Advance Tools and Techniques for Edge Computing in Dynamic Internet of Things Environment
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 5090
Special Issue Editors
Interests: ML & XAI; digital twin and industrial IoT; blockchain for energy and health
Special Issues, Collections and Topics in MDPI journals
Interests: Internet of Things/cyber physical systems; wireless communication; cyber security; smart infrastructures; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The explosion of data necessitates an increase in processing at the edge for reasons such as latency, bandwidth savings, security, privacy, and autonomy. However, because edge computing is inherently distributed, has low computational power, and has a heterogeneous landscape, energy optimization and dynamic load distribution are major concerns for the research community. Edge computing devices are fundamentally low computational devices; however, in the dynamic IoT environment, they may perform high-processing tasks, such as ML model restraining in real-time, automatic data annotation, and so on. Distributed resource allocation is an important solution for transferring their extra loads to similar network edges. In such a scenario, all edge nodes in the same network should be able to share their computational resources in order to complete any high-processing task. In addition, their heterogeneous nature also requires more advanced methods and tools, such as adaptive approaches. For scale edge computing, more energy-efficient distributed edge computing approaches are required. We must counteract this complexity by supporting a variety of deployment energy-efficient resource allocation models in a more standardized and open manner, while also allowing legacy systems to be used.
Dr. Syed Muslim Jameel
Prof. Dr. Ali Kashif Bashir
Guest Editors
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Keywords
- adaptive machine learning
- energy optimization
- dynamic load distribution
- dynamic Internet of Things
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