Abstract

Abstract
Visible Light Communication (VLC) has been emerging as a promising technology to address the increasingly high data-rate and time-critical demands that the Internet of Things (IoT) and 5G paradigms impose on the underlying Wireless Sensor Actuator Networking (WSAN) technologies. In this line, the IEEE 802.15.7 standard proposes several physical layers and Medium Access Control (MAC) sub-layer mechanisms that support a variety of VLC applications. Particularly, at the MAC sub-layer, it can support contention-free communications using Guaranteed Timeslots (GTS), introducing support for time-critical applications. However, to effectively guarantee accurate usage of such functionalities, it is vital to derive the worst-case bounds of the network. In this paper, we use network calculus to carry out the worst-case bounds analysis for GTS utilization of IEEE 802.15.7 and complement our model with an in-depth performance analysis. We also propose the inclusion of an additional mechanism to improve the overall scalability and effective bandwidth utilization of the network.

Abstract

Abstract
In the present-days complex networks modeled on real-world data contain millions of nodes and billions of links. Identifying super spreaders in such an extensive network is a challenging task. Super spreaders are the most important or influential nodes in the network that play the central role during an infection spreading or information diffusion process. Depending on the application, either the most influential node needs to be identified, or a set of initial seed nodes are identified that can maximize the collective influence or the total spread in the network. Many centrality measures have been proposed to rank nodes in a complex network such as 'degree', 'closeness', 'betweenness', 'coreness' or 'k-shell' centrality, among others. All have some kind of inherent limitations. Mixed degree decomposition or m-shell is an improvement over k-shell that yields better ranking. Many researchers have employed single node identification heuristics to select multiple seed nodes by considering top-k nodes from the ranked list. This approach does not results in the optimal seed nodeset due to the considerable overlap in total spreading influence. Influence overlap occurs when multiple nodes from the seed nodeset influence a specific node, and it is counted multiple times during total collective influence computation. In this paper, we exploit the 'node degree', 'closeness' and 'coreness' among the nodes and propose novel heuristic template to rank the super spreaders in a network. We employ k-shell and m-shell as a coreness measure in two variants for a comparative evaluation. We use a geodesic-based constraint (enforcing a minimum distance between seed nodes) to select an initial seed nodeset from that ranked nodes for influence maximization instead of selecting the top-k nodes naively. All models and metrics are updated to avoid overlapping influence during total spread computation. Experimental simulation with the SIR (Susceptible-Infectious-Recovered) spreading model and an evaluation with performance metrics like spreadability, monotonicity of ranking, Kendall's rank correlation on some benchmark real-world networks establish the superiority of the proposed methods and the improved seed node selection technique.

Abstract
The emergency department of a hospital plays an extremely important role in the healthcare of patients. To maintain a high quality service, clinical professionals need information on how patient flow will evolve in the immediate future. With accurate emergency department forecasts it is possible to better manage available human resources by allocating clinical staff before peak periods, thus preventing service congestion, or releasing clinical staff at less busy times. This paper describes a solution developed for the presentation of hourly, four-hour, eight-hour and daily number of admissions to a hospital's emergency department. A 10-year history (2009-2018) of the number of emergency admissions in a Portuguese hospital was used. To create the models several methods were tested, including exponential smoothing, SARIMA, autoregressive and recurrent neural network, XGBoost and ensemble learning. The models that generated the most accurate hourly time predictions were the recurrent neural network with one-layer (sMAPE = 23.26%) and with three layers (sMAPE = 23.12%) and XGBoost (sMAPE = 23.70%). In terms of efficiency, the XGBoost method has by far outperformed all others. The success of the recurrent neuronal network and XGBoost machine learning methods applied to the prediction of the number of emergency department admissions has been demonstrated here, with an accuracy that surpasses the models found in the literature.

Abstract