Technology Development and Deployment Strategy of Carrier Ethernet

Carrier Ethernet(CE) is gradually stepping away from standardization and testing to deployment and application in live networks.Although great improvement has been made in the reliability of various CE technologies,there is need for improvement in their Quality of Service(QoS),Operation,Administration and Maintenance(OAM),especially the multi-vendor interoperability.The deployment of CE should be service-oriented,and the factors,such as maturity of related technologies and standards,deployment costs,and the complexity of network reconstruction as well as the interoperability with other vendors,should be taken into full consideration to flexibly select appropriate networking technology for different application scenarios according to technical features.

Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication,sensing and computing

The integration of communications,sensing and computing(I-CSC)has significant applications in vehicular ad hoc networks(VANETs).A roadside unit(RSU)plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication.Due to the constraints of limited resources,RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method.However,direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue.In this paper,we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks.The renormalization method is compared with two deployment schemes:genetic algorithm(GA)and memetic framework-based optimal RSU deployment(MFRD),to verify the improvement of communication performance.Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm

Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.

Deployment and Exploration of a Gas Storage Well Pattern Based on the Threshold Radius

To tackle the problem that wells that are deployed in a specific pattern based on the requirements of gas reservoir development are not suitable for gas storage,we have conducted concentrically circular injection and production simulation experiments for gas storage,discovered the existence of a threshold radius,denoted by Rt,and derived the expression for Rt.Based on the analysis and discussion results,we propose a strategy for deploying gas storage wells in specific patterns.The expression for Rt shows that it is affected by factors such as the gas storage gas production/injection time,the upper pressure limit,the lower pressure limit,the bottomhole flow pressure at the ends of injection and production,the and permeability.The analysis and discussion results show that the Rt of a gas storage facility is much smaller than the Rt for gas reservoir development.In the gas storage facilities in China,the Rt for gas production is less than the Rt for the gas injection,and Rt increases with the difference in the operating pressure and with permeability K.Based on the characteristics of Rt,we propose three suggestions for gas storage well pattern deployment:(1)calculate Rt according to the designed functions of the gas storage facility and deploy the well pattern according to Rt;(2)deploy sparser,large-wellbore patterns in high-permeability areas and denser,small-wellbore patterns in high-permeability areas;and(3)achieve the gas injection well pattern by new drilling,and the gas production well pattern through a combination of the gas injection well pattern and old wells.By assessing a gas storage facility with a perfect well pattern after a number of adjustments,we found that the Rt of the 12 wells calculated in this paper is basically close to the corresponding actual radius,which validates our method.The results of this study provide a methodological basis for well pattern deployment in new gas storage construction.

Improved Implementation and Evaluation of Wireless Sensor Networks in Intelligent Building

A complete study for the implementation of wireless sensor networks in the intelligent building is presented. We carry out some experiments to find out the factors affecting the network performance. Several vital parameters which are related to the link quality are measured before deploying the actual system. And then, we propose an optimized routing protocol based on the analysis of the test data. We evaluate the deployment strategies to ensure the excellent performance of the wireless sensor networks under the real working conditions. And the evaluation results show that the presented system could satisfy the requirements of the applications in the intelligent building.

Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

In this paper,the establishment of efficientWireless Sensor Network(WSN)networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach(SMOWA)algorithm for solving a multi-objective problem.The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficientWireless Sensor Network to minimize energy consumption.After that,the cluster head for each cluster has been selected with the help of the duty cycle.After configuring the WSN networks,the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks.Energy minimization,as well as the amount of day-saving,has been calculated for the differentWSNswhich has been configured through different deployment policies.The major finding of the research paper is to improve the durability of Wireless Sensor Network(i)applying different deployment strategies:(Random,S pattern and nautilus shell pattern),and(ii)using a new Meta-heuristic algorithm(SMOWA Algorithm).In this research,the lifetime of WSN has been increased to a significant level.To choose the best result set from all the obtained results set some constraints such as“equivalent distribution”,“number of repetitions”,“maximum amount energy storage by a node”has been set to an allowable range.