Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning
Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/17/2098 |
| _version_ | 1797521532750135296 |
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| author | Mumraiz Khan Kasi Sarah Abu Ghazalah Raja Naeem Akram Damien Sauveron |
| author_facet | Mumraiz Khan Kasi Sarah Abu Ghazalah Raja Naeem Akram Damien Sauveron |
| author_sort | Mumraiz Khan Kasi |
| collection | DOAJ |
| description | Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures. |
| first_indexed | 2024-03-10T08:13:50Z |
| format | Article |
| id | doaj.art-9b9453988bb145f1aea7276a18711010 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T08:13:50Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-9b9453988bb145f1aea7276a187110102023-11-22T10:29:55ZengMDPI AGElectronics2079-92922021-08-011017209810.3390/electronics10172098Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement LearningMumraiz Khan Kasi0Sarah Abu Ghazalah1Raja Naeem Akram2Damien Sauveron3Department of Computer Science, FICT, BUITEMS, Quetta 87300, PakistanInformation Security and Cyber Security Unit, King Khaled University, Abha 61421, Saudi ArabiaDepartment of Computer Science, University of Aberdeen, Aberdeen AB24 3FX, UKDepartment of Computer Science, University of Limoges, 23204 Limoges, FranceMobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.https://www.mdpi.com/2079-9292/10/17/2098mobile edge computingmobile edge server placementmultiagent RLedge security |
| spellingShingle | Mumraiz Khan Kasi Sarah Abu Ghazalah Raja Naeem Akram Damien Sauveron Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning Electronics mobile edge computing mobile edge server placement multiagent RL edge security |
| title | Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning |
| title_full | Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning |
| title_fullStr | Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning |
| title_full_unstemmed | Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning |
| title_short | Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning |
| title_sort | secure mobile edge server placement using multi agent reinforcement learning |
| topic | mobile edge computing mobile edge server placement multiagent RL edge security |
| url | https://www.mdpi.com/2079-9292/10/17/2098 |
| work_keys_str_mv | AT mumraizkhankasi securemobileedgeserverplacementusingmultiagentreinforcementlearning AT sarahabughazalah securemobileedgeserverplacementusingmultiagentreinforcementlearning AT rajanaeemakram securemobileedgeserverplacementusingmultiagentreinforcementlearning AT damiensauveron securemobileedgeserverplacementusingmultiagentreinforcementlearning |