• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
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• pp.17-24
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• 2006

We consider the sensor location problem (SLP) on a given sensor field. We present the sensor field as grid of points. There are several types of sensors which have different detection ranges and costs. If a sensor is placed in some point, the points inside of its detection range can be covered. The coverage ratio decreases with distance. The problem we consider in this thesis is called multiple-type differential coverage sensor location problem (MDSLP). MDSLP is more realistic than SLP. The coverage quantities of points are different with their distance form sensor location in MDSLP. The objective of MDSLP is to minimize total sensor costs while covering every sensor field. This problem is known as NP-hard. We propose a new integer programming formulation of the problem. In comparison with the previous models, the new model has a smaller number of constraints and variables. This problem has symmetric structure in its solutions. This group is used for pruning in the branch-and-bound tree. We solved this problem by branch-and-cut(B&C) approach. We tested our algorithm on about 60 instances with varying sizes.

• 대한지리학회지
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• 제43권2호
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• pp.220-234
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• 2008

안전과 보안이 현대사회의 중요한 관심사로 등장하고 있고 그 대상 영역이 실내 공간으로 넘어서 도시로 확대되고 있다. 도심지역에 수 많은 감시 센서들이 설치 운영되고 있다. 많은 보안 모니터링 맥락에서 감시 센서/네트워크의 수행능력 혹은 효율성은 조명의 변화와 같은 환경 조건에 제약을 받는다. 서로 보완적인 상이한 유형의 센서를 설치함으로써 개별 유형의 센서의 고장 혹은 한계를 극복할 수 있다는 것은 익히 잘 알려진 사실이다. 입지 분석과 모델링의 관점에서 관심사는 어떻게 보완적인 상이한 유형의 센서들의 적절한 입지를 결정하여 보안기능을 강화할 수 있느냐 이다. 이 연구는 k 개의 상이한 유형의 감시 센서의 위치를 결정하는 커버리지 기반의 최적화 모델을 제시한다. 이 모델은 상이한 유형의 센서 사이의 공통 커버리지와 동일 유형의 센서 사이의 중복 커버지리를 동시에 고려한다. 개발된 모델은 도심 지역에 센서를 위치시키는데 적용된다. 연구 결과는 공통 및 중복 커버리지가 동시에 모델링 될 수 있으며, 두 유형의 커버지리 사이의 tradeoff를 보여주는 많은 해들이 있음을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권4호
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• pp.344-365
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• 2009

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.