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 2445
Special Issue Editors
Interests: parallel computing on heterogeneous platforms; science gateway design and development; parallel applications in the fields of bioinformatics, astrophysics and earth sciences
Interests: distributed computing platforms; energy-efficient computing; data management services
Interests: bioinformatics; computational biology; systems biology, multi-omic datasets analysis and integration, high-performance computing; big data analytics; machine learning
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
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Keywords
- Internet of Things
- edge computing
- fog layer
- cloud computing
- energy management
- system-on-a-chip
- energy consumption optimizations
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