In this paper, we make use of the replicating asset for statistical arbitrage trading, where the replicating asset is constructed by a portfolio that mimics the returns from a factor model. Using the replicating asset in the context of statistical arbitrage has never been done before in the literature. A novel optimal statistical arbitrage trading model is applied, and we derive the average transaction length and return for the Berkshire A stock and its replicating asset. The results show that the statistical arbitrage method proposed by Bertram (2010) is profitable by using the replicating asset. We also compute the average returns under different transaction costs. For the statistical arbitrage using the replicating asset of the factor model, average annual returns were at least 33%. Robustness is examined with the S&P500. Our results can provide hedge fund managers with a new technique for conducting statistical arbitrage. Pairs trading is a statistical arbitrage concept, and it has two types: one is the statistical arbitrage, and the other is risk arbitrage. It usually involves using two related securities in the same industry or with similar characteristics and is entered into when their prices deviate from the equilibrium state. The application of pairs trading can be roughly divided into three main steps. First, look for two securities whose prices have the same trend in a given period. Second, observe the change in the spread between the two securities during the subsequent trading period. Third, if the two securities have an equilibrium relationship, a long (short) neutral portfolio can be constructed. When the spread of the two securities reverts to its historical mean, the position is reversed. Existing research finds that pairs trading is related to concepts, such as cointegration, correlation of stock prices, mean regression, overreaction, contrarian selection, and price trends –7. It isIf investors want to get the maximum profits, they should look for the spread of pairs with high variance and strong mean reversion. The usual method is to construct a stationary, mean-reverting synthetic asset as a linear combination of securities . HoweverIn statistical arbitrage trading, investors calculate the historical distance between the standardized daily return paths and choose the pair with the smallest trading distance. If the future price is similar to the past, the spread price may converge again, resulting in a positive return in the zero-cost portfolio. Investors can maximize profits by shorting the overpriced and buying the underpriced. However, this method still has some problems, such as when to trade to maximize the profit of paired trading. Bertram  uses theThe relationship between risk and return has always been a worrisome topic in academia and application. Fama and French's three-factor model is designed to capture the relation between average return and size and the price ratios . The thrIn past literature, most research explores the arbitrage of two similar nature assets. There does not exist research on using the factor model to create replicating assets for arbitrage. The main contribution of this paper is using the factor model to form a replicating asset and then constructing the synthetic asset with other assets for statistical arbitrage. Methodologically, we form the replicating asset (portfolio) by using the Buffett- and five-factor model following the method described in Asness et al. . We veriThe remainder of the paper is organized as follows. Section 2 discusses the literature related to statistical arbitrage and factor models. Section 3 presents the data and statistical arbitrage trading model of the Ornstein Uhlenbeck process. Section 4 presents the results of the empirical analysis and examines robustness to varying transaction costs. Section 5 concludes and summarizes the main results of the paper.The most common form of pairs trading involves forming a portfolio of two related stocks whose relative pricing departs from its ‘equilibrium.’ The equilibrium may involve the concepts of cointegration, mean regression, overreaction, and reversal strategies. The success of statistical arbitrage depends on finding two suitable securities, then modeling and forecasting of spread time series. One popular method is the distance approach, which explores different dimensions and implications of pairs trading strategies, such as accounting information, news, liquidity, sensitivity, transaction costs, etc. , 23–25.Jegadeesh and Titman  find theAnother statistical arbitrage concept is cointegration. The method of cointegration describes as follows: first, choose two cointegrated stock price series; second, construct a long (short) position when stocks deviate from their long-term equilibrium; finally, close the position after convergence or at the end of the trading period –30. HongMean reversion is key to pairs trading. The stochastic spread method models the mean-reverting process of pairs trading as an Ornstein Uhlenbeck process. Elliott et al.  advance Bertram  derives Under the capital asset pricing model (CAPM), the expected return of a stock is related to its Beta coefficient. However, this model has difficulties in explaining actual stock market returns. First, its assumptions are difficult to maintain in the actual capital market. Second, the factors affecting expected returns may be more than one. The Arbitrage Pricing Theory (APT) is an extension of CAPM. The pricing model given by both the APT and the CAPM are models under equilibrium. The difference is that the APT model is based on the concept of arbitrage. If the market does not reach equilibrium, there will be risk-free arbitrage opportunities in the market. The APT model uses several factors to explain the expected return on risky assets, and according to the no-arbitrage principle, there is an approximately linear relationship between the equilibrium return of risk assets and the risk factors.Fama and French  find thaAsness et al.  define 'In this paper, we examine the statistical arbitrage trading between the Berkshire Hathaway stock and its replicating portfolio. For clarity of exposition, in the remainder of this paper the term “replicating portfolio” will be replaced with the term “replicating asset” where appropriate for increased clarity. The empirical tests of this study consist of two parts. First, we utilize the five-factor model and Buffett-factor model to construct the replicating asset. Second, we use the Ornstein Uhlenbeck process to test the statistical arbitrage trading of the synthetic asset constructed from the original Berkshire Hathaway stock and its replicating asset.For this study, we utilize the factors data of the five-factor and the Buffett-factor model and the price of Berkshire Hathaway stock. The various factors of the models are obtained from the Kenneth R. French data library and the AQR data library. Berkshire Hathaway’s stock return and price data are from the CRSP database. Berkshire Hathaway’s stock has two classes: class A and class B. Berkshire Hathaway introduced share class B in April 1996. For this study, focus on Class A stocks based on the longer length of the data. We use daily data from 1980/3/17 to 2018/09/28. We use the five-factor model, and the Buffett-factor model to construct the replicating asset for Berkshire Hathaway class A stock. We denote it with BerkA*. We then use the replicating asset and the original Berkshire Hathaway stock to conduct the statistical arbitrage experiments. The data period used in this study spans from 1980/03/17 to 2018/09/28, which gives a total of 9720 daily observations.Fama and French  design tRit is the return on security or portfolio i for period t, Rft is the risk-free return, Rmt is the return on the value-weight (VW) market portfolio. The size factor SMBt is the return from a diversified portfolio of small stocks less the return on a diversified portfolio of large stocks. A positive loading on SMB reflects a tendency to buy small stocks [HMLt is the difference between the returns on diversified portfolios of high and low B/M stocks. Berkshire stock shows a positive tilt, which reflects a tendency of buying stocks that have a high book value relative to their market value [RMWt is the difference between the returns on diversified portfolios of stocks with robust and weak profitability. The investment factor CMAt is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, and eit is a zero-mean residual.A five-factor model that captures the size, value, profitability, and investment patterns in average stock returns can perform better than the three-factor model. In the five-factor model, l stocks , but Berl stocks . The valet value . The proBuffett’s factor model was proposed by Frazzini et al. . They shCarhart  construcarhart's  and add Pt as,Xt denotes the synthetic asset. For our experiments we define Xt = log(Berkshire stock price)−log(replicating asset price). Since the replicating asset is constructed using the theoretically accurate asset pricing models of Eqs  denote the return per trade as a function of a,m, and transaction cost. Given that the Xt represents the log price, the continuously compound rate of return for a single trade after accounting for transaction cost can be written as r = (m−a−c). In a profitable strategy, the return must exceed the transaction costs from entry moving to exit. Bertram [E(T) and V(T) are the mean and variance of T. Even though the process Xt is stationary, the return of every trade is stochastic; the time frame for return realization is random. A trade may take a long time before reaching the exit level and have a significant deviation away from the exit level during the time frame. We can use the first pass time of the Ornstein Uhlenbeck process to calculate the expression of the strategy return and variance. Bertram [a<m, a<0, and −a = m. Ta→m represents the time to transition from a to m, and Tm→a is the time to transition from m to a, and the independence of the two times follows from the Markovian property of the Ornstein Uhlenbeck process. The maximum expected return is then given as follows:a will satisfy the following equation:This section provides the mathematical exposition of the Ornstein Uhlenbeck process statistical arbitrage trading model of Bertram . Under ts of Eqs  and 3);;Pt as,P Bertram  shows th Bertram  assumes This section presents the results of optimal statistical arbitrage trading of Berkshire Hathaway stock with its replicating asset. First, we construct a replicating asset, which will have similar risk and return characteristics with the actual Berkshire A stock price by using the five-factor model (Eq ) and theIn our experiments, we use two different factor models to construct the replicating asset. It should be noted that We next apply the Ornstein Uhlenbeck process arbitrage strategies discussed in Bertram  and CummFigs The results presented show the feasibility of using synthetic assets formed by using the actual stock and a replicating asset constructed from factor models, without the need to find real assets with similar properties for statistical arbitrage. In the experiments, two different factor models were used to replicate the original asset, Berkshire A. Statistical arbitrage applied to the synthetic asset formed from the original Berkshire A and the replicating asset results in arbitrage profits of at least 33% in expected return in To test for the robustness of our method, we redo our experiments using the S&P500 index as the target asset to be replicated instead of Berkshire A stock. We check to see if the proposed optimal arbitrage strategy is feasible under the same method. We again use the Buffett-and five-factor model to form the replicating asset. Estimations of parameters were done by using the daily data of the S&P 500 index. The synthetic asset is formed from the replicating asset and the actual S&P 500. We calculate the difference in the log price of the two assets to create the synthetic asset and name it as SP500*. We then calculate the parameters for the Ornstein Uhlenbeck process and get α = 0.008735, η = 0.007170 for Buffett-factor model, and α = 0.009195, η = 0.007369 for five-factor model. Figs Tables Next, we perform analyses in terms of Sharpe ratios. This study provides a novel method of using replicating assets formed by factor models for optimal statistical arbitrage. The synthetic asset formed by the original target asset and the replicating asset follows the Ornstein Uhlenbeck process closely. Our experiments illustrate the viability of forming the synthetic asset from the original target asset and the replicating asset constructed from factor models. The contributions of this study are as follows: first, we show that the factor model can be used to construct a replicating asset that can be paired with the original target asset to perform statistical arbitrage. This method is shown to be executable under different transaction costs assumptions. Since the synthetic asset in this study is formed from the original target asset and the replicating asset constructed from factor models, the method works even when it is not possible to find a suitable tradable asset to pair with the original target asset. Bertram  solved t


In wireless sensor/ad hoc networks, all wireless nodes frequently flood the network channel by transmitting control messages causing “broadcast storm problem”. Thus, inspired by the physical backbone in wired networks, a Virtual Backbone (VB) in wireless sensor/ad hoc networks can help achieve efficient broadcasting. A well-known and well-researched approach for constructing virtual backbone is solving the Connected Dominating Set (CDS) problem. Furthermore, minimizing the size of the CDS is a significant research issue. We propose a new parallel scatter search algorithm with elite and featured cores for constructing a wireless sensor/ad hoc network virtual backbones based on finding minimum connected dominating sets of wireless nodes. Also, we addressed the problem of VB node/nodes failure by either deploying a previously computed VBs provided by the main pSSEF algorithm that does not contain the failed node/nodes, or by using our proposed FT-pSSEF algorithm repairing the broken VBs. Finally, as nodes in a VB incur extra load of communication and computation, this leads to faster power consumption compared to other nodes in the network. Consequently, we propose the virtual backbone scheduling algorithm SC-pSSEF which aims to find multiple VBs using the VBs provided by the pSSEF algorithm and switch between them periodically to prolong the network life time. Wireless sensor networks (WSNs) consist of small nodes with sensing, computation, and wireless communications capabilities. These networks are widely used in many applications such as environment monitoring, disaster forecast, battlefield detection, traffic control, and disease diagnosis. Such a wide field of applications in wireless networking motivated the interest researchers to investigate an array of wide issues related to the nature of wireless ad hoc and sensor networks . Most ofv of G is said to dominate itself and any vertex adjacent to it in G. A dominating set (DS) is a set of vertices which is collectively dominate all the other vertices in the graph. If the graph is connected, a connected dominating set (CDS) is a DS which is also a connected subgraph of G. The nodes in CDS are called dominators (backbone nodes), other nodes in the network are called dominatees (non-backbone nodes). The MCDS problem seeks to find a connected dominating set of minimum cardinality, which its size is called the domination number of G and written as The topology of the wireless nodes can be modeled as simple undirected graph  as γ(G) .Scatter Search (SS) is one of the meta-heuristic search algorithms that has been successfully applied to hard optimization problems. Unlike other evolutionary methods such as genetic algorithms, SS relies on the hypothesis that systematic designs and methods for creating new solutions afford significant benefits, while the other approaches resort to using randomization. It employs strategies for search diversification and intensification that have proved effective in a wide variety of optimization problems ,6,7.Most basic meta-heuristic algorithms are sequential and commonly used for real-world problems emerging in both academic and industrial domains. However traditional sequential methods do not always satisfy the requirements of these real-world problems. Therefore, parallelism appears quite naturally as an effective approach not only to speed up the search by performing several computations at the same time but also to improve the quality of the provided solutions. On the other hand, the advent of multi-core processors provides a massively huge computation power for scientific and business applications. Therefore, the need for writing efficient optimized codes and data structures became more essential to achieve the expected performance and use of the computing resources .The problem of a sensor node failure, missing of communication link or data, are inescapable in the WSNs. These failure problems are due to various factors such as environmental impact, battery depletion, physical damage, radio interference, etc. Fault-tolerance is the ability of a network to provide a desired level of functionality without interruption even if there are faults in the network . TherefoIn this paper, we investigate the problem of identifying the VBs of WSNs using graph domination and scatter search in parallel environment. In addition to that we consider the problem of making such VB fault-tolerant. Finally, we investigate the problem of increasing the network life time by VB scheduling. Therefore, a new parallel Scatter Search algorithm with Elite and Featured cores (pSSEF) method is proposed for solving the minimum connected dominating set problem in wireless networks. In the pSSEF method, the population is divided among the parallel workers, so that each worker runs different scatter search methods on its solution set. Then, each worker sends its modified solution set to the elite and the featured cores, where each of them selects its solutions among the received solutions and apply special refinement procedures to improve those solutions. This algorithm achieves better results compared to other methods in the literature including the sequential method, particularly for the high dimension graph. Moreover, the algorithm is so stable as each result matches or close to the average result. To recover from (tolerate) failures that may occur in the wireless networks, we propose a new virtual backbone fault-tolerant algorithm based on scatter search called the FT-pSSEF method. The FT-pSSEF method is a modified version of the pSSEF method that keeps network working despite failure in one sensor node or more. If the failed sensor node is not among VB nodes, then it will not affect the network functionality. Otherwise, the FT-pSSEF method looks for another VB among previously obtained from the pSSEF method and kept in VB set. It picks the one that does not contain the failed node/nodes, else the FT-pSSEF method starts to look for a new set of VBs that excludes failed node/nodes. Another version of the algorithm is also designed to increase the network life time by VB scheduling called the SC-pSSEF method.This paper is organized as follows. The next section introduces related works in dominating sets and the network virtual backbone fault-tolerance and scheduling. In A large number of network problems can be converted into graph problems. In this section, we discuss the existent work of modelling the considered network problems using graphs. Moreover, some recent studies are mentioned and discussed about the different mythologies used to solve those problems.Wireless sensor networks can be modeled as a graph, where a sensor node is represented as a vertex in the graph and a communication link is represented as an edge connecting two vertices. Generally, wireless sensor/ad hoc networks has no physical backbone connecting their nodes. Therefore, finding a VB of a given network is crucial in managing the network, saving communication bandwidth and sensor nodes battery, and improving routing. In recent years, it was verified that using domination in network graphs is an optimal choice to find the VBs in wireless ad hoc and wireless sensor networks ,12,13. TBy reviewing most of existing research in managing WSNs, two essential research directions have attracted researchers: the first one pays attention to the fundamental aspects and characteristics of wireless ad hoc networks or wireless sensor networks and their application domains. Consequently, developing new algorithms to identify MCDS is an important step forward to manage such networks. Alternatively, the other research direction focuses on graph theory and solving the MCDS problem heuristically ,18,19,20There are many interests in studying the MCDS problem ,23,24,25Developing other meta-heuristics to solve the MCDS problem has attracted many researchers. Li et al. designed GRASP to solve that problem . JovanovWSNs size for most applications is huge and number of sensor nodes is enormous. Consequently, the need for more computing power steers researchers towards high performance computing and parallel processing. Hedar and El-Sayed proposed a parallel genetic algorithm with elite and diverse cores for constructing a wireless network virtual backbone based on identifying MCDS of wireless nodes to be used in wireless network topology control. Experimental results demonstrated that the proposed parallel algorithm achieved better results than their sequential version .The WSN typically consists of hundreds or maybe thousands of small size, low cost, and low-power sensor nodes. These nodes which have limited memory, processing capability, and storage capacity, communicate with each other via a wireless medium to perform a common task. These networks are most widely used in fire prevention, industrial process control, environmental monitoring, military surveillance, etc. As WSNs are deployed in very large scale areas, the probability of sensor node failure in the network is extremely high which may result in a system disruption and thus can cause a system failure . There aThere are two essential steps to tackle faults in the WSNs: fault detection and fault recovery. Fault detection focuses on detecting the functional failure in the network and whether it will exceed this failure in the near future or not. The second step after the fault detection is fault recovery to enable the system to overcome the detected fault. Some faults such as the depletion of battery and failure in links can be detected by a sensor node itself, this type of detection technique called self-diagnosis. Another technique is the cooperative diagnosis technique which deals with faults require cooperation between a set of sensor nodes to be diagnosed . The mosSince there is no predefined or fixed infrastructure for WSNs, all sensors are frequently flooded with control messages which causes contentions, collisions, and redundancy. As a solution for this issue, the VB has been proposed as the routing infrastructure of these networks. Using VB, routing messages are only forwarded between sensors via the VB, instead of being flooded to all other sensors. Consequently, this technique reduces routing overhead and energy consumption, and prolongs the network lifetime . As sensThe lifetime of a sensor node can be defined as the period from when it starts working to when its energy is depleted . SpecifiA well-known technique is the sleep scheduling, which guarantees the connectivity and coverage simultaneously by switching off some of the unnecessary sensors’ radio and only activating the necessary sensors. Thus, the CDS algorithms can be used to construct multiple virtual backbones to let them work alternatively ,39. Wei Wireless energy transfer technology is another promising technique to prolong the lifetime of WSNs . Jin et Employing both VB sleep scheduling and mobile vehicles for wireless charging as a hybrid technique is able to balance the load of WSNs, and provide a reliable energy replenishment for sustainable wireless sensor networks. Specifically, sensor nodes of recently de-scheduled VB can be considered candidate nodes for WCV strategy.The parallel Scatter Search with Elite and Featured cores (pSSEF) method: finds optimal or near-optimal VBs,the FT-pSSEF method: adapts the obtained VBs to control the network VB fault-tolerance, andthe SC-pSSEF method: adapts the obtained VBs to control the network VB scheduling.The methodology design aims to find VBs of a given network. Then, the obtained VBs can be deployed to manage the network and also to control its fault-tolerance and scheduling in order to extend the network lifetime. The design steps and components of the first method are discussed in this section while the rest of the methods are presented in the next section.p different cores. The first core is the master one which controls the main search process, and is also concerned with developing the elite solutions. The second core is called featured core and develops connected dominating sets since the connectivity is a main featured of the considered problem. The rest of the In the pSSEF method, the proposed parallelization strategy is based on searching the solution space using different search strategies in parallel. To preserve the elitism and diversity, three types of search are used motivated by elite and featured solutions as well as the ordinary search mechanisms of scatter search. These search mechanisms are run on Before explaining the specifics of the proposed method, we begin by defining the representation of the solutions and evaluating their fitness. Then, the scatter search operators are designed including diversification, improvement, reference set update, subset generation and solution combination. Finally, to refine the best obtained solutions, intensification schemes are discussed based on local search and feature refinement.G. The subscript values X has the value 1, then the i-th node in X. Otherwise, the solution X does not contain this i-th node. Therefore, a node subset X, whereThe pSSEF method uses a bit vector X that its corresponding nodes can form a MCDS which works as a VB of the given network.For a given network, the pSSEF method aims to find the best VB which is represented in the above-mentioned binary form. Therefore, the pSSEF method generates different solution candidates to output the best D represented by the solution X, D, and As a particular form of objective function, the pSSEF method uses a special fitness function to calculate the solution quality that increases the probability of finding successful solutions with better coverage and connectivity ,45,46. SThe first objective is the extent to which the solution covers the graph, which represented by the termThe second objective is to measure the solution connectivity which is given by the second termThe third objective is the relative number of nodes in the solution which can be measured by the termIt can be seen that the first objective (coverage) and the second objective (connectivity) are to be maximized. The third objective is related to the solution cardinality which should be minimized. Therefore, the third objective is formulated so that its maximization is equivalent to solution cardinality minimization. Then, we can say that the considered MCDS problem can be reformulated in the following form:The fitness function represented by Equation  can be cIn ,48, the The pSSEF method uses common search mechanisms in SS in addition to some intensification and parallel mechanisms. The details of the SS procedures are explained as follows.P of diverse solutions [The pSSEF method applies the diversification generation procedure to build a large population set olutions ,49. TherThe improvement procedure transforms a trial solution into another one with higher quality. If there is no improvement in the input trial solution results, then the enhanced solution is considered to be the same as the input solution . In pSSEP. Specifically, the RefSet with size b elite solutions, andRefSetRefSetThe reference set update procedure copies the b best solutions from P to RefSetP. For each solution in the updated P, the minimum of distances to solutions in RefSetThe Reference Set (RefSet) is the iterate solution set in SS similar to the population in Evolutionary Algorithms (EAs). Unlike other EAs, the RefSet has limited number of solutions compared to populations in EAs. The reference set update procedure is applied to initialize the RefSet in the search beginning and also to update it in every generation. To generate an initial RefSet, collection of both high quality solutions and diverse solutions are selected from  RefSet2 .At the end of each generation, the RefSet is updated using the same procedure mentioned in creating the initial RefSet, but the set of all solutions generated in this generation are used instead of The pSSEF method operates on the RefSet to produce new solutions by combining each pair of solutions in it. Specifically, the subset generation procedure relies on generating all possible pairs from the set of solutions in the RefSet. This results in The solution combination procedure uses the generated solution subsets to produce new solutions by applying the three-point cut method. In each solution subset, three cut points are selected randomly and the bits between the three points are swapped between the solutions to create new solutions .Elite solutions obtained during the search process are enhanced using two procedures to achieve a faster and a better performance. The details of these procedures are explained as the following.X. If X is a DS, then the LS procedure tries to improve its cardinality by deleting some nodes. On the other hand, if X is not a dominating set, then the LS procedure tries to improve it by adding some nodes to hopefully get a new dominant solution. The main processes of the LS procedure are illustrated in X. This means that the node adding targets the nodes with higher degrees, while the node deleting targets the others that have lower degrees. This process is repeated The pSSEF method applies the local search (LS) mechanism in order to improve the fitness of the obtained solutions by adding or deleting some nodes in these solutions. The addition or removal of nodes is depending on the domination feature of solution In order to find a MCDS, the generated connected dominating solutions are saved in a set denoted by the CD set. The final intensification procedure attempts to improve the cardinality of these solutions by removing all unnecessary nodes without losing the solution coverage or connectivity.p different cores, and the search procedures are applied on these cores in parallel. Two of these core are distinctive. The first one is called “Elite and Master Core” which is the main core that controls the main search process, and it is also responsible for producing new elite solutions. The second core is called “Featured Core” which develops new connected solutions. The rest of cores are called “workers” which generate general solutions represented different possible VBs and pass these solution of the main cores.The proposed method is operated on The Elite and Master Core applies the search procedures on special RefSet called m, the previously mentioned search procedures are applied periodically on each RefSet without migrating any individuals into this RefSet. When the migration interval is achieved, the migration process is initiated to add new elite and featured solutions into each worker RefSet. Moreover, the Elite and Master Core is responsible for collecting all elite and featured solutions from all cores, ordering them and sending the best of them to other cores.Within a certain number of generations, called the migration interval and denoted by How each worker selects and updates its reference set.How the migration process is applied.The pSSEF algorithm starts with setting the initial parameters at the Elite and Master Core and broadcasts the input data to all other cores. In general, parallelism has introduced new degrees of freedom to algorithm design approaches. For investigating the effectiveness of using parallel systems, two versions of the pSSEF algorithm are proposed. The main difference between them is depending on:The pSSEF versions; pSSEF1 and pSSEF2, follow the main structure represented in m consequent generations, where m is the size of the migration interval. After that, all obtained RefSet are collected in the Elite and Master Core which in turn orders all solutions in the received RefSets and sends some of distinctive solutions to them to all workers. Specifically, the Elite and Master Core selects the In the pSSEF1 algorithm, each worker starts to generate its own initial sub-population of size The pSSEF2 algorithm works the same as the first version, except for three main operations as follows.The initial At the beginning of each migration interval, the At the end of each migration interval, a refinement process is applied by calling the final intensification procedure to improve the obtained solutions in the CD solution set.In this section, two extensions of the pSSEF algorithm are proposed to deal with the fault-tolerance and scheduling problems in network virtual backbones. After running the pSSEF algorithm, we can get the best VB and some other alternatives of VBs denoted as backup VBs. Then, two additive algorithms use these obtained VBs to control the network fault-tolerance and scheduling as shown in A modified version of the pSSEF algorithm is composed to deal with the network virtual backbone fault-tolerant. This modified version is denoted by the FT-pSSEF method and aims to tackle the failure that may occur in the VB. The FT-pSSEF algorithm uses two additive mechanisms; solution reconnecting and solution coverage, which are explained before presenting the FT-pSSEF algorithm formally.C in Algorithm 1 Solution Reconnecting.C equal to the nodes in the largest maximal connected component in Set If Randomly select two nodes, Find the shortest path from Set Update If The solution reconnecting mechanism aims to repair the disconnection between the VB nodes caused by the failure of any node. Specifically, the Solution Reconnecting procedure tries to add a minimal number of nodes to the best solution Algorithm 2 Solution Coverage.Collect the nodes represented by If Set Randomly select a node from Set the corresponding component in If The Solution Coverage procedure is another mechanism used by the FT-pSSEF algorithm to refine the coverage of the best solution Since the virtual backbone nodes are prone to failure, a set of different solutions can be obtained from the pSSEF algorithm. These backup solutions are already saved in the CD set. The FT-pSSEF algorithm uses these solutions for providing fault-tolerance to the given network in two ways. First, the FT-pSSEF algorithm searching for another VB from the backup solutions provided by the main pSSEF algorithm. The selected backup solution should not contain any failed node. Second, if there is no such backup solution that matches the specifications in the first step, the FT-pSSEF algorithm repairing the broken VB. The repair process can be achieved by increasing the connectivity between its nodes using the Solution Reconnecting procedure, or by improving the node coverage in the virtual backbone using the Solution Coverage procedure. Another modified version of the pSSEF algorithm is designed to deal with the virtual backbone scheduling, which is called the SC-pSSEF algorithm. This modified algorithm aims to find multiple VBs using the best solutions for MCDS provided by the pSSEF algorithm which are saved in the CD set. During the search process, the SC-pSSEF algorithm finds and stores different connected dominating sets with maximum diversity and minimum cardinality. The SC-pSSEF uses the best VB and backup VBs obtained by the original steps of the pSSEF algorithm, then the SC-pSSEF distinguishes between the obtained VBs to find better virtual backbone scheduling. In order to measure the efficiency of a VB scheduling process, the network lifetime can be estimated using the following mechanism. First, we define the node lifetime as the time period from when it begins to function to when its energy is exhausted. Moreover, the VB lifetime is the minimum life of all its nodes . A schedMinimum approximate domination number (Min) which is the number of nodes in the best connected dominating solution found in all independent runs.Average approximate domination number (Avg) which is the average number of nodes in the best connected dominating solution found in each independent runs.Processing time in seconds which is the average of the code running time over independent runs.The pSSEF algorithm and its extensions was programmed using MATLAB and run on Dell Precision Workstation 7610 with dual Xeon E5-2663-v3 2.8GHz processors including 20 cores. In this experimental section, we analyze the effectiveness of the pSSEF method and its extensions on a group of network graphs as well as their application in virtual backbone fault-tolerance and scheduling problems. First, we discuss the main test bed and parameters setting. Then, we investigate the performance of the proposed algorithm and find its best implementation settings. Moreover, the results of the proposed methods are compared against the results of other benchmark methods presented in ,45,46. TThese performance metrics are used by the compared methods and are commonly used in the literature. Finally, the results of the FT-pSSEF and SC-pSSEF methods on the virtual backbone fault-tolerance and scheduling problems, respectively, are presented and discussed.R. The experiments were performed 20 times for each of the considered networks with different transmission ranges.A large set of sensor/ad hoc network clustering instances are generated. In which, eight different networks (instances) occupy the same area with different number of nodes or vertices. The network sizes very from 80 nodes up to 400 nodes as given in The setting of the pSSEF, FT-pSSEF and SC-pSSEF parameters are shown in The performance of the main proposed method is analyzed through several experiments using different prospective. First, the performance between the two versions; pSSEF1, and pSSEF2 is compared in order to choose the best setting of the main method. Then, another comparison is done between the pSSEF method and the the sequential version ASSA-MCDS . ActuallThe performance comparisons between the two versions; pSSEF1, and pSSEF2, are demonstrated in In this comparison, we compare pSSEF, which is a parallel modified version of the sequential method ASSA-MCDS . The resAnother significant indication for the performance evaluation of the proposed algorithm is the convergence between the results of the average and the minimum solutions that acquired from 20 independent runs on the network instances. The comparison results in pppppThe parallel performance of the pSSEF method is tested on different number of cores. Specifically, the five parallel versions of the proposed method are tested, each version uses one Master and Elite core, one Diverse core and different numbers of Worker cores as follows:The results of running these parallel versions of the proposed method are shown in Processing times for four networks over different number of cores are shown in In this section, the results of the proposed methods are discussed compared to other benchmark methods. Moreover, we discuss the application results on the virtual network fault-tolerance and scheduling problems.GRASP: A greedy randomized adaptive search procedure for connected dominating set problems .MA-MCDS: Memetic algorithm for minimum connected dominating set .SA-MCDS: Simulated annealing for minimum connected dominating set .ASSA-MCDS: Adaptive scatter search algorithm for the minimum connected dominating set .The results of the pSSEF method are compared with those of the following methods:ndations ,54, whicThe performance of the proposed algorithm FT-pSSEF for the fault tolerant virtual backbone in sensor/ad hoc clustering networks shown in The performance of the proposed algorithm SC-pSSEF to determine the a set of VBs for scheduling is tested using four test networks In this paper, we investigated the problem of constructing a virtual backbone network for controlling either wireless ad hoc networks or wireless sensor networks. In graph theory, this problem is equivalent to finding the minimum connected dominating sets. We proposed the pSSEF method that based on the parallelization technique applied on a multi-core system. A special core called the elite have been introduced to maintain the elitism. Another core called featured core been introduced to maintain the feature of special connected solutions. The rest cores are designed to maintain search for different solution types according to scatter search and new designed search procedures. Also two versions, fault-tolerance pSSEF (FT-pSSEF) and scheduling pSSEF (SC-pSSEF), are proposed to deal with the virtual backbone fault-tolerance and scheduling problems.The results of the pSSEF method are compared with other methods in the literature using standard benchmarks. The results proved that the pSSEF method could obtain better solutions. Moreover, the results indicated that the pSSEF method is more efficient than the sequential method, especially in the processing time where the parallel algorithm is approximately twice faster than the sequential one. A parallel implementation using several numbers of cores is also designed to improve the solution qualities. The applications of the proposed method in virtual backbone fault-tolerance and scheduling demonstrate its ability to tolerate node failures and prolong the network lifetime, respectively.


Although China is experiencing a transition from a relation-based society to a rule-based society, relationships among acquaintances still play an important role in resource allocation, such as the allocation of policy resources. This is particularly true in rural China, where targeted poverty alleviation is prevalent and a relation-based social structure still dominates. However, it is still unknown how relationships affect the livelihood strategy of households in rural China and how poverty alleviation policies plays a role between them. Therefore, this paper embeds poverty alleviation into the relation-based society and explores how households respond to the policy in this specific context. Using grounded theory research method and the sustainable livelihoods approach (SLA) framework, this paper contains in-depth interviews and field observations from three poverty-stricken villages in Northeast China. The results show that relationships have a significant impact on the households’ livelihood strategy. In other words, the households’ livelihood strategy is embedded in the relation-based society. The types of relationships induce households to choose maintained or developmental type livelihood strategies, while relationships influence how the poverty alleviation policies affect the livelihood strategy. This study is not only an extension of the SLA in the research context, but also provides a significant perspective for enriching the long-term mechanism of targeted poverty alleviation by building a theoretical model of the relationships between a relation-based society, targeted poverty alleviation and the livelihood strategies of households. Poverty is a global problem facing the world today. As the largest developing country in the world, China has explored an anti-poverty route with Chinese characteristics in the long-term practice of poverty alleviation . RemarkaDespite these great achievements, establishment of a long-term mechanism for poverty alleviation policies remains challenging. The problem is that policy orientation often focuses on the quantitative aspects of poverty alleviation, yet the quality aspect, for instance the livelihood of people, is sometimes neglected . The livIt is worth noting that China is experiencing a transition from a relation-based society to a rule-based society , but relWith the aim to developing an efficient way of alleviating poverty in a sustainable sense, it is imperative to embed the poverty alleviation practices into the specific context of rural areas in China. The sustainable livelihood approach (SLA) provides a useful analytical framework for this purpose . Taking Based on the SLA, this paper explores the role of the specific context of relation-based society and targeted poverty alleviation policies in households’ choice of livelihood strategies in rural areas of Northeast China. In theory, this paper attempts to build a theoretical model of the relationship between a relation-based society, targeted poverty alleviation policies and the livelihood strategies of households. Thus, this study is not only an extension of the SLA in the research context, but also provides a significant perspective for enriching the long-term mechanism of targeted poverty alleviation. In practice, this paper aims to refine the corresponding countermeasures and suggestions from the perspective of both households and the government. In so doing, the paper attempts to provide new ideas for households to achieve sustainable livelihoods and for government departments to formulate effective long-term poverty alleviation policies.The Chinese government has been dedicated to antipoverty efforts for the past decades . In the At the end of 1978, the Third Plenary Session of the Eleventh Central Committee of the Communist Party of China (CPC) clearly pointed out that it is necessary to make some people and some regions rich first, and finally achieve common prosperity. The prelude of reform and opening up was thus initiated. Since then, the poverty standard has been formally defined, and the poverty alleviation work has been separated from the rural relief for the first time. The driving force of poverty reduction at that time mainly came from the rural economic reform . In JanuIn April 1986, the Fourth Session of the Sixth National People’s Congress deliberated the Seventh Five-Year Plan for National Economic and Social Development (the Seventh Five-Year Plan in short). It highlighted the economic development of “old, young, border and poor” areas. In the same year, a special poverty alleviation agency, the Leading Group for Economic Development in Poor Areas under the State Council was established (later renamed as the Leading Group for Poverty Alleviation and Development of the State Council). The agency was to draw up the policy of economic development in poverty-stricken areas, to arrange special support funds, and formulate special reform and development policies according to the characteristics of poverty-stricken areas and people. The establishment of the agency has effectively promoted the development and poverty reduction in economically backward areas. As a result, China’s poverty alleviation has gradually entered the stage of large-scale planned and organized development-oriented poverty alleviation. At the end of 1986, China first established the standards for national poverty-stricken counties. In “the Notice on Strengthening Economic Development in Poverty-Stricken Areas” issued by the State Council in 1987, it was mentioned that “the fundamental transformation from simple relief to economic development has been initially completed in the national rural poverty-stricken areas, and has begun to enter a new stage of development.” Afterwards, a series of poverty alleviation policies including work-relief programs and financial support funds were issued ,15.The promulgation and enforcement of the “National Eight-Seven Poverty Alleviation Program (1994–2000)” in 1994 marked that China’s poverty alleviation has entered a critical stage. The plan decided to concentrate human, physical and financial resources, and strive to basically solve the problem of food and clothing for 80 million rural poverty-stricken people in about seven years from 1994 to 2000 . MeanwhiIn June 2001, the State Council issued the “Outline of China’s Rural Poverty Alleviation and Development (2001–2010)”. The new policy attempted to build a multidimensional and comprehensive pattern of poverty alleviation and changed the focus of poverty alleviation from the original “county” to a more detailed “village” level . The polThe poverty alleviation policy at this stage fully reflects the coming of a new era of poverty alleviation development in China. In 2011, the “Outline of China’s Rural Poverty Alleviation Development (2011–2020)” was released. Differing from the previous poverty alleviation on regional development, the central government pointed out that it is necessary to “establish and improve the identification mechanism of poverty alleviation objects”. Thus, the target of poverty alleviation policy has been focused from “county” and “village” to more accurate “poor households” and “poor population”. In November 2013, when Xi Jinping visited Xiangxi, Hubei, he made the first instruction of “helping the poor with precision” [In summary, the poverty alleviation policies have experienced a transition from relief focused to development centered, the way of poverty alleviation has changed from the government led to the participation of all sectors in the society. In particular, unlike the previous poverty alleviation policies, the targeted poverty alleviation policy is more people-centered and thus highlights the importance of accurate poverty identification to ensure the accurate assistance to the targeted poverty-stricken households ,15. HoweEarly research normally adopts income to measure the poverty level of households. However, a lack of income cannot be used as a useful indicator variable to fully reflect the level of deprivation of individuals or households due to its contingency . To corrThe sustainable livelihoods approach (SLA) established by the UK Department for International Development (DFID) in 2000  synthesiThe choice of livelihood strategy of households highly depends on their own livelihood capital conditions . It has Meanwhile, the SLA incorporates the vulnerability background of individuals and social structure system into the research scope . As indiAlthough the correlation between a certain capital and livelihood decision-making can reflect the choice basis of individuals and households in a certain lifetime capital state, the promulgation and enforcement of the policy as well as the specific relation-based context might change the existing strategy logic and livelihoods capital assets stock of households . HoweverRelation-based social structures dominate in most East Asian countries , such asContrasting to Granovetter’s weak relationship hypothesis , indigenIn a relation-based society, a reliable information infrastructure is absent whereas the key information is often closed, implicit and uncodified, and the free flow of information is normally blocked. In particular, politicians are powerful and control the key information related to some policies. Thus, the access to the key information highly relies on the relationship with the key person . In addiAs people primarily rely on private ordering to obtain and protect their interests, it is common to regard private relations as an important asset and building private relationships as a necessary activity in daily life . HoweverWhen discussing poverty, poverty-stricken households are more likely to enter the vicious “poverty trap” circle in a relation-based society. It is not easy for poverty-stricken households to access to valuable information which may help them get out of poverty. Sometimes the information is available, but the information might be difficult to understand due to the lack of explanation by authoritative persons. The unfair policy enforcement might also become a major obstacle to poverty-stricken households because usually they have few private relations with powerful politicians.In rural China, it is evident that households attach great importance to relations, in particular the relations with the clan and the neighborhood. Whenever important things happen in a household, for instance an unexpected event occurs, the clan are always the first to know and then the neighborhood . DespiteAs a qualitative research method, grounded theory is able to effectively reveal the underlying reasons behind the behavior of research objects without theoretical assumptions . This paConsidering the defects of quantitative methods, this paper attempts to use qualitative methods to offset these defects. One of the significant advantages lie in that qualitative methods pay considerable attention to the nuances of spatial disparity, thus providing a nuanced understanding of a specific place. Meanwhile, many indicators or constructs are difficult to obtain through quantitative methods. For instance, the scope of relationships is so wide that it is difficult to quantify it, thus the quantitative data is not grounded in practice. However, it can be possible to acquire data through qualitative methods, such as narratives and story-telling. In so doing, the background key information and complex relationships can be obtained. In addition, qualitative methods can truly illustrate the imperfection of human-beings. In particular, the core of this paper belongs to the “how” question, i.e., how households make their livelihood decisions, which is of a complex and explanatory nature. Thus, instead of counting the frequency and scope of their occurrence, it is necessary to trace all kinds of interrelated events and figure out the relationship between households’ livelihood decision-making and contextual factors .In short, this paper uses a grounded theory research method to analyze the relationship between relation-based society, targeted poverty alleviation and livelihood decision-making, and forms a theoretical framework of households’ livelihood decision-making under the targeted poverty alleviation in the relation-based society.Tongyu County, which is located in the west of Jilin Province, is one of the key target counties for national poverty alleviation in Northeast China. This paper selects nineteen households in three villages of ninety poverty-stricken villages in Tongyu County as the research object. These three villages are Xinfeng Village of Xinhua Town (X Village), Bianzhao Village of Bianzhao Town (B Village) and Wujingzi Village (W Village) of Bianzhao Town. The major industries in these three villages are dominated by planting, supplemented by animal breeding. The only difference is that W Village is a relocation village. Theoretical sampling has been used to determine the number of cases, that is, when the new cases cannot provide new knowledge, the increase of cases needs to end . We haveThis paper uses several sources to collect data: literature review, questionnaire, semi-structured interview and archival data, among which the primary data source is semi-structured interview. After defining the research topic, targeted poverty alleviation policy, the status of national poverty counties and SLA have been studied through literature review. In terms of questionnaire, its purpose lies in collecting quantitative data, such as age, education level, health condition and cultivated land area of the informants in order to supplement the interview data. The archives collected in this paper include the basic information of the three villages and the archives of poor households. In terms of in-depth interviews, in late July 2019, the research group went to the three poverty-stricken villages in Tongyu County, Jilin Province, to observe the rural governance and the recent situation of targeted poverty alleviation, and paid a visit to the informants for interviews. Each interview lasted about forty minutes, a total of nineteen households were interviewed. The multivariate data could provide more accurate information and obtain more robust results . Table 2The research used the interview outline to guide the interview. The interview outline involves mainly two aspects. First, the informants were asked about their understanding of the targeted poverty alleviation policy, and the degree of participation and satisfaction of the policy. Second, the relational information was collected in two parts. One part is the family information and the other part is the informants’ relationship with the village cadres and other villagers. After outlining the relationships of the informants, the informants were guided to narrate a specific event to demonstrate their relationship with the family members, village cadres and other villagers.Data analysis is the core of grounded theory research and is an important link between the collected data collection and generated theory. Data analysis makes theoretical propositions go beyond specific time and places to produce general interpretation capability for actions and events in different situations . Data anOpen codes generate directly from the original interview data. The original data are smashed up and conceptualized in a new way to achieve the purpose of clarifying phenomena, developing concepts and refining categories . In thisThe task of axis coding (second-order construct) is to discover the potential logical relationship between first-order categories and develop the main category and its subcategories. Through continuous comparison, analysis and induction, this study finally forms eight main categories, including maintained type livelihood, developmental type livelihood, kinship, geographical relationship, transfusion policy, hematopoietic policy, policy quality, and livelihood capital.Selective coding is to excavate the core dimension from the main categories, analyze the relationship between the core dimension and the main categories, and describe the behavior and phenomenon in the way of story line. Ultimately, a number of four categories were generated. A substantive theoretical framework could be developed after completing the story line.In order to test whether the concepts and categories refined in this research have reached theoretical saturation, we conducted coding analysis on the data of Village X. We found that no new concepts and categories appeared. It indicates that the coding has reached theoretical saturation .As it is important to control the data quality, the inter-coder reliability test has been conducted. Prior to the formal interview, a pilot interview was carried out. We had five interviews and transformed the recordings into transcripts for coding. After forming the coding dictionary, the theoretical logic underlying each code was carefully explained. Then the formal interviews were conducted, and two coders who are the co-authors coded the cases. The inter-coder coefficient shows a value of 0.78, indicating a reliable coding procedure .The maintained type livelihood strategy refers that poverty-stricken households keep the status quo and maintain the current source of livelihood to pursue a fragile and unsustainable state of life. Among the poverty-stricken households visited by the research group, it is common that the poverty-stricken households make the maintained type livelihood due to the illness of the family members. The illness not only weakens the ability of one person to make a living, but also increases the burden of the households. In many cases, family members, especially children offer necessary helps, such as medical expenses to their old parents. B2, B3 and B6 confirmed that they received money and daily necessities from their children to maintain the life. Meanwhile, the children such as W3’s husband often stay at hometown to take care of their parents. As the informants indicated:I can’t do any heavy work because of illness. My son used to pay for living expenses and medical expenses.”B2: “My husband gave up the opportunity to develop in the city because he had to take care of his parents in the countryside. Therefore, we make a living by doing odd jobs now.”W3: “In other cases, despite the good health conditions of the family members, the households still choose the maintained type of livelihood strategy. The reason is often due to the undesirable livelihood conditions, more specifically, the insufficiency of the livelihood capital. For instance, family members might stay at countryside to help each other with farming due to a lack of adequate labor force. As the informant explained:If he (son) leaves home, I cannot do farm work by myself. As far as I know, there are few people in our village who work outside could earn more money, so I let my son stay in the countryside to help me with the farm work.”B6:“Differing from the maintained type livelihood strategy, the developmental type livelihood strategy means that households change the status quo and explore new sources of livelihood through further education, skills training and production mode innovation to pursue a potentially sustainable livelihood. During the interviews, the informants delivered a strong expectation of changing their life to pursue a developmental type livelihood, but only a few of them attempted to put these plans into action. An important reason is the livelihood condition of their family members. When the livelihood conditions of their family members are desirable, these family members have the capability to support the informants’ developmental type livelihood. The possession of livelihood capital such as finance capital is a remarkable reflection of the desirable livelihood condition. As is indicated by the informants:This year, my son borrowed forty thousand yuan to buy sheep for me, so that I could earn some income by raising sheep.”B7: “My mother’s family all know that I live a serious life, so when I went to borrow some money to start up my small business, they are glad to help and lend me money.”W16:”Different from the genetic relations such as family members, the geographical relations with neighborhood and villagers have a different effect on the livelihood strategy of households. In general, the neighborhood and villagers have relatively closer contact to households compared to non-neighborhood and non-villagers. When interviewing B7 whether he considered asking his neighborhood and villagers to borrow money for buying sheep, he said with a wry smile that: “all of them are poor and have no money.” But when his wife was hospitalized, he said he had borrowed money from villagers. When buying sheep, he asked favor from his son, while he asked villagers for help only in emergency. This is actually the embodiment of the aforementioned relation-based society. Considering the conditions of genetic relations themselves and other factors, B2 chose to seek help from the genetic relations to maintain life only in case of emergency.Based on the above analysis, this paper forms the following propositions:Proposition 1.Relations have a significant effect on the choice of livelihood strategy of households. The effect of different relationships differs in the livelihood strategy of households. More specifically:Proposition 1a.When the livelihood condition of the genetic relations is undesirable, it has a high possibility to lead to the long-term maintained type livelihood strategy of households.Proposition 1b.When the livelihood condition of the genetic relations is desirable, it has a high possibility to lead to the developmental type livelihood strategy of households.Proposition 1c.When the livelihood condition of geographic relations is undesirable, it has a high possibility to lead to the short-term maintained type livelihood strategy of households.As for the poverty alleviation policy, it includes transfusion   in 2018 as an example, the photovoltaic industry poverty alleviation project provided a dividend of 3000 yuan for each two- or three-star poverty-stricken households , and a dividend of 500 yuan for each one-star poverty-stricken households . The cooperative project of broiler breeding provided an average dividend of 365 yuan per household. For most of the poor households, the subsidy was only used for daily expenses to maintain or improve the temporary living level. The interviews indicated that no poverty-stricken households had change the maintained type livelihood strategy. For example, the informant B9 said that:I’m sick and take a lot of medicine. In any case, if I do some odd jobs, together with the relief fund the government deals out, I feel fine with that.”B9: “The transformation of the development mode belongs to the hematopoietic poverty alleviation policy, but the households have different degrees of acceptance of such policy. For example, W Village launched a sewing handicraft project. The informant W7 could not participate in the training due to the physical reason of low back pain. A similar case happened in Village B when 30 chickens were allocated to each household. Because there was no land to farm and thus no extra food for chickens, the informant B5 sold the chickens and made a profit. The above two cases show that the poverty-stricken households have no desire to change their maintained type livelihood. This might indicate that when the livelihood conditions are not satisfactory, hematopoietic poverty alleviation policies would not lead to an essential change of the poverty-stricken households’ livelihood strategy. Differently, the informant B7 decided to change her maintained type livelihood, and to try the developmental type livelihood. She kept the chickens, looked after them carefully and expected to earn some money through selling the eggs. The whole family supported her decision.My husband helped me build a hen house, and my son helped me mow the grass in the field to feed the chickens. My brother-in-law’s wife often came to help me feed the chickens and clean the hen house. Later eggs can produce more chickens, then I will have more eggs and earn more (money).”B7: “Therefore, the above discussion came to the following propositions:Proposition 2.The poverty alleviation policy has a significant effect on the livelihood strategy of households. The effect of different poverty alleviation policy differs in the livelihood strategy of households. More specifically:Proposition 2a.The transfusion alleviation poverty policy induces the maintained type livelihood strategy of poverty-stricken households.Proposition 2b.When the livelihood conditions are undesirable, the hematopoietic poverty alleviation policy induces the maintained type livelihood strategy of poverty-stricken households.Proposition 2c.When the livelihood conditions are desirable, the hematopoietic poverty alleviation policy induces the developmental type livelihood strategy of poverty-stricken households.Policy-making is the prerequisite for effective enforcement of the targeted poverty alleviation policy, while the policy enforcement provides guarantee for a smooth policy-making realization. Thus, policy-making and policy enforcement are two complementary aspects related to targeted poverty alleviation policies.Through direct observation, the research group found that poverty-stricken households’ files, the poverty alleviation records and poverty alleviation policy posters were pasted on the walls of the poor-stricken households’ houses. Meanwhile, the poverty alleviation information bulletin board was set up at the villagers’ committee. During the interviews, most of the poor households posited that poverty alleviation cadres such as the village secretary often came to visit them. The flow of policy information from the poverty alleviation department to the poor-stricken households and the livelihood information flow from the poor-stricken households to the poverty alleviation departments reflect an important part of the enforcement of targeted poverty alleviation policies.When talking about the communication with the poverty alleviation departments, the informants pointed out that poverty alleviation cadres have the responsibility of conveying policy information, but the quality of information transmission is not satisfactory. For example, although many households have received the leaflets from the poverty alleviation cadres, they are still uncertain about the star rating of their family, subsidy payment and the progress of re-evaluation of minimum living standard. The reason is twofold. First, these informants cannot be able to understand the policy by themselves through reading the files and leaflets, and second, the poverty alleviation cadres are lacking in an effective communication with the poverty-stricken households, which reduces the possibility of these households making full use of policy conditions and achieve the goal of poverty alleviation.The interviews also showed that when the quality of policy enforcement is unsatisfactory, the resource allocation of poverty alleviation policy highly depends on the relationships with village cadres, namely political relationships. The uneven distribution of resources due to the existence of relationships has an impact on the livelihood strategy. For instance, some informants indicated that because of the good relationship with the village cadres, it was relatively quicker to obtain the comprehensive policy information. One case is that B9 received the news about the application for the qualification of poverty-stricken households ahead of time from his cousin who is the village cadre, meanwhile, the coping strategies were explained in detail. Therefore, B9 went to the county hospital for a medical treatment certificate, so that he could make greater use of the qualification conditions to win the application. Finally, B9′s family received the corresponding policy considerations through the qualification of poverty-stricken household and could be able to manage to survive. Conversely, B6 did not get along with the village secretary due to a land dispute with a relative of the village secretary. In spite of the terrible conditions, B6′s family cannot get rated as a poverty-stricken household. As the government has no responsibility to dig wells for free for non-impoverished households, B6′s courtyard was short of water, and the green onions and other vegetables could not be sold. As B6 stated:We don’t have any relatives in the village cadre group. He (the village secretary) just trampled on me... And other village cadres all listen to the village secretary. Now there are college-graduate village officials. They don’t know what’s going on. They just follow the old ways. We could have made a living by selling vegetables, but now it’s really hard.”B6: “Therefore, the following proposition is proposed:Proposition 3.The quality of poverty alleviation policy has a significant effect on the livelihood strategy of households. More specifically:Proposition 3a.When the quality of policy enforcement is unsatisfactory, the political relationships have a high possibility to have a significant impact on the livelihood strategy of households.Theoretically, this paper not only extends the research of poverty analysis framework, but also provides a significant perspective for enriching the long-term mechanism of targeted poverty alleviation. Specifically, first, this research incorporates the specific context of relation-based society into the poverty analysis. Taking the relation-based society as an important contextual factor, this paper explores how different types of relationships influence the livelihood strategy of households. It finds that the livelihood strategy of households highly depends on the livelihood condition of the different types of relationships. The undesirable livelihood condition of the genetic relations has a high possibility to lead to the long-term maintained type livelihood strategy of households, while the desirable livelihood condition of the genetic relations has a high possibility to lead to the developmental type livelihood strategy of households. When the livelihood condition of geographic relations is undesirable, it has a high possibility to lead to the short-term maintained type livelihood strategy of households. In so doing, this research unveils the black box of livelihood strategy in the SLA framework by embedding the strategy-making process in the specific context of relation-based society.Second, this research explores the effect of poverty alleviation policy on affected households’ livelihood strategies. The analysis indicates that poverty alleviation policies have a significant effect on the livelihood strategy of households. More specifically, the transfusion alleviation poverty policy induces the poverty-stricken households to choose the maintained type livelihood strategy. Similarly, the hematopoietic poverty alleviation policy induces the poverty-stricken households to choose the maintained type livelihood strategy when the livelihood conditions are undesirable. By contrast, the hematopoietic poverty alleviation policy induces the poverty-stricken households to choose the developmental type livelihood strategy when the livelihood conditions are undesirable. In so doing, this research distinguishes the poverty alleviation policy into the transfusion and hematopoietic poverty alleviation policies and develops the mechanism with respect to the effect of different types of poverty alleviation policy on different types of livelihood strategy.Third, the livelihood conditions as preconditions are introduced into the analysis of livelihood decision-making of the poverty-stricken households. This is because the households respond to policy changes in accordance with their own livelihood conditions. For example, poverty-stricken households tend to convert the allocated resources into maintenance resources to meet their current needs. The internal logic of their livelihood strategy-making helps to explain the reason why polices cannot meet the expectations in some occasions.Last but not least, this paper explores the interaction between the quality of poverty alleviation policy, the relationships and the livelihood strategy. Specifically, the quality of poverty alleviation policy has a significant effect on the livelihood strategy of households, and the political relationships have a high possibility to have a significant impact on the livelihood strategy when the quality of policy implementation is unsatisfactory. Political relationship has been separated out as an important relationship in relation-based society. The inside logic confirms the research by Bian  that relIn practice, this paper provides new ideas for households to achieve sustainable livelihoods, and for governmental departments to formulate long-term poverty alleviation policies. First, it is vital to establish a sound channel of information dissemination, and to strengthen supervision and return visit mechanism. Policy information, employment information, medical information can be fully covered through online and offline information dissemination channels. In most cases, poverty is attributed to education, health condition and age, the poverty-stricken households often have poor understanding ability and have fewer channels to receive information. Therefore, both online and offline channels need to be taken seriously. Meanwhile, village-level resolutions related to poverty alleviation need to be publicized, in order to avoid internal operational risks. Second, it might be practical to carry out the expatriate system, i.e., sending grassroots cadres to another place. In this way, it might reduce the irregular operation in the process of policy implementation caused by the relationships.With respect to the limitations and future study, first of all, the interview objects in this paper are limited to individuals, the poverty alleviation officers are not included. However, a comprehensive understanding of the role and impact of relationships in poverty alleviation policy implementation in the view of the poverty alleviation officers, such as village cadres is important. Second, this paper excludes the discussion of micro corruption. However, during the interview, the informants touched upon this issue regarding how to build relationships with village cadres. For instance, one of the informants mentioned that the minimum living allowance for his son who has a mental disease has been canceled because he did not present a gift to the village cadre. Therefore, in future studies it is critical to explore the effects of microcorruption on poverty alleviation, and further how it influences the livelihood strategy of households. Third, while livelihood capital is not a focal research topic in this paper, in the future the livelihood capital will be a focus and added to the research of poverty alleviation policy in a relation-based society. It might be interesting to investigate how the variations of livelihood capital influence the livelihood strategy under targeted poverty alleviation. Last but not least, although the qualitative method of case study was used in this paper to explore the livelihood strategy of poverty-stricken households in the relation-based society under the targeted poverty alleviation policy, it is necessary to verify the propositions proposed in this paper by using quantitative research methods, such as questionnaire survey in the future. In addition, it is critical to identify the measurement of important constructs in the conceptual framework, for instance, the measurement of livelihood capital from a multidimensional perspective.Starting with the qualitative research methods of observation and interviews, this paper creatively uses grounded theory to investigate poverty-stricken households’ livelihood strategy when facing targeted poverty alleviation in a relation-based society. The paper finds that relationships have a significant impact on the poverty-stricken households’ livelihood strategy. The types of relationships have different effects on the livelihood strategies of households. This study is not only an extension of the SLA in the research context, but also provides a significant perspective for enriching the long-term mechanism of targeted poverty alleviation by building a theoretical model of the relationship between a relation-based society, targeted poverty alleviation and the livelihood strategies of households.


Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid. With industrialisation, population growth and economic development, the dependence on electricity is ever so increasing, and consequently, electricity consumption is on the hike. This increased electricity consumption causes a problem of energy scarcity and environmental degradation. The conventional grid is unable to solve such problems. Thus, smart grid is stimulated as a smart solution. The smart grid introduces novel ways of electricity generation, namely renewable energy. The smart cities in the smart grid accommodate renewable energy sources, the internet of things enabled devices, communication technologies and dynamic pricing servers to meet this rising electricity demand .2) are expected to reduce due to renewable energy integrated microgrids [The electricity generation from renewable energy sources in smart cities employing internet of things enabled devices is drawing a lot of attention from researchers nowadays due to their environment-friendliness and sustainability. Moreover, due to the high electricity consumption of conventional devices and high operational costs of conventional generators, the internet of things enabled devices, and renewable energy generation is preferred . Keepingcrogrids . The enecrogrids . By instcrogrids . ResearcBy installing agents-based DSLM systems at the consumer end, consumer demand will become more predictable and deterministic in future smart grids ,7. AuthoIn this study, we aim to control the elastic demand for future smart grids. For this, we discussed the problem of the elastic demand control and its automation through dynamic pricing and regulating it to the DR programs at the consumer’s end. Then, a brief discussion is conducted on opportunities of closed-loop elastic demand control by employing a dynamic price of demand response programs. In addition, the STSMC is employed to control a closed-loop elastic demand to minimise energy balance error using dynamic energy price of demand response programs. The operation of the proposed model is briefly explained as dependence on a mismatch between demand and supply called closed-loop feedback error  where STSMC creates the pricing signal and drives the feedback error towards 0 in time. According to the control theory, a closed-loop control system is stable, if its balance error approaches to 0. Thus, in our case, closed-loop elastic demand control based on STSMC obtains energy balance by persistently broadcasting energy prices.For simulations, we have developed a model in MATLAB/Simulink environment, which is composed of STMC and a dynamic pricing demand response model with feedback. The demand response model developed in Simulink is based on a first-order dynamic system with variable gain. The developed model has applied on an example smart microgrid integrated with renewable energy to control closed-loop elastic demand control for demand-side management. The efficacy of the proposed STMC-based model is validated by comparing it to the benchmark models based on PI controller, FOPI controller, FOPID controller and FOPD controller in terms of objectives. Simulation results illustrate that our proposed STSMC based closed demand control is affective to maintain energy balance while generation fluctuating in smart microgrids.The structure of the paper is as follows: In the literature, various techniques are implemented by researchers in recent years to control the demand of consumers on price-based demand models. Price-based DR models are presented ,16. A roThe performance of the integral controller (I), PI controller, PID controller and integral derivative controller (ID) are discussed whose parameters are tuned through the genetic algorithm (GA) through which generation is controlled . The resThe replacement of the integral and derivative operators of the classical PI controller leads to a controller called fractional order PI controller. The application of real order for the integral and derivative operators is the significant foundation of fractional calculus. Numerous descriptions exist in the literature for the FO derivative, but an FOPI controller proposed for the closed-loop elastic demand control is employed . In addiThe transfer function for the version of FOPI controller commonly written as:) method .The PID control loop mechanism is the best possible solution for minimising the online and unknown faults in real time operations. Applications of the PID controller in industry is to access the single fine grained measurements for the improvement of the closed-loop operations. Tuning of the coefficients of PID controller is a challenging step for the researcher and in itself has a wide area of research. For simplicity of the controller, we used the coefficients’ values used for tuning the coefficients of the PID controller . Block dPIDs have 3 coefficients; e is the error signal in negative feedback closed-loop system. Block diagram of using PI controller in simulations for closed-loop demand control is illustrated in In closed-loop industrial processes, the combination of proportional and integral gains is the most widely used classical controller which plays vital role in balancing of level, flow, pressure and other such like industrial process variables that do not involve too much delays. While balancing the industrial plant problems, the PI controller still has problems that inherit with the controller actions. Usually there is high overshoot, initial value and a greater response time of using PI controller for a larger load demand. The author used a PI controller for elastic demand control . There iA specific form of the most common fractional order PID control where I and fractional operator of I is equal to zero which forms a fractional order proportional derivative controller. The relation of I/O for the FOPD controller is commonly written as;e is the error signal, The STSMC algorithm is used for the control of the uncertainty of the linear plants to determine the robustness ,54. STSMOne of the applications of STSMC is controlling nonlinear uncertain systems. STSMC has 3 coefficients; The purpose of this work is to set up tuning rules for achieving an a priori nominative settling time for planar systems. The feedback of the system is formed by using the STSMC to estimate one of the states. States which are estimated and measured, are then used by the STSMC for tuning in the presence of external disruption. We propose STSMC in this work because of its robustness and accuracy. The proposed STSMC technique narrows down the gap and the demand traces the generation profile in an optimal manner. This work is the continuation of our earlier conference paper . With thA power system that consists of generation, transmission lines and distribution system.A communication system between the supply side and the demand side. This communication is done through power lines, modems, routers, wireless technologies, etc.An STSMC which originates a price signal based on the mismatch between the instant demand and generation.The supply for the proposed model is fluctuating renewable energy that will be supplied from a local renewable energy grid station with a utility bulk power supply which is used as a backup source. This bulk electricity from utility will be supplied when the generation from renewable energy is deficient. The renewable energy supply profile values are taken from  as shownG(t) is the instant generation and Do(t) is the instant demand of the consumer which feedback to the system again numerically given by the mismatch between the instant demand and the instant generation known as error (e) into the STSMC.The error signal feeding in to STSMC can be expressed through Equation ;(8)e=G(The following equation is taken as elastic DR model function with time constant response .(9)τdDoDo(t) refers to the demand of consumers at any instant of time and D(p) refers to the price DR function of a market which is taken here as piece-wise-linear function. There is also a delay In Equation , Do(t) r1.There are always limited consumers to which the electricity has to be supplied. We assume that for a low price 2.As there are critical loads, electricity must be supplied to them even at a high price 3.In the range between Equation ;It is the basic principle of the market that when the price of something increases automatically, the demand of it decreases drastically. Based on this principle of the market, this whole system is designed for elastic demand control. The price demand model is designed in such a way that it has a high demand In Equation , the elaBy taking Laplace transform of Equation , we obtaThe price varying first-order dynamic equation for the overall demand of consumers’ response of electricity market models is the above equation. There are uncertain conditions sometimes at the demand side where the demand increases or decreases abruptly. In these cases, the grid can reduce uncertainty and make the DR of consumer easy to predict. In simulations, this uncertainty factor was added through generating random numbers in the overall DR to make the simulation more realistic and similar to operations taking place in smart grids. Figure 13 shows overall renewable energy generation profile.For simulations, MATLAB/Simulink is used for creating the overall environment for the proposed model and to implement the closed-loop elastic demand control. The block diagram of the simulations is shown in The step response of the closed-loop elastic demand control for energy market simulation is demonstrated in As a result of the inverse relationship between demand and price, the coefficients K1 and K3 of the STSMC are tuned negative. K2 will be between 0 and 1. For the response of demand and price to a step generation signal as an input using the STSMC, classical and fractional order controllers are shown in ctricity . Thus, vIn this section, there is a scenario created for closed-loop elastic demand control for which the MATLAB/Simulink model is formulated. The block diagram for the simulations is shown in Equation ;(13)Do=Equation . The supEquation ;(14)DoMTo avoid the excessive energy generation and to cut down the expenses of the production of energy, there is a limit which is equivalent to the maximum demand of the consumer that cannot be exceeded during the supply of energy. This can be represented as shown in Equation ;(15)TR+For the maximum efficiency and reliable utilisation of the renewable energy generation, the overall generation and minimum and maximum local demand of the consumer can be planned as shown in Equation ;(16)DoMIn Equation , for theEquation ;(17)TR=i = 1, 2, 3, ..., 24. When this data set is formed in [Equation  refers tormed in , the valThe elastic demand control of the system is estimated to price using a piece-wise-linear function. The demand function of the system is as follows in Equation :(19)D(t)In Equation , for theThis piece-wise demand function is designed for the closed-loop elastic DR and states that when the price of electricity is low, then there will be high demand of consumers and when the price of the electricity is high, then the demand of the consumer is low, depending on the price signal coming from the STSMC. The piece-wise-linear function formed for the price DR is shown in Equation  is used Equation . The oveEquation .PI controllers have 2 control coefficients Getting response for the system by using PI controller, there is a strong tendency of getting an overshoot at the start . AlthougA PI controller with additional fraction order operator called d system . The DR Same as the PI controller, the coefficient values for the FOPI are kept negative because of inverse relation between demand and price, whereas the value for Proportional integral derivative (PID) is the widely used classical controller. PID controllers have 3 control coefficients consumer . The demWhen the simulation of the closed-loop elastic demand using the FOPD controller starts, the demand starts tracing the generation with overshooting at the start of the simulation. Although generation started to trace generation, the gap between the generation and demand of consumer can be clearly seen in When the simulation starts, the demand of the consumer is controlled successfully with STSMC by continuously providing a price signal to the elastic demand model. The demand started tracing the renewable energy generation at about 1.8 h after starting the simulation. The tracing time of generation by demand is the lowest among the controllers. i.e., PI, FOPI, PID and FOPD. The demand of consumer traced the renewable energy generation to the end of the simulation. The results of the simulation show that closed-loop elastic demand control with STSMC can successfully trace renewable energy generation without overshooting at the start of the simulation. The demand of the system also does not overshoot at the start of the simulation as in ,43 by usThe results of the simulation show that closed-loop elastic demand control in conjunction with STSMC can successfully trace the renewable energy generation without overshooting at the start of the simulation.This whole scenario is discussed in , where tSTSMC outperforms PI, FOPI, PID and FOPD controllers in tracing the generation by the demand of consumer in a renewable energy integrated microgrid. The values of DR of STSMC in comparison with PI, FOPI, PID and FOPD are shown in The demand and price response of the closed-loop elastic demand-control model for the triangular waveform as input is shown in This paper employs elastic demand control of a renewable generation integrated microgrid by a closed−loop STSMC. This STSMC regulates the price signal of the DSLM system which plays a major role in the automation of demand control and also making demand more predictable and deterministic. Price signal from STSMC feeds into the DSLM installed in smart meters at consumers’ premises through which the balance of generation and demand of the microgrid is accomplished via closed-loop STSMC. In this paper, the renewable energy integrated microgrid scenario is discussed through which the closed-loop elastic demand of the system is controlled by tuning the coefficients of the STSMC. For simulations, the whole scenario environment is simulated in MATLAB/Simulink. The price-based demand model for the current scenario is obtained from the hourly price and demand data. In addition, conditions of high demand and low demand of the consumer were discussed for electricity import from the bulk utility supply. This was added in simulations to cut down the additional expenses and operational cost in the smart grid. The process of broadcasting the price to the DSLM agents takes time, in order to show effectiveness of the method in the cases of demand uncertainty and system delays. We used price signal-broadcast delay for making simulations for scenario more realistic. System delay was added after STSMC in the system which is equal to 36 s. We observed from the simulations that price-based elastic demand control by STSMC can exhibit the outstanding performance of tracing of renewable energy integrated microgrid generation. Dynamic energy pricing is a key figure in the smart grid to control the demand of consumers in renewable energy market management. Our findings shows that the STSMC, very quickly after just 1.8 h, starts tracing the generation. The classical and fractional order controller overshoots and follows to trace the generation after 6 h which is significantly higher as compared with STSMC. Thus, our proposed method outperforms the existing methods.