Service Function Chain Migration in LEO Satellite Networks

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

Earthquake disaster chain model based on complex networks for urban engineering systems

According to news reports on severe earthquakes since 2008,a total of 51 cases with magnitudes of 6.0 or above were analyzed,and 14 frequently occurring secondary disasters were identified.A disaster chain model was developed using principles from complex network theory.The vulnerability and risk level of each edge in this model were calculated,and high-risk edges and disaster chains were identified.The analysis reveals that the edge“floods→building collapses”has the highest vulnerability.Implementing measures to mitigate this edge is crucial for delaying the spread of secondary disasters.The highest risk is associated with the edge“building collapses→casualties,”and increased risks are also identified for chains such as“earthquake→building collapses→casualties,”“earthquake→landslides and debris flows→dammed lakes,”and“dammed lakes→floods→building collapses.”Following an earthquake,the prompt implementation of measures is crucial to effectively disrupt these chains and minimize the damage from secondary disasters.

Reliability supply chain network design model for perishable products

The classical supply chain network(SCN)design problem is extended,where the candidate facilities are subject to failure and the products are prone to elapsed time deteriorion.First,the reliable SCN design problem is defined by introducing the probability that a facility may be prone to inactivity based on the analysis of perishable product characteristics.The perishable product SCN design problem is formulated as a 0-1 integer programming model.The objective is to minimize the weighted sum of the operating cost(the fixed plus transportation cost)and the expected failure cost.And then,the perishable product SCN design model is discussed and solved using the genetic algorithm(GA).The results show how to generate the tradeoff curve between the operating costs and the expected failure costs.And these tradeoff curves demonstrate empirically that substantial improvements in reliability are often possible with minimal increase in the operating costs.

Supply Chain Network Equilibrium with Revenue Sharing Contract under Demand Disruptions

Contract is a common and effective mechanism for supply chain coordination,which has been studied extensively in recent years.For a supply chain network model,contracts can be used to coordinate it because it is too ideal to obtain the network equilibrium state in practical market competition.In order to achieve equilibrium,we introduce revenue sharing contract into a supply chain network equilibrium model with random demand in this paper.Then,we investigate the influence on this network equilibrium state from demand disruptions caused by unexpected emergencies.When demand disruptions happen,the supply chain network equilibrium state will be broken and change to a new one,so the decision makers need to adjust the contract parameters to achieve the new coordinated state through bargaining.Finally,a numerical example with a sudden demand increase as a result of emergent event is provided for illustrative purposes.

Function chain neural network prediction on heat transfer performance of oscillating heat pipe based on grey relational analysis

As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a looped copper-water OHP and charging ratio,inner diameter,inclination angel,heat input,number of turns,and the main influencing factors were defined.Then,forecasting model was obtained by using main influencing factors (such as charging ratio,interior diameter,and inclination angel) as the inputs of function chain neural network.The results show that the relative average error between the predicted and actual value is 4%,which illustrates that the function chain neural network can be applied to predict the performance of OHP accurately.

Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

Supply chain network equilibrium model with capacity constraints

The supply chain network with capacity constraints of manufacturers and retailers is considered.The linkage between supply chain network equilibrium and transportation network equilibrium is investigated.By analyzing the relationships among the manufacturers,retailers and consumers in the supply chain network and their own decisions concerning maximum profits,a combined variational inequality for the supply chain network equilibrium mode is proposed.Then an equivalent supernetwork of the supply chain network is constructed by adding additional links which denote the producing of manufactures and the handling of retailers.By the supernetwork,the supply chain network equilibrium model can be reformulated as a transportation network equilibrium model.Equivalent mathematical programming is,therefore,obtained based on the principle of a Wardrop user equilibrium.The corresponding Lagrange multiplier of the capacity constraints can be explained as the extra cost of marginal production when capacity is exceeded.A numerical example is given to illustrate the rationality of the model and the validity of the results.

Service Function Chain in Small Satellite-Based Software Defined Satellite Networks

Software Defined Satellite Networks(SDSN) are proposed to solve the problems in traditional satellite networks, such as time-consuming configuration and inflexible traffic scheduling. The emerging application of small satellite and research of SDSN make it possible for satellite networks to provide flexible network services. Service Function Chain(SFC) can satisfy this need. In this paper, we are motivated to investigate applying SFC in the small satellite-based SDSN for service delivery. We introduce the structure of the multi-layer constellation-based SDSN. Then, we describe two deployment patterns of SFC in SDSN, the Multi-Domain(MD) pattern and the Satellite Formation(SF) pattern. We propose two algorithms, SFP-MD, and SFP-SF, to calculate the Service Function Path(SFP). We implement the algorithms and conduct contrast experiments in our prototype. Finally, we summarize the applicable conditions of two deployment patterns according to the experimental results in terms of hops, delay, and packet loss rate.

A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks

As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.

Literature Review on Network Design Problems in Closed Loop and Reverse Supply Chains

The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.

Achievement of Interference Alignment in General Underlay Cognitive Radio Networks: Scenario Classification and Adaptive Spectrum Sharing

Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user(PU), which may lead to low signalto-noise-ratio at cognitive radio receivers(CRRs). Consequently, sum rate of cognitive users(CUs) may fall short of the theoretical maximum through IA. To solve this problem,we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

Purpose:With the availability and utilization of Inter-Country Input-Output(ICIO)tables,it is possible to construct quantitative indices to assess its impact on the Global Value Chain(GVC).For the sake of visualization,ICIO networks with tremendous low-weight edges are too dense to show the substantial structure.These redundant edges,inevitably make the network data full of noise and eventually exert negative effects on Social Network Analysis(SNA).In this case,we need a method to filter such edges and obtain a sparser network with only the meaningful connections.Design/methodology/approach:In this paper,we propose two parameterless pruning algorithms from the global and local perspectives respectively,then the performance of them is examined using the ICIO table from different databases.Findings:The Searching Paths(SP)method extracts the strongest association paths from the global perspective,while Filtering Edges(FE)method captures the key links according to the local weight ratio.The results show that the FE method can basically include the SP method and become the best solution for the ICIO networks.Research limitations:There are still two limitations in this research.One is that the computational complexity may increase rapidly while processing the large-scale networks,so the proposed method should be further improved.The other is that much more empirical networks should be introduced to testify the scientificity and practicability of our methodology.Practical implications:The network pruning methods we proposed will promote the analysis of the ICIO network,in terms of community detection,link prediction,and spatial econometrics,etc.Also,they can be applied to many other complex networks with similar characteristics.Originality/value:This paper improves the existing research from two aspects,namely,considering the heterogeneity of weights and avoiding the interference of parameters.Therefore,it provides a new idea for the research of network backbone extraction.

Improved Enhanced Chain Based Energy Efficient Wireless Sensor Network

In this paper, we have proposed an energy efficient chain based protocol which is an improvement over ECBSN (Energy Efficient Chain Based Sensor Network). ECBSN protocol has certain deficiencies like the non optimal selection of leader nodes in rounds, aggregation and transmission of data by head nodes that leads to unbalanced energy consumption. Aiming at these problems, an improved chain based protocol is proposed. IECBSN adopts a new method of selection of leader nodes based on selection value (SV) parameter .To lower energy consumption further, one more level of hierarchy has been added with a head leader node, which will aggregate data from the leader nodes and pass it to the base station. IECBSN shows an improvement of 20% - 35% as compare to PEGASIS (Power Efficient Gathering in Sensor Information System) and 5% t to 7% from ECBSN on energy consumption and improves network lifetime.

Closed-loop supply chain network equilibrium strategy model with environmental protection objectives

With the increasing popularity of ecological civilization and sustainable development,enterprises should consider environmental protection measures in their operations in addition to pursue their economic interests.This paper establsihes a closed-loop supply chain network model composed of multiple suppliers,manufacturers,retailers,recyclers,and demand markets—regarding their dual goals of the profit maximization and the minimization of carbon emissions.The conditions necessary for establishing overall equilibrium and an equilibrium model of the entire closed-loop supply chain network are determined by applying variational inequality and dual theory.A modified projection contraction algorithm is used to design a model-solving program.Finally,using numerical examples,the paper conducts a comparative static analysis on important parameters such as the weight coefficients of environmental protection objectives and consumers'awareness of low-carbon environmental protection and attains some beneficial enlightenment on management.The results indicate that when the environmental protection objectives of a certain type of enterprise increases,both the economic benefits and environmental protection performance will improve;when the environmental protection objectives of all enterprises increases simultaneously,environmental protection performance improves significantly,but the changes in economic benefits of different enterprises are inconsistent and profit coordination is more complex.Although consumers’awareness of low-carbon preference could improve environmental performance,it reduces the overall profits of network members and the entire closed-loop supply chain network as a whole.The above conclusions can be used as a reference for the government in designing low-carbon environmental protection policy and in closed-loop supply chain research.

Chain-type wireless sensor network node scheduling strategy

In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network （WSN）, sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamental y different from other types of WSNs, in which the sensor nodes are deployed along elongated geographic areas and form a chain-type network topo-logy structure. This paper investigates the node scheduling prob-lem in the chain-type WSN. Firstly, a node dormant scheduling mode is analyzed theoretical y from geographic coverage, and then three neighboring nodes scheduling criteria are proposed. Sec-ondly, a hybrid coverage scheduling algorithm and dead areas are presented. Final y, node scheduling in mine tunnel WSN with uniform deployment （UD）, non-uniform deployment （NUD） and op-timal distribution point spacing （ODS） is simulated. The results show that the node scheduling with UD and NUD, especial y NUD, can effectively extend the network survival time. Therefore, a strat-egy of adding a few mobile nodes which activate the network in dead areas is proposed, which can further extend the network survival time by balancing the energy consumption of nodes.

A Transportation Network Equilibrium Reformulation of Eco-Industry Chain Network

In this paper, we demonstrate that the eco-industrial network equilibrium model of link flow version previously introduced can be reformulated as a transportation network equilibrium problem of path flow version. Then, some methodological tools mainly applied in the field of transportation science can be used to discuss the eco-industrial chain network problem. What the highlighted contribution lies in is that the paper not only expands theory of supply chain model with reducing path flow but also generalizes the traditional transportation network equilibrium problem by new applications.

Network topology optimization of supply chain information systems

Network topology optimization has been widely researched. Since market competition has gradually developed into competition among the supply chain information systems, the network to- pology optimization of supply chain information systems has been in urgent need. However, the net- work topology optimization of supply chain information systems is still in its early stages and still has some challenges. So a description of typical seven network topologies for various supply chain infor- mation systems has been given. The generic characteristics of each network topology can be summa- rized. To analyze the optimization of network topology optimization of supply chain information sys- tems, a numeric model has been established based on these general characteristics. A genetic algo- rithm is applied in the network topology optimization of supply chain information systems model to a- chieve the minimum cost and shortest path. Finally, our experiment results are provided to demon- strate the robustness and effectiveness of the proposed model.

A New Coordinated Transition-metal Linked Network Compound Constructed by Octamolybdate Chain

A novel transition-metal ion coordination-linked network compound {Na4[Co- (H2O)2(NH2NH2)2Mo8O27]?16H2O}n 1 was synthesized by the reduction reaction of Na2MoO4? 2H2O, NH2NH2?2HCl and Co(OAc)2?4H2O in aqueous solution at ambient temperature and structurally characterized. Crystal data for 1: triclinic system, space group P1, a = 9.5544(2), b = 9.8640(2), c = 11.6338(3) ?, α = 103.3790(10), β = 100.5600(10), γ = 96.2750(10)o, V = 1035.32(4) ?3, Z = 1, Dc = 2.789 g/cm3 and R = 0.0453. The X-ray crystal structure analysis shows that 1 is constructed by octamolybdate anions linked via corner-sharing interactions and hetero-metal links into the polymeric anionic sheet [Co(H2O)2(NH2NH2)2Mo8O27]n 4n-, and further allied by [Na4(H2O)12]n 4n+ sodium chains into a 3D framework with z-shaped channels. The mag- netic study of compound 1 indicates that weak antiferromagnetic coupling interaction occurs be- tween the cobalt centers.