 99%

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Kovács

Bolemányi

Botzheim

2022

Sensors

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“…Where R s is the perceptual radius of sensor s i . Based on equation ( 10), the coverage of the grid point T by all the sensors in the network can be determined according to the following equation [34]:…”

Section: Bagoa For Deployment Optimization 61 Coverage Model Descriptionmentioning

confidence: 99%

“…Where S is the set of sensors and sn is the total number of sensors. At last, the coverage rate of all the grid points is computed using equation ( 12) [34].…”

Section: Bagoa For Deployment Optimization 61 Coverage Model Descriptionmentioning

confidence: 99%

Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks

Deghbouch

Debbat

2021

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This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithmâ€™s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.

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“…Therefore, most of the methods proposed by researchers aim to perform additional computations causing in reduce the needed communication to decrease the overall wasted energy [8]. Whilst in another side some researchers proposed solutions to treat coverage holes caused by node death using mobile sensors [18], both sides targeting energy consumption management in WSNs and prolong the network lifetime as possible. The methods and techniques generally classified into sub approach as follows:…”

Section: The Approaches To Energy Consumption Managmentmentioning

confidence: 99%

Intelligent Routing to Enhance Energy Consumption in Wireless Sensor Network: A Survey

Razooqi

Al-Asfoor

2021

Mobile Computing and Sustainable Informatics

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Nowadays, the network and the Internet applications have gained a substantial importance in the digital world, due to the great impact which it provides for health and community services. Among the most important services that have been provided are smart devices and vital factor measurement devices for patients, whether in a hospital or outside the hospital. Furthermore, sensors that collect medical data or measurements of temperature and humidity, in various critical environments. The proper types of network that may be used in such difficult environment are Wireless Sensor Networks, that used to sense and process data. Additionally, the Wireless Sensor Networks have been used in the environment of Internet of Things and smart cities in the general services and health fields. All these reasons have made researchers focus on Wireless Sensor Networks and addressing the challenges that face them. The most important challenge facing this type of network is energy consumption and increase battery life. This paper discusses the methodologies used in energy conservation in Wireless Sensor Networks, such as data reduction technology, shortest path selection and artificial intelligence algorithms used in smart routing and energy saving. Besides, we have introduced comparisons between the standard algorithms which are suggested by the researchers, to make a clear picture of the energy consumption problem and propose some effective solutions in Wireless Sensor Networks field.

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Regional Optimization Dynamic Algorithm for Node Placement in Wireless Sensor Networks

Cited by 6 publications

References 24 publications

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks

Intelligent Routing to Enhance Energy Consumption in Wireless Sensor Network: A Survey

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