Dataset Information

Efficient Route Management Method for Mobile Nodes in 6TiSCH Network.

ABSTRACT: The combination of time slotted access and channel hopping technology in IEEE 802.15 TSCH networks enables high reliability and low power operation to meet the stability and real-time requirements of industrial applications. Basically, TSCH and RPL, a routing protocol for TSCH, are proposed for static nodes that generate fewer control messages, so they allow collisions in shared cells when they exchange control messages. In a topology containing mobile nodes, the collision of control messages in a shared cell makes the network difficult to recover quickly. The proposed scheme minimizes the collision of control messages by allocating dedicated control cells to form preferred parent nodes quickly for mobile nodes. We also proposed a method for establishing a fixed route from the root node to the mobile node in RPL to minimize the delay time. Through the simulation using the 6TiSCH simulator, it was confirmed that the performance of the proposed method was approximately 2.5 times better in terms of overhead and resource use, and 33% better in terms of network participation time of mobile nodes compared with existing solutions.

SUBMITTER: Kim MJ

PROVIDER: S-EPMC8125023 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Efficient Route Management Method for Mobile Nodes in 6TiSCH Network.

Kim Min-Jae MJ Chung Sang-Hwa SH

Sensors (Basel, Switzerland) 20210428 9

The combination of time slotted access and channel hopping technology in IEEE 802.15 TSCH networks enables high reliability and low power operation to meet the stability and real-time requirements of industrial applications. Basically, TSCH and RPL, a routing protocol for TSCH, are proposed for static nodes that generate fewer control messages, so they allow collisions in shared cells when they exchange control messages. In a topology containing mobile nodes, the collision of control messages in ...[more]

PMID: 33925073

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Project description:BackgroundA significant health challenge is evident in the United States, with 6 in 10 adults having a chronic disease and 4 in 10 adults having 2 or more. Chronic disease self-management aims to prevent or delay disease progression and disability and reduce mortality risk. The evidence to support the use of information technology tools, including mobile apps, web-based portals, and web-based educational interventions, that support disease self-management and improve clinical outcomes is growing. Customer discovery and value proposition design methodology is a form of stakeholder engagement and is based on marketing and lean start-up business methods. As applied in health care, customer discovery and value proposition methodology can be used to understand the clinical problem and articulate the product's hypothesized unique value proposition relative to alternative options that are available to end users.ObjectiveThis study aims to describe the experience and findings of academic researchers applying the customer discovery and value proposition methodology to identify stakeholders, needs, adaptability, and sustainability of a chronic disease self-management mobile app (CDapp). The motivation of the work is to make mobile health app interventions accessible and acceptable for all segments of patients' chronic diseases.MethodsData were obtained through key informant interviews and analyzed using rapid qualitative analysis techniques. The value proposition framework was used to build the interview guide. The aim was to identify the needs, challenges (pains), and potential benefits (gains) of the CDapp for our stakeholders.ResultsOur results showed that the primary consumers (end users) of a CDapp were the patients. The app adopters (decision makers) can be medical center leaders including population health department managers or insurance providers, while the consumer adoption influencers (influencers or saboteurs) are clinicians and patient caregivers. We developed an ecosystem map to visualize the clinical practice workflow and how an app for chronic disease management might integrate within an academic health care center or system. A value proposition for the identified customer segments was generated. Each stakeholder segment was working within a different framework to improve patient self-management. Patients needed help to adhere to self-care activities and they needed tailored health education. Health care leaders aim to improve the quality of care while reducing costs and workload. Clinicians wanted to improve patient education and care while reducing the time burden. Our results also showed that within academic medical centers, there were variations regarding patients' self-reported abilities to manage their diseases.ConclusionsCustomer discovery is a useful form of stakeholder engagement when designing studies that seek to implement, adapt, and sustain an intervention. The customer discovery and value proposition methodology can be used as an alternative or complementary approach to formative research to generate valuable information in a brief period.

Project description:Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the directed edge structure of the graph.

Dataset Information

Efficient Route Management Method for Mobile Nodes in 6TiSCH Network.

Publications

Efficient Route Management Method for Mobile Nodes in 6TiSCH Network.

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