A Grooming Nodes Optimal Allocation Method for Multicast in WDM Networks

Grooming of multicast sessions in metropolitan WDM ring networks

통상적으로 표준적인 최적배분은 층별 조사비용을 고려하여 표본을 배분한다. 만일 조사단위당 비용이 서로 다를 경우 보다 현실적인 배분방법을 고려할 필요가 있다. 즉, 개별 조사단위의 특성에 따라 이익비용비를 최대로 하는 단위를 먼저 표본으로 고려하는 동적배분을 고려하였다. 이러한 관점에서 층별 표본수를 배분하고, 배분된 표본규모에 따라 임의로 표본을 선정하는 방식인 표준적인 최적배분과는 차이가 있다. 이 논문은 약물오용, 낙태, 알콜중독 등과 같은 민감한 특성을 조사하는 층화확률화 응답기법에 대해 각 층별로 표본을 배분할 경우 최적 동적배분을 고려하여 보다 현실적인 문제를 해결하고자 하였으며, 수치적 예제를 통해 동적배분 방법의 효과성을 증명하였다. Typically the standard optimal allocation method distributes the sample for each stratum considering survey cost. In case of varying survey cost for each survey unit, we need to consider more practical allocation method. In other words, according to characteristics of an individual unit, we consider the optimal dynamic allocation method which first selects the survey unit having maximum value of benefit cost ratio. In terms of this, the proposed allocation method is different from standard optimal allocation method which allocate samples for each stratum and selects the random sample according to each size of sample. This paper is considered the dynamic optimal allocation method for the stratified randomized response technique which surveys for sensitive characteristic of survey units such as drug abuse, abortion, alcoholic. We prove the practical usefulness of proposed method using the numerical example.

To solve the question of guaranteeing the safety of a system with a complex structure and tight couplings, a new method of optimal allocation exploiting the dynamical planning theory is presented. Two key factors that can affect system safety, i.e., accident loss and accident rate are analyzed. Then, to measure comprehensively requirements of system safety, accident safety level is defined based on these two factors. Subsequently, optimal allocation for complex systems’ safety resource is implemented according to the network properties of the inherent structures of the systems and constraint conditions for safe investments. Finally, a case study shows that the method using dynamical planning for optimal allocation of system safety can ensure preferential allocations of the parts with high safety requirements. Besides, this optimal allocation method is feasible and valid.

Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices

Queuing theory is an important concept in current internet technology. As the requirement of bandwidth goes on increasing it is necessary to use optical communication for transfer of data. Optical communication at backbone network requires various devices for traffic grooming. The cost of these devices is very high which leads to increase in the cost of network. One of the solutions to this problem is to have sparse traffic grooming in optical WDM mesh network. Sparse traffic grooming allows only few nodes in the network as grooming node (G-node). These G-nodes has the grooming capability and other nodes are simple nodes where traffic grooming is not possible. The grooming nodes are the special nodes which has high cost. The possibility of faults at such a node, or link failure is high. Resolving such faults and providing efficient network is very important. So we have importance of such survivable sparse traffic grooming network. Queuing theory helps to improve the result of network and groom the traffic in the network. The paper focuses on the improvement in performance of the backbone network and reduction in blocking probability. To achieve the goals of the work we have simulated the model. The main contribution is to use survivability on the sparse grooming network and use of combiner queues at each node. It has observed that Combiner queuing alone does the job of minimizing blocking probability and balancing the load over the network. The model is not only cost effective but also increases the performance of network and minimizes the call blocking probability

Optimal effort allocation in stratified, large area trawl surveys, with application to shrimp surveys in the Barents Sea

Many research works have been developed for stereo image compression purpose where most of them aim at encoding a reference image, a residual one and a disparity map. While the disparity field is often losslessly encoded, we are mainly interested in this paper in the bit allocation problem between the reference and residual images. Generally, the bit allocation is expressed as an optimization problem which involves the computation of the operational rate-distortion (RD) functions for all the wavelet subbands and for different quantization steps. However, this strategy is computationally intensive. To solve this problem, we consider the uniform scalar quantization of the wavelet subbands of both images modeled by a Generalized Gaussian distribution. Thanks to recent approximations of the entropy and distortion functions, we develop an optimal and fast bit allocation method. The obtained results confirm the efficiency of the proposed bit allocation method in the context of stereo image coding.

This paper proposes a new reliability optimization allocation for multifunction systems with multistate units based on goal-oriented (GO) methodology. First, this optimization allocation method is expounded in terms of establishing GO model, establishing reliability optimization allocation model, and solving algorithm. Then its process is formulated. Finally, the new method is applied in reliability optimization allocation of power-shift steering transmission (PSST), whose goal is to minimize the system cost. The results analysis shows that the system costs for different operation times turn to a relatively stable value, and the allocated reliability indices of unit are satisfied with engineering requirements. All in all, this new optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex multifunction systems efficiently. In addition, the analysis process shows that the reliability optimization allocation method based on GO method can provide a new approach for the reliability optimization allocation of multifunction systems with multistate units.

In this paper, we consider the saturation problem in the 3GPP LTE cellular system caused by the expected huge number of machine‐type communication (MTC) devices, leading to a significant impact on both machine‐to‐machine (M2M) and human‐to‐machine H2H traffic. M2M communications are expected to dominate traffic in LTE and beyond cellular networks. In order to address this problem, we proposed an advanced architecture designed for 5G LTE networks to enable the coexistence of H2H/M2M traffic, supported by different priority strategies to meet QoS for each traffic. The queuing strategy is implemented with an M2M gateway that manages four queues allocated to different types of MTC traffic. The optimal radio resource allocation method in LTE and beyond cellular networks was developed. This method is based on adaptive selection of channel bandwidth depending on the QoS requirements and priority traffic aggregation in the M2M gateway. Additionally, a new simulation model is proposed which can help in studying and analyzing the mutual impact between M2M and H2H traffic coexistence in 5G networks while considering high and low priority traffics for both M2M and H2H devices. This simulator automates the proposed method of optimal radio resource allocation between the M2M and H2H traffic to ensure the required QoS. Our simulation results proved that the proposed method improved the efficiency of radio resource utilization to 13% by optimizing the LTE frame formation process.

The authors propose an optimal structure decision method which can determine cell type, arrangement, degree of freedom, and link length. It can be used for fixed-base and mobile-base manipulators, so that universal/modular manipulators can be made. Simulation results show the differences in evaluation for different candidates and tasks and also demonstrate that the structure decision method works efficiently. With respect to the software structure, optimal knowledge allocation is one of the most important issues for CEBOT. The authors propose a method of optimal knowledge allocation based on the amount of communication among cells. Communication among cells for the control proposed is described. >

Comparison in power consumption of static and dynamic WDM networks

The regional integrated energy system, as a comprehensive energy production form which integrates the production and conversion of various energy sources, is characterized by high energy utilization rate, multi-energy complementation, cleanness and low carbon, and its optimal allocation method is of great significance for realizing energy conservation and emission reduction and ensuring regional energy safety. In this paper, an optimal allocation model of regional IES with topology planning is established to construct a mixed integer linear programming model with the objective of economic optimization by introducing the selection of power lines and heat transmission pipelines and capacity allocation constraints and effectively taking into account factors such as network loss, upper capacity limit and voltage amplitude, with the distributed energy stations as basic node units. The test results show that the planning model established in this paper can effectively reduce the installation capacity of redundant equipment, improve the economy of the planning scheme and the comprehensive energy efficiency of the system, and has certain reference value for the planning and construction of IES.

Optimal allocation of sea area utilization is a necessary method to coordinate the conflicts between different departments and realize the dual goals of development and ecological protection. This paper presents an optimal allocation method of sea area utilization based on genetic algorithm. Taking the estuarine sea area of Dengsha Estuary Sea Area as an example, the empirical study was conducted. The results show that the sea area utilization optimization allocation method based on genetic algorithm can produce the optimization result scheme with higher comprehensive benefit and lower ecological loss based on the initial scheme. Under the guidance of constraint conditions, the problem of conflicts between different departments can be solved by optimizing the spatial distribution and the demand of each department can be fully met. Different spatial distribution optimization alternatives can be produced under the same sea area utilization structure.

Advanced metering infrastructure targets the real-time collection of massive multisource data with smart meters (SMs), forming a key component of active distribution networks (ADNs). To achieve comprehensive situation awareness for ADNs, the use of high-speed and reliable two-way communications, such as 5G communications, is the basis. However, it is challenging to optimally allocate wireless resources for massive data, especially when emergency events occur randomly. This study discusses an energy-efficient and optimal wireless resource allocation method employing 5G uplink-based machine-type communications for achieving ADN situation awareness. We propose a 5G-based framework that can reasonably reserve resource blocks for emergency data and model the resource allocation scheme as a problem of maximizing energy efficiency (EE). Then, by using the sample average approximation theory, we transform the stochastic constraint and summarize the scheme as a mixed-integer linear programming problem. Finally, we construct an iterative algorithm based on the Lagrange dual algorithm for searching the global optimal allocation scheme. The results of simulations and experiments illustrate that even if emergency events occur randomly, our algorithm can optimally support the cooperative transmission of all sampled data while providing the best EE for large-scale SM communications.

This paper suggests a stratified Kuk model to estimate the proportion of sensitive attributes of a population composed by a number of strata; this is undertaken by applying stratified sampling to the adjusted Kuk model. The paper estimates sensitive parameters when the size of the stratum is known by taking proportional and optimal allocation methods into account and then extends to the case of an unknown stratum size, estimating sensitive parameters by applying stratified double sampling and checking the two allocation methods. Finally, the paper compares the efficiency of the proposed model to that of the Su, Sedory and Singh model and the adjusted Kuk model in terms of the estimator variance.