Information Exchange Strategy Research Articles

Algorithm-Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks

The user-centric cell-free network has emerged as an appealing technology to improve the wireless communication’s capacity of the internet of things (IoT) networks thanks to its ability to eliminate inter-cell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network. In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMM) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learning-based deep distributed ADMM (D-ADMM) network framework. To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D-ADMM adaptively learns hyper-parameters and non-convex solvers of the intractable RIS design problem through data-driven end-to-end training. Finally, numerical results demonstrate that the proposed D-ADMM achieve around 210% improvement in capacity compared with the distributed noncooperative algorithm and almost 96% compared with the centralized algorithm.

Digitalisation and inter‐organisational information exchange strategies of German companies—A survey with focus on small and medium sized enterprises

AbstractThe reliability of well‐performing production processes not only depends on internal but also on external factors in the upstream supply chain. Therefore, companies require fast and reliable information exchange with cooperating organisations. Since existing researches usually put focus on reviewing intra‐organisational solutions, the authors have conducted a survey to capture the current state and needs regarding the inter‐organisational information exchange between German companies. The main outcomes of the authors’ survey are outlined. In a first step, the current status of digitalisation in the companies is analysed. The authors have found that the use and implementation of modern methods correlates positively with the company size. Taking a look on the current methods for inter‐organisational information exchange in a second step, it is seen that only a few companies are completely satisfied with their current methods. Companies see the greatest potential for the improvement of information exchange in standardisation and the increase of required resources. This indicates that companies would be best supported by the development of solutions that can be implemented easily and help to form the heterogeneous landscape of data exchange strategies towards a structured, standardised way.

Current State of the Inter-Organizational Information Exchange Strategies of German SME - A Survey

The reliability of well-performing production processes not only depends on internal but also on external factors in the upstream supply chain. Therefore, companies require fast and reliable information exchange with cooperating organizations. We aim to support the industry in improving their communication methods and, therefore, have conducted a survey to capture the current state and needs in this area — addressing inter- instead of the usually surveyed intra-organizational degree of digitalization. In this paper, we outline the main outcomes of our survey. We received 135 evaluable questionnaires from manufacturing companies based in Germany, predominantly from small and medium-sized companies. Based on the responses, we detected that the use of modern methods correlates with the company size. Furthermore, we showed that only a few companies are completely satisfied with their current methods for inter-organizational information exchange. The greatest potential for improvement is seen in standardization and the increase of required resources.

Coordination of specialised energy aggregators for balancing service provision

In the present context of evolution of the power and energy systems, more flexibility is required on the generation and demand side, to cope with the increasing uncertainty mostly introduced by variable renewable energy resources. This paper presents a conceptual framework that encompasses different types of aggregators, including local network aggregators, demand-side general aggregators, specialised energy aggregators (SEAs), and energy community aggregators. In this framework, this paper focuses on the coordination of SEAs to provide balancing services to the system operator. Each SEA manages a specific type of load, so that these loads can be managed by exploiting their control capabilities in a detailed way considering response time, dynamics and available flexibility. Moreover, the presence of the SEAs increases the privacy protection of the users, as only the information on a specific type of user’s load is sent to the SEA. The SEA Coordinator interacts with the Balancing Service Provider aimed at procuring frequency containment, frequency restoration and replacement reserve services. This paper contains the SEA Coordinator formulation, information exchange and control operation strategies. Case study applications are presented by using SEAs for three specific types of loads (thermoelectric refrigerator, water booster pressure systems and electric vehicle charging stations). The results show how the control algorithm of the SEA Coordinator is effective in providing balancing services at different timings with the different types of loads. Various scenarios are considered, comparing an ideal situation without command propagation delays with realistic situations that take into account the command propagation delays.

Real-Time Information Exchange Strategy for Large Data Volumes Based on IoT.

In this paper, we study and analyse the real-time information exchange strategy of big data in the Internet of Things (IoT) and propose a primitive sensory data storage method (TSBPS) based on spatial-temporal chunking preprocessing, which substantially improves the speed of near real-time storage and writing of microsensory data through spatial-temporal prechunking, data compression, cache batch writing, and other techniques. The model is based on the idea of partitioning, which divides the storage and query of perceptual data into the microperceptual data layer and the perceptual data layer. The microaware data layer mainly studies the storage optimization and query optimization of raw sensory data and cleaned valid data; the aware data is the aggregation and statistics of microaware data, and the aware data layer mainly studies the storage optimization and query optimization of aware data. By arranging multiple wireless sensors at key monitoring points to collect corresponding data, building the core data service backend of the system, defining multifunctional servers, and constructing an optimal database model, we effectively solve the parameter collection and classification aggregation processing of different devices. To address the requirement of reliable and secure transmission in the process, we design a highly concurrent and high-performance TCP-based socket two-layer transmission framework and introduce the asymmetric encryption method (RSA) and data integrity verification method to design a transmission protocol that is both reliable and secure. The integration of big data and IoT is bound to bring the intelligence of human society to a new level with unlimited development prospects.

Intelligent Discrete Sliding Mode Predictive Fault-Tolerant Control Method for Multi-Delay Quad-Rotor UAV System Based on DIECOA

This paper introduces a novel intelligent sliding mode predictive fault-tolerant control method based on the Dynamic Information Exchange Coyote Optimization Algorithm (DIECOA), which is applied to a quad-rotor UAV system with multi-delay and sensor fault. First, the system nonlinearity and sensor fault are dealt with by means of interpolation transformation and system state expansion, and an equivalent system is obtained. Second, the quasi-integral sliding mode surface is used to construct the prediction model so that the initial state of the system is located on the sliding mode surface, and the global robustness is guaranteed. Third, this paper introduces an improved fault and disturbance compensation term, which effectively weakens the adverse effect of time delays and enhances the FTC performance of the system. Fourth, the Dynamic Information Exchange (DIE) strategy is designed to further improve the coyote individual replacement mechanism and speeds up the solution and convergence speed of the method in this paper. Finally, the simulation is carried out on the fault-tolerant simulation platform of the quad-rotor Unmanned Aerial Vehicle (UAV), and the results show the rationality of the method.

Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game

Ant colony optimization (ACO) is prone to get into the local optimum and has a slow convergence speed when it is applied to the traveling salesman problem (TSP). Therefore, for overcoming the drawbacks of ACO, a heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game is proposed. Firstly, three subpopulations with different characteristics are constructed into heterogeneous ant colony to enhance the performance of the ant colony. Secondly, the adaptive interactive learning mechanism is adopted when the algorithm diversity decreases, in which the objects to be communicated are selected adaptively according to the population similarity. In this mechanism, the way of communication is to pair the inferior individuals with the superior individuals, which enlarges the searching range and speeds up the convergence speed. Finally, an elite information exchange strategy based on non-zero-sum game is adopted when the algorithm falls into local optimum, in which each subpopulation selects the partners for elite information exchange according to the normalized comprehensive evaluation operator, which is helpful for each subpopulation to select the most appropriate strategy for getting out of the local optimal. Through this model, the accuracy of the solution is further improved. The data that used for this experiment is from the TSPLIB library under MATLAB simulation with various ranges of TSP datasets. Experimental results indicate that the proposed algorithm has a higher quality solution and faster convergence speed in solving the traveling salesman problem.

Patterns of health information exchange strategies underlying health information technologies capabilities building

ABSTRACT The combination of electronic health records (EHRs), health information exchange (HIE), and telehealthholds a high potential for improving the coordination of care and saving lives. As well, the benefits of the three HIT on hospitals’ depend on the patterns of capabilities that are available and used by clinicians. However, little is known about how the three HIT, actually empirically coexist and about the strategies underlying the use of HIE in hospital settings. Based on data from a European Union survey, we use a combination of hierarchical and non-hierarchical clustering and discriminant analysis to identify patterns of hospitals’ HIT capabilities. Five statistically significantly separated configurations were derived from a data set of 1038 acute care hospitals. The actual empirical coexistence of the three HIT capabilities and associated HIE strategies revealed by this study can be counter-intuitive and shed light on misalignments that may impede the realisation of the potential benefits.

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy.

Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

A Multidimensional Model of Public Health Approaches Against COVID-19.

COVID-19 is appearing as one of the most fetal disease of the world’s history and has caused a global health emergency. Therefore, this study was designed with the aim to address the issue of public response against COVID-19. The literature lacks studies on social aspects of COVID-19. Therefore, the current study is an attempt to investigate its social aspects and suggest a theoretical structural equation model to examine the associations between social media exposure, awareness, and information exchange and preventive behavior and to determine the indirect as well as direct impact of social media exposure on preventive behavior from the viewpoints of awareness and information exchange. The current empirical investigation was held in Pakistan, and the collected survey data from 500 respondents through social media tools were utilized to examine the associations between studied variables as stated in the anticipated study model. The findings of the study indicate that social media exposure has no significant and direct effect on preventive behavior. Social media exposure influences preventive behavior indirectly through awareness and information exchange. In addition, awareness and information exchange have significant and direct effects on preventive behavior. Findings are valuable for health administrators, governments, policymakers, and social scientists, specifically for individuals whose situations are like those in Pakistan. This research validates how social media exposure indirectly effects preventive behavior concerning COVID-19 and explains the paths of effect through awareness or information exchange. To the best of our knowledge, there is no work at present that covers this gap, for this reason the authors propose a new model. The conceptual model offers valuable information for policymakers and practitioners to enhance preventive behavior through the adoption of appropriate awareness strategies and information exchange and social media strategies.

Asynchronous parallel disassembly sequence planning for multi-manipulator based on improved shuffled frog leaping algorithm

To solve the problem of low disassembly efficiency of complex products, an asynchronous parallel disassembly sequence planning method of multi-manipulator based on improved shuffled frog leaping algorithm was proposed. Depending on the characteristics of this method, the disassembly manipulators are taken as the research object, and the minimum completion time of disassembly is taken as the optimization objective. Based on the basic disassembly time of parts, the disassembly preparation time caused by the change of disassembly tools and directions is considered, so that the objective function evaluation model is more realistic. Then, the mathematical model of asynchronous parallel disassembly sequence planning is constructed and the disassembly information modeling method is optimized. We proposed a new method based on the fastener constraint matrix and the part constraint matrix to build the information model of product disassembly. Based on this model, the detachable conditions are derived and the feasible disassembly sequence is obtained. In order to improve the performance of the proposed algorithm, a double-link encoding method and an efficient decoding operator is proposed. The adjustment orders which based on adjustment position and the substitution formulas are constructed to carry out the new segmental local evolution of frog individuals, and the update formula of frog position is reconstructed. Furthermore, the two-point mutation operator is added to the global information exchange strategy to enhance the search ability of the algorithm. The proposed algorithm is applied to a screw lifting mechanism and four products of varying complexity, and the results are compared with two algorithms. The results show that the proposed method can solve disassembly problems of different scales and is especially effective in solving large-scale disassembly problems.

A parallel WOA with two communication strategies applied in DV-Hop localization method

Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results.

An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation.

Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particle swarm optimizer (NMSPSO) to optimize the current configuration of double layer coil array. To balance the exploration and exploitation abilities, three novel improved strategies are used in NMSPSO based on multi-swarm particle swarm optimizer. Firstly, a novel information exchange strategy is achieved by individual exchanges between sub-swarms. Secondly, a novel leaning strategy is used to control knowledge dissemination in the population, which not only increases the diversity of the particles but also guarantees the convergence. Finally, a novel mutation strategy is introduced, which can help the population jump out of the local optimum for better exploration ability. The method is examined on a set of well-known benchmark functions and the results show that NMSPSO has better performance than many particle swarm optimization variants. And the superior electric field distribution in mTMS can be obtained by NMSPSO to optimize the current configuration of the double layer coil array.

Resilience evaluation for UAV swarm performing joint reconnaissance mission.

The resilience of unmanned aerial vehicle (UAV) swarm is its joint capability to resist possible threat, adapt to disruptive events, and restore its intended performance under a specific time period. The quantitative assessment of the UAV swarm resilience requires a thorough understanding of its missions. In this paper, a mission-oriented framework is proposed to implement the resilience evaluation for the UAV swarm. Guided by the framework, the resilience evaluation for the UAV swarm performing joint reconnaissance mission is studied. A UAV swarm model is developed for joint reconnaissance mission based on complex networks and agent-based models. The following aspects of the UAV swarm are considered in the proposed model, namely, the mission orientation, UAV attributes, swarm topology, UAV cooperative strategy, UAV information exchange and fusion strategy, potential threats, recovery strategies, etc. Then, a novel performance metric is proposed to measure the mission capability of the UAV swarm performing joint reconnaissance mission. Results from the simulations show that, compared with existing studies, the proposed approach can provide more realistic and objective resilience evaluation for the mission-oriented UAV swarm. The above works can be used to support the decision making and the optimal design of the UAV swarm, given different missions.

Информационные и стратегические основы коммуникационного менеджмента

The article discusses a quite new sphere of management which response for creating a corporative informational environment. Communication management is investigated in this case as the development of a complex of strategically necessary measures and processes directed both inside and outside the company. Analyzing the features of communication management in modern conditions, the authors focus their attention on the information and analytical aspect of communication management, which allows to realize its objectives and goals. Information is intended to serve the interests of the organization, employees must be able to analyze it in order to avoid over-saturation, and also to determine its authenticity. Creating a favorable communication climate, and in the wake of this, and effective information exchange strategies inside and outside the company is possible only if the following conditions are met: knowledge of the overall structure of the organization’s communication management processes, clear formulation of corporate values and goals, establishment of information exchange channels within the organization, serious analyzing the requests of various groups of consumers and creating an adequate “answer”. In general, effective communication management in the context of information culture seems to be a necessary tool for creating a competitive advantage of an organization on the modern market.

Improved fireworks algorithm with information exchange for function optimization

The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta-heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are presented after an analysis of the drawbacks of EFWA. These improvements are a new explosion scheme, GS-Gaussian explosion operator, and deep information exchange strategy. The proposed IFWA is tested on 23 benchmark function optimization problems and a real engineering problem, namely, optimal controller design for automotive active suspension. Optimization results prove that IFWA has competitive advantage compared with EFWA and other popular meta-heuristic algorithms and demonstrates the potential to solve real problems effectively.

Survey of Multi-agent Communication Strategies for Information Exchange and Mission Control of Drone Deployments

Understanding how multiple drones can coordinate and communicate is essential for advancing multi-agent robotics. Multiple drone applications are growing with technology and have reached areas such as: farming, meteorology, pollution detection, and forest fire monitoring. The focus of this paper is to illustrate how current systems manage mission control and communication strategies for multi-agent drone deployments. The primary scope was to examine papers that provide promising experimental results and analyze popular communication hardware used. Principal results included the classification of two main mission control strategies: centralized and decentralized. In addition the two most popular formation strategies leader-follower and virtual structure were compared. Finally, successful experimental frameworks that were used for practical applications were introduced and classified. Most results were limited in number of agents or were in their initial experimental stages. The literature review revealed a need for a greater focus on overall robustness of multi-agent systems.

Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm

Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.

A parallel variable neighborhood search for the vehicle routing problem with divisible deliveries and pickups

A well-known variant of the vehicle routing problem involves backhauls, where vehicles deliver goods from a depot to linehaul customers and pick up goods from backhaul customers to the depot. The vehicle routing problem with divisible deliveries and pickups (VRPDDP) allows vehicles to visit each client once or twice for deliveries or pickups. In this study, a very efficient parallel approach based on variable neighborhood search (VNS) is proposed to solve VRPDDP. In this approach, asynchronous cooperation with a centralized information exchange strategy is used for parallelization of the VNS approach, called cooperative VNS (CVNS). All available problem sets of VRPDDP have been successfully solved with the CVNS, and the best solutions available in the literature have been significantly improved.

Multi-Population Ant Colony Algorithm for Virtual Machine Deployment

With the recent rapid development of cloud computing technology, how to reduce the costs of a cloud data center effectively has become an important issue. The study on virtual machine deployment mainly aims at deploying virtual machine resources required by users on a physical server rationally and effectively. This paper proposes a multi-population ant colony algorithm to solve problems of virtual machine deployment. With resource wastage and energy consumption as optimization objectives, this algorithm uses multiple ant colonies for the solution and determines strategies for information exchange among ant colonies according to the information entropy of each population to guarantee the balance of its convergence and diversity. The simulation results show that this algorithm has better performance than the single-population ant colony algorithm and can reduce resource wastage and energy consumption effectively for high-demand virtual machine deployment.