An optimization framework for monitoring multi-channel multi-radio wireless mesh networks

Shin, Donghoon; Bagchi, Saurabh

doi:10.1016/j.adhoc.2012.10.004

Cited by 19 publications

(8 citation statements)

References 20 publications

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“…The normalized energy threshold of Th is set to 0.2 and 0.5, respectively. Compared to the DRA algorithm in [13] and the budget algorithm in [14], our proposed PSO-CTA algorithm achieves higher crowdsensing effect function. As the number of mobile users increases, the crowdsensing effect function increases.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [10], authors propose a game-theoretic distributed power control mechanism based on channel sensing results of users in cognitive wireless sensor network. To maximize the sensing quality, authors study the problem of multichannel sensing assignment in the multichannel system [11][12][13]. These literatures use a simplistic objective function and there is no budget constraint.…”

Section: Related Workmentioning

confidence: 99%

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Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Zhai

Wang²

2017

Wireless Communications and Mobile Computing

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Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Zhai

Wang²

2017

Wireless Communications and Mobile Computing

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“…During the research, Mikrotik DynaDish 5 is a cheap 5 GHz device designed for outdoor use and equipped with a Gigabit Ethernet port to take full advantage of 802.11ac standard power. Theoretically, using a 5 GHz device with 802.11ac standard provides the fastest data transfer rate, at speeds of about 800 Mbps (Shin and Bagchi, 2013).…”

Section: Figure 12 Vulnerability Mapmentioning

confidence: 99%

Public Participation in Gis via Mobile Applications for Crisis Management Process: A Case Study of an Earthquake, Tehran, Iran

Farhadpour

Hosseinali

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The effects of earthquakes on societies are greater than what most people think. These effects range from structural damages to economic impacts and fatalities. An earthquake only lasts for a few seconds and the aftershocks may continue for days, but the damage does continue for years. The lack of integrated and automated tools for estimating resources needed in the aftermath of an earthquake during the disaster response phase motivate the design and implementation of a comprehensive, interactive, and user-friendly model. The objective of this research is to use geospatial analyses for examining vulnerable areas and evaluating the hazard of building damage by earthquake. Hence, the effective parameters of damage are identified and by using the fuzzy inference, the degree of damage in the study area is determined. Subsequently, with the provision of internet service in a troubled region, it is possible to collect voluntary information from people in the region using their mobile smartphones. For this purpose, an app in the Android Studio environment has been developed for convenience and sending the exact location of the affected sites. Moreover, the result of this research identified areas with a high priority for relief and optimal use of time, especially in the early hours of the crisis.

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“…In [10] it is proposed the theoretic-game mechanism of the power distribution based on the results of measuring the channel of users in the cognitive radio network. To improve the analysis quality, the authors consider the multichannel recognition problem in the multichannel system [11][12][13]. These systems use the simplified objective function and absent BC (BC -budget constraint).…”

Section: Introductionmentioning

confidence: 99%

Mobile Users’ Multiple Detection Method on the Basis of the Particle Swarm Optimization in the Cognitive Radio Network

Obikhod¹

2018

A.I.S.

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In the article, the object is mobile users' multiple detection processes based on the particle swarm optimization in the cognitive radio network. The aim of the research lies in the field of the algorithms of the mobile users' detection of the cognitive radio system. In addition, it lies in the area of development of the generalized algorithm PSO-NN and improvement the method of multiple detection by using the particle swarm method and convolutional neural network and its realization. The tasks are to develop the multiple detection architecture, the generalized algorithm PSO-NN, to realize the multiple detection algorithm and to model PSO-NN as the algorithm of the multiple detection effectiveness for 50 mobile devices. The methods used are mathematical models based on the principles of the organization and operations of biological neural networks, mathematical learn models, and NP-hard algorithm theory methods. The following results were received. The multiple detection architecture was developed, which differs from the known ones since each location is divided into the subzones. In the different subzones, a mobile user can receive different measuring results in the same channel. Such a division can be used for more flexible data using. Developed generalized algorithm PSO-NN differs from the known ones as it is configured more correctly for the real conditions that inherent in the architecture of the cognitive systems. This algorithm uses particle swarm optimization controlled by the convolutional neural network. Therefore, strict access to spectrum analysis based on mobile users' energy component is provided. Due to the use of the micro particle architecture and convolutional neural networks, detection effectiveness function and global particle location are detected in a more accurate way. Further implementation of the multiple detection algorithm differs from the known ones since after uniting the detected channel users' matrices, only one user is assigned to a specific channel. Such an implementation assumes more realistic search area and speed of the users' detection with found channels. Modeling PSO-NN as the multiple detection effectiveness algorithm for 50 mobile users has several convolutional layers that were generalised with each other. Such architecture can be a confirmation of the fact that the neural network chosen in a practical way completely satisfies the tasks. The modeling result showed that at 20 locations the detection effectiveness with using the algorithm PSO-NN increased by 10% in 20 locations, by 20% in 25 locations, by 20% in 30 locations, by 20% in 35 locations, while the results did not change in 40 locations. K e ywor d s : cognitive radio; particle swarm algorithm; convolutional neural network; radio frequency resource; NP-hard class problem.

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An optimization framework for monitoring multi-channel multi-radio wireless mesh networks

Cited by 19 publications

References 20 publications

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Public Participation in Gis via Mobile Applications for Crisis Management Process: A Case Study of an Earthquake, Tehran, Iran

Mobile Users’ Multiple Detection Method on the Basis of the Particle Swarm Optimization in the Cognitive Radio Network

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