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Energy Management System for Internet of Things
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Energy Management System for Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 1531

Special Issue Editors

Dr. Daniele D'Agostino

E-Mail Website
Guest Editor
Institute of Electronics and Information Engineering and Telecommunications, National Research Council, 16149 Genoa, Italy
Interests: parallel computing on heterogeneous platforms; science gateway design and development; parallel applications in the fields of bioinformatics, astrophysics and earth sciences
Prof. Dr. Daniele Cesini

E-Mail Website
Guest Editor
Data Handling Group, Italian National Institute for Nuclear Physics, 00186 Rome, Italy
Interests: distributed computing platforms; energy-efficient computing; data management services
Dr. Ivan Merelli

E-Mail Website
Guest Editor
Institute for Biomedical Technologies, Italian National Research Council, 20054 Segrate, Italy
Interests: bioinformatics; computational biology; systems biology, multi-omic datasets analysis and integration, high-performance computing; big data analytics; machine learning
Dr. Lucia Morganti

E-Mail Website
Guest Editor
Data Management and Storage team, INFN-CNAF, 40127 Bologna, Italy
Interests: physics; calculus; programming; numerical algorithms; statistics; big data

Special Issue Information

Dear Colleagues,

We are observing the explosive growth of the Internet of Things (IoT) paradigm, mostly due to its ability to connect physical devices (i.e., the Things) to analytics and machine learning applications, which can help to gather insights from device-generated data enabling the devices themselves to make smart decisions without human intervention. The IoT is becoming pervasive in our lives, and due to the huge amount of data it generates, the challenge is to push computing power back to places where the data are generated—the so-called fog/edge computing—while maintaining a high energy efficiency to meet the requirements often imposed by the operational conditions. This means that for modern big data computing platforms, the best possible tradeoff between time-to-solution and energy-to-solution has to be provided. This Special Issue aims at presenting and investigating state-of-the-art energy management systems for all the layers of a big data platform (IoT/edge/fog/cloud) considering both hardware and software dimensions. The list of interesting topics includes (but is not limited to) low-power devices and system-on-a-chip architectures, software techniques to improve the flops-per-watt ratio, low-power scientific data communication systems, low-power distributed storage systems, efficient power supply systems for computing devices, energy-saving techniques for low-power devices and parallel processing, low-power sensors and data sources, and Artificial Intelligence techniques to optimize energy consumption of computation, data storage, and analytics.

Dr. Daniele D'Agostino
Prof. Dr. Daniele Cesini
Dr. Ivan Merelli
Dr. Lucia Morganti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • edge computing
  • fog layer
  • cloud computing
  • energy management
  • system-on-a-chip
  • energy consumption optimizations

Published Papers (1 paper)

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Research

17 pages, 1006 KiB  
Article
Efficient Integrity-Tree Structure for Convolutional Neural Networks through Frequent Counter Overflow Prevention in Secure Memories
by Jesung Kim, Wonyoung Lee, Jeongkyu Hong and Soontae Kim
Sensors 2022, 22(22), 8762; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228762 - 13 Nov 2022
Viewed by 1091
Abstract
Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection [...] Read more.
Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection technique that can protect the sensitive data of CNNs. However, naively applying secure memory to a CNN application causes significant performance and energy overhead. Furthermore, ensuring secure memory becomes more difficult in environments that require area efficiency and low-power execution, such as the Internet of Things (IoT). In this paper, we investigated memory access patterns for CNN workloads and analyzed their effects on secure memory performance. According to our observations, most CNN workloads intensively write to narrow memory regions, which can cause a considerable number of counter overflows. On average, 87.6% of total writes occur in 6.8% of the allocated memory space; in the extreme case, 93.9% of total writes occur in 1.4% of the allocated memory space. Based on our observations, we propose an efficient integrity-tree structure called Countermark-tree that is suitable for CNN workloads. The proposed technique reduces overall energy consumption by 48%, shows a performance improvement of 11.2% compared to VAULT-128, and requires a similar integrity-tree size to VAULT-64, a state-of-the-art technique. Full article
(This article belongs to the Special Issue Energy Management System for Internet of Things)
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