Digital Twins (DT) have evolved from static digital mirrors into executable cyber-physical counterparts that predict, optimize, and control complex systems. However, the practical deployment of DT in Internet of Things (IoT) environments suffers from limited data fidelity, model brittleness, and resource constraints across the edge–cloud continuum. Generative DT (GDT) is DT augmented with Generative AI (GenAI). They enable the synthesis of high-fidelity data, bridge model-driven and data-driven paradigms, and provide adaptive decision support under uncertainty. This paper systematically reviews the research progress on GDT in the Manufacturing Internet of Things (MIoT), covering system architectures, key enabling technologies, and representative application scenarios. It also summarizes the main limitations of existing studies and outlines future research directions.

Many-objective computation offloading (MOCO) has emerged as a critical research issue in vehicular edge computing. A key challenge in the MOCO problem is how to optimize task offloading under limited edge computing resources to effectively balance multiple objectives, such as latency, energy consumption, and load balancing. To address this challenge, we formulate the MOCO problem by modeling the task computation and offloading procedure of vehicle terminals based on queuing theory, which aims to minimize the average delay time, average energy consumption, and average offloading cost for each vehicle terminal task, as well as the average load variance of edge resources. To tackle the MOCO problem, we propose a novel evolutionary algorithm based on Bayesian Maximum Entropy and incremental learning (BMEILEA) for efficient optimization of all objectives. A novel many-objective fitness evaluation mechanism based on Bayesian maximum entropy is proposed to evaluate and select solutions in the evolving population. An adaptive dynamic reference point strategy based on incremental learning is developed to effectively guide the evolutionary process. Extensive experimental results show that BMEILEA outperforms other well-known many-objective algorithms in solving the MOCO problem and achieves better convergence and diversity in the obtained nondominated solutions.

To address issues with Automated Guided Vehicles (AGVs) in ports, such as low utilization, poor flexibility, and weak adaptability, this paper presents a new collaborative transport solution. This solution enables the virtual connection of two AGVs via the Internet of Things (IoT) without physical connections. A hybrid collaborative control framework is designed to implement this solution, integrating feedback and feed-forward control. Within this framework, the transport AGV and container are treated as a single rigid body. A hierarchical decoupling approach is applied to separately design lateral and longitudinal feedback controllers for the leader AGV. Additionally, a centralized feed-forward controller for the follower AGV is developed based on rigid-body consensus theory and the AGV’s kinematic model. Typical port transportation conditions are established on both a high-fidelity dynamics software platform and a self-developed port platform using real-world port data. Tests with two AGVs show a distance error of 0.019 m and an angle error of 0.2∘ at handling points, meeting port operational metrics. These results demonstrate the scheme’s effectiveness in addressing gaps in research on nonholonomic constraint robots in collaborative transport, offering solutions for scenarios with multiple constraints and high precision requirements. Importantly, this work provides insights for developing IoT-enabled smart ports.

The research on human-following robot is important for practical applications. It is a hot field of human–machine technology. This article proposes an adaptive recurrent proportional integral differential (PID) control algorithm with self-tuning filter based on vision to address the issue of insufficient recognition accuracy of specific following targets in the presence of occlusion, multiple people, or deformation. It also aims to further improve the control accuracy and immunity of a human-following robot. First, a depth camera-based red green blue (RGB) picture and a depth image are acquired. The person reidentification algorithm and the YOLOv8 algorithm are used to detect and track the targets. The spatial position information of the targets is calculated by the depth image. Additionally, the orientation proportional differential (PD) controller and the speed proportional integral (PI) controller are built. Its foundation is the discrepancy between the relative posture of the user and the robot. In order to minimize sensor data fluctuations and lessen the negative impacts of relative positional instability, a self-tuning filter is developed. To remember the relative postures between the robot and the user in the history window, an adaptive recurrent mechanism is suggested. The controller has the ability to output the control quantity in an adaptive manner based on the current system state. Finally, experiments are conducted to verify the reliability of the proposed method. The experimental findings demonstrate that the visual pedestrian tracking algorithm proposed in this article is highly adaptable. Compared to the traditional PID, fractional-order PID, and virtual spring model, our method demonstrates significant enhancements, reducing the average distance error by 64.29%, 57.14%, and 60.52% in steering scenarios, and by 42.86%, 40.00%, and 40.00% in straight-ahead scenarios, respectively.

To enhance the flexibility of port horizontal transportation systems and improve the smoothness, accuracy, and efficiency of Automated Guided Vehicles (AGVs) motion in high-frequency disturbance environments, a longitudinal and lateral hierarchical formation control strategy for AGVs based on angle and velocity tracking is proposed. Based on the leader-follower formation control model, an AGV control system is designed, comprising a lateral controller for Sliding Mode Control (SMC) based on angle tracking and longitudinal controllers for SMC and Proportion Integration (PI) based on velocity tracking. To address the impact of high-frequency disturbance signals in the port environment, a first-order Low Pass Filter (LPF) is designed to enhance the robustness of the AGVs formation control system. Finally, tests were conducted in a combined simulation environment using Simulink and Trucksim, focusing on typical operational conditions for empty AGV formations. Simulation results indicate that the proposed scheme significantly enhances the robustness of the port AGV control system.

The error compensation and suppression effects of traditional ultrawideband (UWB) ranging in non Line of Sight (NLOS) environments are limited. The contribution of specific occlusions to UWB spatial perception in NLOS is ignored. To achieve comprehensive sensing of spatial information by UWB, we initially analyze the channel impulse response (CIR) and the underlying parameters of registers during UWB communication. By comparing the difference between NLOS and line-of-sight (LOS) environments for each parameter on a continuous time series, a fast discriminative method for UWB environment conversion is proposed. Further, combining the ensemble learning XGBoost classifier, an efficient NLOS occlusion recognition method is proposed. At the same time, an algorithm optimization based on a discrete degree threshold is designed. It is based on loss function probability matrix-weighted predictive labeling. The prediction matrix of the loss function of the XGBoost algorithm is used as label weights. The UWB prediction labels of continuous time series are weighted, which mitigates the effect of low-probability data on the overall prediction results. Finally, UWB spatial sensing experiments are carried out to verify the reliability of the proposed method. The experimental results show that the mutation in the parameter profile can effectively perceive the LOS/NLOS transition. The recognition accuracy of the proposed occlusion recognition method in conditions of human, metal, and wall occlusion is 94.44%, 92.00%, and 95.87%, respectively. In contrast to the origin method, the suggested algorithm’s average recognition accuracy has increased by 16.71%. Its precise recognition accuracy makes UWB spatial sensing more effective.

An integrated container scheduling problem (ICSP) is a significant challenge to improve the overall efficiency of (un)loading, transshipment, and reduce energy consumption in container terminals. In this study, we address a ICSP with two transshipment routes (ICSP$\_$TR) for sea-road containers. First, a multi-objective optimization mathematical model is formulated for the ICSP$\_$TR. The objectives are to minimize the maximum completion time, the total load waiting time of automatic guided vehicles (AGVs), and the total energy consumption of quay cranes (QCs) and yard cranes (YCs). Second, an improved non-dominated sorting genetic algorithm II (INSGAII) is proposed to solve the ICSP$\_$TR. The order crossover and two-point mutation are employed for container sequence. A following rule is designed for transshipment routes and external trucks. The early complete time rule is adopted in equipment allocation to configure QCs, AGVs, and YCs. Third, an external archive technology and a variable neighborhood local search strategy are developed to improve the exploitation ability. Finally, 80 instances based on ICSP$\_$TR and the single route ICSP are solved, respectively. Two storage situations of containers are compared between ICSP$\_$TR and the single route ICSP. The results based on ICSP$\_$TR show higher feasibility and effectiveness for hybrid transshipment. Furthermore, the results of the algorithm analysis show that INSGAII outperforms the original NSGAII and two other prominent multi-objective algorithms in convergence, diversity, and distribution for the ICSP_TR. Moreover, experimental results verify that INSGAII is capable of generating higher-quality scheduling schemes, offering container terminal managers a broader array of superior options.

In practical cyber-manufacturing systems (CMS), the node component is the forwarder of information and the provider of services. This dual role makes the whole system have the typical physical-services interaction characteristic, making CMS more vulnerable to cascading failures than general manufacturing systems. In this work, in order to reasonably characterize the cascading process of CMS, we first develop an interdependent network model for CMS from a physical-service networking perspective. On this basis, a realistic cascading failure model for CMS is designed with full consideration of the routing-oriented load distribution characteristics of the physical network and selective load distribution characteristics of the service network. Through extensive experiments, the soundness of the proposed model has been verified and some meaningful findings have been obtained: (1) attacks on the physical network are more likely to trigger cascading failures and may cause more damage; (2) interdependency failures are the main cause of performance degradation in the service network during cascading failures; and (3) isolation failures are the main cause of performance degradation in the physical network during cascading failures. The obtained results can certainly help users to design a more reliable CMS against cascading failures.

Because of COVID-19, factories are facing many difficulties, such as shortage of workers and social alienation. How to improve production performance under limited labor resources is an urgent problem for global manufacturing factories. This work studies an energy-efficient job-shop scheduling problem with limited workers. Those workers can have multiskills. A many-objective model with five objectives, that is: 1) makespan; 2) total tardiness; 3) total idle time; 4) total worker cost; and 5) total energy, is built. To solve this many-objective optimization problem (MaOP), a novel fitness evaluation mechanism (FEM) based on fuzzy correlation entropy (FCE) is adopted. Two construction methods for reference points are proposed to build the bridge between MaOP and a fuzzy set. Based on FCE and cluster methods, an environmental selection mechanism (ESM) is proposed to achieve a balance between solution convergence and diversity. With the proposed FEM and ESM, two many-objective evolutionary algorithms are proposed to solve MaOP. The effect of FCE-based FEM and ESM on the performance of algorithms is verified via experiments. The proposed algorithms are compared with four well-known peers to test their performance. The extensive experimental results show that they are very competitive for the considered many-objective scheduling problem.

Energy-efficient production scheduling research has received much attention because of the massive energy consumption of the manufacturing process. In this article, we study an energy-efficient job-shop scheduling problem with sequence-dependent setup time, aiming to minimize the makespan, total tardiness and total energy consumption simultaneously. To effectively evaluate and select solutions for a multiobjective optimization problem of this nature, a novel fitness evaluation mechanism (FEM) based on fuzzy relative entropy (FRE) is developed. FRE coefficients are calculated and used to evaluate the solutions. A multiobjective optimization framework is proposed based on the FEM and an adaptive local search strategy. A hybrid multiobjective genetic algorithm is then incorporated into the proposed framework to solve the problem at hand. Extensive experiments carried out confirm that our algorithm outperforms five other well-known multiobjective algorithms in solving the problem.

The emerging information technology and the rising computational thinking make it possible to establish new theoretical methods and engineering practices for planning, scheduling, control and decision-making of complex logistics systems. Consequently, the complex logistics systems oriented neural-physical fusion computation (CLSO-NPFC) is proposed initially to discuss and explore the decision issues of complex logistics systems at all the strategic, tactical and operational levels. By CLSO-NPFC, the container terminal oriented logistics generalized computing mechanization, automation and intelligence are constructed uniformly and tentatively, and the typical deep learning model neural computing architecture (DLM-NCA) in CLSO-NPFC is designed for the prediction of calling liner handling volume that is measured by container units rather than twenty-feet equivalent unit. A typical regional container terminal along the coast of China is selected to implement, execute and evaluate the DLM-NCA, and the DLM-NCA shows the agile, efficient and robust performance for forecasting with the low and stable computation consumption. It demonstrates the feasibility, credibility and practicality of the abstract principles, design paradigms and computing architecture in CLSO-NPFC preliminarily.

In the context of new retail and personalized, small-batch, distributed collaborative production, orders arrive in real time, and each workshop needs to organize production lines based on suborders in real time under the constraints of smart contracts. However, the existing cloud centralized scheduling method has very high calculation and communication costs when inserting orders in real time, and the fully reactive edge scheduling method is difficult to meet the various order-level requirements of customers. Therefore, this article proposes a real-time edge scheduling model that considers real-time trial insertion of orders based on order-level requirements. Further, a real-time edge adjustment method to eliminate fluctuations is proposed. The proposed strategies were implemented using the lightest methods, avoiding cascading effects, to apply to the edge. Experimental results show that proposed strategies have significant advantages in order-level indicators such as customer satisfaction and have slightly better performance in workshop-level indicators such as resource utilization, energy consumption, and makespan.

A Bi-swarm Particle Swarm Optimizer with novel neighborhood topology strategy (BPSO-NT) is proposed in this paper. The increase of its population diversity helps to improve its global search ability. The strategy of updating neighborhood topology that plays a vital role in the particle swarm optimization algorithm (PSO) is studied by leveraging link prediction techniques. Different learning strategies are utilized to update the velocity of individuals in the two swarms of BPSO-NT. From comparison results with the state-of-the-art PSO variants on ten benchmark functions, the superiority of the proposed algorithm is demonstrated. Furthermore, BPSO-NT is applied to the intermodal transportation planning, and statistical results show that BPSO-NT outperforms other PSO variants in this practical optimization problem.

Human-following robots can bring a lot of convenience to our lives. The function of human-following can be decomposed into three processes: human detection, tracking and following. Considering cost and adaptability. In this paper, a low-cost laser scanner is used to achieve it. And an improved method of threshold-based clustering is proposed, which can effectively solve the problem of excessive clustering when it is far away from the laser scanner. Three types of features are extracted to recognize the legs from other objects by the random forest classifier. The position in the middle of the legs is used to indicate the position of the target person, which will be tracked by the robot using a Kalman filter algorithm and nearest neighbor algorithm. After inputting the coordinates of the followed target into the controller, the linear velocity and angular velocity of the following can be obtained. Experiments show that the robot can follow the target person stably.

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO's strong global search capability and LS's fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants.

Wireless sensor networks (WSNs) have well known limitations such as battery energy, computing power and bandwidth resources that sometimes limit their widespread use. Current researches are mainly concentrated to propose solutions for nodes energy optimization, network load balancing and the improvement of WSN robustness; however, the software defined network (SDN) paradigm uses the theory of forwarding phase separating from control, simplifying management and configuration of the network to improve network extension and flexibility. It could further optimize WSNs deployment and improve their transmission performance. In this paper, we firstly describe the general architecture and the main features of software defined networks; then, we analyze the current integrated SD-WSN scheme and summarize these results in detail.

Existing routing protocols of wireless sensor networks (WSNs) attempted to optimize the energy efficiency and the routing reliability from the perspective of the network itself and failed to take into consideration the environmental impact from outside, causing them cannot make prompt reaction to the dynamic changes of the environments (e.g., wildfire). Thus, in these routing protocols the routing survivability under harsh environments is questionable. To tackle this issue, in this paper by referencing the concept of potential field, we design an environment-cognitive multipath routing protocol (ECMRP) in order to provide sustainable message forwarding service under harsh environments. In ECMRP, routing decisions are made according to a mixed potential field in terms of depth, residual energy and environment. The basic idea of this approach is to instruct data packets to select routes with the tradeoff among latency, energy conservation and routing survivability. As the environmental field is constructed and updated using the sensing capability of WSN itself, constructed routes can avoid crossing through the danger zones to keep the paths safe. The experimental results show that ECMRP can obtain significant improvements in packet delivery ratio and network lifetime under harsh conditions.

The Internet of things (IoT) is embedded into the industrial scene and combined with various devices, facilities and materials to form Smart Objects (SOs). The SOs could be thought of as a heterogeneous swarm in a smart factory under Industrial IOT environment. The process of production is the coordinated operation of various production resources, but dynamic events often interfere with production activities. To solve this problem, an swarm collaboration architecture is proposed. Specifically, it considers the influence of human factors with Human-in-the-Loop on workshop intelligent equipment interaction, and the multi-level dynamic scheduling method of swarm collaboration under the framework is discussed.

Exposure to air pollution poses a significant risk to human health, particularly to urban dwellers. When correlated with individual health outcomes, high resolution information on human mobility, and the spatial and temporal distribution of the pollutants can lead to a better understanding of the effects of pollution exposure. People-centric sensing is normally carried out by data sharing through a central cloud server. This system architecture is not designed to serve the ever-growing number of high fidelity connected devices, particularly when crowdsourcing urban data on location and environmental conditions. Here, we outline an architecture for a people-centric and cognitive Internet of Things (PIoT) environmental sensing platform, which involves closed loops of interactions among people nodes and physical devices as well as servers and recommendations on device connections by cognitive computing. Taking advantage of smart objects and virtual node technology in PIoT, an algorithm to aggregate on-demand user data from smart devices is proposed. A PIoT prototype sensing system is designed and deployed to measure the space-time distribution of particulate matter in air (PM2.5), and mobility counts, for quantifying personal exposure to air pollution. A case study of particulate matter PM2.5exposure in New York City is presented to illustrate the potential application of people-centric measurement system and data analysis.

In a smart factory environment, a much larger amount of data are transmitted in the workshop networks bringing big challenges to data transfer capability and energy usage efficiency. In the workshop, two main networks, i.e., wired/wireless fieldbus networks and wireless sensor networks, are usually used to collect and transmit data separately; thus, this paper proposes a mobile and hierarchical data transmission architecture to integrate these two networks also taking advantages from the existing mobile intelligence in smart factories, such as automatic guided vehicles (AGVs), to implement a novel data and materials delivery scheme well suited for modern industrial wireless sensor networks (IWSNs). Simulation experiments demonstrated how the proposed approach, running within the IWSN, significantly increases data delivery efficiency along with achieving better energy usage, by 4 times, with respect to the separated networks without any mobile intelligence support.

Since increasing citizen are associated with a major increase in street vehicles and air pollution, and air pollution exposure is a significant risk to urban dwellers. Spatially and temporally high-resolution air pollution measurement is highly concerned. Conventional people-centric sensing approaches is based on vertical and closed way, and measurement are based on private sensor accessing, gateway-based data bundled and data shared trough cloud server. However, the growing connected devices leads to a certain of high-density crowdsourced surroundings around human in smart city, but how to efficiently read spatiotemporal air pollution data from self-organized smart objects is still poorly understood. To address the issue, this paper proposes a service-oriented service architecture of people-centric internet of things(PIoT) based on smart object. And then, a directly Device-to-Device (D2D) service aggregation mechanism is presented. Finally, a case study of low-cost measurement system for street level air pollution and traffic volume is illustrated.

Mobility impaired individuals need the wheelchair to support their independent life, so monitor activities performed on the wheelchair can provide significant insights on their general health status. Activity recognition related to healthy people is a well established research area; however, only few works addressed this problem for wheelchair users. This paper proposes a novel approach based on dynamic Bayesian networks to recognize physical activities performed on a wheelchair. We equipped the wheelchair seat with a pressure detection unit and attached two inertial measurement units on the user's wrists. We focus on common basic activities and specifically, to experimentally evaluate our method, we defined four dynamic activities (moving forward, moving backward, moving left-circle, moving right-circle) and two static activities (left-right swing, forward-backward swing). Data is collected using a smart wheelchair system we developed in previous research. Firstly, we generate the posture sequence from the pressure signals and detect the raw acceleration data from inertial measurement units; then, we fuse the posture sequence and inertial features to detect the postural-based activities. Results shows that our proposed method can achieve an overall classification accuracy of 91.88%.

The Asymmetric information among different entities can contribute to the imbalance between supply and demand in agricultural supply chain. Negotiation is an effective method to address information symmetry. Many researchers can be found in the literature on agent-based negotiation in industrial supply chain, but very few in agricultural supply chain, primarily because of the interior instability. This paper presents an agent-based negotiation framework for agricultural supply chain to address the information symmetry by introducing the third party logistics. The third party logistics, with more functions than a traditional broker, integrates logistics services and intermediary services to guarantee the relative stability in dynamic agricultural environments. A negotiation interaction process is also designed to facilitate the interaction among these agents. Finally, a case is used to validate the proposed framework.

In recent years, the elderly population is increasing enormously, from 9% in 1994 to 12% in 2014, and is expected to reach 21% by 2050. Elderly live often alone today and even conducting an independent daily life, some of them move with the aid of walkers or using wheelchairs. Monitoring elderly activity in mobility has become a major priority to provide them an effective care service. This paper focuses on an enhancement of a smart wheelchair based on pressure sensors to monitor users sitting on the wheelchair. If the wheelchair user assumes a dangerous posture, the system triggers audio/visual alarms to avoid critical consequences such as wheelchair overturn. The paper discusses the hardware design of the system, then analyzes and compares posture recognition methods that have been applied on pressure data we collected. The experiments demonstrate the effectiveness of the proposed method and 99.5% posture recognition accuracy has been observed.

This paper studies the fault-tolerant cooperative control problem of agent groups in the context of multi-agent flocking tasks with second order linear dynamics. A distributed flocking algorithm with biconnected topology is proposed which is composed of two parts: motion strategy of biconnectivity and fault-tolerant flocking algorithm with bounded control input. The proposed algorithm handles the movement and reconfiguration of the flock, while maintaining the desired shape. It is proved that the proposed control algorithm can not only achieve the resultant biconnected network which is able to tolerate temporary node failures, but also guarantee the stable flocking motion. Several simulations are presented to demonstrate the efficiency of the theoretical results.

The logistics and transport operations, which have become more information intensive and more technologically dependent, are undergoing dramatic revolutions. Among various logistics activities, the role of transport is becoming more and more important. In order to provide high quality service and obtain higher return on investment, information sharing and cooperative decision making become a must in this dynamic changing environment. In particular, multi agent (MA) technologies have drawn significant attention for enabling autonomous information sharing and cooperation in a complex and distributed environment. This paper presents the development of coordination and decision making mechanisms for the implementation of autonomous control with MA technology in the domain of hinterland transport. It tackles the challenges of the hinterland transport planning caused by limited information sharing and lack of cooperation. The objective of the developed system is to provide quality transport plan to achieve high level of performance and robustness in hinterland logistics. The paper specifies and implements the MA cooperation, negotiation and decision-making processes. A case study is presented to illustrate the effectiveness of the developed system.

How to shorten time delay and enhance delivery ratio is still an open problem in the study of opportunistic networks. Most proposals are trying to deal with this issue by introducing infrastructures. Although related research has been proven to be useful in improving the routing performance of the network, there is still room for further improvement. In this article, inspired by the powerful message synchronization capability of wireless sensor networks (WSNs), we propose a new opportunistic network framework called WON that introduces WSNs into opportunistic network. With the support of WSNs, fast message delivery and high success ratio can be achieved. We specifically present the layered architecture of WON and compare WON to existing architectures in opportunistic networking. The simulation results concerning delivery delay and success ratio are highly encouraging. Finally, open issues are outlined.

The resources that enable cloud logistics have the characteristics of heterogeneity, discrete distribution, dynamics and autonomy. They have an influence on the efficiency of the integration and configuration of the resources. Based on fully considering the classification and the features of the cloud logistics resources, this paper establishes a uniform resource expression model. This model does not only transform the heterogeneity and discrete distribution into attribute or function information, but also transforms the dynamic and autonomy characteristics into capability information, achieved the mapping from cloud logistics physical resources to virtual resources. The use of ontology and web service technology encapsulated virtual resources into a Web service achieves the cloud service encapsulation of logistics resources. Finally, a transportation service application case has been used to validate the feasibility of the method.

Research relating to the invulnerability of Wireless Sensor Networks (WSNs) has made gratifying progress. However, most of them concentrate on the statistic features of networks, and ignore the cascading failure of network caused by dynamic load changes. In this study, considering the realistic characteristics of WSNs, random network, scale-free network, WS small-network and NW small-network models have been built and the invulnerability performance and cascading process of these network models under random attack are researched respectively. The study presents that the increase of the coefficient of tolerance-T is beneficial to improving the invulnerability of all networks, especially NW small-world network model. By evaluating the distribution of causes (i.e., Traffic overload, invalid connectivity) to node failures, it was found that invalid connectivity is major reason for failure nodes. Besides that, scale-free network shows more steady performance than other networks in terms of error-tolerance.

In traditional manufacture enterprises, a popular production mode is parallel lines within a department and sequential between departments. Such production mode may cause a large buffer between departments, long and complex transportation routes, and components mismatched at the assembly department. Based on the concept of collaborative production, this paper proposes a mathematical model and relative algorithm for collaborative production of products with components of different colors. A case study has been used to validate the proposed model, and the related future value stream mapping has been reached, compared with the current value stream mapping. The analysis results show that the proposed approach is promising.

With more and more applications of Internet of things (IoT) technologies, the quality of life of residents is one of the most important aspects in smart cities. Specially, home healthcare monitoring for the disabled and / or the elderly has become a focus of recent researches and developments. Existing home healthcare systems have drawbacks such as simple and few functionalities, weak interaction and poor mobility. This paper presents a home mobile healthcare (mHealth) system for wheelchair users, based on the emerging IoT technologies. The paper focuses on the proposed system architecture and the design of wireless body sensor networks (WBSNs). The nodes of WBSNs include wireless heart rate and ECG sensors, wireless pressure detecting cushion, home environment sensing nodes and control actuators. A prototype system implementation shows that the proposed people-centric sensing system is efficient in monitoring human activities and in interacting with the living environment.

Network invulnerability is an important property of networks that operate under very likely physical attacks and failures due to operating environmental conditions. A notable example of such networks is wireless fire alarming networks (WFANs) that are strongly related to the safety of the public and to the efficiency of rescuing. WFANs based on wireless sensor networks (WSN) are gaining momentum as they considered a viable and effective solution. However, the current research on invulnerability in the WSN domain mainly focuses on the optimization of the sensor node layout in the initialized network and on routing protocols, whereas the importance of optimization of the deployed network is less explored. In this paper, we show that the invulnerability of WSNs can be improved by introducing two new elements: super wires and super nodes. Moreover, on the basis of the definition of a novel centrality measurement, we propose two layout schemes based on super wires and super nodes for enhancing network invulnerability. The simulation analysis indicates that the proposed schemes are able to enhance the invulnerability of the network with low network construction costs.

Material safety and traceability is of great importance in toy manufacturing because there have been tougher requirements on toy product safety imposed by new international regulations. We investigate and analyze the production workflow in small and medium toy manufacturing enterprises by SADT and simulation analysis. We find out that tracking information is incomplete and information flow and material flow are out-sync due to lacking material and production process collaboration in current system. Thus, the tracking objective creates a need for systems to collaborate material flow and production flow in manufacturing enterprises. In this paper, aiming at enhancing efficiency of production, a new concept of collaborative material and production tracking based on a supply chain view is presented. Then we also propose collaborative architecture of toy material and production tracking system based on the Internet of Things to improve tracking accuracy and efficiency. Finally, we take the paint material for example to implement the production tracking system. The application example shows that it can collect real-time data accurately and reliably. This system has practical significance in improving product quality management and realizing enterprise information construction.

Opportunistic network as a representative network evolved from social networks and ad hoc networks, has been on cutting edges in recent years. Due to its inherent characteristics serving for intermittent networking setting specifically, the opportunistic network has been also widely applied in the domain of Internet of Things (IoT). Many researchers have focused on the realistic mobility model and cost-effective routing scheme. Community as one of the most inherent attributes of the opportunistic network has been proved to be much helpful in simulating mobility traces of human society and selecting suitable message forwarders. This paper proposes a community-structured mobility model with consideration of geographical location preference and time-variance in human behavior patterns. Based on this model, a novel routing algorithm is presented by jointly considering utilities generated by social degree and relation. The results show that our routing scheme is able to improve success rate while control the routing cost and transmission delay into a reasonable range.

The acquisition and application of people's various physiological and psychological states will play a very important role in future smart society. In this paper, body sensor network is used to perceive human physiological parameters, especially human skin resistance in and out of adjacent fingers and the pulse information in ulnar-sided position on the right hand. Then the feature combination which contributes to the emotion recognition is obtained through wavelet analysis and the fear emotion is recognized by uncertainty data fusion algorithm of D-S Evidence Theory. The prototype experiments have proved that this method has a good recognition effect. The perspective of vibration sense of human pulse based on Traditional Chinese Medicine and computer technology is achieved in this study. It perceives psychological states of the human objectively and directly, which will provide a prototype model to obtain the human physiological and psychological indexes objectively, as well as an example to monitor real-time physiological and psychological states of human body.

It is often assumed that a multi-robot network is connected initially. However, it doesn't agree with the actual case that robots are randomly placed in an unknown environment. This paper explores a control algorithm combined artificial potential field function with the virtual leader in wireless sensor network environment to control the connectivity of a multi-robot system, even initial network is unconnected. Potential field function provides foundation of establishing connectivity for subsequent control in a multi-robot system. Finally, comparison between the proposed algorithm and others are analyzed through simulation experiments. It is demonstrated that the proposed algorithm can overcome local optima and control communication topology relationships for the multi-robot system.

Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).

As the most important facilities Container terminals (CT) play a valuable role in international trade. The efficiency of quay side determines the productivity of the CT mostly. Considering continuous berth allocation (CBA) and quay crane assignment (QCA), the objective of this paper is to minimize the sum of extra cost of berthing at non-optimal location and penalty cost of time delay. A berth allocation heuristic strategy(BAHS) is provided to dispose continuous berth allocation problem. Due to different stages, quay crane assignment heuristic strategies at berthing (QCAHSB) and quay crane assignment heuristic strategy after departure (QCAHSD) are introduced. A hybrid model integrated with the two heuristic strategies and particle swarm optimization (PSO) is proposed to solve the collaborative scheduling of continuous berth and quay crane. Experimental results show that the hybrid model is available for the collaborative scheduling problem effectively.

With the rapid development of China's network economy, the strong demand for the types and personalization of logistics services is increasing, thus they embody an evident trend of network and socialization. The integration of Internet of Things technology is exactly to meet this trend, therefore, a new intelligently networked logistics service mode called “cloud logistics” has been proposed under the environment of IoT in this paper, which can provide services on demand. We analyze the concept, operating principle and features of cloud logistics. Then, the cloud logistics-based One-stop Service Platform for logistics center has been put forward. Through a unified, centralized, intelligent management and operation of cloud logistics platform, we can provide the supply chain users with comprehensive, fast and efficient logistics services. In addition, on the basis of research on the one-stop service platform architecture, a platform instance is also presented.

The existing methods of Web Service Composition (WSC) seldom consider the initiative and dynamic adaptability of Web Service (WS), which cannot make WS perceive the change of users' requirements in the changeable network environment. The result leads to the failure of Automatic Web Service Composition (AWSC). Meanwhile, the quantity of WS is so large in the existing network environment that the result of the AWSC does not meet users' requirements. Therefore, to improve the success rate of AWSC and solve the optimization of WS, on the basis of establishing the model of AWSC guided by the users' demand, this paper puts forward an optimal model of WSC based on particle swarm optimization (PSO) algorithm with Quality of Service (QoS) constraints, then illustrates the feasibility of the model by simulation results.

For the existing Web Services integration methods have shortages in the areas of dynamic, flexibility and intelligence, and the existing programs in industry have the disadvantages that their robustness and fault tolerance are poor, network traffic is too much and network delay frequently happens, a Mobile Agent based Web Service Integration (Mobile Agent based Web Service Integration, MAWSI) framework has been proposed. The function of each part of the framework is analyzed, and the organizational structure and interactive mode of the Agent subsystems in the framework have been designed in detail. Furthermore, we construct an open, distributed, dynamic service composition system, and take an example to illustrate the feasibility and effectiveness of the framework.

This paper presents a review of the recent developments of collaborative wireless sensor networks (CWSN). CWSN focuses on collaboration in wireless sensor networks (WSN). It has applied on almost every research field of wireless sensor networks, such as localization, topology, protocol, environment sensing and coverage, energy aware, security, and so on. As the resource on each node is limited and there are lots of nodes in WSN, now it is becoming a significant and basic method of WSN, especially when there are bottlenecks that affect the performance of a node or the networks. This paper presents and discusses the concept and features of CWSN, its research trends and its applications, along with our recent researches. The relationship between CWSN and IOT (Internet of things)/CPS (Cyber physical systems) is also briefly discussed.

Energy consumption is one of the fundamental and challenging problems in the wireless sensor networks. It is considered from the view of energy consensus in this paper. Algebraic graph theory is used to describe topology structure of the wireless sensor networks(WSN). The energy consumption of nodes is analyzed and modeled. A hybrid model is built to describe the process of updating continuous energy state and the process of dynamic, discrete topology of WSN. Considering surplus energy information coupled with control message from base station, consensus algorithm for energy consumption is proposed. Integration analysis of the hybrid system reveals that the surplus energy state of each node converges to energy consumption balance. We evaluate the system with the simulation experience and performance data. The results can be a guideline to design and develop collaborative WSN with energy aware.

Container terminal handling operation is the main task of terminal service. The handling time and the operation efficiency at container terminals are the service quality evaluation standard in the resource limit. This paper based on the discreteness and dynamic characteristic of Container Terminal Logistics Operation System (CTLOS), establishes the architecture of CTLOS with the multi-agents technology, introduces the structure and function of two agent groups, and studies on communication and cooperation among agents. Finally, it takes the wharf apron subsystem in the CTLOS as an example to show the operation mechanism of the agents and effectiveness of the modeling method.

Wireless sensor networks (WSN) consist of many small battery powered nodes with resources constraint. Low power design is a pivotal step of deploying a new platform. A typical sensor node consists of three major components: sensors, micro-controller, and transceiver. It's important to know their dynamic power consumption before optimization. Current research focuses on simulation, or integrating software probes inside the node. They are not always applicable. We proposed an energy efficiency testbed for wireless sensor nodes. It is portable and can realtime sample the data of power consumption. Our device, based on measurements of current and voltage information, enables accurate energy efficiency analysis of nodes in real world. It's useful for evaluating and modeling a new platform at design phase. Moreover, it can be used to monitor health status of sensor nodes from the aspect of power. Experimental results on WSN platform, MicaZ, show that our device can well monitor the energy efficiency of wireless sensor nodes.

A swarm is a decentralized and self-organized collective with lots of simple but autonomous and homogeneous individuals. Swarm intelligence is defined to describe its emergent behaviors. Both sensor networks and mobile multi-robots can have swarm features. The combination and cooperation of these two systems is a tendency recently. From the view of swarm organisms, the challenges of combination of sensor networks and mobile multi-robots are discussed and a layered dual-swarm framework is presented, which has a 3D communication structure, and which is possible to organically inherit traditional swarm technology while building an efficient interaction channel for both swarms to cooperatively work together. Finally, a control strategy based on virtual entities is introduced to induce and control the behaviors of the robot swarm through wireless sensor networks.

The problem of multiple robots system formation using a distributed control method is studied in this paper. The main idea of this paper is that uses swarm flocking control algorithm to implement the ldquobiodsrdquo model of Reynolds among multi-robots. With the help of graph theory, we propose a provably-stable flocking control law, which ensures that the internal group formation is stabilized to a desired shape, while all the robotspsila velocities and directions converge to the same. Player/stage simulation results show that the proposed method can be efficiently applied to multiple robots formation control. With the characteristic of player/stage, the algorithm in this paper can be implemented on the real robots with so few or no changes.

This paper mainly uses flocking control algorithm to implement the ldquobiodsrdquo model of Reynolds among multi-robots. We present two flocking algorithms: the first algorithm is a gradient-based algorithm with a velocity consensus protocol, and the second one is the main flocking algorithm for robot formatin control in a free workspace with an additional target steering agent that takes the group target into account. We find that the first algorithm only guarantees the creation of flocking in some special initial states and the second algorithm can create flocking for a generic set of initial states. Simulation results show that the proposed method ensures the group formation is stabilized to a desired shape, while all the robots' velocities and directions converge to the same.

In this paper, we developed a local-centralized adaptive clustering algorithm, two-phase cluster formation algorithm (TCF), which concerns not only energy efficiency but also the reliability and the steadiness of the wireless sensor networks. TCF proposes a local-centralized mechanism to elect cluster-heads which makes election of cluster-head more reasonable and improves the efficiency of energy consumption. TCF provides a twice cluster-formation model which imposes the feature of load balance on wireless sensor networks by distributing evenly the communication load and the load of data fusion among cluster-heads. TCF also suggests a parameter to measure the probability of network congestion in hierarchical applications of WSNs.

This paper is concerned with the design and implementation of a control architecture for a mobile robot that is to navigate in dynamic unknown indoor environments. It is based on the framework of Open Robot Control Software @ KTH, which is discussed and evaluated in this paper. As a hybrid architecture, it is decomposed into several basic components which can be classified as either deliberative or reactive. Each component can concurrently execute and communicate with another using unified communication interfaces. Scalability and portability and reusability are the goals of the design.