Feasibility of P2P-STB based crowdsourcing to speed-up photo classification for natural disasters

Loor, Fernando; Manriquez, Manuel; Gil-Costa, Verónica

doi:10.1007/s10586-021-03381-6

Cited by 7 publications

(5 citation statements)

References 42 publications

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“…There are applications for emergency management designed for P2P networks [4,32]. However, managing a P2P network requires implementing a distributed hash table (DHT) and communication protocols such as Pastry or Chord.…”

Section: Related Workmentioning

confidence: 99%

“…They are typically coordinated by social, public, and national organizations responsible for sending the appropriate resources to the most affected areas and also for reporting the news. Emergency response teams need to work fast, and the decision makers must base their decision on the collected data from different sources as soon as possible [4].…”

Section: Introductionmentioning

confidence: 99%

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Bot-Based Emergency Software Applications for Natural Disaster Situations

2022

Self Cite

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Upon a serious emergency situation such as a natural disaster, people quickly try to call their friends and family with the software they use every day. On the other hand, people also tend to participate as a volunteer for rescue purposes. It is unlikely and impractical for these people to download and learn to use an application specially designed for aid processes. In this work, we investigate the feasibility of including bots, which provide a mechanism to get inside the software that people use daily, to develop emergency software applications designed to be used by victims and volunteers during stressful situations. In such situations, it is necessary to achieve efficiency, scalability, fault tolerance, elasticity, and mobility between data centers. We evaluate three bot-based applications. The first one, named Jayma, sends information about affected people during the natural disaster to a network of contacts. The second bot-based application, Ayni, manages and assigns tasks to volunteers. The third bot-based application named Rimay registers volunteers and manages campaigns and emergency tasks. The applications are built using common practice for distributed software architecture design. Most of the components forming the architecture are from existing public domain software, and some components are even consumed as an external service as in the case of Telegram. Moreover, the applications are executed on commodity hardware usually available from universities. We evaluate the applications to detect critical tasks, bottlenecks, and the most critical resource. Results show that Ayni and Rimay tend to saturate the CPU faster than other resources. Meanwhile, the RAM memory tends to reach the highest utilization level in the Jayma application.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bot-Based Emergency Software Applications for Natural Disaster Situations

2022

Self Cite

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“…As a result, each node knows which photo needs to be sent to the other node and which picture needs to be received. In [ 20 ], the authors proposed a distributed framework for processing images captured by crowds within the target area following a natural disaster. The goal of this work is to reduce the overhead of processing photos on the server side.…”

Section: Related Workmentioning

confidence: 99%

“…However, we could not compare our work with any of them for different reasons. For instance, in [ 20 ], the architecture of the used platform is composed of a server and a set of volunteers connected through digital television, which is implemented with a set top box connected to the television. Our work, on the other hand, focuses on resource-constrained environments.…”

Section: Performance Evaluationmentioning

confidence: 99%

Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments

Mowafi

Awad

Al-Quran

2022

Sensors

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Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers’ interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed.

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“…erefore, this paper conducts in-depth research around BSA. After a comprehensive analysis of the optimization mechanism of the natural optimization algorithm and its advantages and disadvantages, two improved backtracking search optimization natural optimization algorithms based on nature inspiration are proposed, which improve the performance of the natural optimization algorithm [20][21][22][23][24]. e research work in this paper not only improves the optimization performance of BSA, but also provides a new direction for the improved design of other metaheuristic natural optimization algorithms and also has important significance for the intelligent optimization design of metaheuristic natural optimization algorithms in the engineering field.…”

Section: Related Workmentioning

confidence: 99%

Construction of Legal Reporting Information Platform Based on Natural Optimization Algorithm

Feng

Zhou

2022

Scientific Programming

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Natural optimization algorithms have attracted much attention from researchers because they can simulate or explain certain prediction processes. The traditional method of predicting the factor value of legal reporting information based on causal window has shortcomings caused by individual weak classifiers, so the prediction adaptability is poor. Aiming at the construction of the early warning model of legal reporting information, this paper proposes a semi-integrated natural optimization algorithm. The natural optimization algorithm uses the variance of the supporting area factor to characterize the smoothness of the factor neighborhood and uses the optimal threshold parameter for factor classification. It solves the capacity-distortion problem of the hidden algorithm of traditional legal reporting information. The experimental results show that the natural optimization algorithm has better performance. The classification error rate in the question is reduced to 0.137, which effectively promotes the practicability of classification prediction of legal reporting information.

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Feasibility of P2P-STB based crowdsourcing to speed-up photo classification for natural disasters

Cited by 7 publications

References 42 publications

Bot-Based Emergency Software Applications for Natural Disaster Situations

Bot-Based Emergency Software Applications for Natural Disaster Situations

Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments

Construction of Legal Reporting Information Platform Based on Natural Optimization Algorithm

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