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Semantic Communication (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarized. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarized. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarized. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.

This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous multicore platforms. Each processor is composed of a set of multi-speed cores with limited energy resources. A reconfigurable system is sensible to unpredictable reconfiguration events from related environment, such as the activation, removal or update of tasks. The problem is to handle feasible reconfiguration scenarios under energy constraints. Since any task can finish execution before achieving its worst-case execution time (WCET), the idea is to distribute this execution on different processor cores for meeting related deadlines and reducing energy consumption. The methodology consists in using lower processor speeds first to consume less energy. If the system is still non-feasible after reconfiguration, then we adjust the task periods as a flexible solution or migrate some of them to the least loaded processors. Accordingly, an integer linear program (ILP) is formulated to encode the execution model that assigns tasks to different cores with optimal energy consumption, thereby realizing energy-efficient computing/green computing. The potency and effectiveness of the proposed approach are rated through simulation studies. By measuring the energy consumption cost, our solution offers better than 11% of gain than recently published methods and improves by 85% the overall number of adjusted periods.

Several semantics models are adopted by time Petri nets for different applications. Yet they have some limitations on schedulability analysis of flexible manufacturing systems. The scheduling scope of a strong semantics model is greatly limited because of the impact of strong timing requirements, perhaps keeping some optimal schedules out of the consideration. A weak semantics model cannot guarantee the scheduling timeliness as there lacks strong timing enforcement. A mixed semantics model cannot ensure that independent transitions with overlapping firing interval fire in an interleaving way, thus affecting the search for the optimal schedules. In this paper, we present a relaxed mixed semantics model for time Petri nets to address these problems by redefining the firability rules of transitions. In our model, the firability of a transition is determined by maximal concurrent sets containing the transition. This treatment not only extends the scheduling scope of TPN model greatly while avoiding the generation of invalid schedules, but also solves the problem of concurrent scheduling of independent transitions. A state class method is then proposed to support the verification and analysis of temporal properties. Finally, we apply the proposed model to schedulability analysis of a job shop scheduling problem, and compare the features of four semantics models.

Ocean temperature, salinity, and electric conductivity are essential ocean properties. Their structure and changes directly impact physical, chemical and biological processes in oceans. Since the 1970s, numerous researches have focused on the morphological analysis of vertical profiles in oceanography. However, due to the complexity of an ocean environment, most of them are conducted at local scales or only focus on single elements, e.g., ocean temperature or salinity. This work aims to achieve the joint classification of the vertical structure of ocean properties at a global scale and present two-dimension regional characteristics. Based on 150 seawater profiles from the National Oceanographic Data Center, this work explores such characteristics of ocean temperature, salinity and electric conductivity in the deep sea and achieves global-scale joint classification. We demonstrate that their vertical features have clear regional characteristics and can be classified into four types, i.e., bidirectional gradient, homo-dromous contravariant, homo-dromous gradient, and homodromous gradient (salinity) ones. In addition, our results prove that there exist the power-law distributions of these three factors in intermediate water, which may be explained through the self-organization theory. Moreover, the `up-tail' phenomenon is widely discovered in the vertical structure of electric conductivity, and it may be considered as a combined effect caused by temperature, pressure, and salinity.

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm. It focuses on solving multiple optimization tasks concurrently while improving optimization performance by utilizing similarities among tasks and historical optimization knowledge. To ensure its high performance, it is important to choose proper individuals for each task. Most MTO algorithms limit each individual to one task, which weakens the effects of information exchange. To improve the efficiency of knowledge transfer and choose more suitable individuals to learn from other tasks, this work proposes a general MTO framework named individually guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones, and each individual is fully explored to learn experience from the execution of other tasks. By using the concept of skill membership, individuals with higher solving ability are selected. Besides, to further improve the effect of knowledge transfer, only inferior individuals are selected to learn from other tasks at each generation. The significant advantage of IMTO over the multifactorial evolutionary framework and baseline solvers is verified via a series of benchmark studies.

Industry 4.0 is widely accepted to guide a novel and promising production paradigm where many advanced intelligent machines and latest technologies are utilized. The self-optimization and self-training behaviors of advanced intelligent machines make them more and more proficient when processing jobs; while the abrasion of their components reduces their work efficiency in the manufacturing process. Therefore, we address a flow shop scheduling problem with deteriorating and learning effects, where the processing time of jobs is a function of their starting time and positions in a schedule. In order to solve it efficiently, an artificial-molecule-based chemical reaction optimization algorithm is proposed. A set of artificial molecules are constructed based on some elitist solutions and adaptively injected into the population, which can enhance and balance exploration and exploitation abilities. The simulation experiments are carried out on a set of stochastic test problems with different sizes. The experimental results show that the proposed algorithm performs better than its peer algorithms in solving the investigated problem.

Nowadays, Weibo has become a significant and popular information sharing platform in China. Meanwhile, spammer identification has been a big challenge for it. To mitigate the damage caused by spammers, classification algorithms from machine learning have been applied to distinguish spammers and non-spammers. However, most of the previous studies overlook the class imbalance problem of real-world data. In this paper, by analyzing the characteristics of spammers in Weibo, we select microblog content similarity, the average number of links, and the other 12 features to construct a comprehensive feature vector never seen before. Considering the existence of imbalance problems in spammer identification, an ensemble learning method is used to combine multiple base classifiers for improving the learning performance. During the training stage of base learners, fuzzy-logic-based oversampling and cost-sensitive support vector machine are considered to tackle imbalanced data at both data and algorithmic levels. The experimental results demonstrate that compared with the existing state-of-the-art methods, the recall rate of our proposed approach increases by 6.5% and reaches the precision value of 87.53% when used to deal with real-world Weibo datasets we collected.

Forecasting emergency calls are of great importance in practice. By forecasting the occurrence of unfortunate events, we can learn from these events and further prevent their occurrence in the future. However, because of the uncertainty of event occurrences, it is hard to guarantee their prediction accuracy. In this paper, a combined model, which consists of two parts, is proposed. The first part is a Poisson neural network model (PNN). It is responsible for basic forecasting, and its initial weights and thresholds are trained by applying a genetic algorithm. The second part consists of multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), and multivariate gray (GM), which are responsible for estimating residual errors. The basic prediction result adjusted by the residual error is used as the final forecasting result. The proposed model fully takes the advantages of PNN, MLR, ARIMA, and GM, and thus improves forecasting performance. Our method has been applied to the emergency calls of Ningbo, China. The experimental results show that the proposed model has advantages over some existing forecasting models, such as a neural network model, Poisson regression, and stochastic configuration networks in terms of mean absolute percentage error.

In recent years, there is an increasing synergy between computational technologies and physical components. So-called Cyber-Physical Systems (CPS) are composed of a collection of devices interacting with each other and communicating with the physical world. They integrate computation and communication aspects together with control and monitoring techniques. Various CPS applications can be found in almost all areas of human life, such as manufacturing systems, smart grids, robotics, transportation systems, medical devices, military, home area networks and smart buildings.

Twitter spam classification is a tough challenge for social media platforms and cyber security companies. Twitter spam with illegal links may evolve over time in order to deceive filtering models, causing disastrous loss to both users and the whole network. We define this distributional evolution as a concept drift scenario. To build an effective model, we adopt K-L divergence to represent spam distribution and use a multiscale drift detection test (MDDT) to localize possible drifts therein. A base classifier is then retrained based on the detection result to gain performance improvement. Comprehensive experiments show that K-L divergence has highly consistent change patterns between features when a drift occurs. Also, the MDDT is proved to be effective in improving final classification result in both accuracy, recall, and f-measure.

A dandelion algorithm (DA) is a recently-proposed intelligent optimization algorithm and shows an excellent performance in solving function optimization problems. However, like other intelligent algorithms, it converges slowly and falls into local optima easily. To overcome these two flaws, a dandelion algorithm with probability-based mutation (DAPM) is proposed in this paper. In DAPM, both Gaussian and Levy mutations can be used interchangeably according to a given probability model. In this paper, three probability models are discussed, namely linear, binomial, and exponential models. The experiments show that DAPM achieves better overall performance on standard test functions than DA.

A colored traveling salesman problem (CTSP) is a path optimization problem in which colors are used to characterize diverse matching relationship between cities and salesmen. Namely, each salesman has a single color while every city has one to multiple salesmen’s colors, thus allowing salesmen to visit exactly once the cities of their colors. It is noteworthy that cities’ accessibilities to salesmen may change over time, which usually takes place in the multiwarehouse distribution of online retailers. This work presents a new CTSP with dynamically varying city colors for describing and modeling some scheduling problems with variable city accessibilities. The problem is more complicated than the previously proposed CTSP with varying edge weights. In particular, the solution feasibility changes as the cities change their colors, that is, a feasible original solution path may become no longer feasible after city colors change. A variable neighborhood search (VNS) algorithm is presented to solve the new problem. Specifically, a dynamic environment simulator with an adjustable frequency and amplitude is designed to mimic such color changes. Then, direct-route encoding, greedy initialization, and appropriate population immigrant are proposed to form an enhanced VNS, and then its performance is evaluated. The results of extensive experiments show that the proposed VNS can quickly track the environmental changes and effectively resolve the problem.

Nowadays an increasing number of companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a provider to maximize its revenue by intelligently scheduling tasks in its software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, which may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. Its core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. This work compares it with state-of-the-art methods, experimentally. The results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-trip time of tasks for all applications.

A transportation (automotive service) facility location problem is important in urban infrastructure planning and construction. To handle it, researchers have proposed a number of stochastic/random models for locating an automotive service enterprise. However, most of them fail to describe all kinds of uncertainty, e.g., data imprecision. By considering regional constraints, this work proposes a new random fuzzy cost-profit equilibrium model by using uncertainty data and management methods. It presents a hybrid algorithm integrating stochastic fuzzy simulation and particle swarm optimization to solve the location problem of an automobile service enterprise. In addition, since risk factors can impact a decision, this work conducts a risk performance analysis when locating an automotive service enterprise. A practical example is given to illustrate the proposed model and algorithm.

It is a great challenge to find an optimal one-wafer cyclic schedule for a single-arm multicluster tool that is widely adopted in semiconductor fabrication. Aiming to tackle this significant problem, an optimal scheduling strategy is determined first for each individual tool under the condition that the bottleneck individual tool is transport-bound. Then, by developing a Petri net model with robot waiting being explicitly described to reveal the properties of the entire system, this paper shows that to schedule such a tool optimally is to allocate the robot waiting time properly. Then, this paper presents the necessary and sufficient conditions for the existence of an optimal one-wafer cyclic schedule. Thereafter, an efficient algorithm is developed to check the given conditions and find such a schedule efficiently if existing. Finally, two industrial examples are used to verify that the proposed method is applicable and effective.

Mobile cyber-physical systems (CPS) that take the advantages and extend the application domains of CPS have become increasingly popular in recent years. For example, mobile CPS could be a kind of foundational techniques to support the development of vehicular networking systems, thereby improving security and privacy of users in the dynamic environments of vehicular networks. In this paper, we first distinguish mobile CPS from traditional CPS. Then, we introduce their three emerging application areas, i.e., vehicular networking systems, healthcare systems, and mobile education. After that, we discuss four main research challenges of mobile CPS regarding security, energy consumption, mobile dynamic environment, and system stability. Also, we consider the corresponding techniques, which may address these challenges, and analyze the inter-relations among them. Finally, we outline the possible research directions and applications of mobile CPS in the future.

The unit commitment problem is a large-scale, nonlinear, and mixed-integer optimization problem in an electric power system. Numerous researchers concentrate on minimizing its total generation cost. Cuckoo search is an efficient metaheuristic swarm-based approach that balances between local and global search strategy. Owing to its easy implementation and rapid convergence, it has been successfully used to solve a wide variety of optimization problems. This paper proposes an improved binary cuckoo search algorithm (IBCS) for solving the unit commitment problem. A new binary updating mechanism is introduced to help the IBCS choose a right search direction, and a heuristic search method based on a novel priority list can prevent it from being trapped into local optima. A 4-unit system is used as an example to validate the effectiveness of the proposed method.

Particle swarm optimization (PSO) is a population-based algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly when optimization problems are subjected to noise. PSO is strongly influenced by its previous best particles and global best one, which may lead to premature convergence and fall into local optima. This also holds true for various PSO variants dealing with optimization problems in noisy environments. Opposition-based learning (OBL) is well-known for its ability to increase population diversity. In this paper, we propose hybrid PSO algorithms that introduce OBL into PSO variants for improving the latter's performance. The proposed hybrid algorithms employ probabilistic OBL for a swarm. In contrast to other integrations of PSO and OBL, we select the top fittest particles from the current swarm and its opposite swarm to improve the entire swarm's fitness. Experiments on 20 benchmark functions subject to different levels of noise show that the proposed hybrid PSO algorithms outperform their counterpart PSO variants as well as composite differential evolution in most cases.

Cloud computing is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. In contrast to grids and other traditional high-performance computing systems, clouds provide a customizable infrastructure where scientific workflows can provision desired resources ahead of the execution and set up a required software environment on virtual machines (VMs). Nevertheless, various challenges, especially its quality-of-service prediction and optimal scheduling, are yet to be addressed. Existing studies mainly consider workflow tasks to be executed with VMs having time-invariant, stochastic, or bounded performance and focus on minimizing workflow execution time or execution cost while meeting the quality-of-service requirements. This work considers time-varying performance and aims at minimizing the execution cost of workflow deployed on Infrastructure-as-a-Service clouds while satisfying Service-Level-Agreements with users. We employ time-series-based approaches to capture dynamic performance fluctuations, feed a genetic algorithm with predicted performance of VMs, and generate schedules at run-time. A case study based on real-world third-party IaaS clouds and some wellknown scientific workflows show that our proposed approach outperforms traditional approaches, especially those considering time-invariant or bounded performance only.

The emergency disposals are usually charged by several geographically dispersed and logically collaborated emergency organizations. In addition, each emergency organization owns its private emergency response process and can operate independently. In this case, modeling such cross-organizational emergency response processes is really a challenging issue when considering: 1) effective collaboration and coordination among different emergency partners; and 2) reasonable privacy and security protection mechanism to maintain the sensitive information confidential and unseen by other involved partners. In this paper, we propose a package reduction-based privacy protection approach to facilitate the modeling. Specifically, a kind of workflow nets extended with time, resource, and message factors, called TRM_WF_nets for short, is first introduced to model cross-organization emergency response processes. Then, a three-layered framework is proposed to model cross-organization emergency response processes by taking into account their privacy protection and temporal performance evaluation. Next, a set of reduction rules are proposed to reduce the scale of a TRM_WF_net while keeping its external observable timing, message, and resource factors invariant. These reduction rules not only improve the efficiency of temporal performance evaluation but also protect the business privacy of each emergency organization during its collaboration and interaction with others. Finally, a real-life scenario of cross-organization fire emergency response processes is used to validate our proposed approaches.

A mobile ad hoc network (MANET) can be constructed when a group of mobile users need to communicate temporarily in an ad hoc manner. It allows mobile services to be shared through device-to-device links and composed by combining a set of services together to create a complex, value-added, and cross-organizational business application. Nevertheless, various challenges, especially the reliability and quality-of-service of such a MANET-based mobile service composition, are yet to be properly tackled. Most studies and related composition strategies assume that mobile users are fully stable and constantly available. However, this is not realistic in most real-world scenarios where mobile users are mobile. The mobility of mobile users impact the reliability of corresponding mobile services and consequently impact the success rate of mobile service compositions. In this paper, we propose a reliability-aware mobile service composition approach based on prediction of mobile users' positions. We model the composition problem as a multi-objective optimization problem and develop an evolutionary multi-objective optimization-based algorithm to solve it. Extensive case studies are performed based on a real-world mobile users' trajectory data set and show that our proposed approach significantly outperforms traditional ones in terms of composition success rate.

An uncertainty facility location allocation (FLA) problem is a complex nonlinear optimization problem. Currently, researchers have discussed the stochastic cost/profit issues of FLA. However, rising energy conservation awareness and environmental concerns, energy-efficientcy and low-carbon emission should be regarded as key criteria in solving it. To do so, this paper presents a new FLA problem from a sustainable development point of view. By taking a typical facility service enterprise as a case, a new stochastic energy-efficient FLA model subject to carbon emission, economical, capacitated, and regional constraints is formulated. Then, stochastic simulation and scatter search are integrated as an intelligent algorithm to resolve it. Some examples are demonstrated to prove the availability of the proposed model and solution algorithm.

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

A crane system often works in a complex environment. It is difficult to model or learn its true dynamics by traditional system identification approaches. If a dynamics model is created by minimizing its prediction error, its use tends to introduce inaccuracies and thus lead to suboptimal performance. Is it possible to learn the dynamics model of a crane that can achieve the best performance, instead of learning its true dynamics? This work answers the question by presenting a performance-driven model predictive control (P-MPC) algorithm for a two-dimensional underactuated bridge crane. In the proposed dual-layer control architecture, an inner-loop controller uses a proportional–integral–derivative controller to achieve anti-sway rapidly. An outer-loop controller uses MPC to ensure accurate trolley positioning under control constraints. Compared with classical MPC, this work proposes a data-driven method for plant modeling and controller parameter updating. By considering the control target at the learning stage, the method can avoid adjusting the controller to deal with uncertainty. We use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing control performance and then used in conjunction with optimal control schemes to efficiently design a controller for a given task. The model is updated directly based on the performance observed in experiments on the physical system in an iterative manner till a desired performance is achieved. The controller parameters and prediction models of the best closed-loop performance can be found through continuous experiments and iterative optimization. Simulation and experiment results show that we can explicitly find the dynamics model that produces the best performance for an actual system, and the method can quickly suppress swing and realize accurate trolley positioning. The results verified its effectiveness, feasibility, and superior performance on comparing it with state-of-the-art methods.

Through Internet, a cloud computing system provides shared resources, data, and information to users or tenant users in an on-demand and pay-as-you-go styles. It delivers large-scale utility computing services to a wide range of consumers. To ensure that their provisioned service is acceptable, cloud providers must exploit techniques and mechanisms that meet the service-level-agreement (SLA) performance commitment to their clients. Thus, performance issues of cloud infrastructures have been receiving considerable attention by both researchers and practitioners as a prominent activity for improving service quality. This paper presents an analytical approach to percentile-based performance analysis of unreliable infrastructure-as-a-service clouds. The proposed analytical model is capable of calculating percentiles of the request response time under variable load intensities, fault frequencies, multiplexing abilities, and instantiation processing time. A case study based on a real-world cloud is carried out to prove the correctness of the proposed theoretical model. To achieve optimal performance-cost tradeoff, we formulate the performance model into an optimal capacity decision problem for cost minimization subjected to the constraints of request rejection and SLA violation rates. We show that the optimization problem can be numerically solved through a simulated-annealing method.

Collaborative filtering is now successfully applied to recommender systems. The availability of extensive personal data is necessary for generating high quality recommendations. However, traditional collaborative filtering methods suffer from sparse and sometimes cold-start problems, particularly for newly deployed recommenders. Currently, several recommender systems exist in good working order, and data collected from these existing systems should be valuable for newly deployed recommenders. This paper introduces a privacy preserving shared collaborative filtering problem in order to leverage the data from other parties (contributors) to improve its own (beneficiaries) collaborative filtering performance, with the privacy protected under a differential privacy framework. It proposes a two-step methodology to solve this problem. First, item-based neighborhood information is selected as the shared data from the contributor with guaranteed differential privacy, and a practical enforcement mechanism for differential privacy is proposed. Second, two novel algorithms are developed to enable the beneficiary to leverage the shared data to support improved collaborative filtering. The extensive experimental results show that the proposed methodology can increase the recommendation accuracy of the beneficiary significantly while preserving data privacy for the contributors.

As more and more ontologies are defined with different terms, ontology matching plays a crucial role in addressing the semantic heterogeneity problem in many different disciplines. Many efforts have been made to discover correspondences among terms in different ontologies. Most studies directly match two ontologies by utilizing terminological and structural methods that rely on ontologies themselves only. However, the decentralized characteristic of ontologies raises the uncertainty in ontology matching. To address this problem, we propose a four-stage ontology matching framework (FOMF) to enhance ontology matching performance. It is built upon the commonly accepted claim that an external comprehensive knowledge base can be used as a semantic bridge between domain ontologies for ontology matching. First, FOMF semantically maps domain ontologies to a knowledge base and then produces different types of alignments, including equivalence, subclass, sameas, and instance alignments. Similarities between two domain ontologies are next employed to enhance the equivalence and sameas alignments discovery. Finally, based on acquired alignments, inferred alignments are deduced to guarantee the completeness of matching results. Our experimental results show the superiority of the proposed method over the existing ones.

Extended logical Petri nets are proposed to improve logical Petri nets, and the related firing rules and state reachability graph are introduced. Their attributes, place arrival time, and priority function are defined for each token, batch processing wait time and transition firing duration are defined for each logical input/output transition, and firing duration is defined for each ordinary transition. Their token removal and generation functions are defined to operate the attributes and arrival time of tokens in places. A state in their reachability graph is redefined. They are used to model an e-commerce system. The reachability, time cost superiority of tokens with different priorities, and fairness of the resulting model are analyzed and correct end-states are illustrated.

This paper considers the multiobjective scheduling of flexible manufacturing systems (FMSs). Due to high degrees of route flexibility and resource sharing, deadlocks often exhibit in FMSs. Manufacturing tasks cannot be finished if any deadlock appears. For solving such problem, this work develops a deadlock-free multiobjective evolutionary algorithm based on decomposition (DMOEA/D). It intends to minimize three objective functions, i.e., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. A type of a discrete differential evolution (DDE) algorithm is also developed for solving each subproblem. The mutation operator of the proposed DDE is based on the hamming distance of two randomly selected solutions, while the crossover operator is based on Generalization of Order Crossover. Experimental results demonstrate that the proposed DMOEA/D can significantly outperform a Pareto domination-based algorithm DNSGA-II for both 2-objective and 3-objective problems on the studied FMSs.

In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.

Complex multi-cluster tools have been extensively used in semiconductor manufacturing. It is crucial to increase their productivity by their effective operation. With structural complexity, multiple robots, and the interaction among individual tools, it is very challenging to schedule a tree-like multi-cluster tool. This paper investigates the scheduling problem of such a tool whose bottleneck individual tool is process-bound. The system is modeled by well-known discrete-event models, i.e., resource-oriented Petri nets. Based on the models, for the first time, this work develops necessary and sufficient conditions under which a one-unit (wafer) periodic schedule exists and shows that an optimal one-unit periodic schedule can always be found. Algorithms with polynomial complexity are presented to find the optimal cycle time and the one-unit periodic schedule. Industrial examples are used to illustrate the proposed method, and they show that a significant reduction in cycle time can be obtained in comparison with the existing method.

An inherently non-negative latent factor model is proposed to extract non-negative latent factors from non-negative big sparse matrices efficiently and effectively. A single-element-dependent sigmoid function connects output latent factors with decision variables, such that non-negativity constraints on the output latent factors are always fulfilled and thus successfully separated from the training process with respect to the decision variables. Consequently, the proposed model can be easily and fast built with excellent prediction accuracy. Experimental results on an industrial size sparse matrix are given to verify its outstanding performance and suitability for industrial applications.

Due to a battery constraint in wireless sensor networks (WSNs), prolonging their lifetime is important. Energy-efficient routing techniques for WSNs play a great role in doing so. In this paper, we articulate this problem and classify current routing protocols for WSNs into two categories according to their orientation toward either homogeneous or heterogeneous WSNs. They are further classified into static and mobile ones. We give an overview of these protocols in each category by summarizing their characteristics, limitations, and applications. Finally, some open issues in energy-efficient routing protocol design for WSNs are indicated.

Non-local choice mismatch is one of the most important problems in the Internet-scale and service-based workflow ecosystems. The state-of-the-art method can solve it by generating adaptors to check deadlock-freeness based on a reachability graph. The states in the reachability graph give clues to re-design the composition. Deadlocks are resolved via an iterative process. However, this method is inefficient due to the overlook of the future deadlock state and the requirement of many interactions with a developer. In this paper, an abnormity prevention strategy based on an optimal controller is proposed for collaboration services described by Web Services Choreography Description Language. To overcome the deficiencies of the previous work, we describe service choreography by using service workflow nets. Then, by combining structure and reachability analyses, we formulate a different reachability graph called controlled reduced one. We next develop a maximally permissive state feedback control policy to prevent abnormity. We finally construct an optimal controller for the administrator of service composition to avoid deadlocks in service choreography. The advantage of our methodology is verified via an example.

Workflow nets (WF-nets) as a class of Petri nets are widely used to model and analyze workflow systems. Soundness is an important property of WF-nets, which guarantees that the systems are deadlock- and livelock-free and each task has a chance to be performed. Van der Aalst has proven that the soundness problem is decidable for WF-nets and we have also shown that it is PSPACE-complete for bounded ones. Since the definition of soundness is based on reachability and Van der Aalst has proven that a sound WF-net must be bounded, the soundness detection can be carried out via the reachability graph analysis. However, the state explosion problem is a big obstacle to this technique. The unfolding technique of Petri nets can effectively avoid/alleviate this problem. This paper proposes an algorithm to generate a finite prefix of the unfolding of a WF-net, called basic unfolding. Furthermore, a necessary and sufficient condition is proposed to decide soundness based on basic unfolding. In addition, some examples illustrate that the unfolding technique can save the storage space effectively.

In automated manufacturing systems, resource failures are often inevitable. They reduce the number of available resources and may cause some processing routes of parts to halt and sometimes the whole system to shutdown. This article focuses on the robust deadlock control problem in automated manufacturing systems with multiple resource failures. To obtain such a robust controller, we first put forward a new concept of blocked states of automated manufacturing systems. From such a state, the production of some part types through one of their routes is blocked as caused by resource failures, and only after some failed resources are repaired, these parts can resume their normal processing. Then, these blocked states are characterized in terms of emptied siphons caused by resource failures. In order to prevent the system from deadlocks and blocked states, a robust controller is proposed by the following two steps. First, for siphons without unreliable resources, optimal deadlock control places are added. Then for siphons which contain unreliable resources, new control places are devised to ensure that they could be marked even when resource failures happen. It is proved that the proposed controller can guarantee that all types of parts can be processed repeatedly as long as one unit of unreliable resources can still work. This means that the proposed controller is of greatest robustness, that is, it can tolerate a maximum number of resource failures. Some examples are provided to illustrate the proposed method and show the advantage over the previous ones.

Information and computer technologies provide the spur to burgeoning man-made, highly automated systems.[...]