We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: (a) Are IoT applications key to the improvement of people's health and the environment? (b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health?

https://www.mdpi.com/1424-8220/21/7/2330/pdf

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals.

A review of energy efficiency evaluation metrics for data centers

JANUARY 6,1965 Mr. ANDERSON (for himself, Mr. GORE, Mr. JAVTTS, Mr. MCNAMARA, Mr. BARTLETT, Mr. BATH, Mr. BIBLE, Mr. BREWSTER, Mr. BURDICK, Mr. CASE, Mr. CHURCH, Mr. CLARK, Mr. DODD, Mr. DOUGLAS, Mr. GRUENING, Mr. HARTKE, Mr. INOUYE, Mr. JACKSON, Mr. KENNEDY of Massachusetts, Mr. KENNEDY of New York, Mr. KUCHEL, Mr. LONG of Missouri, Mr. MANSFIELD, Mr. MCCARTHY, Mr. MCGEE, Mr. MCGOVERN, Mr. MOINTYRE, Mr. METCALF, Mr. MONDALE, Mr. MONRONEY, Mr. MONTOYA, Mr. MORSE, Mr. Moss, Mr. MUSKIE, Mrs. NEUBERGER,, Mr. PASTORE, Mr. PELL, Mr. PROXMIRE, Mr. RANDOLPH, Mr. RIBICOFF, Mr. TYDINGS, Mr. WILLIAMS of New Jersey, Mr. YARBOROUGH, and Mr. YOUNG of Ohio) introduced the following bill; which was read twice and referred to the Committee on Finance

The Senate of the United States

Software-defined networking (SDN) paradigm, with the flexible and logically centralized control, enables dynamically minimizing the network energy consumption by redirecting paths of packets. However, the links and switches are designed to accommodate maximum traffic volume and their power consumption is not traffic proportional. Moreover, there exists a trade-off between energy efficiency and network performance that need to be considered together. Addressing these issues, we propose an energy efficiency metric named Ratio for Energy Saving in SDN (RESDN) that quantifies energy efficiency based on link utility intervals. We provide integer programming formulation and method for maximizing the RESDN of the network. To the best of our knowledge, RESDN approach is novel as it measures how links are profitably utilized in terms of the amount of energy they consume with respect to their utility. We analyze our approach considering various metrics of interest and different types of SDN enabled switches. Experiments show that maximizing the RESDN value improves energy efficiency while maintaining acceptable network performance. In comparison to state-of-the-art utility-based heuristics, RESDN method achieves up to 30% better ratio for energy saving, 14.7 watts per switch power saving, 38% link saving, 2 hops decrease in average path length, and 5% improved traffic proportionality.

RESDN: A Novel Metric and Method for Energy Efficient Routing in Software Defined Networks

Information and communication technologies (ICT) are increasingly permeating our daily life and we ever more commit our data to the cloud. Events like the COVID-19 pandemic put an exceptional burden upon ICT. This involves increasing implementation and use of data centers, which increased energy use and environmental impact. The scope of this work is to summarize the present situation on data centers as to environmental impact and opportunities for improvement. First, we introduce the topic, presenting estimated energy use and emissions. Then, we review proposed strategies for energy efficiency and conservation in data centers. Energy uses pertain to power distribution, ICT, and non-ICT equipment (e.g., cooling). Existing and prospected strategies and initiatives in these sectors are identified. Among key elements are innovative cooling techniques, natural resources, automation, low-power electronics, and equipment with extended thermal limits. Research perspectives are identified and estimates of improvement opportunities are mentioned. Finally, we present an overview on existing metrics, regulatory framework, and bodies concerned.

Strategies for improving the sustainability of data centers via energy mix, energy conservation, and circular energy

We propose a new approach to real-time monitoring and management of data center infrastructure using low-power wireless sensors. This system enables monitoring and optimization of parameters such as power, temperature, humidity, airflow, among others. Given the mission critical nature of data centers, the monitoring system must be reliable and deployed through a non-invasive process. This paper describes a monitoring system that collects data to help users make more informed decisions, reduce downtime and cost of operations, improve efficiency, perform energy audits, plan space usage, and develop predictive behavior models.

A new approach to data center infrastructure monitoring and management (DCIMM)

Article history: Received: 13 February, 2018 Accepted: 04 April, 2018 Online: 11 April, 2018 Data centers’ mission critical nature, significant power consumption, and increasing reliance on them for digital information, have created an urgent need to monitor and adequately manage these facilities. Metrics are a key part of this effort as their indicators raise flags that lead to optimization of resource utilization. A thorough review of existing data center metrics presented in this paper shows that while existing metrics are valuable, they overlook important aspects. New metrics should enable a holistic understanding of the data center behavior. This paper proposes a novel framework using a multidimensional approach for a new family of data center metrics. Performance is examined across four different sub-dimensions: productivity, efficiency, sustainability, and operations. Risk associated with each of those sub-dimensions is contemplated. External risks are introduced, namely site risk, as another dimension of the metrics, and makes reference to a methodology that explains how it is calculated. Results from metrics across all subdimensions can be normalized to the same scale and incorporated in one graph, which simplifies visualization and reporting. The new family of data center metrics can help to standardize a process that evolves into a best practice to help evaluate data centers, to compare them to each other, and to improve the decision-making process.

/pdf/an-overview-of-data-center-metrics-and-a-novel-approach-for-grmmstt064.pdf

An Overview of Data Center Metrics and a Novel Approach for a New Family of Metrics

Article history: Received: 30 May, 2017 Accepted: 09 August, 2017 Online: 21 August, 2017 Recent standards, legislation, and best practices point to data center infrastructure management systems to control and monitor data center performance. This work presents an innovative approach to address some of the challenges that currently hinder data center management. It explains how monitoring and management systems should be envisioned and implemented. Key parameters associated with data center infrastructure and information technology equipment can be monitored in real-time across an entire facility using low-cost, low-power wireless sensors. Given the data centers’ mission critical nature, the system must be reliable and deployable through a non-invasive process. The need for the monitoring system is also presented through a feedback control systems perspective, which allows higher levels of automation. The data center monitoring and management system enables data gathering, analysis, and decision-making to improve performance, and to enhance asset utilization.

/pdf/a-reliable-non-invasive-approach-to-data-center-monitoring-q1m34l7m7n.pdf

A Reliable, Non-Invasive Approach to Data Center Monitoring and Management

This paper describes a predictive model using MATLAB machine learning tools for nonlinear regression, support vector machine and neural network. Data center performance measures such as run time, waiting time and energy consumption, are predicted based on past events, total workload input and number of IT resources. Predictive analytics enables evidence-based decision making in the case of uncertainty and risk, and helps to evaluate areas of improvement.

Data Center Predictions using MATLAB Machine Learning Toolbox

Given the mission critical nature of data centers, metrics must provide a holistic understanding of the data center behavior, which is only viable if risks to the actual operation are considered. This paper proposes a framework for measuring data center site risk. The proposed methodology is aimed at standardizing a process to help evaluate data center sites and compare them to each other, or compare different scenarios where the data center operates. It establishes a process under defined criteria for the site risk analysis, despite the fact that much of that criteria is based on standards and expert knowledge. The metric is also a way to communicate and better understand risks due to the data center location, and help evaluate mitigation strategies.

A framework for data center site risk metric

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