• Title/Summary/Keyword: mobile crowd sensor system

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A management scheme of crowd group for the critical region (위험 지역 탐색을 위한 군집 그룹 관리 방안)

  • Park, Sangjoon;Lee, Jongchan
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.539-540
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    • 2021
  • In this paper, we consider the management scheme of the crowd group in the sensor networks. In the case of the networks searching the critical area, the operation of crowd group can affect the mission implement. The mobile sink system leading the group networks can change the network configuration as the dangerous data gathered from group sensor nodes. The dynamic network management provides the important role to the mission of mobile sink to react the dangerous environments.

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A study of path alteration in mobile crowd sensor systems (이동형 군집형 센서 시스템에서 경로 변경 연구)

  • Park, Sangjoon;Lee, Jongchan
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2020.07a
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    • pp.563-564
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    • 2020
  • 본 논문에서는 이동형 군집형 센서 시스템에서 상황에 따른 경로 변경에 대해 고려한다. 이동형 군집형 센서 시템은 임무 수행을 위해 무선으로 상호 네트워크 연결을 유지한 채로 그룹 이동을 수행한다. 이러한 그룹 이동의 경우 경로 변경의 상황에서 싱크 시스템의 명령에 따라 경로 변경을 수행한다. 그러므로 본 논문에서는 이동 센서 시스템들의 경로 변경을 발생하는 환경에 대해 고려한다.

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A study of area assignment to the crowd sensor system (군집형 센서 시스템의 영역 지정의 연구)

  • Park, Sangjoon;Lee, Jongchan
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2020.01a
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    • pp.249-250
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    • 2020
  • 본 논문에서는 무선 이동 환경에서 군집형 센서 시스템의 영역 지정 방식에 대해 고려한다. 이동 기능을 보유한 다수의 군집형 센서 시스템들이 임무를 수행할 경우 이동 경로 상에서 센서 시스템들의 이동 영역을 설정한다. 이동 영역의 설정을 위해 센서 세스템들의 영역 지정을 위한 네트워크를 형성한다. 형성된 네트워크 안에서 센서 시스템들은 이동 지점을 통해 경로를 따라 이동을 수행한다.

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A hybrid tabu search algorithm for Task Allocation in Mobile Crowd-sensing

  • Akter, Shathee;Yoon, Seokhoon
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.4
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    • pp.102-108
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    • 2020
  • One of the key features of a mobile crowd-sensing (MCS) system is task allocation, which aims to recruit workers efficiently to carry out the tasks. Due to various constraints of the tasks (such as specific sensor requirement and a probabilistic guarantee of task completion) and workers heterogeneity, the task allocation become challenging. This assignment problem becomes more intractable because of the deadline of the tasks and a lot of possible task completion order or moving path of workers since a worker may perform multiple tasks and need to physically visit the tasks venues to complete the tasks. Therefore, in this paper, a hybrid search algorithm for task allocation called HST is proposed to address the problem, which employ a traveling salesman problem heuristic to find the task completion order. HST is developed based on the tabu search algorithm and exploits the premature convergence avoiding concepts from the genetic algorithm and simulated annealing. The experimental results verify that our proposed scheme outperforms the existing methods while satisfying given constraints.

Multi-Object Tracking using the Color-Based Particle Filter in ISpace with Distributed Sensor Network

  • Jin, Tae-Seok;Hashimoto, Hideki
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.1
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    • pp.46-51
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    • 2005
  • Intelligent Space(ISpace) is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.