Collaborative Spectrum Research Articles

Negotiating care in organizational borderlands: a grounded theory of inter-organizational collaboration in coordination of care

BackgroundAlthough coordination of care and integrated care models aim to enhance patient satisfaction and perceived care quality, evidence regarding their practical implementation remains scarce. Understanding the nuances of collaboration across care providers to achieve effective coordination of care is imperative for seamless care integration. The aim of this study was to construct a grounded theory of how inter-organizational collaboration is performed to support coordination of care for patients with complex care needs.MethodsA qualitative design with a constructivist grounded theory approach was applied. In total, 86 participants with diverse backgrounds were recruited across multiple care settings, including hospitals, ambulance services, primary care centers, municipal home healthcare and home care services. The grounded theory was developed iteratively, based on a combination of observations and interviews, and using constant comparative analysis.ResultsCoordination of care, a complex process that occurs across interconnected healthcare organizations, is manifested as “Negotiating care in organizational borderlands.” Care coordination evolves through a spectrum of inter-organizational collaboration, ranging from “Dividing care by disease-specific expertise” to “Establishing paths for collaboration” and ultimately “Co-constructing a comprehensive whole.” These categories highlight the challenges of coordinating care across both professional and organizational boundaries. In the multifaceted healthcare landscape, effective care coordination occurs when healthcare professionals actively bridge the divides, leveraging their collective expertise. Importantly, organizational boundaries may serve a purpose and should not be dissolved to facilitate effective care coordination.ConclusionsThe key to effective care coordination lies in robust inter-organizational collaboration. Even when patients receive integrated care, healthcare professionals may have fragmented roles. This research emphasizes the importance of clearly defined lines of accountability, reinforcing mutual responsibility and facilitating bridging of professional and organizational boundaries. Healthcare professionals and policymakers can use these insights to effectively utilize inter-organizational collaboration in supporting care coordination for patients with complex care needs.

Reputation-Based Self-Differential Sequential Mechanism for Collaborative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

Reputation-Based Self-Differential Sequential Mechanism for Collaborative Spectrum Sensing Against Byzantine Attack in Cognitive Wireless Sensor Networks

Energy consumption optimization in green cognitive radio networks based on collaborative spectrum sensing

In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models.

Retraction Note: A novel linear SVM-based compressive collaborative spectrum sensing (CCSS) scheme for IoT cognitive 5G network

Retraction Note: A novel linear SVM-based compressive collaborative spectrum sensing (CCSS) scheme for IoT cognitive 5G network

Co-creating multimodal transportation hubs in Switzerland: How to close the gap between actors across different scales, levels, and sectors

Transforming railway stations and their surroundings into multimodal transportation hubs (MMTHs) involves numerous actors at different scales and from different economic sectors and levels of government. Successful transformations offer a wide range of benefits to sustainable development, including increased public transportation use and mixed-use, high-density station districts. Intensive collaboration and, ideally, co-creation are critical to achieving these outcomes through MMTHs; however, orchestrating all involved actors is challenging and requires supporting methods, and the knowledge required to develop and refine methods is scarce and rarely subjected to systematic analysis. Based on 15 semistructured interviews and two design thinking workshops attended by 13 and 20 MMTH experts, our study shows that the challenges of co-creating MMTHs relate not only to professional matters but also to managing collaboration and implementation among a large number of actors with various roles and interests. In this paper, we develop design guidelines for reviewing and evaluating two current methods and a prototypical method (the functional model) with the goal of identifying potential improvements and supporting MMTH co-creation in Switzerland. These guidelines cover the broad spectrum of co-creation activities, from organization and design collaboration with relevant actors to the development of a shared vision to support financing and the planning process. The functional model encompasses many aspects of the design guidelines and closes gaps between actors across different scales, economic sectors, and governmental levels. Due to the relatively low effort involved, the method can be repeated as needed throughout MMTH development, which often takes several years. Our study demonstrates that existing MMTH co-creation methods require improvement, and the design guidelines developed here suit this purpose. Our work thus contributes to the further development of MMTH co-creation methods, ultimately supporting sustainable development such as CO2 emission reduction and responsible land use.

Navigating the spectrum of human-robot collaboration: Addressing robophobia-robophilia in the hospitality industry

Analyzing human-robot collaboration, this study draws on insights from cognitive science and psychology, with a particular focus on developing strategies for instructing robots and addressing the dynamics of robophobia and robophilia. Robophilia signifies the evolving, positive human-robot relationship, emphasizing the attribution of personhood to robots, while robophobia represents fear of robots. Although hospitality studies have explored anthropomorphism in robots, there is a gap in understanding robophobia impact in this context. This research investigates the transition from robophilia to robophobia and its implications for human-robot interactions in hospitality. Past studies primarily emphasized positive aspects, like anthropomorphism and robot personhood neglecting potential negatives such as robophobia. Guided by analogical transfer theory, fear acquisition theory, and uncanny valley theory using three experimental studies, this research explore factors driving this transition, its impact on embarrassment, customer outrage, and employee sabotage intentions during joint robot interactions. This research enriches our understanding of complex human-robot dynamics, shedding light on robophobia's mediating role within the context of service disruptions.

Collaboration in the return-to-work process after sick leave due to common mental disorders: a qualitative study of stakeholders’ views on goals and roles

BackgroundThis study explores how the goals of collaboration in the return-to-work (RTW) process for people with common mental disorders are described by the stakeholders involved, and how they experience stakeholders’ roles and responsibilities in relation to these goals.MethodsInterviews were conducted with 41 participants from three Swedish regions. Nine of the participants were workers, six employer representatives, four occupational health professionals, four social insurance officers, 18 RTW coordinators and five physicians. Thematic analysis was conducted.ResultsThree main themes and overarching goals when collaborating on RTW were identified. In the first theme, ‘creating an informative environment’, all stakeholders emphasised clear roles and responsibilities. The second theme, ‘striving for consensus in an environment of negotiations’, addressed negotiations about when and how to collaborate, on what and with whom, and reveal different views on stakeholders’ goals, roles and responsibilities in collaboration. The third theme identified goals for ‘creating a supportive environment’ for both workers and other stakeholders. Coordinators are found to have an important role in achieving a supportive environment, and in neutralising power imbalances between workers and their employers and social insurance officers.ConclusionsCompeting goals and priorities were identified as hindering successful collaboration, contributing to a spectrum of complex versus easy RTW collaboration. This study suggests some basic conditions for achieving a collaborative arena that is neutral in terms of power balance, where all stakeholders can share their views.

A Double-Threshold Cooperative Spectrum Sensing Algorithm in the Internet of Vehicles

To address the shortage of wireless spectrum resources caused by the rapid development of the Internet of Vehicles, spectrum sensing technology in cognitive radio is employed to tackle this issue. In pursuit of superior outcomes, a double-threshold cooperative spectrum sensing algorithm is introduced. This algorithm enhances traditional energy detection technology to mitigate the high sensitivity to noise interference in the Internet of Vehicles environment. A double-threshold judgment mechanism can be established based on the uncertainty of noise. Varying fusion rules are implemented in the collaborative spectrum sensing scheme according to the density of vehicles and the spectrum resource demand. Simulation results demonstrate that the performance of the double-threshold cooperative spectrum sensing algorithm surpasses that of the traditional single-threshold energy detection scheme, particularly evident under lower Signal-to-Noise Ratio (SNR) conditions. Moreover, the proposed algorithm exhibits superior sensing performance in environments characterized by higher noise uncertainty.

Communication spectrum prediction method based on convolutional gated recurrent unit network

In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources.

Who Determines What Is Relevant? Humans or AI? Why Not Both?

A spectrum of human-artificial intelligence collaboration in assessing relevance.

Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things.

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds.

Joint Collaborative Big Spectrum Data Sensing and Reinforcement Learning Based Dynamic Spectrum Access for Cognitive Internet of Vehicles

Cognitive Internet of Vehicles (CIoV) is an intelligent vehicle network envisioned to opportunistically access spectrum licensed to primary users (PUs) on the premise of not interrupting their normal communications. Dynamic spectrum access enables the CIoV to choose the best possible spectrum for communications based on the outcomes of spectrum data sensing, which can improve the spectrum access performance effectively. In this paper, we enable the CIoV to adapt to various spectrum states through: a) a collaborative big spectrum data sensing scheme to sense a massive amount of spectrum data; and b) a reinforcement learning (RL) based dynamic spectrum access scheme to optimize spectrum selection strategies. Q-learning, which is a popular RL approach, is proposed for underlay, overlay, and collaborative spectrum access modes to allocate spectrum resources to the CIoV intelligently. The Q-learning models, which include the spectrum state vector, the action vector of CIoV, and the spectrum access reward received in different spectrum situations, are defined for the spectrum access modes. A Q-learning based spectrum access algorithm is proposed to improve the communication performance of the CIoV in different spectrum access modes. Simulation results indicate that the collaborative spectrum access mode can achieve higher average throughput, lower interference power and lower communication outage compared with the underlay and overlay spectrum access modes.

Cooperative Spectrum Sensing Algorithm Based on Eigenvalue Fusion

A novel algorithm is introduced to improve collaborative spectrum sensing under low cognitive capabilities and insufficient signal-to-noise ratio. The algorithm is based on the difference of random matrix eigenvalues and uses the theory of random eigenvalues and the extreme distribution of the minimum eigenvalue. It makes use of the average, both arithmetic and geometric, as well as the minimum and maximum values of eigenvalues as the detection metric. It calculates the fusion power parameter through local energy spectrum sensing. Simulation results demonstrate that the algorithm outperforms the DMM algorithm and the NMME algorithm under users with low cognitive capabilities and Insufficient signal-to-noise ratio, making it more suitable for low signal-to-noise ratio environments.

Cognitive Radio Spectrum Sensing-Based QAM Technique Using Blockchain

Cognitive radio (CR) is a novel concept developed to solve concerns such as spectrum underutilization and scarcity. Spectrum detection methods are employed in the blockchain-based CR to make optimum use of the spectrum reserves. In this paper, an attempt is made to evaluate the effectiveness of an energy sensor using collaborative spectrum detection. Wideband is defined as the frequency range between 470 MHz and 790 MHz, and additive white Gaussian noise (AWGN) is employed. The probability of detection ( P d ) under different situations is examined using detection in the receiver operational curve (ROC). According to the findings, the P d increases with the number of samples. This form of sensing, which is thought to be the easiest and best, uses energy-detecting spectrum sensing. To address ambiguity, the M-ary QAM technique is provided, which increases aggregate effectiveness in terms of the percentage of false alarm ( P f ) and probability of missed detection ( P m ) by 5% at a comparable delay period. When CR encounters shadowing and impacts situations, the client cannot tell the difference between an underutilized zone and fading. In comparison to the existing model, this study increases the likelihood of detecting a 3 dBm SNR for a 64-QAM modulated signal by at least 15%.

5G-RCOLAB: A system level simulator for 5G and beyond in rural areas

This paper describes 5G-RCOLAB, an open source simulation tool for performance evaluation of 5G/6G wireless networks in rural areas. The tool is designed with a layered architecture, based on three modules: (i) a link-level simulator of a communication channel for remote areas, with a mapping Link to System (L2S) interface; (ii) a Long-Term Evolution (LTE)-based physical layer adapted for remote areas, together with a Medium Access Control (MAC) layer that includes a novel Collaborative Spectrum Sensing (CSS) technique with security features for Byzantine attacks and spectrum usage efficiency; and (iii) an application oriented network layer that emulates the behavior of typical users in rural areas. The tool has the goal to reduce simulation complexity of large scale 5G rural networks and at the same time, to represent coherently the features of the physical and MAC layers, that include new channel models that cover long distances with the use of TV White Spaces (TVWS) in the Ultra High Frequency (UHF) and Very High Frequency (VHF) bands, as well as novel CSS techniques to improve spectrum efficiency. The 5G-RCOLAB was validated both by field test measurements from a Proof of Concept (PoC) prototype and by the simulation and evaluation of three 5G common core use cases in rural areas. The results show that the 5G-RCOLAB features allow a better evaluation of novel technologies implemented in the physical and MAC layers and provide paths for improvements and deployments of cost effective solutions of 5G in rural or remote areas.

Spectrum Sensing Method Based on Residual Dense Network and Attention.

To address the problems of gradient vanishing and limited feature extraction capability of traditional CNN spectrum sensing methods in deep network structures and to effectively avoid network degradation issues under deep network structures, this paper proposes a collaborative spectrum sensing method based on Residual Dense Network and attention mechanisms. This method involves stacking and normalizing the time-domain information of the signal, constructing a two-dimensional matrix, and mapping it to a grayscale image. The grayscale images are divided into training and testing sets, and the training set is used to train the neural network to extract deep features. Finally, the test set is fed into the well-trained neural network for spectrum sensing. Experimental results show that, under low signal-to-noise ratios, the proposed method demonstrates superior spectral sensing performance compared to traditional collaborative spectrum sensing methods.

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

생성형 AI가 출판 환경에 미치는 영향에 관한 연구

ChatGPT is popular all over the world and shows the potential to exert a great influence on society as a whole. The importance of ChatGPT is that it became a catalyst for the public to directly experience generative AI services. Generative AI is already being evaluated as having enough potential to bring about many changes in all areas of society. The publishing sector is no exception. Rather, publishing is more likely to be impacted by generative AI than other fields. This study was conducted to know and prepare for how generative AI will affect the future of publishing. Among the currently existing generative AI services, marketing work, image, and audio fields were analyzed as those that can directly affect the publishing process. Subsequently, as a result of analyzing how generative AI can change writing, which is the core of publishing, it was found that generative AI is already being actively used in a wide spectrum of planning, question-and-answer, collaboration, and mixed content writing. The development and proliferation of generative AI is bringing new opportunities and challenges to publishing.

A Collaborative Spectrum Sensing Algorithm Based on Reputation Update against Malicious User Attacks

In cognitive radio networks, collaborative spectrum sensing (CSS) algorithms could improve spectrum detection performance; however, most explorations are based on reliable network environments. In the real network environment, there may be malicious users that bring wrong spectrum sensing results and attacks designed by them remarkably reduce the spectrum efficiency. In order to resist the attacks of malicious users, this paper proposes a CSS method based on the reputation update. By setting an appropriate reputation threshold, the user fusion center selects the sensing user with a higher reputation to participate in the CSS. Each user’s reputation value is then updated according to whether its local sensing result matches the final judgment result. This article chiefly discusses scenarios for application of three information fusion rules. The simulation results reveal that the proposed approach with reputation update outperforms the conventional CSS algorithm for a variety of judgment rules. The proposed algorithm is capable of preventing lower reputation users from participating in the CSS, filtering out malicious users, and eliminating the impact of malicious users’ attacks.

SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

In this paper, the software and hardware of a software-defined radio (SDR) platform are used to implement and verify the blind real-time sensing act of intelligent collaborative spectrum sensing based on a new theoretical formula for constructing denoised mixed features named MSKU3 and paired with an unsupervised machine learning K-Medoids algorithm. Two low-cost RTL-SDR dongle hardware receivers are used as two cooperative unlicensed secondary users to capture the radio frequency of a licensed primary user channel. A host personal computer is used as a fusion center to run GNU-Radio software signal processing blocks to implement the developed method, and a single Universal Software Radio Peripheral (USRP) N210 hardware transmitter based on FPGA is used to take up unoccupied desired radio frequency bandwidth. Two scenarios of signal-to-noise ratio levels have been adopted to verify and test the sensing performance of the developed system. The first one occurs when unlicensed secondary users have equal signal-to-noise ratio values. The second occurs when unlicensed secondary users have different signal-to-noise ratio values since each secondary user has their location. The experimental results of detecting action in terms of the probability of detection for the proposed method show that the theoretical and practical results are very close to each other.