• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권4호
• /
• pp.1773-1795
• /
• 2020

Unmanned aerial vehicles (UAVs) network are a very vibrant research area nowadays. They have many military and civil applications. Limited bandwidth, the high mobility and secure communication of micro UAVs represent their three main problems. In this paper, we try to address these problems by means of secure clustering, and a security clustering algorithm based on integrated trust value for UAVs network is proposed. First, an improved the k-means++ algorithm is presented to determine the optimal number of clusters by the network bandwidth parameter, which ensures the optimal use of network bandwidth. Second, we considered variables representing the link expiration time to improve node clustering, and used the integrated trust value to rapidly detect malicious nodes and establish a head list. Node clustering reduce impact of high mobility and head list enhance the security of clustering algorithm. Finally, combined the remaining energy ratio, relative mobility, and the relative degrees of the nodes to select the best cluster head. The results of a simulation showed that the proposed clustering algorithm incurred a smaller computational load and higher network security.

• 한국정보통신학회논문지
• /
• 제16권1호
• /
• pp.198-204
• /
• 2012

무선센서 네트워크의 계층구조형 클러스터링 알고리즘은 센서노드의 효율적인 관리를 위해서 다양한 분야에 사용하고 있다. 계층형 클러스터링 구조에 많이 사용되는 LEACH(Low Energy Adaptive Clustering Hierarchy)는 확률 함수식을 사용하기 때문에 클러스터 헤드노드의 선출 개수가 일정하지 않다. 본 논문에서는 LEACH 알고리즘의 단점을 보완하여 매 라운드마다 고정된 개수의 클러스터 헤드노드를 선출하는 i-LEACH 알고리즘을 제안한다. i-LEACH(improved-LEACH)는 BS이 고정된 개수의 클러스터 헤드노드를 선출하여 네트워크 전체에 통보하기 때문에 클러스터링 구성과정의 네트워크 트래픽량을 줄일 수 있고, 네트워크의 에너지를 효율적으로 관리할 수 있다. 제안한 i-LEACH와 LEACH를 시뮬레이션 한 결과 i-LEACH에서는 클러스터 헤드노드의 선출과정이 제외되었기때문에 LEACH 보다 소비된 전력량은 25%, 네트워크 트래픽 량은 16% 향상되었다.

• ETRI Journal
• /
• 제34권6호
• /
• pp.922-931
• /
• 2012

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy-efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near-optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near-optimal energy-efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy-efficient routing technique produces a longer network lifetime and achieves better energy efficiency.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
• /
• pp.465-468
• /
• 2008

무선 센서 네트워크의 클러스터링(Clustering) 기법은 센서 노드의 에너지 소모를 최소화하기 위한 목적으로 개발되어 Network Lifetime을 증대시키는 효과를 보인다. 기존의 클러스터링 기법들은 센서 노드들이 CH(Cluster Head) 역할을 교대로 수행함으로써 각 노드의 에너지 소모를 균등하도록 하여 Network Lifetime을 향상시키는 방법을 제안하였지만, 싱크(Sink) 노드와 인접한 노드들의 에너지 소모를 최소화하는 방안은 제시하지 못했다. 본 논문에서는 싱크 노드의 POS(Personal Operating Space)내에 존재하는 인접 노드의 일부를 클러스터의 멤버(Member) 노드로 가입시키지 않고, 직접싱크 노드와 통신하게 함으로써 싱크 노드와 인접한 CH의 에너지 소모를 줄여 Network Lifetime을 연장하는 클러스터링 알고리즘을 제안하였다.

• 한국정보통신학회논문지
• /
• 제12권6호
• /
• pp.1107-1112
• /
• 2008

무선 센서 네트워크의 클러스터링(Clustering)기법은 센서 노드의 에너지 소모를 최소화하기 위한 목적으로 개발되어 Network Lifetime을 증대시키는 효과를 보인다. 기존의 클러스터링 기법들은 센서 노드들이 CH(Cluster Head) 역할을 교대로 수행함으로써 각 노드의 에너지 소모를 균등하도록 하여 Network Lifetime을 향상시키는 방법을 제안하였지만, 싱크(Sink) 노드와 인접한 노드들의 에너지 소모를 최소화하는 방안은 제시하지 못했다. 본 논문에서는 싱크 노드의 POS(Personal Operating Space)내에 존재하는 인접 노드들의 일부를 클러스터의 멤버(Member) 노드로 가입시키지 않고, 직접 싱크 노드와 통신하게 함으로써 싱크 노드와 인접한 CH의 에너지 소모를 줄여 Network Lifetime을 연장하는 클러스터링 알고리즘을 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권3호
• /
• pp.1213-1237
• /
• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• Smart Structures and Systems
• /
• 제8권1호
• /
• pp.69-92
• /
• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2017년도 춘계학술대회
• /
• pp.649-651
• /
• 2017

클러스터링 문제는 무선 애드 혹 네트워크의 네트워크 수명과 확장성을 향상시키는 문제 중 하나이다. 이 문제는 무선 애드 혹 네트워크의 설계 및 운영과 관련된 어려운 조합 최적화 문제이다. 본 논문에서는 네트워크 수명을 최대화하고 무선 애드 혹 네트워크의 확장성을 고려한 효율적인 클러스터링 알고리즘을 제안한다. 클러스터링 문제는 NP-hard 문제로 알려져 있습니다. 따라서 본 논문에서는 노드의 수가 많은 네트워크에서 합리적인 시간 내에 최적의 해를 효율적으로 얻을 수 있는 최적화 방식을 사용하여 문제를 해결한다. 제안된 알고리즘은 노드의 전력과 클러스터링 비용을 고려하여 클러스터 헤드를 선택하고 클러스터를 구성한다. 우리는 노드의 전송에너지 측면에서 시뮬레이션을 통해 성능을 평가한다. 시뮬레이션 결과는 제안된 알고리즘이 기존의 알고리즘보다 성능이 우수함을 보여 준다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권4호
• /
• pp.1437-1459
• /
• 2020

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
• /
• pp.134-138
• /
• 1998

Unification of Kohone SOM(Self-Organizing Maps) neural network with the branch-and-bound algorithm is presented for clustering large set of patterns. The branch-and-bound search technique is employed for designing coarse neural network learning paradaim. Those unification can be use for clustering or calssfication of large patterns. For classfication purposes further usefulness is possible, since only two clusters exists in the SOM neural network of each nodes. The result of experiments show the fast learning time, the fast recognition time and the compactness of clustering.