Nowadays, many universities are employing metrics that are used by other countries as the focus moves towards academic management. A shared vision and collaboration is required to identify success cases. Leaders at senior and middle management need to be guided by a road map to get a clear vision, a list of different strategies and successful outcomes. Consequently, this article proposes an academic management strategy to guarantee student-centred education. This strategy has an emphasis on hierarchical process in layers, in order to optimise and achieve efficiency, reliability and resilience. In this paper, the “what”, “ how” and “where” are taken into account in order to respond to academic and administrative adjustments which are necessary to reduce the risk of investment in training and formation of human capital, which warns about the need to acquire knowledge, especially from countries with scientific expertise. It is also shown the indicators that motivate the effort based on the merit that human capital produces. A methodology of flipped learning or blended learning is applied to presume a human capital that is able to break down barriers, such as: English as a universal language. A bibliometric analysis has been based over 2000 scientific articles from the Web of Science and Scopus databases. So It was possible to identify countries, universities and researchers specifically for each area of knowledge based on the results of this analysis. Besides, university careers can even be validated according to the development and scientific interest that is presented by the bibliometric analysis, which could be compared with studies based on economics and wealth from sources such as Forbes.

Citizens are expected to require the growth of multiple Internet of Things (IoT) -based applications to improve public and private services. According to their concept, smart cities seek to improve the efficiency, reliability, and resilience of these services. Consequently, this paper searches for a new vision for resolving problems related to the quick deployment of a wireless sensor network (WSN) by using a sizing model and considering the capacity and coverage of the concentrators. Additionally, three different routing models of these technology resources are presented as alternatives for each WSN deployment to ensure connectivity between smart meters and hubs required for smart metering. On the other hand, these solutions must reduce costs when this type of wireless communication network is deployed. The present work proposes various optimization models that consider the physical and network layers in order to integrate different wireless communication technologies, thus reducing costs in terms of the minimum number of data aggregation points. Using a heterogeneous wireless network can reduce resource costs and energy consumption in comparison to a single cellular technology, as proposed in previous works. This work proposes a sizing model and three different models for routing wireless networks. In each case, constraints are evaluated and can be associated with different real-world scenarios. This document provides an optimization model that encompasses all of the proposed constraints; due to the combinatorial nature of the problem, this would require a heuristic technique.

This work presents techniques for obtaining a reliable electrical load-curve based on comparative analysis between the different compressed sensing algorithms. Therefore, the goal is implementing compressed sensing (CS) when a wireless heterogeneous network, that exchanges information between electrical enterprise and smart meters, has a fault. Then, the data cannot be sent totally, and we would have the data only of some smart meters; thus, using the adequate technique of compressed sensing is possible to the reconstruction of load-curve required for generating demand response (DR) with the minimum error. In the advanced metering infrastructure (AMI) there may be communication faults; then, it is necessary to have other forms for estimating the demand response using few measurements. In addition, using a dictionary based on the DCT transform does not mean that the sea is the best option for the representation of a signal. For example, among other results, in this work we obtain an average of percent root mean square difference nearest to the 5% in relation with a Gaussian function or Wavelet basis with values between 1.4 and 1.7% average PRD.

The present work warns of the potential performance of the mapreduce technique for handling big data from the intelligent measurement of electrical energy, this information is managed by the measurement data management system and is used to find the energy consumption pattern of the users. The large amount of information from the intelligent measurement of electrical energy requires a short-term analysis for a subsequent decision-making required to obtain the answer to the demand of the energy in each electric distribution company. In this way, this work presents a technique to handle a large amount of information in a short time to generate trends and statistics of electric energy consumption information from the information coming from several years of information storage.

This document presents a performance analysis on network data traffic channel mathematical modeling using polynomial regression applied to Potential Polynomials of degree one (P1P) to propose an easier computational alternative for traditional methods and even those whose uses neural networks. It proposes an alternative to traditional methods that use neural networks. There are several studies that look into modeling and traffic prediction as auto-regression in movement average (ARIMA) or Wavelet based. In this sense, this paper analyzes the Mean Absolute Error (MAE) and the Mean Square Error (MSE) to compare P1P polynomial regression other mentioned methods. Proofs were realized with a set of 100 sample signals from different network traffics analyzing TCP and UDP packets to compute average MAE and MSE values. Finally, graphics will demonstrate the statistical performance of the proposed method, and comparative tables with different modeling and predictor algorithms will be presented.

Wireless cellular networks have become increasingly important in providing data access to cellular users via a grid of cells. Many applications are considered to read data from smart meters for potable water, gas, or electricity. This paper proposes a novel algorithm to assign paired channels for intelligent metering through wireless connectivity, which is particularly relevant due to the commercial advantages that a virtual operator currently provides. The algorithm considers the behavior of secondary spectrum channels assigned to smart metering in a cellular network. It explores spectrum reuse in a virtual mobile operator to optimize dynamic channel assignment. The proposed algorithm exploits the white holes in the cognitive radio spectrum and considers the coexistence of different uplink channels, resulting in improved efficiency and reliability for smart metering. The work also defines the average user transmission throughput and total smart meter cell throughput as metrics to measure performance, providing insights into the effects of the chosen values on the overall performance of the proposed algorithm.