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Optimizing Age of Information and security of the next-generation Internet of Everything systems
We analyze the information exchange and interactions among the major components, i.e., people, things, data, and processes, of the Internet of Everything (IoE) system, where raw data generated by people and things must be processed to obtain relevant higher level information that can be utilized by...
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| Format: | Default Article |
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2022
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| Online Access: | https://hdl.handle.net/2134/25918528.v1 |
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| _version_ | 1818164503737532416 |
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| author | Alia Asheralieva Dusit Niyato |
| author_facet | Alia Asheralieva Dusit Niyato |
| author_sort | Alia Asheralieva (17562462) |
| collection | Figshare |
| description | We analyze the information exchange and interactions among the major components, i.e., people, things, data, and processes, of the Internet of Everything (IoE) system, where raw data generated by people and things must be processed to obtain relevant higher level information that can be utilized by IoE processes for decision making and actions. Accordingly, the value of information obtained in the IoE system depends on the Age of Information (AoI) - time elapsed from the moment when raw data is generated to the moment when the data is processed and delivered to the processes. To reduce the AoI, the system is realized in the multiaccess edge computing network, where data can be processed by the edge devices (EDs) in proximity to people, things, and processes. The system security and resilience are further enhanced through coded distributed computing when each data input of EDs is encoded with a specific encoding function so that the final result of data processing by EDs can be recovered even if some processing outputs of EDs are erroneous or delayed. We then define a stochastic optimization problem where the AoI, security, and resilience are optimized jointly to maximize the expected long-term system payoff - difference between the value of information and data processing costs. Since this problem is hard to solve directly due to hidden information about the correctness of processing outputs returned by EDs, we develop a machine learning (ML) framework to obtain the problem solution. |
| format | Default Article |
| id | rr-article-25918528 |
| institution | Loughborough University |
| publishDate | 2022 |
| record_format | Figshare |
| spelling | rr-article-259185282022-05-09T04:00:00Z Optimizing Age of Information and security of the next-generation Internet of Everything systems Alia Asheralieva (17562462) Dusit Niyato (13395561) Engineering Communications engineering Information and computing sciences Distributed computing and systems software Age of Information (AoI) Bayesian methods Coded distributed computing (CDC) Incomplete information Internet of Everything (IoE) Internet of Things (IoT) Lagrange interpolation Machine learning (ML) Multiaccess edge computing (MEC) Resilience Resource allocation Security Stochastic optimization Value of information We analyze the information exchange and interactions among the major components, i.e., people, things, data, and processes, of the Internet of Everything (IoE) system, where raw data generated by people and things must be processed to obtain relevant higher level information that can be utilized by IoE processes for decision making and actions. Accordingly, the value of information obtained in the IoE system depends on the Age of Information (AoI) - time elapsed from the moment when raw data is generated to the moment when the data is processed and delivered to the processes. To reduce the AoI, the system is realized in the multiaccess edge computing network, where data can be processed by the edge devices (EDs) in proximity to people, things, and processes. The system security and resilience are further enhanced through coded distributed computing when each data input of EDs is encoded with a specific encoding function so that the final result of data processing by EDs can be recovered even if some processing outputs of EDs are erroneous or delayed. We then define a stochastic optimization problem where the AoI, security, and resilience are optimized jointly to maximize the expected long-term system payoff - difference between the value of information and data processing costs. Since this problem is hard to solve directly due to hidden information about the correctness of processing outputs returned by EDs, we develop a machine learning (ML) framework to obtain the problem solution.<p></p> 2022-05-09T04:00:00Z Text Journal contribution 2134/25918528.v1 https://figshare.com/articles/journal_contribution/Optimizing_Age_of_Information_and_security_of_the_next-generation_Internet_of_Everything_systems/25918528 All Rights Reserved |
| spellingShingle | Engineering Communications engineering Information and computing sciences Distributed computing and systems software Age of Information (AoI) Bayesian methods Coded distributed computing (CDC) Incomplete information Internet of Everything (IoE) Internet of Things (IoT) Lagrange interpolation Machine learning (ML) Multiaccess edge computing (MEC) Resilience Resource allocation Security Stochastic optimization Value of information Alia Asheralieva Dusit Niyato Optimizing Age of Information and security of the next-generation Internet of Everything systems |
| title | Optimizing Age of Information and security of the next-generation Internet of Everything systems |
| title_full | Optimizing Age of Information and security of the next-generation Internet of Everything systems |
| title_fullStr | Optimizing Age of Information and security of the next-generation Internet of Everything systems |
| title_full_unstemmed | Optimizing Age of Information and security of the next-generation Internet of Everything systems |
| title_short | Optimizing Age of Information and security of the next-generation Internet of Everything systems |
| title_sort | optimizing age of information and security of the next-generation internet of everything systems |
| topic | Engineering Communications engineering Information and computing sciences Distributed computing and systems software Age of Information (AoI) Bayesian methods Coded distributed computing (CDC) Incomplete information Internet of Everything (IoE) Internet of Things (IoT) Lagrange interpolation Machine learning (ML) Multiaccess edge computing (MEC) Resilience Resource allocation Security Stochastic optimization Value of information |
| url | https://hdl.handle.net/2134/25918528.v1 |