• 제어로봇시스템학회논문지
• /
• 제12권11호
• /
• pp.1138-1143
• /
• 2006

Sensor deployment is an important issue in the mobile wireless sensor network. In this paper, we propose a deployment algorithm for mobile sensor network to spread out mobile sensor nodes widely as well as regularly. Since the proposed algorithm uses tree topology in deploying the sensor nodes, calculating power as well as spreading speed can be reduced compare to other deployment algorithms. The performance of the proposed algorithm is simulated using NS-2 simulator and demonstrated.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
• /
• pp.387-388
• /
• 2020

As the utilization of microservice architectural style in diverse applications are increasing, the microservice deployment cost became a concern for many companies. We propose an approach to reduce the deployment cost by generating an algorithm which minimizes the cost of basic operation of a physical machine and the cost of resources assigned to a physical machine. This algorithm will produce optimal resource allocation and deployment location based on genetic algorithm process.

• 정보처리학회논문지D
• /
• 제13D권3호
• /
• pp.391-398
• /
• 2006

본 논문에서는 최근 많은 연구가 진행되고 있는 무선 센서 네트워크 분야에서 이동 센서 노드의 배치 알고리즘인 DSSA(Distributed Self Spreading Algorithm)와 TBDA(Tree Based Deployment Algorithm)을 모델링 하고, 이들 알고리즘의 안정성과 정확성을 널리 사용되고 있는 모델 검증 도구인 SPIN을 이용하여 검증한다. 그리고 두 알고리즘이 무선 센서 네트워크의 중요사항인 에너지 소비면에서 효율적으로 동작하는지 SPIN 검증 도구를 이용하여 비교분석하고, DSSA에서 발생하는 진동에 대한 보완점을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권4호
• /
• pp.1568-1589
• /
• 2021

Deployment of access point (AP) is a problem that must be considered in network planning. However, this problem is usually a NP-hard problem which is difficult to directly reach optimal solution. Thus, improved AP deployment optimization scheme based on swarm intelligence algorithm is proposed to research on this problem. First, the scheme estimates the number of APs. Second, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the location and transmit power of APs. Finally, the greedy algorithm is used to remove the redundant APs. Comparing with multi-objective whale swarm optimization algorithm (MOWOA), particle swarm optimization (PSO) and grey wolf optimization (GWO), the proposed deployment scheme can reduce AP's transmit power and improves energy efficiency under different numbers of users. From the experimental results, the proposed deployment scheme can reduce transmit power about 2%-7% and increase energy efficiency about 2%-25%, comparing with MOWOA. In addition, the proposed deployment scheme can reduce transmit power at most 50% and increase energy efficiency at most 200%, comparing with PSO and GWO.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권3호
• /
• pp.990-1013
• /
• 2015

Deploying sensors into a target region is a key issue to be solved in building a wireless sensor network. Various deployment algorithms have been proposed by the researchers, and most of them are evaluated under the ideal conditions. Therefore, they cannot reflect the real environment encountered during the deployment. Moreover, it is almost impossible to evaluate an algorithm through practical deployment. Because the deployment of sensor networks require a lot of nodes, and some deployment areas are dangerous for human. This paper proposes a deployment approach to solve the problems mentioned above. Our approach relies on the satellite images and the Virtual Force Algorithm (VFA). It first extracts the topography and elevation information of the deployment area from the high resolution satellite images, and then deploys nodes on them with an improved VFA. The simulation results show that the coverage rate of our method is approximately 15% higher than that of the classical VFA in complex environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권5호
• /
• pp.1708-1734
• /
• 2022

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권11호
• /
• pp.3544-3564
• /
• 2022

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.

• 한국멀티미디어학회논문지
• /
• 제16권3호
• /
• pp.354-365
• /
• 2013

무선 센서 네트워크의 QoS (Quality of Service)에 큰 영향을 미치는 요소들 중 하나는 센서 노드의 배치이다. 본 연구는 감시정찰 센서 네트워크에서 센서 필드 설계시에 사용자의 의사결정을 효과적으로 지원하기 위하여 다양한 변수들을 고려한 배치 결과를 제공하고, 정량적인 분석을 실시하며, 또한 세밀한 센서 배치 시뮬레이션을 제공하는 시스템을 개발하는 것이다. 배치 형태를 영역 채우기, 경로 감시, 그리고 장벽 감시의 3가지 형태로 분류하여 격자 기반의 초기 배치 알고리즘을 제공하고, 배치 영역의 비정형성과 센서들의 감지 범위의 상이성을 고려하여 초기 배치를 수정하는 알고리즘을 제공한다. 배치된 노드들에 대해서 경로 커버리지와 장벽 커버리지를 분석하는 기능과 네트워크 시뮬레이션을 제공한다. 제안된 시뮬레이터는 감시 정찰용 센서네트워크 시스템의 개발 환경의 일부로 활용될 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권8호
• /
• pp.2606-2626
• /
• 2022

In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

• 한국지능시스템학회논문지
• /
• 제22권6호
• /
• pp.687-693
• /
• 2012

본 논문에서는 군집로봇의 효과적인 협조탐색을 위한 탐색영역에 대한 군집로봇의 최적배치을 제안한다. 먼저, 탐색영역에 대한 최적의 배치를 위해 보로노이 테셀레이션과 K-mean 알고리즘을 이용하여 탐색영역을 분할한다. 분할된 영역을 안전한 주행을 위해 전역경로계획과 지역경로계획을 한다. 전역경로계획은 A*알고리즘을 이용하여 전역경로계획을 하여 최적의 전역경로를 찾고, 지역경로계획은 포텐셜 필드방법을 이용하여 장애물 회피 통해 안전하게 목표점에 이르게 한다. 마지막으로 제안한 알고리즘은 시물레이션을 통해 그 응용가능성을 검토한다.