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Efficient security patch distribution is of essential importance for updating anti-virus software to ensure effective and timely virus detection and cleanup. In this paper, we propose a mixed strategy of patch distribution to combine the advantages of the traditional centralized patch distribution strategy and decentralized patch distribution strategy. A novel network model that contains a central node and a multiplex network composed of patch dissemination network layer and virus propagation network layer is presented, and a competing spreading dynamical process on top of the network model that simulates the interplay between virus propagation and patch dissemination is developed. Such a new framework helps in effectively analyzing the impacts of patches distribution on virus propagation, and developing more realizable schemes for restraining virus propagation. Furthermore, considering the constraints of the capacity of the central node and the bandwidth of network links, an optimal allocation approach of patches is proposed, which could simultaneously optimize multiple dynamical parameters to effectively restrain the virus propagation with a given budget. Accepted version

The linear threshold model is widely adopted as a classic prototype for studying contagion processes on social networks, where nodes, representing individuals, are assumed to be in one of two states: inactive or active. Each inactive node can be activated via a threshold rule during evolution. Although both contagion mechanisms and network impacts have been well studied, very few studies paid attention on the effect of interacting strengths on the threshold rule. In this paper, a modified linear threshold model on weighted networks is proposed. On one hand, the weight of a link in the network is characterized by a power-law function of the product of endpoint degrees. On the other hand, peer influences on a node incorporate both the number of its active neighbors and associated link weights. The systematic dynamics is explored by the combination of the spin-glass theory and Monte-Carlo algorithm. In analogy to unweighted networks, a global cascade is not triggered in weighted networks when the average degree of nodes is either too small or too large, however, large cascades are realized within an intermediate range, which is referred to as the cascade window. Moreover, two regimes of the power exponent of the weight function are identified in which the system exhibits distinct behaviors: when networks are very sparse, there exist one extreme of the weight exponent making the system susceptible to large cascades; when the networks are relatively dense, on the contrary, there exists the other extreme of the weight exponent causing the system to maintain optimal robustness. All these results demonstrate the importance of both network connectivity and link weights, and offer a sophisticated description of social contagions. Ministry of Education (MOE) This work was partly supported by Natural Science Foundation of China under Grant No. 11331009, Science and Technology Commission of Shanghai Municipality, China under Grant No. 17ZR1445100, and Ministry of Education of Singapore under Grants MOE2014-T2-1-028 and MOE2016-T2-1-119.

The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks. The main contributions of this study are four-fold. First, the multiscale principal component analysis method is utilized in the preprocessing module to obtain robustness against noise. Second, a novel automated channel selection strategy is proposed and then is further verified with comprehensive comparisons among three different strategies for decoding channel combination selection. Third, a sub-band alignment method by utilizing MEWT is adopted to obtain joint instantaneous amplitude and frequency components for the first time in MI applications. Four, a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load. Extensive experiments for subject-specific and subject independent cases are conducted with the three-benchmark datasets from BCI competition III to evaluate the performances of the proposed method by employing typical machine-learning classifiers. For subject-specific case, experimental results show that an average sensitivity, specificity and classification accuracy of 98% was achieved by employing multilayer perceptron neural networks, logistic model tree and least-square support vector machine (LS-SVM) classifiers, respectively for three datasets, resulting in an improvement of upto 23.50% in classification accuracy as compared with other existing method. While an average sensitivity, specificity and classification accuracy of 93%, 92.1% and 91.4% was achieved for subject independent case by employing LS-SVM classifier for all datasets with an increase of up to 18.14% relative to other existing methods. Results also show that our proposed algorithm provides a classification accuracy of 100% for subjects with small training size in subject-specific case, and for subject independent case by employing a single source subject. Such satisfactory results demonstrate the great potential of the proposed MEWT algorithm for practical MI EEG signals classification. Published version

As one of the key techniques determining the overall system performances, efficient and reliable algorithms for improving the classification accuracy of motor imagery (MI) based electroencephalography (EEG) signals are highly desired for the development of brain-computer interface (BCI) systems. In this study, we propose, for the first time to the best of our knowledge, a novel data adaptive empirical wavelet transform (EWT) based signal decomposition method for improving the classification accuracy of MI based EEG signals. Specifically, to reduce the system complexity and execution time, the proposed method selects 18 electrodes out of 118 to analyze the non-stationary and nonlinear EEG signal behaviors. Meanwhile, the method adopts the Welch power spectral density (PSD) analysis method for single mode selection out of the total 10 for each channel, and the Hilbert transform (HT) method for both instantaneous amplitude (IA) and instantaneous frequency (IF) signal components extraction for each selected mode. With seven commonly used machine-learning classifiers adopted, extensive experiments were conducted with the benchmark dataset IVa from BCI competition III to evaluate the performance of the proposed method. Results show that with the IA and IF component features being tested using the least-square support vector machine (LS-SVM) classifier, the EWT method achieves an average classification accuracy of 95.2% and 94.6% respectively, which is higher as compared with the existing methods. While for every participant, a classification accuracy of at least 80% could be achieved by employing a single feature only. Results also show that a combination of EWT and higher order statistics features, which contain both kurtosis and skewness of the extracted instantaneous components, help achieve a higher success rate. The better performances of EWT over those of the existing methods demonstrate the effectiveness and great potential of EWT for BCI system applications.

For the future development of integrated energy systems with high penetration of renewable energy, an integrated community energy systems (ICES) dispatch model is proposed including various renewable energy sources and energy conversion units. Energy coupling matrices of ICES based on traditional energy hub (EH) models are constructed. Uncertainties of long‐term forecast data of renewable energy sources and internal loads are depicted by multi‐interval uncertainty sets (MIUS). To cope with the impacts caused by uncertainties of renewable energy sources and internal loads, the whole dispatch process is divided into two stages. Considering various constraints of ICES, we solved the dispatch model through the improved particle swarm optimization (IPSO) algorithm in the first stage. The optimal evolutionary dispatch is then proposed in the second stage to overcome the evolution and errors of short‐term forecast data and obtain the optimal dispatch plan. The effectiveness of the proposed dispatch method is demonstrated using an example considering dramatic uncertainties. Compared with the traditional methods, the proposed dispatch method effectively reduces system operating costs and improves the environmental benefits, which helps to achieve a win‐win situation for both energy companies and users. Published version

Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic components or modes from electroencephalogram (EEG) signals for the development of robust brain-computer interface (BCI) systems. This study proposes a novel automated computerized framework for proficient identification of motor and mental imagery (MeI) EEG tasks by employing empirical Fourier decomposition (EFD) and improved EFD (IEFD) methods. Specifically, the multiscale principal component analysis (MSPCA) is rendered to denoise EEG data first, and then, EFD is utilized to decompose nonstationary EEG into subsequent modes, while the IEFD criterion is proposed for a single conspicuous mode selection. Finally, the time-and frequency-domain features are extracted and classified with a feedforward neural network (FFNN) classifier. Extensive experiments are conducted on four multichannel motor and MeI data sets from BCI competitions II and III using a tenfold cross-validation strategy. Results compared with the other existing methods demonstrated that the highest classification accuracies of 99.82% (data set IV-A), 93.33% (data set IV-b), 91.96% (data set III), and 88.08% (data set V) in subject-specific scenarios, while 82.70% (data set IV-A) in the subject-independent framework are achieved for IEFD with FFNN classifiers collectively. The overall exploratory results authenticate that the proposed IEFD-based automated computerized framework not only outperforms the conventional SD methods but is also robust and computationally efficient for the development of subject-dependent and subject-independent BCI systems. This work was supported in part by the National Key Research and Development Program of Shaanxi (2021SF-342), Fundamental Research Funds for the Central Universities (G2018KY0308), China Postdoctoral Science Foundation under Grant (2018M641013), Postdoctoral Science Foundation of Shaanxi Province (2018BSHYDZZ05).

BackgroundA great concern around the globe now is to mitigate the COVID-19 pandemic via contact tracing. Analyzing the control strategies during the first five months of 2020 in Singapore is important to estimate the effectiveness of contacting tracing measures.MethodsWe developed a mathematical model to simulate the COVID-19 epidemic in Singapore, with local cases stratified into 5 categories according to the conditions of contact tracing and self-awareness. Key parameters of each category were estimated from local surveillance data. We also simulated a set of possible scenarios to predict the effects of contact tracing and self-awareness for the following month.FindingsDuring January 23 - March 16, 2020, the success probabilities of contact tracing and self-awareness were estimated to be 31% (95% CI 28%-33%) and 54% (95% CI 51%-57%), respectively. During March 17 - April 7, 2020, several social distancing measures (e.g., limiting mass gathering) were introduced in Singapore, which, however, were estimated with minor contribution to reduce the non-tracing reproduction number per local case (Rι,2). If contact tracing and self-awareness cannot be further improved, we predict that the COVID-19 epidemic will continue to spread in Singapore ifRι,2≥ 1.5.ConclusionContact tracing and self-awareness can mitigate the COVID-19 transmission, and can be one of the key strategies to ensure a sustainable reopening after lifting the lockdown.SummaryWe evaluate the efficiency of contact tracing and self-awareness in Singapore’s early-stage control of COVID-19. Then use a branching model to simulate and evaluate the possible prospective outcomes of Singapore’s COVID-19 control in different scenarios.

We study opinion dynamics on complex social networks where each individual holding a binary opinion on a certain subject may change her/his mind to match the opinion of the majority. Two rules of interactions between individuals, termed as classic majority and influence majority rules, respectively, are imposed on the social networks. The former rule allows each individual to adopt an opinion following a simple majority of her/his immediate neighbors, while the latter one lets each individual calculate the influence of each opinion and choose to follow the more influential one. In this calculation, the influences of different opinions are counted as the sum of the influences of their respective opinion holders in neighborhood area, where the influence of each individual is conveniently estimated as the number of social connections s/he has. Our study reveals that in densely-connected social networks, all individuals tend to converge to having a single global consensus. In sparsely-connected networks, however, the systems may exhibit rich properties where coexistence of different opinions, and more interestingly, multiple steady states of coexistence can be observed. Further studies reveal that low-degree and high-degree nodes may play different roles in formulating the final steady state, including multi-steady states, of the systems under different opinion evolution rules. Such observations would help understand the complex dynamics of opinion evolution and coexistence in social systems. Ministry of Education (MOE) Published version The research is partially supported by Ministry of Education, Singapore, under contract MOE2016-T2-1-119. QW’s work is partially supported by National Natural Science Foundation of China (NSFC) under the Grant No. 61663015, and CX’s work is partially supported by NSFC underGrants No. 61773286.

Match making is of significant importance in some social systems. People may need to seek for romantic partners, teammates, collaborators, etc. In this paper, we propose a minimalist framework of match making in complex networks. Specially we adopt a simple model where each individual would greedily seek for making a match with the strongest partner within his/her social connection range. We explore a few matching schemes including greedy mode, roulette wheel selection mode and completely random mode on different networks. We also investigate when social systems become more densely connected, how the match making process would be affected. Our observations show that, in a more densely connected social network, individuals’ efforts for seeking for matches with the strongest partners would be more likely to end up with matching with someone similar to themselves. Meanwhile, the cost of such an approach may be quickly increased. The implications of such observations in real-life systems and open problems are briefly discussed. Ministry of Education (MOE)

Integrating massive electric vehicles (EVs) into the power grid requires the charging of EVs to be coordinated to reduce the cost and guarantee system stability. The coordination becomes more challenging when the EV owners have different charging preferences. To tackle this problem, a hybrid centralized–decentralized charging control scheme is designed in this paper which mainly includes three parts, as follows. On the centralized charging side, an offline optimal scheduling approach is first presented, aiming at minimizing the energy cost while satisfying the charging requirements of EVs. To deal with the system dynamics and uncertainties, a model predictive control-based adaptive scheduling strategy is then developed to determine the near optimal EV charging profiles in real time. On the decentralized charging side, the interactions between EVs and the charging system controller is modeled as a leader–follower noncooperative Stackelberg game in which the system controller acts as the leader and the EVs act as followers. The existence of the equilibrium state and its optimality are proved and analyzed. It is shown that by adopting the proposed decentralized charging algorithm, the communication burden between EVs and the system controller is low and that the charging scheme is robust to poor communication channels. Last, we investigate how the size of these two charging groups impacts the system utility and propose an algorithm maximizing the total revenues of the whole system. Simulation results evaluate the performances of the scheme and investigate the parameters’ impacts on the system utilities. The proposed approach and obtained results may provide guidelines for improving the efficiency of the charging park operation and provide useful insights, helping the system operator develop rational investment strategies.

The accurately estimated state is of great importance for maintaining a stable running condition of power systems. To maintain the accuracy of the estimated state, bad data detection (BDD) is utilized by power systems to get rid of erroneous measurements due to meter failures or outside attacks. However, false data injection (FDI) attacks, as recently revealed, can circumvent BDD and insert any bias into the value of the estimated state. Continuous works on constructing and/or protecting power systems from such attacks have been done in recent years. This survey comprehensively overviews three major aspects: constructing FDI attacks; impacts of FDI attacks on electricity market; and defending against FDI attacks. Specifically, we first explore the problem of constructing FDI attacks, and further show their associated impacts on electricity market operations, from the adversary's point of view. Then, from the perspective of the system operator, we present countermeasures against FDI attacks. We also outline the future research directions and potential challenges based on the above overview, in the context of FDI attacks, impacts, and defense.

This paper investigates the problem of defending against false data injection (FDI) attacks on power system state estimation. Although many research works have been previously reported on addressing the same problem, most of them made a very strong assumption that some meter measurements can be absolutely protected. To address the problem practically, a reasonable approach is to assume whether or not a meter measurement could be compromised by an adversary does depend on the defense budget deployed by the defender on the meter. From this perspective, our contributions focus on designing the least-budget defense strategy to protect power systems against FDI attacks. In addition, we also extend to investigate choosing which meters to be protected and determining how much defense budget to be deployed on each of these meters. We further formulate the meter selection problem as a mixed integer nonlinear programming problem, which can be efficiently tackled by Benders’ decomposition. Finally, extensive simulations are conducted on IEEE test power systems to demonstrate the advantages of the proposed approach in terms of computing time and solution quality, especially for large-scale power systems.

This paper aims to investigate the risk identification problem of power transmission system that is integrated with renewable energy sources. In practice, the fluctuation of power generation from renewable energy sources can lead to the severe consequences to power transmission network. By treating the fluctuation of power generation as the control input, the risk identification problem is formulated with the aid of optimal control theory. Thus, a control approach is developed to identify the fluctuation of power generation that results in the worst-case cascading failures of power systems. Theoretical analysis is also conducted to obtain the necessary condition for the worst power fluctuations on power system buses. Finally, numerical simulations are implemented on IEEE 9 Bus System to demonstrate the effectiveness of the proposed approach.

Integrating massive electric vehicles (EVs) into the power grid requires charging to be coordinated to reduce energy costs and the peak-to-average ratio (PAR) of the system. The coordination becomes more challenging when the highly fluctuant renewable energies constitute a significant portion of the power resources. To tackle this problem, a novel two-stage EV charging mechanism is designed in this paper, which mainly includes three parts. At the first stage, based on the prediction of future energy requests and considering the elastic charging property of EVs, an offline optimal energy generation scheduling problem is formulated and solved in a day-ahead manner to determine the energy generation in each time slot the next day. Then, at the second stage, based on the planned energy generation day-ahead, an adaptive real-time charging strategy is developed to determine the charging rate of each vehicle in a dynamic manner. Finally, we develop a charging rate compression (CRC) algorithm, which tremendously reduces the complexity of the problem solving. The fast algorithm supports real-time operations and enables the large-scale small-step scheduling more efficiently. Simulation results indicate that the proposed scheme can help effectively save energy costs and reduce the system PAR. Detailed evaluations on the impact of renewable energy uncertainties show that our proposed approach performs well in enhancing the system fault tolerance against uncertainties and the noises of real-time data. We further extend the mechanism to track a given load profile and handle the scenario where EVs only have several discrete charging rates. As a universal methodology, the proposed scheme is not restricted to any specific data traces and can be easily applied to many other cases as well.

Opportunistic underwater sensor networks (OUSNs) are deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. However, the storage capacity of the sensor nodes in such networks may be insufficient, especially when a wealth of data messages are generated rapidly in some emergency response applications. The message dissemination in OUSNs therefore may differ significantly from those in wireless sensor networks or delay-tolerant networks, where network throughput should be taken as one of the primary objectives of network performance. To this end, the strategies for message storing, disseminating and discarding are investigated, and a Message Dissemination Approach for Storage-Limited (MDA-SL) OUSNs is proposed. In MDA-SL, the messages are preferred to be disseminated to the nodes with higher speed or larger residual storage. In addition, the copies of newer messages are inclined to be discarded when their message holders’ storage is full. Simulation results demonstrate the excellent performance of MDA-SL, showing that it can achieve satisfactory network throughput with propagation delay being restricted according to the diverse application requirements.

Motivated by recent developments in Wireless Sensor Networks(WSNs), we present distributed clustering algorithms for maximizingthe lifetime of WSNs, i.e., the duration till the first node dies. Westudy the joint problem of prolonging network lifetime by introducing clustering techniques and energy-harvesting (EH) nodes. Firstlywe propose distributed clustering algorithm for maximizing the lifetime of clustered WSN, which includes EH nodes, serving as relaynodes for cluster heads (CHs). Secondly graph-based and LP-basedEH-CH matching algorithms are proposed which serve as benchmarkalgorithms. Extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently

Recent advances in wavelength division multiplexing (WDM) networks have helped enhance the popularity of multicasting services. However, as a single network failure may disrupt the information transmission to multiple end-users, protecting multicast requests against network failures becomes an important issue in network operation. This paper investigates the sub-wavelength level protection for dynamic multicast traffic grooming. A new method named lightpath-fragmentation based segment shared protection (LF-SSP) scheme is proposed. By carefully splitting primary/backup lightpaths into segments to improve resource sharing for both traffic grooming and protection, LF-SSP aims to minimize the network resources allocated for request protection. Extensive simulations are carried out to compare the performance of LF-SSP to some existing approaches, on sub-wavelength-level as well as wavelength-level multicast protections in different cases. Results show that LF-SSP steadily outperforms these existing methods as long as the network resources are not too limited. Influences of the add/drop port resources and the average number of destinations per connection request on the LF-SSP performance are also evaluated. Accepted version

Today’s transportation section accounts for a significant portion of petroleum consumption and greenhouse gases emissions worldwide. Statistics show that \(63.7\%\) of the petroleum consumed in the world in 2012 was due to the transport sector, which caused emission of 7135 million tons of carbon dioxide into the environment (International energy agency: key world energy statistics (2014), [1]). The world’s fossil fuel scarcity, as well as the growing environmental crisis associated with their wide usage, is driving the electrification of transportation and extensive use of electric vehicles (EVs). EVs are emerging as an efficient and sustainable alternative for private and public road transportation.

Energy generation scheduling to achieve robust and economic power supply is an essential component in microgrids. Two features of microgrids are the integration of large-scale renewable sources and the use of combined heat and power (CHP) generators. Such features, however, impose significant difficulties on the design of intelligent control strategies for microgrids.

In the world today, fossil fuels are the dominant energy sources for both transportation sector and electricity generation industry.

Community structures widely exist in various complex networks. Extensive studies have been carried out on defining and quantifying community structures as well as developing algorithms for detecting them in extra-large complex systems. Despite all these efforts, however, our understanding of why community structures widely exist in so many real-life systems, or in other words, the benefits/drawbacks for real-life systems to have community structures, remains to be rather limited. In this work, we discuss on the effects of community structures on infection propagation, detection and control in complex networks. Specifically, we investigate (i) the effects of community structures on transmission speed and infection size; (ii) when monitors can be deployed in the network to detect the infection spreading, the effects of community structures on early-stage infection detection and (iii) in adaptive networks with link rewiring for isolating the infected nodes, the effects of community structures on infection control. Our results show that the existence of community structures generally speaking helps slow down the infection spreading; whether it helps reduce the overall infection size when no control method is adopted however, depends on the network topology. When infection detection and controlling methods such as link rewiring are adopted, the existence of community structures steadily helps improve the efficiency of infection detection and control, though having too many communities may not necessarily bring along additional benefits. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version

It is interesting and of significant importance to investigate how network structures co-evolve with opinions. The existing models of such co-evolution typically lead to the final states where network nodes either reach a global consensus or break into separated communities, each of which holding its own community consensus. Such results, however, can hardly explain the richness of real-life observations that opinions are always diversified with no global or even community consensus, and people seldom, if not never, totally cut off themselves from dissenters. In this article, we show that, a simple model integrating consensus formation, link rewiring and opinion change allows complex system dynamics to emerge, driving the system into a dynamic equilibrium with co-existence of diversified opinions. Specifically, similar opinion holders may form into communities yet with no strict community consensus; and rather than being separated into disconnected communities, different communities remain to be interconnected by non-trivial proportion of inter-community links. More importantly, we show that the complex dynamics may lead to different numbers of communities at steady state with a given tolerance between different opinion holders. We construct a framework for theoretically analyzing the co-evolution process. Theoretical analysis and extensive simulation results reveal some useful insights into the complex co-evolution process, including the formation of dynamic equilibrium, the phase transition between different steady states with different numbers of communities, and the dynamics between opinion distribution and network modularity, etc., Comment: 12 pages, 8 figures, Journal

Individual decision to accept a new idea or product is often driven by both self-adoption and others’ persuasion, which has been simulated using a double threshold model [Huang et al., Scientific Reports 6, 23766 (2016)]. We extend the study to consider the case with limited persuasion. That is, a set of individuals is chosen from the population to be equipped with persuasion capabilities, who may succeed in persuading their friends to take the new entity when certain conditions are satisfied. Network node centrality is adopted to characterize each node’s influence, based on which three heuristic strategies are applied to pick out persuaders. We compare these strategies for persuader selection on both homogeneous and heterogeneous networks. Two regimes of the underline networks are identified in which the system exhibits distinct behaviors: when networks are sufficiently sparse, selecting persuader nodes in descending order of node centrality achieves the best performance; when networks are sufficiently dense, however, selecting nodes with medium centralities to serve as the persuaders performs the best. Under respective optimal strategies for different types of networks, we further probe which centrality measure is most suitable for persuader selection. It turns out that for the first regime, degree centrality offers the best measure for picking out persuaders from homogeneous networks; while in heterogeneous networks, betweenness centrality takes its place. In the second regime, there is no significant difference caused by centrality measures in persuader selection for homogeneous network; while for heterogeneous networks, closeness centrality offers the best measure. MOE (Min. of Education, S’pore) Published version

Overlay IP/MPLS over WDM network is a promising network architecture starting to gain wide deployments recently. A desirable feature of such a network is to achieve efficient routing with limited information exchanges between the IP/MPLS and the WDM layers. This paper studies dynamic label switched path (LSP) routing in the overlay IP/MPLS over WDM networks. To enhance network performance while maintaining its simplicity, we propose to learn from the historical data of lightpath setup costs maintained by the IP-layer integrated service provider (ISP) when making routing decisions. Using a novel historical data learning scheme for logical link cost estimation, we develop a new dynamic LSP routing method named Existing Link First (ELF) algorithm. Simulation results show that the proposed algorithm significantly outperforms the existing ones under different traffic loads, with either limited or unlimited numbers of optical ports. Effects of the number of candidate routes, add/drop ratio and the amount of historical data are also evaluated.

Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the “peculiar” dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version

The self-clocking principle (SCP) of Transmission Control Protocol (TCP) had been analyzed for a network implementing a per-flow buffering scheme. The ideal SCP is yet unknown for the Internet which implements a first-in–first-out buffering scheme. This paper derives an ideal SCP for the Internet by formulating the traffic transmission control as a typical control problem and then solving it by a control-theoretic approach. The ideal SCP reveals the defect of the SCP being deployed in the Internet that it is insufficient to avoid congestion by adjusting the packet effective window based on records of the outstanding packets of a single source; instead outstanding packets from other sources also have to be counted. The ideal SCP also reveals the difficulties of developing and implementing an effective self-clocking scheme for congestion control in the Internet.

The available methods for selection of controlled variables (CVs) using the concept of self-optimizing control have been developed under the restrictive assumption that the set of active constraints remains unchanged for all the allowable disturbances. To keep the input and output variables within their allowable bounds, the use of cascade controllers, and to track the optimal set of active input constraints, the use of split-range controllers is suggested in literature. In this paper, we propose a different strategy, where CVs are selected as linear combinations of measurements to minimize local average loss, while ensuring that all the constraints are satisfied over the allowable set of disturbances. This result is extended to select a few of the available measurements, whose combinations are used as CVs. The proposed approach offers simpler implementation of operational policy for processes with tight operational constraints. We use the case study of forced-circulation evaporator to illustrate the usefulness of the proposed method.

Scale-free networks are prone to epidemic spreading. To provide cost-effective protection for such networks, targeted immunization was proposed to selectively immunize the hub nodes. In many real-life applications, however, the targeted immunization may not be perfect, either because some hub nodes are hidden and consequently not immunized, or because the vaccination simply cannot provide perfect protection. We investigate the effects of imperfect targeted immunization in scale-free networks. Analysis and simulation results show that there exists a linear relationship between the inverse of the epidemic threshold and the effectiveness of targeted immunization. Therefore, the probability of epidemic outbreak cannot be significantly lowered unless the protection is reasonably strong. On the other hand, even a relatively weak protection over the hub nodes significantly decreases the number of network nodes ever getting infected and therefore enhances network robustness against virus. We show that the above conclusions remain valid where there exists a negative correlation between nodal degree and infectiousness.

An integrated configuration is proposed to convert tunable slow light from signal to another frequency in a wide bandwidth by using a 70 m-long highly nonlinear photonic crystal fiber (HN-PCF). A 10 GHz RZ signal is delayed by a 10 Gbit/s 231-1 pseudo random bit sequence (PRBS) stimulated Brillouin scattering (SBS) pump, and the slow light is converted to another frequency in a broadband by four-wave mixing (FWM). By this way, not only the slow light is converted, but the idler power is enhanced greatly. In our experiment, all-optical controlled 37.5 ps delay time is converted in a 40 nm bandwidth flatly, and 4.7 dB idler power is enhanced simultaneously. The experimental results are in good agreement with those of the theory.

In distributed lightpath restoration in wavelength-routed networks, different restoration operations may compete for the same wavelength on the same link and get blocked though there are still plenty of idle capacities on the link. In this paper, we propose two different wavelength assignment methods within the same framework for lowering such type of blocking. Simulation results show that the proposed methods significantly outperform the existing ones. Theoretical analysis confirms the optimality of the proposed methods for a special case; while for a few most important more general cases, it is shown that the optimal performance cannot be guaranteed by any distributed wavelength assignment method with a predefined wavelength searching sequence. For different cases where the original lightpath establishment has adopted random and first-fit wavelength assignments respectively, we discuss the different concerns in developing efficient distributed restoration schemes.

Distributed lightpath provisioning in wavelength-division multiplexing (WDM) networks has gained wide research interests. In this article, we study the performance of distributed lightpath provisioning in WDM networks with dynamic routing and wavelength assignment (RWA). Specifically, we consider the case where routing of each lightpath is calculated based on globally flooded link-state information, and wavelength assignment is decided through local information exchanges. Simulation results show that such schemes steadily outperform those schemes with only global flooding or only local information exchanges. More significantly, the impacts of various factors on the proposed scheme, including RWA algorithm, network topology, number of wavelengths per fiber, global flooding interval, and traffic load, have been evaluated. Such evaluations help to achieve some insights useful for the future developments of efficient lightpath provisioning schemes.

Motivated by recent developments in the theory of complex networks, we examine the tolerance of communication networks for intentional attacks that aim to crash the network by taking down network hubs. In addition to providing a brief survey of key existing results, we investigate two different effects that largely have been ignored in past studies. Many communication networks, such as the Internet, are too large for anyone to have global information of their topologies, which makes accurate, intentional attacks virtually impossible; most attacks in communication networks must propagate from nodes to adjacent nodes, utilizing local-network topology information only. We show that incomplete global information has a different impact on intentional attacks in different circumstances, and local information-based attacks can actually be highly efficient. Such insights will be helpful for the future development of efficient protection schemes against network attacks.

In this paper, we study the impacts of traffic allocations on the performance of optical packet switches (OPS). In particular, two different cases are investigated where traffic loads are distributed over different (i) time slots (time dimension); and (ii) output ports (space dimension), respectively. These two cases are of significant importance as each has different applications and they form the basis of some more complicated schemes. Our main contributions are three fold. Firstly, for the most fundamental OPS configuration, we prove that its packet loss is a convex function of traffic load. For any other node configuration, we prove that its packet loss remains as a convex function of traffic load as long as a simple condition is satisfied. Secondly, for any OPS with its packet loss as a convex function of traffic load, we propose a simple algorithm for efficiently comparing some different traffic allocations and telling which one of them leads to the lowest packet loss. We also show that in either time or space dimension, the best packet-loss performance is achieved when traffic loads are uniformly distributed. Thirdly, for OPS with limited capability of adjusting a given traffic distribution, we propose a Load Balancing (LB) algorithm to minimize the packet loss. These contributions provide some useful guidelines and algorithms for achieving efficient traffic allocations in various OPS networks.

Distributed lightpath provisioning is expected to play a key role in next-generation WDM optical networks. A major challenge in distributed lightpath provisioning is the potentially significant degradation of network blocking performance caused by outdated link-state information, occurring especially under traffic with short average durations of connections. To address this problem, various parallel reservation schemes have been proposed, with the common feature of applying multiple capacity-search and/or reservation operations executed simultaneously. In this paper, we evaluate the performance of these distributed parallel reservation schemes in wavelength-routed networks. Specifically, we develop general yet accurate analytical models to provide insights into the behavior of the different schemes. We also conduct extensive simulation study. Numerical results show that by using simple parallel reservation schemes, in particular through multiple reservations on several different routes, blocking probabilities caused by outdated link-state information can be drastically lowered and network performance can be significantly improved. The tradeoff between control traffic loads and network blocking performance is also evaluated.

Multi-fiber WDM networks are becoming the major telecommunication platforms for transmitting exponentially increasing data traffic. While today's networks are mainly providing circuit-switched connections, optical packet-switching technologies have been investigated for years, aiming at achieving more efficient utilizations of network resources. In this paper, we have evaluated, for the first time, the packet-loss performance of multi-fiber optical packet switches (MOPS). Our main contributions are threefold. Firstly, we have proposed simple and accurate analytical models for analyzing packet-loss performance of (i) the most fundamental MOPS configuration, (ii) MOPS equipped with fiber delay lines (FDLs) and (iii) shared wavelength converters (SWCs). Secondly, we have shown that the MOPS network cannot achieve the same performance as the one with full wavelength conversion (FWC), which is quite different from the well-known conclusion in circuit-switched networks. However, MOPS does significantly outperform the classic single-fiber switches. By introducing a small number of FDLs or SWCs, it outperforms the highly expensive FWC solution as well. Finally, we have taken the hardware constraints into consideration by evaluating the performance of MOPS configurations having multiple limited-sized switching boards, which leads to some insights helpful for developing cost-effective MOPS configurations in the future.

Opinion formation in the population has attracted extensive research interest. Various models have been introduced and studied, including the ones with individuals' free will allowing them to change their opinions. Such models, however, have not taken into account the fact that individuals with different opinions may have different levels of loyalty, and consequently, different probabilities of changing their opinions. In this work, we study on how the non-uniform distribution of the opinion changing probability may affect the final state of opinion distribution. By simulating a few different cases with different symmetric and asymmetric non-uniform patterns of opinion changing probabilities, we demonstrate the significant effects that the different loyalty levels of different opinions have on the final state of the opinion distribution., Comment: 5 pages, 3 figures, published in Proc. IEEE DSP'2015

Overlay IP/MPLS over WDM network holds great promise for the Next-Generation Internet. One key design challenge with such a network is the limited information exchange between its two layers. We evaluate the influences of limited WDM-layer link cost information and routing strategies on network performances for dynamic multicast traffic grooming. Simulation results show that both WDM-layer link cost information and routing strategy have significant impacts on the network performances, especially when the network resources are abundant. In addition, the impacts of WDM-layer link cost information are more significant compared to those of the routing strategy. The results suggest that there is significant potential for network performance improvements by designing more efficient schemes for overlay traffic grooming and sharing of link-state information between the WDM and IP/MPLS layers.

Inspired by clustering and energy harvesting techniques, we study multiple-cluster wireless sensor networks with energy harvesting (EH) sensors serving as relay for cluster heads. In this paper, we derive the model for realistic energy harvesting rate. Then we propose distributed matching algorithm for EHs to serve as relay for CHs. The proposed algorithm could find optimal/near-optimal CH-EH matching in short time and still achieve good performance. We evaluate the performance of our method through theoretical analysis as well as simulation.

As sensor networks collect different kinds of data to make better-informed decisions, we need multimodal fusion for basic analytical tasks such as event prediction, error reduction and data compression. Inspired by unsupervised feature discovery methods in deep learning, we propose an approach based on the stacked autoencoder: a multi-layer feed-forward neural network. After extracting key features from multimodal sensor data, the algorithm computes compact representations which can also be used directly in analytical tasks. Using simulated and real-world environmental data, we evaluate the performance of our approach for data fusion and regression. We demonstrate improvements over situations where only single modality data is available and where multimodal data are fused together without learning intermodality correlations. For regression, we attained more than 45% improvement in root-mean-square errors (RMSE) over linear approaches, and up to 10% improvement over shallow neural network methods.

In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and combined heat and power (CHP) generators. In such systems, the fluctuant net demands (i.e., the electricity demands not balanced by renewable energies) and heat demands impose unprecedented challenges. To cope with the uncertainty nature of net demand and heat demand, a new flexible uncertainty model is developed. Specifically, we introduce reference distributions according to predictions and field measurements and then define uncertainty sets to confine net and heat demands. The model allows the net demand and heat demand distributions to fluctuate around their reference distributions. Another difficulty existing in this problem is the indeterminate electricity market prices. We develop chance constraint approximations and robust optimization approaches to firstly transform and then solve the prime problem. Numerical results based on real-world data evaluate the impacts of different parameters. It is shown that our energy generation scheduling strategy performs well and the integration of combined heat and power (CHP) generators effectively reduces the system expenditure. Our research also helps shed some illuminations on the investment policy making for microgrids.

In WDM networks, traffic grooming is a fundamental and important operation for aggregating multiple low-rate channels from the end users into a high-rate wavelength channel. In the literature, several traffic grooming problems were formulated where the common objective is to minimize the number of add-drop multiplexers required, and each of these problems was solved by specific algorithms. In this paper, we propose a network flow approach for solving a class of static and dynamic traffic grooming problems. In this approach, we transform a traffic grooming problem onto the minimum edge-cost flow problem through proper graph transformation and assignment of edge weights, and then solve this resulting network flow problem by an efficient heuristic algorithm. The network flow approach has two main advantages: (1) It is generally applicable to many static and dynamic traffic grooming problems. It can solve some existing traffic grooming problems as well as new problems that cannot be solved by the existing algorithms. (2) It performs at least as good as the best existing algorithm for static traffic grooming and significantly outperforms the existing algorithms for dynamic traffic grooming.

With the development of optical packet-switching (OPS) technologies, multi-fiber OPS networks will play an important role in the future data transmissions. In such networks, instead of constructing some extremely expensive node configurations with strictly non-blocking switching function, a more practical solution is multi-board switches that contain a number of small-sized switching boards. In this article, we have evaluated the performance of several different multi-board switches, based on the following two main objectives: (i) better understanding the effects of different connection schemes between switching boards and optical buffers and (ii) investigating possible schemes for achieving comparable performance to that of the ideal, strictly non-blocking switches. Extensive simulation results have shown that unlike circuit-switched net- works, multi-board OPS cannot easily perform comparably to the strictly non-blocking switch by having slightly more fibers per link. Also, such a problem can be tackled by several different approaches. The most efficient one is to equip the switch with more buffers rather than to increase the switching-board size or to enhance the buffer sharing between different switching boards.

We present a new algorithm for the registration of three-dimensional partially overlapping surfaces. It is based on an efficient scheme for the rejection of false point correspondences (correspondence outliers) and does not require initial pose estimation or feature extraction. An initial list of corresponding points is first derived using the regional properties of vertices on both surfaces. From these point correspondences, pairs of corresponding rigid triplets are formed. The normal vectors at the vertices of each corresponding triplet are used to compute the candidate rotations. By clustering the candidate rotation axes and candidate rotation angles separately, a large number of false correspondences are eliminated and an approximate rotation is decided, from which an approximate translation is also obtained. Finally, the optimal transformation parameters are determined by further refining the estimated parameters in an iterative manner. Mathematical analysis and experimental results show that the registration process is fast and accurate even when the objects are regularly shaped and contain many regionally similar surface patches.

Link-state update is a critical component of the routing protocol in wavelength-routed networks. High-frequency updates impose heavy traffic on network control channels as well as excessive burden of electronic processing, while stale link-state information seriously degrades network performance. Therefore, a tradeoff has to be made between control overhead and network blocking probability. This paper proposes a novel link-state update method. By actively regulating the link-state update rate, the proposed method efficiently handles the inherent burstiness of link-state changes without overloading control network with excessive update messages. To improve the blocking performance under limited control bandwidth, it assigns different types of updates with different priorities such that the stale link-state information of more negative impact has a higher chance to be removed. Comprehensive performance evaluations show that the method successfully enforces the control bandwidth quota while achieving much lower blocking probability than existing link-state update methods

In this paper, we consider path protection in wavelength-routed networks with shared risk link groups (SRLGs). Specifically, we study diverse routing, where two paths without sharing any SRLG have to be found between each pair of source–destination nodes, and its applications in dynamic shared protection as well. For the NP-complete diverse routing problem, a heuristic method is proposed, which steadily outperforms an existing algorithm within the first few iterations. When more iterations of calculations are allowed, we demonstrate that the two different algorithms perform nearly the same. This interesting observation helps to achieve some insight into how to further improve the performance of the heuristics in the future.

We present a new method for three-dimensional partial-surface registration that utilizes both regional surface properties and shape rigidity constraint to align a partial object surface and its corresponding complete surface. The statistical properties of the vertices on the object surface are first computed and compared with each other to find the initial candidate correspondences. We use the overall object-shape rigidity constraint and a clustering method to obtain an approximation of the transformation parameters while, at the same time, rejecting correspondence outliers. The transformation parameters can be further refined with an iterative approach. The algorithm does not require any feature extraction or initial pose estimation, and is especially applicable when the object surfaces are formed by a large number of vertices, smooth with few salient features, and contain many regionally similar surface patches. Experiments confirm that the proposed scheme can achieve accurate registration results in an efficient manner.

In this paper, we analyze the blocking probability of distributed lightpath establishment in wavelength-routed WDM networks by studying the two basic methods: destination-initiated reservation (DIR) and source-initiated reservation (SIR). We discuss three basic types of connection blocking: 1) blocking due to insufficient network capacity; 2) blocking due to outdated information; and 3) blocking due to over-reservation. It is shown that the proposed models are highly accurate for both the DIR and the SIR methods, in both the regular and irregular network topologies, under the whole range of traffic loads.

Distributed wavelength provisioning is becoming one of the most important technologies for supporting next-generation optical networks. This paper describes the evaluation of the performance of distributed wavelength provisioning in wavelength-division-multiplexing (WDM) optical networks with sparse wavelength conversion (i.e., where wavelength conversion is available at only a subset of network nodes). Using the well-known destination-initiated reservation method as a case study, a highly accurate analytical model supported by comprehensive simulation validation is proposed. Both analytical and simulation results show that, in optical networks with distributed wavelength provisioning, sparse wavelength conversion still helps to significantly lower the connection-blocking probabilities. However, unlike that in centralized wavelength provisioning, sparse wavelength conversion may not easily achieve nearly the same performance as that of full wavelength conversion, especially under light traffic loads. This paper evaluates how the potential contribution of sparse wavelength conversion depends on different factors, such as the number of wavelength converters, the number of wavelength channels per fiber, the burstiness of traffic loads, and the network size, and discusses the influence of the signaling scheme.

A problem of many distributed lightpath provisioning schemes is wavelength contention, which occurs when a connection request attempts to reserve a wavelength channel that is no longer available. This situation results from the lack of updated global link-state information at every node. In networks with highly dynamic traffic loads, wavelength contention may seriously degrade the network performance. To overcome this problem, we propose a new framework for distributed signaling and introduce a class of schemes referred to as intermediate-node initiated reservation. In the new scheme, reservations may be initiated at any set of nodes along the route; in contrast, reservations can only be initiated by the destination node in the classic destination initiated reservation (DIR) scheme. As a result, the possibility of having outdated information due to propagation delay is significantly lowered. Specifically, we consider two schemes within this framework, for networks with no wavelength conversion and for networks with sparse wavelength conversion, respectively. Theoretical and simulation results show that, compared with the classic DIR scheme, the new schemes can significantly improve the network blocking performance. The accuracy of the analytical models is also confirmed by extensive numerical simulations.