Earticle

The most important factor in the wireless sensor network is the use of effective energy and increase in lifetime of the individual nodes in order to operate the wireless network more efficiently. For this purpose, various routing protocols have been developed. The LEACH such a protocol, well known among typical cluster routing protocols. However, when a cluster head is selected, the energy consumption may not be equal because it does not take into account the energy of the nodes. In this paper, we seek to improve the cluster head selection method according to residual energy of each sensor node. This method then adaptively applies the LEACH algorithm and the cluster head section algorithm with consideration of node energy in accordance with the energy of the whole sensor field. Through the simulation, it was found that this proposed algorithm was effective.

Focusing on the problem that users would remove or delete the task at any time in cloud computing environment, a new scheduling algorithm for adapting to dynamic changes of user task is proposed. At first, the algorithm uses directed acyclic graph (DAG) to describe the association relation of cloud computing tasks. When a task needs to be removed, the algorithm utilizes a cost function to determine whether to cancel this task, and then updates the DAG according to its dependence on removed task. Finally, it uses a heuristic algorithm to perform scheduling. Experimental results show that this algorithm can better avoid scheduling execution of revoked tasks and improve the executive efficiency of cloud computing resource. Its performance is better than Min-Min and Max-Min in executive time span.

E-mail classification is an effective method to manage, improve process efficiency and filter junk mail. The extraction of E-mail characteristic is the key problem of exactness classification. In order to make the classification has a more distinct division characteristic words, IDF (Inverse document frequency) is used to epurate further the characteristic. The procedure which users deal with E-mail is a natural half-supervised learning. By using this process, proposed algorithm corrects classification results, adjust classification rule to adapt the individuation requirement of user automatically. The evaluation experiments indicate the availability of proposed algorithm.

In order to use Kriging, one has to estimate three parameters(nugget, sill and range) of semivariogram, which shows the relationship in the given two sites. A visual fit of the semivariogram parameters to a few standard models is widely used. But, it does not give the suitable results and not provide the automated process of Kriging. The gradient based nonlinear least squares is another choices to estimate three parameters, but it has some problems such as initial value problem. In this paper, we suggest the genetic algorithm as a compatible alternative method to solve the above mentioned problem. Finally, we estimate three parameters of semivariogram of rain-fall by adapting the genetic algorithm, compute Kriging estimate and conclude its effectiveness and compatibility.

AQM은 라우터에서 사용되고 있으며 큐의 순간적 길이나 평균길이를 관찰하여 혼잡을 예측하고 탐지하는 알고리즘이며, 평균 큐길이가 임계 값을 초과하면 혼잡이 발생하였다고 추론하고 라우터로 유입되는 패킷을 미리 폐기시키거나 패킷 전송 노드에게 혼잡을 미리 알림으로써 전송노드에서 전송률을 줄여 혼잡을 피하도록 한다. AQM 알고리즘의 하나인 RED (random early detection)는 패킷을 무작위로 폐기시켜 혼잡제어를 하는 큐 기반의 혼잡 제어기술이다. RED는 TCP의 시간경과 및 큐 지연을 줄이며 링크 활용도를 높이고 버스트한 트래픽이 유입되는 것을 관리하는 기술로써 널리 사용되어지고 있으며 혼잡도의 정도를 큐의 평균 길이로 추정한다. 그러나 파라미터($P_{max}$, $TH_{min}$)값들이 고정되어 있어서 특정한 네트워크 환경에서는 동작을 잘 못한다. 본 논문에서는 네트워크 상태를 지속적으로 감지함으로써 $P_{max}$와 $TH_{min}$의 값을 네트워크 상태에 적합한 값으로 자동적으로 바꾸어줌으로서 다양한 환경에서도 동작을 잘할 수 있는 확장 RED를 제안한다.

Active queue management (AQM) algorithms run on routers and detect incipient congestion by typically monitoring the instantaneous or average queue size. When the average queue size exceeds a certain threshold, AQM algorithms infer congestion on the link and notify the end systems to back off by proactively dropping some of the packets arriving at a router or marking the packets to reduce transmission rate at the sender. Among the existing AQM algorithms, random early detection (RED) is well known as the representative queue-based management scheme by randomizing packet dropping. To reduce the number of timeouts in TCP and queuing delay, maintain high link utilization, and remove bursty traffic biases, the RED considers an average queue size as a degree of congestions. However, RED do not well in the specified networks conditions due to the fixed parameters($P_{max}$ and $TH_{min}$) of RED. This paper addresses a extended RED to be adapted in various networks conditions. By sensing network state, $P_{max}$ and $TH_{min}$ can be automatically changed to proper value and then RED do well in various networks conditions.