Nodes Deployment Method across Specific Zone of NB-IoT Based Heterogeneous Wireless Sensor Networks

Fan, Zhaoyong

doi:10.1109/iccsn49894.2020.9139062

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…It can be known that PSO algorithm faces the multimodal problem in most application-oriented sensors deployment scenarios. Therefore, the optimized deployment algorithm must track and observe the value of each peak in the actual application, as each peak may be the optimal peak that we need [6]. In general, the particle swarm optimization (PSO) algorithm initializes the solution set of the multi-objective problem that needs to be optimized as a group of random particles.…”

Section: Pso Algorithmmentioning

confidence: 99%

“…The deployment problem is formulated as a MOPT problem two or more conflicting objectives in many researches, the aim of the optimization process is to obtain reasonable trade-offs between the two objectives [5]. However, while sensors are used in greater numbers for residential and supermarket environments, the optimal deployment strategies become increasingly significiant [6]. While the coverage quality and network connectivity have been optimized in many literature, the most scenes of deployment only considerre the terrestrial two-dimensional networks.…”

Section: Introductionmentioning

confidence: 99%

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An optimized deployment strategy of smart smoke sensors in a large space

2022

KSII TIIS

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With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

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Section: Pso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An optimized deployment strategy of smart smoke sensors in a large space

2022

KSII TIIS

View full text Add to dashboard Cite

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“…The results showed that the proposed scheme has more efficient coverage and connectivity than other similar algorithms. In [ 40 ], Zhaoyong proposed a node deployment scheme for heterogeneous wireless sensor networks. The deployment model was designed based on the prediction of the possible consumption of traffic and energy.…”

Section: Background and Related Workmentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

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A Multi-objective optimized node deployment algorithm for Wireless Sensor Networks Based on the Improved ABC

Liu

Junli

Huang

2021

J. Phys.: Conf. Ser.

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In the Internet of Things(IoT) for smart fire applications, sensors are deployed at different places and collect multiple types of data during their work time. Since the coverage of a sensor is limited, how to deploy sensors is important to reduce the number of sensors and improve the quality of wireless sensor networks(WSNs) service. The service quality and network lifetime of wireless sensor networks also are dramatically varied across different coverage and node deployment strategies. There are two crucial problems in WSNs, i.e. node deployment with lowest cost, and coverage optimization with maximum coverage rate, need to be solved. In this research, we focus on the problem of coverage optimization and node deployment. Based on the binary coverage probability model, we develop the joint binary coverage probability model and Improved Artificial Bee Colony(IABC) algorithm. The result of simulation shows that our proposed deployment algorithm have a greater coverage ratio in the case of fewer iterations when the volume of deployed sensors is the same compared with the standard ABC algorithm.

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Nodes Deployment Method across Specific Zone of NB-IoT Based Heterogeneous Wireless Sensor Networks

Cited by 4 publications

References 9 publications

An optimized deployment strategy of smart smoke sensors in a large space

An optimized deployment strategy of smart smoke sensors in a large space

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

A Multi-objective optimized node deployment algorithm for Wireless Sensor Networks Based on the Improved ABC

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