Co‐production is gaining increasing recognition as a good way of facilitating collaboration among different stakeholders, including members of the public. However, it remains an ambiguous concept as there is no definitive or universal model of co‐production or clarity on what constitutes a good co‐production approach. This paper draws on the reflections of the academic researchers, practitioners and public advisors involved in co‐producing a priority‐setting exercise. The exercise was conducted by the Primary and Community Health Services (PCHS) Theme of the National Institute for Health and Care Research Applied Research Collaboration for Kent, Surrey and Sussex (NIHR ARC KSS).We collected data through written and verbal reflections from seven collaborators involved in the PCHS priority‐setting exercise. We used Gibbs' model of reflection to guide the data collection. We then analysed the data through an inductive, reflexive thematic analysis.A common thread through our reflections was the concept of ‘sharing’. Although co‐production is inherently shared, we used the virtuous cycle to illustrate a sequence of sharing concepts during the research cycle, which provides the underpinnings of positive co‐production outcomes. We identified six themes to denote the iterative process of a shared approach within the virtuous cycle: shared values, shared understanding, shared power, shared responsibilities, shared ownership and positive outcomes.Our results present a virtuous cycle of co‐production, which furthers the conceptual underpinnings of co‐production. Through our reflections, we propose that positive co‐production outcomes require foundations of shared values and a shared understanding of co‐production as a concept. These foundations facilitate a process of shared power, shared responsibilities and shared ownership. We argue that when these elements are present in a co‐production exercise, there is a greater potential for implementable outcomes in the communities in which the research serves and the empowerment of collaborators involved in the co‐production process.Three members of the public who are public advisors in the NIHR ARC KSS were involved in the priority‐setting exercise that informed this paper. The public advisors were involved in the design of the priority‐setting exercise and supported participants' recruitment. They also co‐facilitated the focus groups during data collection and were involved in the data analysis, interpretation and preparation of the priority‐setting report. For this current manuscript, two of them are co‐authors. They provided reflections and contributed to the writing and reviewing of this manuscript. Both public involvement and co‐production are largely advocated for by research organisations, policymakers and the healthcare system as two practices that greatly determine the translation of research findings into practice.One of the hallmarks of co‐production is the involvement of nonacademic individuals in different phases of the research process.It is also worth noting that not all cited works of co‐production follow the same process of involving the public along the NIHR guidelines, so, co‐production remains a somewhat elusive or perhaps contested concept fraught with ambiguities.One way to better understand the elements that contribute to successful collaboration in research activities has been to gather reflections from participants from co‐produced projects. For instance, Tanay et al.1.1Primary and Community Health Services (PCHS) is one of the eight themes of the NIHR Applied Research Collaboration for Kent, Surrey and Sussex (ARC KSS). The overall ARC objectives are of providing and evaluating (improvements in) sustainable and integrated regional careThe public involvement comprised members of the public and representatives of community organisations who participated in focus group discussions to provide insights on what the research priorities ought to be for the PCHS Theme Box . While tBox 1Purpose: To refine the research priorities of the PCHS Theme, and to ensure they are defined by local communities.Date: August 2021 to August 2022Setting: Kent, Surrey and SussexApproach: Co‐production and public involvement.Co‐production: Eight collaborators as outlined in Figure Public involvement: 22 participants  were involved through online focus group discussions via Zoom. Participants were recruited widely through adverts shared with local organisations, word of mouth and snowballing. The inclusion criteria for involving participants was that they had to be at least 18 years old and resident in Kent, Surrey or Sussex. The participants had an age range of 18–75 years. Sex distribution was 5 males, 11 females; 6 participants did not disclose their sex.Findings: [1] Improving the ‘front door’ to the NHS, [2] addressing problems in the healthcare system, [3] developing person‐centred care and [4] focus on seldom‐heard groups .The co‐production element involved eight collaborators who are involved in overseeing and supporting research activities of the PCHS Theme Figure .The two academic researchers  are employed by Brighton and Sussex Medical School and they coordinate research activities within the PCHS Theme. The academic researchers work closely with the Academic Health Science Network (AHSN) to provide dissemination and implementation support. Two representatives from the AHSN , an implementation manager and an implementation assistant were therefore involved in the exercise, along with a Public and Community Involvement and Engagement (PCIE) coordinator working for the ARC KSS (K. F.‐S.) and three public advisors. For the public advisors, we collaborated with the two advisors attached to the PCHS Theme  and we extended an invitation to public advisors working with other themes of the ARC KSS. Our reason for inviting other public advisors was to ensure we capture as many community views as possible. Only one public advisor from the Social Care Theme was available to collaborate with us at the time. The public advisor was no longer available after we completed the studies and declined the invitation to participate in this publication.Public advisors are recruited by the ARC KSS through an expression of interest and selection process, and they are attached to each theme to support governance and ensure the theme's activities align with the needs of the local communities. The NIHR ARC for KSS selects public advisors based on their knowledge of their local community, lived experience of health and social care and the extent to which they are embedded into their communities, for example, through their links to local organisations. Public advisors are usually taken through an orientation process by the PCIE Theme of ARC KSS to enable them to perform their roles effectively. K. T. is a community‐based massage therapist in Sussex and he was recruited as a public advisor in 2020. H. R. has over 21 years' experience of working with community and voluntary organisations in Surrey and he was recruited in 2021 just before the priority setting was conceptualised. Both public advisors have links with community and voluntary organisations focusing on mental health, dementia, carers, young people and family support. K. T. and H. R. have established rapport with other members of the PCHS Theme as they have been supporting the theme since their recruitment. Public advisors were paid in line with ARC KSS's PCIE guidelines.1.2During the co‐production, the team collaborated through Zoom, telephone calls and texts, emails and MS Teams. As the priority‐setting exercise took place during COVID‐19, all interactions were carried out virtually. The co‐production process for the priority setting involved working with collaborators across different phases of the priority setting The first theme we identified was that of shared values. From our reflections, we realised that the starting point of our co‐production process was the identification of a clear purpose for the priority‐setting exercise and why this should be co‐produced. One of the researchers and AHSN implementation manager reflected on how the PCHS Theme was operating within a different primary and community health services landscape as a result of the COVID‐19 pandemic. They explained how this changing landscape spurred the need to interact with local communities to define the ARC's primary care research priorities. The reflection shows that the recognition of the shifting landscape and the need to work with communities were shared among the collaborators during the conceptualisation of the project. It was therefore easy to get their buy‐in on the project.3.2Also, more should be done to improve public members’ awareness of co‐production so they have better understanding of how to negotiate the interactions with other collaborators. My opinion from this exercise is that public members are generally familiar with being involved in projects as study participants and consulted on project ideas, thus being involved yet distanced from the project. For our study, we were clear from the start what their role would be as collaborators and we produced a briefing document to explain what this meant. I believe this approach empowered the public advisors to actively collaborate with us. (D. I.)The collaborators' reflections demonstrated the need for a shared understanding of co‐production from the start of the project, in a general sense and as applied to specific research activity. To engender a shared understanding of how the priority‐setting exercise would be coproduced, we took a pragmatic approach that involved guidance from the co‐production and PCIE Themes of the NIHR ARC KSS. With their advice, at the start of our project, we prepared a briefing document to share with collaborators who were public advisors to explain and get their input on their involvement and the process of co‐production.3.3I was able to follow the insights of the public advisors [during the data analysis]…The public advisors helpfully provided a narrative and context to support the themes generated by the research assistant from their experiences of the discussions and as active members of their communities. (D. G.)Our reflections further highlighted how power was shared among collaborators, although to varying degrees and at different points, depending on the responsibilities of each collaborator. The reflections showed that having a shared understanding of how the priority‐setting exercise should be co‐produced gave the collaborators the power to actively contribute in a way that highlighted their strengths and expertise. Even though one of the collaborators stated: ‘I think co‐production is so much about everyone being able to share power’ (S. F.), our reflections highlighted that power was not equally shared throughout the entire process. Although there was equal power sharing during focus groups where all collaborators acted as co‐facilitators, our reflections showed a system of power shift among the collaborators during the other phases, whereby each collaborator led different aspects of the priority setting. For example, during the definition of the themes, the power shifted to the public advisors. We also observed that the involvement of different academic collaborators in later phases could support a shift of power to the public advisors.…there is still the need to have a person who would have overall responsibility and be held accountable for the project (D. I.)We also observed that despite the power sharing, there should be a named person accountable for the project, in our case the academic researchers.3.4It was a really good team effort. It says somethings about interdisciplinary team working I think, about the different skills and insight people bring and the value of working in this way…That is one of the things I enjoy about the ‘Theme’ approach in the ARC KSS, the whole Theme can support a project and in varying ways, regardless of their official role. (K. F.‐S.)All collaborators commented on the element of shared responsibilities in their reflections. They highlighted how they saw co‐production as a collaborative effort and valued the different expertise of each collaborator, particularly public members. We reflected on how sharing responsibilities throughout the priority‐setting exercise by negotiating roles and relationships allowed interdisciplinary working and fostered a connected team.It [the priority setting exercise] was also the first piece of co‐produced research I have been involved with, so it was a learning experience for me personally but it was nice to have so much support. (G. G.)Our reflections further revealed that shared responsibilities require reciprocity and drawing on others' strengths. For instance, although public advisors did not have academic research experience, they brought in a wealth of experience as community representatives with diverse and multifaceted backgrounds.3.5I cannot as a researcher claim ownership of the findings and that is a good thing because those findings reflect the actual needs of the communities we serve. (D. I.)From our reflections, we observed that by sharing responsibilities, collaborators were able to own aspects of the priority‐setting in a way that would not have been possible in conventional research. The collaborators expressed various feelings around ownership of the priority‐setting process and outcomes. Generally, collaborators felt they were each equally embedded within the process and expressed ownership of that, for example, one collaborator described being part of the ‘actual mechanism’ (K. T.). There was no sense of personal ownership of the outcomes among collaborators, rather a perception of joint ownership as the outcomes were grounded in communities' needs.…But that's fine because what I've learned when being involved in various activities is not to become attached to the outcome. I leave it to the Research Lead, as the team may have gathered other views as well. Often, I don't have time to go back and check anyway unless invited by the Research Team to do so…I find it better not to become too attached and instead leave it to their best judgment and move on to the next assignment. So that's the way, I've continued working with ARC. (H. R.)However, one collaborator expressed that they did not feel a sense of ownership towards the outcome of the priority‐setting exercise.3.6The process of reviewing the themes with the Public Advisors and their input into the development of the outputs enhanced the quality of the outputs and the project more generally…The priority‐setting exercise has also sparked a collaboration with other Themes [of ARC KSS] and has led to the conceptualisation of projects that align with the priorities identified for PCHS as well as other fields, such as Social Care. For instance, a cross‐Theme working group has been formed to initiate projects relating to the needs of carers. (D. G.)From our reflections, we observed that the co‐produced priority‐setting exercise resulted in the generation of evidence that was aligned with local communities' needs and realities, and had greater potential to be implemented. For instance, one of the priorities we identified was to focus on the needs of informal carers. This priority was also identified through a separate priority‐setting exercise conducted by the Social Care Theme of ARC KSS. This has led to the establishment of a working group focused on co‐designing project(s) to assess and address the needs of informal carers in KSS.So, there are multiple benefits of co‐production rather than seeing it as a standalone exercise of just collecting information from members of the public. By getting the members of the public in a co‐production exercise, you get their creative juices flowing i.e., their grey cells being more actively engaged in a joint endeavour. (H. R.)It gives us a voice really and I think that's very important for the community to have voice and to actually feedback and have some ability to help shape it actually because we live in a community we know… I think that's really good that we can be the ‘go‐between’ and help guide the research…I did feel we were listened to, very much so, it was really good… otherwise, you are not really in contact, you just feel that things are done to you or things are asked from you, but you never really hear any more about it and that's it. (K. T.)We also identified the impacts of the co‐production process on public advisors. The public advisors felt the co‐production exercise sparked their creativity, gave them a voice, and reinforced their role as boundary spanners, that is the link between the ARC and local communities;3.7We emphasise the interlink between the themes derived from our reflections on the community priority‐setting exercise and propose a virtuous cycle of co‐production. As mentioned, a virtuous cycle illustrates a sequence of elements that facilitate desirable outcomes. The six themes we identified denote elements of an iterative process of sharing within a virtuous cycle Figure . The firWe suggest foundations of shared values and shared understanding can facilitate co‐production activity that mobilises the skills and unique contributions of all collaborators. A transparent understanding of the co‐production process can empower active and equal contribution from different collaborators (shared power), and in turn, allows for sharing of responsibilities with consideration for the strengths of individual collaborators. A shared sense of power and responsibility can foster equitable feelings of ownership and influence in the project activity and outcomes. We suggest an iterative process of sharing within this virtuous cycle can empower community collaborators and give rise to research outcomes that have greater potential to be implemented in the communities that the research serves.We note that in our community priority‐setting exercise, some collaborators did not feel an ongoing sense of ownership over the project. However, they stated they felt influence over the project outcomes as an active and valued member of the team.4The current study analysed the reflections of seven collaborators involved in a co‐produced community priority‐setting exercise. We presented the resulting six themes as a virtuous cycle of co‐production, which furthers our understanding of the conceptual underpinnings of sound co‐production activity. We argue that shared values and a transparent understanding of the application of co‐production provide the necessary foundations for co‐produced research. These foundations facilitate the process of shared power, shared responsibilities and shared ownership. When these elements are present, co‐produced research has the potential to produce research outcomes that represent the needs of communities and are more likely to be implemented. Within the virtuous cycle, we emphasise the importance of sharing power, responsibilities and ownership across all collaborators, which can empower community members and ensure their voices are represented and heard.We recognise that what can be perceived as the ambiguity of co‐production can make it a difficult concept to grasp, and there is no ‘the’ or correct co‐production approach.The first element of our proposed virtuous cycle is shared values. Shared values are another ambiguous concept closely linked to various disciplines.Co‐production exercises often do not follow a definitive model, but some frameworks have been introduced to foster collaborative or participatory research, for example, Experience Based Co‐Design and Always Events®. Although these frameworks are pertinent to health service development and quality improvement,Although sharing power has been identified as a key principle of co‐production, a truly equal distribution of power in co‐production exercises is challenging as there is no clarity on how this can be achieved.,p.9 Another approach is through reflexivity on how power is being shared through the co‐production process.Another way of mitigating power tensions is through a transparent definition of roles and responsibilities.The greater the power wielded by collaborators, the greater their sense of responsibility. Responsibilities can only be effectively shared where there is diversity of roles, strengths and experiences. Expectations and roles need to be clear and well communicated to ensure that all collaborators can carry out their roles effectively. Although we used a briefing document to delineate roles, another good way to facilitate transparency in roles and responsibilities is to use an online shared platform for open and continuous communication.The last aspect of sharing identified from our reflections is shared ownership. The ownership of the research data or findings is a crucial consideration in qualitative research.Research projects are co‐produced for several reasons, including the potential for improved quality of research findings and the increased possibility of meaningful impact.We also highlight the empowerment of public advisors as an impact of co‐production. Empowering public advisors amplified their roles as community‐based boundary spanners, that is individuals who live in local communities in which they serve and facilitate the flow of information between the communities and service providers.5The transferability of our findings may be limited as the perspectives presented in this paper draw on a regionally co‐produced project . Also, the priority setting was community focused and we did not collaborate with primary and community care providers. We further realise that the extent to which the public advisors act as boundary spanners may be restricted by the number, location and type of local organisations they have links to. For instance, the two public advisors involved in our co‐production exercise were from Surrey and Sussex, there was no collaborator from Kent. These limitations emphasise the need for a shared understanding of the context  of the co‐produced research project.6Embedding co‐production in research is regarded as best practice that facilitates a process of collaboration among different stakeholders and can greatly determine the quality of research findings. Despite the growing shift towards co‐production as a way of creating impactful research, there remains limited evidence on the conceptual underpinnings of successful co‐production. Drawing on reflections from collaborators of a recent priority‐setting exercise conducted within ARC KSS, we contribute three additions to co‐production research. First, we propose a virtuous cycle of co‐production, which presents an iterative process of sharing as one way of conceptualising co‐production. The virtuous cycle demonstrates that the principles of co‐production go beyond sharing power and responsibility. There is also a need for shared values, shared understanding and shared ownership of the process and outcomes. Second, we showed that our co‐production exercise resulted in research outcomes that may have more potential to be implemented given their alignment with the public advisors and their local communities. Third, we show how the co‐production process empowers public advisors as boundary spanners between research and local communities. This paper provides a new model of co‐production, which can guide applied health and care researchers in co‐production activities and help maximise positive research outcomes.Deborah Ikhile conceptualised the idea for this manuscript and designed the data collection guide and led the data analysis with contributions from other authors. Deborah Ikhile, Sam Fraser, Devyn Glass, Kat Frere‐Smith, Hasu Ramji, Keith Turner and Georgie Gremesty provided reflections that informed the manuscript. All authors contributed to the first draft of the manuscript, reviewed, edited and approved the final manuscript.The authors declare no conflict of interest.


One of the most important problems in complex networks is the location of nodes that are essential or play a main role in the network. Nodes with main local roles are the centers of real communities. Communities are sets of nodes of complex networks and are densely connected internally. Choosing the right nodes as seeds of the communities is crucial in determining real communities. We propose a new centrality measure named density-based entropy centrality for the local identification of the most important nodes. It measures the entropy of the sum of the sizes of the maximal cliques to which each node and its neighbor nodes belong. The proposed centrality is a local measure for explaining the local influence of each node, which provides an efficient way to locally identify the most important nodes and for community detection because communities are local structures. It can be computed independently for individual vertices, for large networks, and for not well-specified networks. The use of the proposed density-based entropy centrality for community seed selection and community detection outperforms other centrality measures. Complex networks represent complex interactions among multiple nodes representing objects in many real systems. One of the most important problems in complex networks is the location of nodes that are essential or play a main role within the network. Nodes having main local roles are the centers of real communities.Communities are sets of nodes joined together in tightly connected groups, with only a few connections with nodes belonging to other communities. They can be used as an intermediate step to select the most influential nodes. By incorporating information on the community structure of the input network into the optimization process of influence maximization, the efficiency of the most influential nodes is improved .An analysis of complex networks can uncover new knowledge and improve our understanding of the processes and structures of networks . One impMany community-detection methods  have beeSeveral centrality measures take advantage of various network topological properties to estimate the influence. However, the majority of centrality measures ignore the community structure, although it is one of the main features of many real-world networks. The centrality of a node in a network depends on two influences: its local influence on the neighboring nodes belonging to its community and its global influence on the nodes belonging to the other communities. The goal of our research was to create a new centrality measure, which can enable better estimation of the local importance and identification of the locally most important nodes. They are the centers of real communities. The new centrality measure can be used for the ranking nodes regarding the node’s importance in one network, and it is not appropriate for a comparison of the importance of values from different complex networks. The important nodes from different complex networks can have different centrality values, while the complex networks may not have the same structure. Centrality measures have to provide a quick prediction of real communities in large complex networks for the task of community detection. Therefore, an efficient centrality measure should not require processing of a whole network but require only local information of the subnetwork. The new local centrality measure should also be based on empirical observations rather than on a theoretical analysis, so that it can be used in real systems to identify the most important nodes as the centers of the communities in a network. The realization of this goal has practical value, since the nodes with the highest centrality values can be used directly for the identification of real communities. On the other hand, our research also has a theoretical value: achieving an understanding of the ability to identify communities using the new proposed factor density-based entropy, which can be estimated by humans or calculated automatically.We introduce a local entropy-based centrality measure that can enable better identification of the most important central vertices as the seeds of real communities in networks. Furthermore, the proposed entropy-based centrality measure requires no control parameters to be tuned to achieve the optimal solution. The entropy-based centrality measure used considers the nodes and the number of links in the neighborhood of a node.In this work, we make the following contributions. We extend the current list of the significant factors described in previous research on node centrality with entropy. We demonstrate that the proposed density-based centrality measure is correlated with the local centrality and correlated weakly with the global nodes’ centrality measures. This makes it useful for the identification of the locally most important nodes in a network, which are the seeds of the communities, while the communities are local structures.We use an extended simple label propagation community-detection algorithm, LPA , with diWe show that the proposed density-based entropy centrality outperforms the other local centrality measures in identifying real community structures. The proposed density-based entropy centrality measure identifies all the centers that are related to the centers of natural communities. The proposed centrality and extended LPA community-detection method enable local community detection. The density-based entropy centrality is a local node centrality index, and the link strength used in the extended LPA algorithm for community identification is a local similarity index. They enable local community identification of any number of communities, while no prior knowledge is required about the number of communities.The rest of this article is organized as follows. Each centrality measure considers some of the properties of complex systems and establishes its own definition of centrality, while each community-detection method considers some of the properties of the complex systems and establishes its own definition of a community . We intrThe modern science of networks has brought significant advances to our understanding of complex systems and different community-detection methods .Many community-detection algorithms select the seed vertices randomly to detect communities using methods that enhance or expand the k-means clustering method . The labFinding influential nodes in a complex network is an important research topic ,19. DiffNode centrality is one of the most intuitive methods to identify the important nodes in a network. Node centrality evaluates the efficiency of the propagation of information from a central node and estimates the influence and the strength of the connection of the node with its neighborhood .The popular centrality measures are degree centrality ,23, betwV and a set of edges A is an adjacency matrix, sometimes also called the connection matrix, with rows and columns labeled with the graph vertices, with values i and j are adjacent (connected with an edge) or 0 otherwise.Let i is proportional to the degree (or number of directly connected neighbors) of that vertex.i and j.Degree centrality finds the most connected vertex with the greatest degree as the most central. The degree centrality j to k, and j to k that pass through graph vertex i. The use of betweenness and closeness centrality makes the algorithm costly because they use the whole network for evaluation of the centrality of each vertex, while the degree centrality is a local centrality measure. Studies in  = −1;for all : for all :       search the neighbor for all :   greater = 1;if . The time complexity of the calculation of density-based entropy centrality for n vertices is m is the number of edges. In the third step, the label propagation of the community central candidates has near linear time complexity For listing all the maximal cliques in sparse graphs, the node-ordering version of the Bron–Kerbosch algorithm can be made to run in time arseness . Computaial-time . Then, iWe tested the performance of the proposed density-based entropy centrality measure on real network structures. All the real-world datasets used for testing are listed in Q [The measures that are used most often for the evaluation of community-detection methods are Normalized Mutual Information (NMI)  for the Q  for evalQ .We tested the performance of the community detection using a density-based entropy on the twelve real networks listed in The Zachary karate network contains 34 members of a university karate club. The links model the interaction of the members outside the club. A conflict between an administrator and an instructor led to the split of the club into two clubs.The Dolphins’ dataset is a network of 62 dolphins living in New Zealand. The nodes in the network represent the dolphins, and the links connect two dolphins with frequent contact. There are two communities of dolphins.US politics books is a social network of books about US politics. Each node in the network represents a book, and a link between two books indicates that they are often bought together. There are three communities in the network.US political blogs is a network of Internet blogs on the subject of US politics with partitioning of the graph into liberal and conservative bloggers.The American College football network models American football games between Division IA colleges during the regular season Fall 2000.The Jazz network is the collaboration network among Jazz musicians.We also tested our method on several networks without ground-truth community partitions see : the EcoWe also tested the proposed centrality on some large-scale network datasets from the SNAP datasets : DBLP, YYouTube is a popular video-sharing website, where the users can create groups that other users can join. The user-defined groups are the ground-truth communities of the network.The Amazon network is based on Customers-Who-Bought-This-Item-Also-Bought feature of the Amazon website. An undirected edge between two products denotes that the products are co-purchased together frequently. The product category provided by Amazon defines each ground-truth community.The application of the proposed method to the Zachary karate network is shown in In For the dolphin datasets, political books, and political blogs, all the real datasets were uncovered with a resulting modularity better than that of the other considered methods see .Community detection of the American football dataset divides the football teams into 12 groups or conferences, with more frequent intra-conference matches than inter-conference matches. All the real communities are identified and shown in The Jazz network is separated into three real communities, and all three identified are shown in The results of Jazz communities prove that the proposed method and the use of density-based entropy centrality enables the efficient identification of also overlapping communities.The improved label propagation method CDCE using the proposed centrality uncovered all the real communities  in the upper real-world datasets often used as a test-bed for the evaluation of community-detection methods. We also evaluated the efficiency on the large datasets below.For assessing the performances of a community-detection algorithm for large datasets, we used the F1-score measure proposed by Rossetti et al. . The F1-From the upper F1-scores, we can see that the use of the proposed centrality is efficient, although cliques larger in size than four tend to be very sparse in large networks. The proposed density-based entropy centrality emphasizes the power of the maximal cliques in defining the central node and in community detection. The vertices in each community tend to be interconnected densely and may form multiple cliques with large sizes. We considered the sum of all the maximal sizes of these cliques and not only above some certain threshold. The maximal cliques allowed for encoding all clique information adaptively, based on whatever clique sizes are available.We compared the results of CDCE using different centrality measures for small real-world datasets often used as a test-bed for the evaluation of community-detection methods. We used the density-based entropy centrality, clique centrality and degree centrality. Even for these small datasets, the density-based centrality performed better than degree and graph centrality, as shown in The correlation of different centrality measures is shown in the correspondence graph in Ecoli, Power Grid, and the PGP dataset), we can see that the modularity values of our partitions are lower than those obtained with the Louvain, as CDCE does not optimize modularity as the Louvain algorithm does. However, the modularity values obtained via our method were almost always equal or better than those of the other two algorithms, i.e., Infomap and Label propagation  and also for overlapping communities .For large datasets, the F1-measure obtained with the proposed method and compared with values published by Rossetti et al.  also shoThis article introduces density-based entropy centrality. It is a local measure of node centrality. The proposed density-based entropy centrality can be applied efficiently for community detection, which is also efficient for the identification of dense and overlapping communities. We identified the seed vertices, and then, the extended LPA method was used to identify the final communities. The empirical evaluations on real-world networks show that the used method identifies more ground-truth community members more efficiently than the other considered methods. The use of density-based entropy centrality gave better results than two other considered local centrality measures: clique centrality and degree centrality.The use of the centrality measure and a community-detection method for identifying communities in specific real-world networks like Facebook can be our future research work. The enhancement of a method for identifying dynamic communities can be a promising direction of our research.


Global Navigation Satellite System (GNSS) multipath has always been extensively researched as it is one of the hardest error sources to predict and model. External sensors are often used to remove or detect it, which transforms the process into a cumbersome data set-up. Thus, we decided to only use GNSS correlator outputs to detect a large-amplitude multipath, on Galileo E1-B and GPS L1 C/A, using a convolutional neural network (CNN). This network was trained using 101 correlator outputs being used as a theoretical classifier. To take advantage of the strengths of convolutional neural networks for image detection, images representing the correlator output values as a function of delay and time were generated. The presented model has an F score of 94.7% on Galileo E1-B and 91.6% on GPS L1 C/A. To reduce the computational load, the number of correlator outputs and correlator sampling frequency was then decreased by a factor of 4, and the convolutional neural network still has an F score of 91.8% on Galileo E1-B and 90.5% on GPS L1 C/A. Global Navigation Satellite Systems (GNSSs) are at the heart of the positioning applications. These applications are progressively catered toward highly accurate solutions. However, in urban and suburban environments, the challenge for an accurate positioning solution persists. Indeed, cycle slips on carrier phase measurements  and larg• Only Non-Line-of-Sight (NLOS) signals arriving to the antenna.• A Line-of-Sight (LOS) signal with one or multiple reflections of the LOS signal.In the existing literature, most algorithms focus on the detection of one category at a time with a heavy focus on NLOS detection with a wide array of techniques. C/N0). This is due to the fact that NLOS signals and multipath interference often affect the positioning solution very differently  has been applied to GNSS data processing especially for multipath detection/mitigation. For example • 0: No multipath present• 1: Presence of multipathThe CNN inputs are obtained due to correlator output values as a function of their delay (with respect to the prompt correlator) and as a function of time yielding a 2D image. As CNNs are particularly efficient in image analysis  using GNSS data only. LOS reflections happen very often in challenging environments and excluding all affected measurements would lead to a low positioning availability, and not all LOS reflections severely degrade the positioning solution . The defanalysis , these iFor any supervised neural network to be trained, it needs pre-labeled data. Without this preliminary classification, the CNN cannot operate successfully. The labeling approach taken in this paper is to use multiple correlator outputs from the tracking stage to compute the discriminator function. The tracking error is then used to determine whether the measurement is affected by multipath or not. This method is further detailed in section 2.1. The computational gain that CNNs bring with respect to computing the tracking bias is also investigated.IX is the in-phase correlator • QX is the quadrature phase correlator • A is the amplitude of the incoming signal• Rc is the autocorrelation function of the pseudorandom noise (PRN) code• EL is the early–late spacing in chips• k is the discrete integration time interval• ɛθ(k) is the difference between the estimated phase and the phase of the incoming signal• ɛf(k) is the difference between the estimated frequency and the frequency of the incoming signal• ɛτ(k) is the difference between the estimated delay and the delay of the incoming signal• Tcoh is the coherent integration time• IX• QX• For a single GNSS satellite, the arriving signal has to be acquired and then tracked so that its data bits can be demodulated. After tracking without multipath, the expression of the in- and quadrature-phase (I and Q) correlator for the prompt, early, and late correlator outputs are as follows:When tracking a GNSS signal, the delay-locked loop (DLL) and phase-locked loop (PLL) use discriminators to retrieve the delay and phase error, respectively. The DLL tries to estimate the travel time of the incoming signal by matching a locally generated code with a known time delay, if the delay is within one PRN code length. When the signal is emitted from the satellite, the satellite clock delay and other hardware delays are added onto the signal travel time. The atmospheric delays induced by different media are also accounted for in the travel time. When estimating the travel time of the incoming signal with the discriminator, the atmospheric errors and the satellite clock bias shift the value of the travel time but do not distort the autocorrelation function (ACF) of the PRN code.To retrieve the delay error, the DLL uses at least three correlators . The delay error is used to refine the frequency of the locally generated replica with the incoming signal. If the locally generated code replica is aligned with the incoming PRN sequence, then the early and late correlator values are equal in an error-free scenario. The difference in chips between the early and late correlator positions is called the early–late (EL) spacing. This parameter has a quintessential role for the DLL as a large EL spacing will lead to a better tracking of the dynamics, while a smaller one will reduce the effect of multipath .EL is the early–late spacing in chips• The value of τ for DEMLP(τ) = 0 yields the extra delay caused by the errors affecting the DLL. To find this delay, the zero crossing of the discriminator must be found. The slope of the EMLP discriminator must be negative for it to be a stable lock point. As the autocorrelation function of the PRN code is an even function τ is zero. Therefore, the discriminator will have a zero crossing where the tracking delay is equal to 0 as shown in In this study, only non-coherent discriminators are used as they do not make any assumption on the state of the PLL—on the phase error. The discriminator function is given for the EL power, a non-coherent discriminator. In the absence of noise and multipath, its function is given as follows:In a realistic scenario, tracking errors will always be present in GNSS receivers. Noise is not the sole cause for errors being propagated from the tracking stage to the pseudorange but also multipath. As conveyed by Indeed, when adding multipath, (1) and (2) become:i is the index of the signal received (direct or reflected)• N is the number of multipath reflections• τi is the delay error introduced by multipath i in seconds• whereτ = 0 anymore. This is illustrated in Due to the several multipath delays, the zero crossing for the discriminator function is not at By reconstructing the ACF of each tracking epoch with multiple correlators, the discriminator function can be obtained with great accuracy. However, if the discriminator function is subsampled, the tracking error has to be linearly interpolated at the zero crossing. The further the correlators are spaced from one another, the less accurate the interpolation is. This phenomenon is depicted in red in When the number of correlators is insufficient or does not cover enough of the ACF, the delay may not be computed due to the lack of zero crossing. In this study, when this occurs (only with low correlator resolutions), the delay is assumed to be zero to not reset the low pass filter.The discriminator function can have several zero crossings. The zero crossing that is the closest to the prompt index and with a negative slope is chosen as the stable lock point from which to compute the tracking bias—since the EMLP discriminator is used. On the presented dataset, when this occurs, the second zero crossing is much further in terms of delay than the one chosen as the stable point with respect to the prompt correlator.Tobs is the maximum delay for which the discriminator is observed• c is the speed of light in m/s• b is the tracking bias in meters• TrLPF is the low-pass filter of the DLL modelled by a Butterworth filter of order 2 with a bandwidth of BDLL• ρb is the pseudorange bias induced from the tracking bias in meters• When the computed tracking delay in seconds is multiplied by the speed of light, the tracking bias is obtained in In this paper, up to 101 correlators were used to reconstruct the ACF of the PRN code. Only the non-coherent discriminator will be studied because this is the most robust type of discriminator since it does not assume any PLL lock.Tc is the chip length in meters• BDLL is the equivalent loop filter bandwidth of the DLL in Hz• EL is the EL spacing in chips• C/N0 is the carrier-to-noise-density ratio in Hz• Tcoh is the coherent integration time in seconds• 2• Since the DLL noise is assumed to follow a Gaussian law with a known distribution σNoise can be considered to be caused by multipath. However, labeling every error exceeding this threshold would lead to a lot of measurements being classified as affected by multipath, especially in urban and suburban environments. If excluded, this would not only leave the most accurate measurements for positioning but would surely leave very few measurements for the positioning solution. In GNSS, a minimum of four satellites, when one constellation is used, is needed to compute a positioning solution . If too many satellites are excluded due to being labeled as affected by multipath, the following may occur:• Not enough satellites to compute a position• An increase in the dilution of precision valuesOn the other hand, if faulty measurements are not excluded from the positioning, then the positioning solution cannot be trusted.In a DLL where only additive white Gaussian noise affects the signal, the propagated noise can be estimated as zero mean Gaussian noise . For the follows :ρb≈ϵMP+vs. multipath challenge occurs in degraded environments, the multipath sources can be identified. Indeed, multipath will often come from buildings, roads, or other vehicles. When looking at attenuation factors of common surfaces at a normal incidence, a threshold can be established based on materials that yield a tolerable multipath error. The values of these materials, for the L1 frequency, are given as MEE is the multipath error envelope value for a given early–late spacing and relative multipath amplitude factor• tMP is the multipath delay in chips• bMaterial is the maximum multipath error caused by reflection from a given material• αMaterial is the relative multipath amplitude factor of a given material• To establish a new threshold, the idea was to find a value that would be small enough to not cause a large bias in the pseudorange but also large enough to not cause availability issues due to frequent multipath labeling. As the largest availability For example, the MEE of brick is given in As assumed in the study by bMaterial = 2.23 m). As shown by The early–late spacing used on GPS L1 C/A was higher than that for Galileo E1-B as a low EL spacing on L1 C/A led to a prolonged convergence of the tracking loops of the receiver. This change also induces an increase in the bias of bBrick is the maximum multipath error caused by a reflection from brick• This gives the following classification:bbrick.Thus, the threshold for multipath can be written as follows:The decision given in When first plotting the tracking biases and its filtered version, it was noticed that they were not centered around zero. These biases were slightly offset by a different value depending on the satellite number and constellation. Because of this, setting a threshold would not be possible according to the decision given in 1) Convolutional layer2) Activation function3) Pooling layer4) Output layer5) Loss functionThe accuracy of the tracking bias in GNSS receivers is limited by the sampling frequency in the correlation domain of the signal. The receiver used in this study, presented in section 2.5, can have a correlation domain sampling frequency of 200 MHz at most. A total of 101 correlators at 200 MHz were used to compute the most accurate tracking bias possible with the equipment used. Furthermore, computing an accurate tracking bias in GNSS receivers is time consuming due to the large number of correlators needed. Therefore, this method is best reserved for theoretical classification, but a more lightweight solution is needed to be more applicable to GNSS receivers. Training a CNN can be cumbersome, so it only has to be done once. When trained, the model can then be used in receivers as the prediction is fast. CNN images can be constructed with fewer correlators than used for the theoretical classification presented in section 2.2. Evidently, reducing the number of correlators affects the accuracy of the model as discussed in section 3.Despite having a large freedom of implementation for CNNs, all models share some properties and layers which are listed as follows:k is the number of channels in the image (1 for black and white and 3 for RGB)• o indicates the number of channels in the output image• • * is the convolution product• • • As the name indicates, convolutional neural networks rely on the convolution operation. The input, for example, an image, is a 2D matrix (or 3D if in color). This input is then convolved with multiple filters of the same size but of different values. Filters can be thought of as feature-detection matrices. In computer vision, kernels can be used to detect edges or sharpen the images . CNNs arOnce the feature maps are generated from the convolutional layer, an activation function is used to transform the values of the maps. A lot of activation functions exist, such as ReLU . These fPooling layers are used to downsample feature maps in order to reduce the computational load created by the convolutional layers. Pooling is most often performed as max pooling or average pooling. The pooling layer uses a filter of specified size (N x N) that slides over the whole feature map with a given step. Max pooling selects the highest value of the filter. Average pooling takes the average value of the filter. Their differences are given in the study by • The trained data is not fully representative of the potential input data• The network is too complex for the problem at hand• The network has been trained for too many iterations• Data leakageHence, special attention was paid to overfitting in the proposed model. To make sure that the model was less likely to occur, dropout was implemented. Dropout deactivates a neuron with a given probability (chosen by the user) to reduce reliance on certain connections. As shown by J(θ) is the existing cost function• θ is the parameter to minimize• j is the sum index (L1 or L2)• λ is the regularization factor (L1 or L2)• βj is the weighting penalty (L1 or L2)• p is the chosen degree of regularization (L1 or L2)• ENET(θ) is the new cost function with L1 and L2 regularization• A very common problem in ML is overfitting. Overfitting is when a model works very well for the training data but performs poorly when fed new data. The most frequent reasons are as follows:At last, comparing the loss function values of the training and validation dataset is a good metric to check overfitting.To construct the CNN for multipath mitigation, TensorFlow  and Kera• 0 → Not multipath• 1 → MultipathThe first layer is a convolutional layer made of 20 filters of size 3 × 3 with a stride of 1, computed using λ = 0.001 for both). The loss function chosen in this architecture is the binary cross-entropy. The batch size was of 64. The full architecture is given in This layer is implemented with a dropout rate of 0.5, meaning that each neuron connection has a 50% chance of being ignored at each epoch. This layer is also regularized with the elastic net method given in The dataset was obtained from a dynamic scenario. The dynamic data were collected in all challenging conditions (suburban and deep urban). This was to obtain multipath errors to detect and exclude them. The dataset is on the L1 frequency band (1575.42 MHz) on GPS and Galileo with a 200 MHz sampling frequency. This large sampling frequency was used because it enabled a better reconstruction of the ACF over the span of the collect. An IFEN SX3 was used as the GNSS receiver . The IFEThis dataset was obtained with a car. Data collection took place in the city center of Toulouse, France, in a mix of urban and suburban environments for a total duration of approximately 40 min. The neural network had more than 4,000 images to be trained with, for each signal. There were more than 4 million correlators used per signal, and the satellite geometry was diverse. Having more multipath scenarios would likely be beneficial for the networks but the number of images and correlators was deemed sufficient to validate the proof of the concept. The assumptions were made on the material types because most buildings are made out of either concrete or bricks with glass windows, and the road, of asphalt. The IFEN SX3 was connected to a Novatel GPS 704-X pinwheel antenna. The data were replayed in post-processing to retrieve the full correlators for each epoch as it cannot be done in real-time with this number of correlators. The main parameters for each signal are given in This section is divided into subsections: presenting the metrics used to analyze the results and the different configurations, the results for Galileo E1-B, and the ones for GPS L1 C/A. In order to investigate the benefits of using correlator images to detect multipath, the number of correlators used in images varies. To obtain accurate data labeling, the pseudorange bias has been computed with the most amount of correlators—101 at 200 MHz. In total, there are seven correlator variations presented in vs. the full theoretical autocorrelation function for GPS L1 C/A.y-axis) with respect to the prompt correlator and time (on the x-axis) to yield 2D images. The CNN architecture and the labeled data remain identical no matter the correlator configuration. However, each correlator configuration induces a training period with the corresponding images of the chosen correlator configuration. The results for these correlators will be presented for both signals. The images that are input to the CNN are the correlator values as a function of the chip delay , indicating that it is much worse at detecting multipath. This is further highlighted by the tracking bias results using 51@200&100. Indeed, the recall for this configuration is of 49.1% but has a perfect precision score.When decreasing the number of correlators to 25, this becomes even more apparent as the 25@200 or 25@200&50 have recall scores of 0, meaning that these configurations classify every measurement as not affected by multipath. This can be explained by the fact that the early–late spacing is higher for GPS L1 C/A. Indeed, when the early–late spacing is increased, the linear region of the discriminator function is also increased. On the downside, the number of correlators needed to cover the linear region to find the zero crossing of the discriminator function increases. The zero crossing can even be shifted further in the delay domain due to multipath and noise. Hence, the two variations using 25@200 and 25@200&50 do not cover enough of the ACF to get a correct estimate of the tracking bias.Moreover, when using 25@50, the F score of the CNN-based approach is 1.6% higher, which is due to its much higher precision with respect to the tracking-based approach despite its lower recall. Similar to Galileo E1-B, this conveys the fact that when the number of correlators is reduced, the CNN is a more viable solution to predict the presence of multipath than the tracking bias method.Similar to Galileo E1-B, CNNs are much faster than the tracking bias method. Indeed, the CNN is 21 times faster on the 25@50 configuration as shown in From all the correlator variations, the CNN configurations using less correlators at a reduced correlator sampling frequency (51@100 and 25@50) have only slightly lower metrics than the CNN benchmark variation of 101@200 with respect to other correlator configurations. This can be explained by the higher early–late spacing chosen for this signal with respect to E1-B. Hence, the distortion caused by the multipath on the tracking bias is larger for higher EL spacing . So, maiFurthermore, the two CNN configurations using 51@200 and 25@200 perform the worst. This is due to the fact that both of these variations do not show enough of the ACF—especially the 25 correlators’ configurations that show just enough correlators between the early and late correlators. Hence, these variations combine too little information on the ACF while still overcrowding the image with narrow correlators that do not add enough benefit to the classification.For the CNN-based approach, the 51@200&100 to 25@200&50 variations show a performance that is in between the two previous configurations. As this configuration still requires a high sampling frequency of the signal and yet does not improve the classification, a lower correlator sampling frequency for correlators is favored for both computational and metric performances.In this paper, a theoretical classification of multipath has been proposed that uses GNSS tracking correlator outputs. This classification has been used to label data to train a convolutional neural network. Then, this CNN has been used to predict whether multipath was present or not on the correlator outputs from GPS L1 C/A and Galileo E1-B. This CNN has been developed to be low in complexity so that it can be trained with a low amount of data while avoiding under-- or overfitting.The model uses tracking correlator values as a function of delay (with respect to the prompt correlator) and time in the form of images of 256 × 256 pixels. The inputs are lightweight, helping the predictions yielded by the CNN to be swiftly computed. This is illustrated by the fact that it is around 21 times faster to compute than the tracking bias for GPS L1 C/A and two to six times faster for Galileo E1-B, when using 25@50. The metrics between the CNN approach and the tracking-based approach were also compared, and the CNN performed better as the number of correlators was lowered. For example, when using 25@50, the F score was 91.8% for the CNN and 80.8% for the tracking bias on Galileo E1-B. The same conclusion applies to GPS L1 C/A as the F score was 88.9% for the tracking bias and 90.5% for the CNN with 25@50.Several variations have been tested in the CNN to test how the CNN performs when reducing the number of correlators to generate the input images. The favored configuration is to decrease the number of correlators along with the correlator sampling frequency. Indeed, it was shown that by reducing the number of correlators from 101 to 25 and by reducing the resolution by a factor of 4, the F score only decreased from 94.7 to 91.8% on Galileo E1-B and from 91.6 to 90.5% on GPS L1 C/A. This highlights the robustness of this method to detect multipath and then exclude the corrupted measurement from the positioning solution.Indeed, as mentioned previously, recall is how well the model detects multipath, thus excluding it. So, if a model correctly detects multipath, the confidence in the positioning solution would be higher. On the other hand, solutions that satisfy a certain precision requirement would improve the solution availability, i.e., having enough GNSS observations to have a full-rank system that can yield a GNSS position, velocity, and time (PVT) solution. As precision is how well the model classifies multipath, if there are very few false positives, then the model rarely falsely excludes useful measurements. In an accurate model, precision and recall are inversely correlated , meaningy. In this paper, availability was deemed more important than integrity as shown by the models having higher precision than recall. This is because additional integrity algorithms can be added on top of this solution such as fault detection and exclusion (By lowering the threshold to below 0.5, it would increase the recall at the expense of precision: it would only classify not multipath for very low values of xclusion . OrientiIn the future, the number of correlators could be further reduced and their positions varied to investigate what could be the minimum number of correlators to classify multipath at a given rate. This could lead the method to be more implementable on real-time receivers so that the number of computed correlators is more achievable. Even 3D images can be built by taking into account the Doppler offset of the received signal, as done by Excluding measurements because they are affected by multipath does not always lead to better positioning solution as it can lead to too few measurements to compute a position solution, or sometimes, this satellite was more important to the overall geometry than excluding the error was. Hence, the next step is to find a criterion to know if removing the multipath- affected measurement is worth it. Then, based on the results of the positioning accuracy, the multipath threshold can be redefined and optimized accordingly.


Innovative technological solutions are required to improve patients’ quality of life and deliver suitable treatment. Healthcare workers may be able to watch patients from a distance using the Internet of Things (IoT) by using big data algorithms to analyze instrument outputs. Therefore, it is essential to gather information on use and health problems in order to improve the remedies. To ensure seamless incorporation for use in healthcare institutions, senior communities, or private homes, these technological tools must first and foremost be easy to use and implement. We provide a network cluster-based system known as smart patient room usage in order to achieve this. As a result, nursing staff or caretakers can use it efficiently and swiftly. This work focuses on the exterior unit that makes up a network cluster, a cloud storage mechanism for data processing and storage, as well as a wireless or unique radio frequency send module for data transfer. In this article, a spatio-temporal cluster mapping system is presented and described. This system creates time series data using sense data collected from various clusters. The suggested method is the ideal tool to use in a variety of circumstances to improve medical and healthcare services. The suggested model’s ability to anticipate moving behavior with high precision is its most important feature. The time series graphic displays a regular light movement that continued almost the entire night. The last 12 h’ lowest and highest moving duration numbers were roughly 40% and 50%, respectively. When there is little movement, the model assumes a normal posture. Particularly, the moving duration ranges from 7% to 14%, with an average of 7.0%. The Internet of Things (IoT) has made it possible for users to gather and analyze data from a variety of surroundings, gadgets, and other things used in routine everyday activities. IoT items often feature sensors that generate enormous volumes of diverse data. The methods and tools required to analyze this data are provided by the big data analytics discipline . IoT hasIn this context, the Internet of Medical Things (IoMT) leverages the Internet of Things (IoT) to enable the gathering and transmission of data from medical devices or sensors, which can then be used to improve patient outcomes . In the https://www.zionmarketresearch.com/report/smart-home-market (20 January 2022)], the global smart bed market was valued at USD 6.38 billion in 2019 and is expected to reach USD 18.2 billion by 2025, growing at a compound annual growth rate of 19.6% during the forecast period from 2020 to 2025. The current research in the area of smart beds is mainly focused on developing new technologies and sensors to improve the user experience. However, there is limited research on modeling smart beds for emergency use for patients. The need for smart healthcare services is growing with an aging population, yet there is not enough physical staff in hospitals to meet the need     # For each pressure sensor in the array: FOR each other_sensor IN Array:  # If the pressure sensor is within a certain distance of the current pressure sensor:IF distance  < threshold:    # Add it to the current clusternew_cluster.append (other_sensor)    # Add the cluster to the list of clustersclusters.append (new_cluster)    # Return the list of clustersRETURN clusters  # Initialize a variable to hold the sensor datavar sensorData;    # Loop through all the data from the sensorFOR each data from the sensor    # Store the data in the sensorData variablesensorData = data;  IF data is within the range    # Send the sensorData to the Network ClusterSend sensorData to Network Cluster  IF the data is not within the range    # Generate an alertRETURN sensorData# CLOUD STORAGE process # Install the gcloud component that comes with the Python AppEngine extension:gcloud elements: app-engine-python package# Put Google Cloud Storage in assemble a bucketUpload information from smart bed gadgets to the bucket.# Transfer clinical form data to the bucket in step k.# A program to export local files to Google’s cloud storage.Import cloud storageCustomer = storageClient (project = ‘proj1’)‘Proj1 bucket’ in client.get bucket;Bucket. Blob (‘proj1.csv’); blob2Blob2.upload from filename (filename = ‘Y: /proj1 data.csv’)End of processThe suggested technique’s complete process, which is based approximately on the feature-based method of time series clustering , is compA. Extraction of Features from the Behavior LogsIn the first stage, the behavior logs are used to extract the patient’s placement on the bed and represent it as a vector. The bed is divided into five sectors with 10 nodes. The place vector’s components are represented by each of the five regions, and the value of each component indicates how many times the patient changed positions. When a person moves from one position to another, the value is divided in half and added to both the start and destination places. The place vector , for instance, represents a scenario where three time positions are changed that are located in area 1, two time positions are changed that are located in area 3, and one time position is changed that is located from area 3 to area 4. We acquire a time series of these location vectors that correspond to the patient’s position at a certain moment in time.B. Spatial-Temporal ClusteringAlgorithm 1: Algorithm for the proposed spatio temporal clusteringInput: First phase place vectorOutput: Output clusters in the second phase clustering (in this work it is limited to 5).The collection of place vectors is subjected to two-phase clustering. Algorithm 1 presents the steps for the proposed spatio-temporal clustering. The first phase of clustering concentrates on the geographic component of the data, whereas the second phase concentrates on the time component. In the first phase, the location vectors are divided into several sets of vectors based on a similarity metric. The goal of this stage is to categorize the special attention service for patients. The weighted directed graphs depict the transition of the staff service for patients, which is clustered in the second phase. This grouping is used to categorize patient-moving actions. The clustering methodology is based on the Ward method , one of First-phase Place Vector Clustering:Step 1:  In this approach, the collection of place vectors is divided into a number of clusters. When the appropriate number of clusters, which is determined by a priori information, is reached during clustering, cluster merging is halted.Graph Generation:Step 2:  From the collection of clusters acquired in the previous phase, weighted directed graphs are built in this stage. Each graph shows how the patient’s placement has changed throughout time. The specific steps are explained as follows:Step 2.1: The location vector time series is converted into a time series of the first phase clustering’s discovered clusters.  ●Let   ●The time series of location vectors   ●Each interval on the timeline has a length, which is a predetermined parameter.  ●Each time period of length is further broken down into Step 2.2: The representative cluster is then chosen for each sub-interval, where the representative cluster is the one that spends the majority of the sub-interval together.  ●At the Step 2.3: Finally, the transition of clusters in the time series is used to create the edge-weighted directed graph   ●According to the definition, if   ●A change from   ●The total of the edge weights is the same amount Directed graph second-phase clustering:Step 3:  There are many graph similarity metrics that may be used for clustering [ustering ,27,28. Hustering ,30,31. TStep 4: The clusters are obtained in the second-phase clustering as C. Cluster to Situation MappingThe last stage involves mapping the generated clusters from the second phase into known operational scenarios. By examining the characteristics of derived clusters and operating scenarios, the mapping is carried out.The prescribed network cluster platform is created to investigate big data analytics within the IoT environment, especially in the context of smart beds. It is possible to simulate the movement on the bed and record the sensor states throughout the course of the simulation. A log file is where this data is kept. This attribute is internally configurable based on the class file. The created file includes data from all the sensor nodes for each minute that are associated with the patient’s position on the bed and their orientation . ●We utilize the usage logs for three days. There are six behavior logs in total each day.●There are now six nodes employed across all of the use logs.●Eight clusters will be used in the initial round of clustering.●The graph building step’s time interval length and number of sub-intervals are both set to 200 s and 10, respectively.●Five clusters will be used in the second step of clustering.The following conditions are used to apply the strategy to the usage logs received from the bed nodes in order to assess the efficiency of the suggested approach:A. Initial stage ClusteringThere are 1057 place vectors, and after grouping, we get 8 clusters.The time series of the aforementioned clusters during the night time is shown in B. Phase two ClusteringOne hundred and six graphs in all are created. The adjacency matrices of these graphs are then subjected to the Ward approach. By carefully examining the produced clusters, we may draw the following conclusions:●●●Graphs in ●●Graphs in The time series of the aforementioned clusters during the day time is shown in C. Cluster to Situation MappingBased on an examination of the time series of clusters and their center vectors, derived clusters are mapped into recognized operational circumstances. The findings of the analysis are summed up as follows.●●●●●If there is a correlation between clusters and operational conditions that we are already aware of, we may automatically determine the operational state from a behavior record.There are times when clusters transit often in the time series of the clusters acquired in the first phase, and some clusters are dispersed across the other figures . Figure However, there are still some issues with the suggested approach. The fact that the location vectors are deterministically grouped and that the outcomes of the first phase of clustering heavily influence the second-phase clustering may be due to this: For the purpose of transferring information into the graph creation step, the spatial properties are oversimplified. Instead of using hierarchical clustering, it is beneficial to employ a fuzzy clustering technique.Additionally, during the graph formation phase, types of edges connecting many nodes have a higher volume than kinds of self-loops from a single node. As a result, changes in patient position are not accurately represented in the second-phase clustering. Since such flow information is one of the key indicators used to discern between working staff conditions, this issue needs to be addressed in our ongoing research.The suggested approach is also intended to be utilized for anomaly detection. If the present directed graph differs from previously known ones, an abnormality in the patient is likely to have occurred, and immediate medical attention is required.We offer several instances of the simulated evolutions of the various movement activities in order to officially demonstrate if they behaved as predicted. With the use of node clustering and the big data produced by the sensor nodes, the current strategy has provided a framework for enabling researchers to test algorithms in a simulated smart bed. We employed two distinct estimators based on two-phase clustering. We conducted the studies and discovered that the attribute that may have the most detrimental impact on the estimator’s capacity to identify the patient’s movement when it changes randomly. Due to the simulations’ increased realism and wide range of scenarios, we chose to preserve these random motions. These changes also provide the potential for enhancing the algorithms that smart beds use to accurately recognize the positions of the sensor nodes. Thus, in the future, other researchers might test more sophisticated algorithms in the context of big data analytics using the created logs.The present method makes it simple to test smart beds with various sensor nodes to accurately identify patient positions, allowing for the construction of inexpensive smart beds in the real world. To gain better trade-offs between accuracy and cost, the presented smart bed system may need to be expanded to take into account sensor layouts other than grids.Despite the fact that there are several scenario detection methods for spatio-temporal data, our primary emphasis is on the movement of individuals. The scenario is specified for a group of clusters, which is the major contribution of the suggested technique. Even though the suggested technique mainly succeeds in capturing the spatio-temporal characteristics of the operational circumstances, there is still potential for improvement. We are thinking of using multi-agent simulation to generate behavior logs since the data is insufficient to validate the suggested strategy.Future extensions of this work are anticipated in a number of different ways. From the perspective of a big data analytics study, this may be sufficient since the logs capture every movement. However, some end users may want to see an animation of the changes made by the bed sensors. To boost performance, this will be offered as an optional feature. An enhanced future study will provide some user-centered ideas for enhancing the design of large data analyses. Additionally, research will be carried out to identify which grid sensors are really helpful for movement detection in order to achieve a much smaller collection of sensors for this detection. This will lower the price of putting the existing strategy with actual sensors in a smart bed into practice.