Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Nursing Staff Time and Care Quality in Long-Term Care Facilities: A Systematic Review

Background and Objectives In long-term care (LTC) facilities, nursing staff are important contributors to resident care and well-being. Despite this, the relationships between nursing staff coverage, care hours, and quality of resident care in LTC facilities are not well understood and have implications for policy-makers. This systematic review summarizes current evidence on the relationship between nursing staff coverage, care hours, and quality of resident care in LTC facilities. Research Design and Methods A structured literature search was conducted using four bibliographic databases and gray literature sources. Abstracts were screened by two independent reviewers using Covidence software. Data from the included studies were summarized using a pretested extraction form. The studies were critically appraised, and their results were synthesized narratively. Results The systematic searched yielded 15,842 citations, of which 54 studies (all observational) were included for synthesis. Most studies (n = 53, 98%) investigated the effect of nursing staff time on resident care. Eleven studies addressed minimum care hours and quality of care. One study examined the association between different nursing staff coverage models and resident outcomes. Overall, the quality of the included studies was poor. Discussion and Implications Because the evidence was inconsistent and of low quality, there is uncertainty about the direction and magnitude of the association between nursing staff time and type of coverage on quality of care. More rigorously designed studies are needed to test the effects of different cutoffs of care hours and different nursing coverage models on the quality of resident care in LTC facilities.