Earticle

수중무선센서망에서의 센서위치추정은 수중환경의 급격한 신호감쇠 특성으로 인해 기존 지상기반의 무선 주파수 방식이나 GPS를 효과적으로 사용할 수 없다는 구조적 난제를 안고 있다. 음향통신이 가장 현실적인 대안이지만, 수중환경은 심각한 에너지 제약과 더불어 센서배치에 따른 기하학적 특이성 문제를 야기한다. 본 논문은 이러한 문제를 해결하기 위해 ToA 기반의 음향 거리추정 방식을 활용한 UWSN 맞춤형 분산형 센서위치추정 알고리즘을 제안한다. 종종 기하학적 오차요인을 간과하는 기존 분산형 접근법들과 달리, 제안하는 기법은 GDoP 기반의 재선택 메커니즘을 명시적으로 도입하였다. 이는 기하학적 배치가 열악한 기준노드 집합을 능동적으로 배제함으로써 위치추정의 강인성을 보장한다. 제안된 알고리즘은 반복적인 절차를 통해 수행되며, 위치를 성공적으로 파악한 센서노드가 이후 인접 노드들의 새로운 기준노드 역할을 수행함으로써 위치 추정영역을 점진적으로 확장한다. 시뮬레이션 결과는 위치 추정성능이 통신반경과 노드밀도 간의 상호 보완적인 관계에 의해 결정됨을 보여준다. 또한 에너지 효율성 분석 결과, 제안된 분산형 방식은 단거리 음향 전송을 활용함으로써 기존 중앙집중식 방식 대비 전체 네트워크 에너지 소비를 약 30% 절감하는 것으로 나타났다. 결론적으로 본 연구는 자원이 제한된 수중환경에 효과적으로 적용 가능한 강인하고 확장성 높으며 비용 효율적인 센서위치추정 솔루션을 제시한다.

Localization in Underwater Wireless Sensor Networks(UWSNs) faces significant challenges due to signal attenuation affecting standard RF and GPS methods, alongside severe energy and geometric constraints in acoustic environments. To address these challenges, this paper proposes a distributed cooperative localization algorithm tailored for UWSNs using Time of Arrival(ToA) acoustic ranging. A key innovation is the explicit inclusion of a Geometric Dilution of Precision(GDoP)-based re-selection mechanism, which ensures robust positioning by mitigating geometric errors often ignored by traditional methods. Through an iterative process, localized nodes serve as new references, progressively expanding network coverage. Simulation results demonstrate that performance relies on balancing communication range and node density, while energy analysis reveals approximately a 30% reduction in consumption compared to centralized approaches through short-range transmission. Consequently, this study presents a robust, scalable, and cost-effective solution for resource-constrained underwater deployments.

Consider a large-scale distributed system in which each computing device is observing triggers from an external source. Distributed Trigger Counting (DTC) algorithm is used to detect the state of the system when the aggregated number of the observed triggers reaches a predefined value. In this paper, we propose a simple and efficient DTC algorithm: Cascading Thresholds (CT). We mathematically show that CT is an optimal DTC algorithm in terms of the total number of exchanged messages among the devices (message complexity). For the maximum number of received messages per device (MaxRcv), CT is sub-optimal. The average message complexity of CT is , and MaxRcv of it is , where is the number of triggers to be detected, is the number of devices, and is the degree of a node in the tree-like structure. Compared to the previous optimal algorithm (TreeFill), CT is much simpler: in our implementation the code size is about 2.5 times smaller. Also, unlike TreeFill CT does not require complicated mechanisms including distributed locking. Experimental results show that CT has a lower message complexity and MaxRcv compared to the previous work (CoinRand and RingRand). Furthermore, CT and TreeFill show a similar performance. From its simplicity, CT is more practical than previous work including TreeFill, CoinRand and RingRand.

현재의 ICT 인프라(인터넷과 서버/Client 연동)는 다양한 장치, 서비스, 비즈니스 및 기술 진화에 따른 신속한 대응 에 어려움을 겪고 있다. 클라우드 컴퓨팅(Cloud Computing)은 구름 같은 네트워크 환경에서 원하는 작업을 요청하여 실행한다는 데서 기원하였으며, 인터넷 기술을 활용하여 IT 자원을 서비스로 제공하는 컴퓨팅을 뜻하고 오늘날 IT 트 렌드의 하나로 가장 주목 받고 있다. 이러한 분산클라우드 환경에서는 네트워크 및 컴퓨팅 자원에 대한 통합 관리 체계 를 통하여 관리 비용 증가 문제를 원천적으로 해결하고 분산된 마이크로 데이터센터(Micro DC(Data Center))를 통하여 코어 네트워크 트래픽 폭증 문제를 해결하여 비용 절감 효과를 높일 수 있다. 그러나 기존의 Flooding 방식은 인접한 모든 DC들에게 전송하기 때문에 많은 트래픽을 유발 할 수 있다. 이를 위해 Restricted Path Flooding 알고리즘이 제안 되었으나 대규모 네트워크에서는 여전히 트래픽을 발생할 수 있는 단점이 있어서 본 논문에서는 홉수 제한을 통하여 이를 개선한 Lightweight Path Flooding 알고리즘을 제안하였다.

Current ICT(Information & Communication Technology) infrastructures (Internet and server/client communicatio n) are struggling for a wide variety of devices, services, and business and technology evolution. Cloud computing or iginated simply to request and execute the desired operation from the network of clouds. It means that an IT resour ce that provides a service using the Internet technology. It is getting the most attention in today's IT trends. In the distributed cloud environments, management costs for the network and computing resources are solved fundamentall y through the integrated management system. It can increase the cost savings to solve the traffic explosion problem of core network via a distributed Micro DC. However, traditional flooding methods may cause a lot of traffic due to transfer to all the neighbor DCs. Restricted Path Flooding algorithms have been proposed for this purpose. In large networks, there is still the disadvantage that may occur traffic. In this paper, we developed Lightweight Path Floodi ng algorithm to improve existing flooding algorithm using hop count restriction..

분산 스트림 처리 시스템을 위한 고가용성 알고리즘에는 Passive Standby, Active Standby, Upstream Backup 알고리즘 등이 있다. 기존의 고가용성 알고리즘은 복구 시 필요한 데이터의 백업을 위한 bandwidth overhead가 크며 노드의 연산결과를 다수의 downstream 노드들이 공유하며 데이터의 유입률이 폭발적으로 증가하는 경우에 출력 큐의 오버플로우로 인한 데이터의 손실 문제가 발생 할 수 있다. 본 논문은 이러한 문제들을 해결하기 위해 데이터 스트림의 유입량과 노드들의 연산처리율 모니터링을 통해 백업 방법을 유동적으로 변경시키는 적응적 Upstream Backup 알고리즘을 제안한다.

There have been High-Availability algorithms for Distributed Stream Processing System such as Passive Standby, Active Standby and Upstream Backup. Existing High Availability Algorithms have high bandwidth overhead when they backup data needed for restoring from the system failure and data can be lost by overflow of output queue in case of explosive increasing of input data rates. In this paper, we suggest adaptive Upstream Backup algorithm which changes fluidly the way to backup using monitor input rates of data stream and throughput of operation to solve those problems.

This paper proposes a method to realize sensor function allocation and effective data aggregation simultaneously in wireless sensor networks. This method realizes dynamic allocation of sensor functions so as to balance the distribution of each sensor function in a target monitoring area. In addition, effective data aggregation is performed by using a tree network topology and time division multiple access (TDMA), which is a collision-free communication scheme. By comparing the results from the proposed method with the results from non-optimized methods, it can be validated that the proposed method is 1.7 times more efficient than non-optimized methods in distributing sensor functions. With this method, the network lifetime is doubled, and the number of data packets received at a base station is considerably increased by avoiding packet collisions.

Aiming at the inherent deficiencies of large-scale wireless sensor network for distributed positioning algorithm, the DV-hop algorithm is mainly studied and improved, and two kinds of improved algorithms DV-hop and DV-hop+Lastdist DV- hop-MinMax are proposed. The DV- hop+Lastdist improves the positioning accuracy of the node by only adding a message variable, while the DV-hop-MinMax effectively reduces the costs of floating-point operations and power consumption, and can select the appropriate algorithm according to actual application environment. Finally, the paper gives the method to realize OMNeT++ in the simulation environment, and carries out the simulation analysis.

In view of the operating mode of wireless sensor networks, the node initial probability and channel listening, a distributed clustering algorithm DCOS based on the optimal search model is proposed, along with the corresponding mathematical optimization model. The goal of model optimization is to find a feasible allocation scheme for search resource so that the probability of successful communication reaches its maximum under the constrained condition for search resource. Clustering algorithm is designed to balance the network energy consumption, thereby maximizing network lifetime. In the clustering algorithm, the cluster head is generated in the comprehensive consideration of the minimum number of nodes in the overlay network, and the node residual energy and the number of neighboring nodes, during the operation of the network, setting the maximum wait time for cluster heads may avoid the their energy consumption. Analysis and simulation results show that, compared to several important clustering algorithms, the performance of DCOS is proven to be superior in balancing node energy consumption and prolonging the network lifetime.

Satellite network, especially low earth orbit (LEO) satellite network, which has advantages of global coverage and short round-trip time (RTT), has played an increasingly important role in the future generation of global communication network. Due to fast velocity, frequent handover and time-varying topology, designing effective routing algorithm for LEO satellite network has always been challenging. An Agent-based distributed Routing Algorithm with Traffic Prediction (TPARA) is proposed in this paper. TPARA algorithm, which fully takes traffic density of the surface of the Earth into account, consists of two parts: traffic prediction and routing decision. In the former, not only the heterogeneity of traffic on the ground is considered, but upcoming traffic from terrestrial terminals is predicted using an improved Kalman filtering as well. In the latter, mobile agents (MAs) are used to explore satellite network and collect routing information. The ultimate routing decision is determined by both current status and future status of satellite network. Simulation results show that, compared with the classical algorithm of ACO, TPARA not only has shorter delay, but alleviates congestion as well.

In recent years, distributed computing technology has been one of the cutting edge technologies for its low power and cost, which makes numerous IT organizations extend their hands in order to improve their financial ability. Because of these new features, grid computing, the original task scheduling mechanism, can’t work effectively in distributed computing environments, hence, we need a new task scheduling method to solve the problems. With considering the complex characters of the task in different distributed computing applications, firstly, we construct a more comprehensive task scheduling model, which has three sub objective functions. Secondly, we present an improved genetic algorithm to solve the multi- objective NP-hard problem. Finally, we implement some simulation experiments, and the evaluation results show us that the proposed model and improved GA are efficient and effective. The first part is the research status and related problems. The second part is the establishment of system architecture and task scheduling model. The last part is the experimental analysis and conclusion.

Clustering problem is one of the significant issues for wireless sensor networks concerned with energy consumption and large-scale deployment. Several energy-efficient clustering algorithms have been proposed to improve the energy utilization efficiency and prolong the network lifetime. In this paper, we propose a new clustering scheme after a comprehensive analysis on existing protocols. In our algorithm, named WPCA (Weighted Probabilistic Clustering Algorithm), every node independently decides whether to be a cluster head according to a weighted probability, which is related to the ratio between node’s residual energy and average remaining energy. The nodes with more residual energy are assigned larger weight value to further increase the chances to be elected as cluster heads. In addition, the rotation procedure of cluster heads in previous algorithms is totally abandoned. Simulation results show that WPCA achieves longer lifetime than previous probabilistic-based clustering algorithms and gets a very close approximation compared with a deterministic clustering method.

Localization of sensors nodes is a key and fundamental issue in wireless sensor networks due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade the problem scale in our approach and a moderate number of common nodes between clusters are kept during clustering. Inner cluster local coordinates are calculated and then mapped into global coordinates according to the tree structure formed by clustering. Simulations on MATLAB are conducted and the results show that the proposed algorithm has better localization coverage and higher accuracy than the traditional MDS based algorithms.

When the system is with DG (distributed generation), Power system structure has changed The structure has change to complicated new model with distributed generations from traditional open network. The voltage and power losses of traditional network will be influenced by the location connected with DG, reactive power, active power and the number of DG. The purpose to connect DG is to improve reliability of the system, reduce the loss of network and reduce the cost. In order to achieve this goal, this paper analyzes the indicator of power loss and takes it as objective function. Considering the superior properties of particle swarm optimization algorithm in solving discrete values problem, the algorithm is improved by the tabu search mechanism, we use the TSPSO (Tabu Search mechanism Particle Swarm Optimization) algorithm to study the problems which include the positions, capacity and numbers of DGs. At last, verify the validity of the method by simulation experiment.

With the rapid development of the 3G network, traditional calculation methods are unable to adapt to the data scene that telecom users' Network access behavior's data scale increase rapidly dozens of TB. The cloud techniques such as Hadoop platform are introduced to solve the data storage problem. The appropriate data mining algorithms are designed from the perspective of practical application. This paper improves the traditional decision tree SPRINT algorithms, proposes a parallel computing program and successfully applies to the Hadoop platform.

In this paper, we propose a new boosting algorithm for distributed databases. The main idea of the proposed method is to utilize the parallelism of the distributed databases to build an ensemble of classifiers. At each round of the algorithm, each site processes its own data locally, and calculates all needed information. A center site will collect information from all sites and build the global classifier, which is then a classifier in the ensemble. This global classifier is also used by each distributed site to compute required information for the next round. By epeating this process, an ensemble of classifiers, which is almost identical to the one built on the whole data, will be produced from the distributed databases. The experiments were erformed on 5 different datasets from the UCI repository [9]. The experimental results show that the accuracy of the proposed algorithm is almost equal to or higher than the accuracy when pplying boosting algorithm to the whole database.

In Quorum based cross datacenter distributed storage systems, data replication is a widely used technology. Such systems usually choose weak consistency, means they prone to select less number of replicas in read request in order to improve the performance, which lead to the possibility of getting stale data. In this paper, we propose an adaptive replica selection algorithm to make sure the system does not exceed specific stale read ratio while achieving good performance. Firstly, we choose a suitable distribution model from many candidates to estimate the time interval between current read request and nearest write request before it. Then we use Monte-Carlo method to simulate the processing order of read request and write request in different replicas. At last we use those estimated values to determine minimal number of replicas each read request needs to select in order to achieve a specific consistency level. We conduct a comprehensive experiments and the result shows our algorithm is effective.

This paper analyzes the present situation of the current network security problems and points out the research and development of intrusion detection system has very important significance on the basis of comparative analysis of the traditional static security model and PPDR dynamic security model, and according to this model, using ant colony algorithm is a distributed computing network intrusion of metrics, the determination of index contrast and invasion route, increase the accuracy of testing operation and calculation results show that the effectiveness of the solution and the convergence speed. For distributed network intrusion is put forward a new kind of means.

Support vector machine (SVM) is an important algorithm in data mining; it can transform the nonlinear classification problem into a linear classification problem by increasing the dimension of the data. The author points out the shortcomings of the traditional data analysis methods, and puts forward the method of complex simulation data analysis based on distributed SVM data mining algorithm. In the empirical part, through construct the evaluation index system of the school sports balanced development mode, the results show that the primary indicators of the sports balanced development are resource allocation(0.3774), school physical education process(0.2781), school physical education results(0.2450), and school sports social environment(0.1000).Overall, the balanced development of school physical education is a long and gradual process, sports evaluation index system also needs to be constantly updated and revised.

To meet the demand of digitizing progress of pharmaceutical retail industry, there are various kinds of software are made for pharmaceutical retail enterprise. However, much commercial software on the market fails to meet the demand of data transmission safety. Therefore, a novel system was designed for medium size pharmaceutical retail enterprise with multiple branches. It is designed to digitize the process of retail operations including management of stock, order client, staff, product, branch and warehouse. Advanced Encryption Standard (AES) algorithm was used to secure the data transmission on the Internet. The system can be deployed1 in pharmaceutical retailing, hospital prescriptions issues, pharmaceutical company management, medical supplies market, medical devices market and hospital staff management. With minor modification the system can be deployed to other retail or wholesale industry.

With the advent of the information age, the data volumes of various industries are in explosive growth, which increasingly highlights the importance of data. When a disaster occurs, how to recover the data completely and rapidly is one of the important issues receiving common concerns throughout the current industrial and academic circles. Based on the traditional disaster recovery system, this paper researches and implements a PRS algorithm-based distributed disaster recovery system[1, 2] called OsnDDR, which ensures the high availability of the system under the circumstance of continuous disasters or multi-node damage. The system adopts PRS algorithm based on RS erasure code [3] to reduce the storage resource consumption caused by the data redundancy and designs the load balancing strategy to guarantee the load balancing of each disaster recovery center in the system.