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| Title | Research On Lightweight Topology Optimization Strategy Of Internet Of Things Based On Network Motif; [基于网络模体的轻量级物联网拓扑优化策略研究] |
|---|---|
| ID_Doc | 45493 |
| Authors | Chen N.; Qiu T.; Guo D.-K.; Xu T.-Y. |
| Year | 2023 |
| Published | Jisuanji Xuebao/Chinese Journal of Computers, 46, 6 |
| DOI | http://dx.doi.org/10.11897/SP.J.1016.2023.01121 |
| Abstract | With the development of fifth-generation telecommunication technology (5G) and smart hardware devices, the scale and diversity of Internet of Things (IoT) applications are greatly expanding, like smart buildings, smart agriculture, smart homes, intelligent transportation, and so on. Numerous sensing devices can be networked to provide better network services for all aspects of life. However, the networking of massive intelligent sensing devices has brought a great threat to the quality of service (QoS) of the IoT, where the threat includes communication interrupts, data traffic congestion, and other unexpected factors. The robustness and performance of the IoT topology directly influence the network applications' services and thus are associated with network reliability and resilience. The failure of key device nodes and network cyber-attacks can lead to the collapse of the IoT topology, which affects the QoS of the whole network system. Then, the system will finally fail without the ability to communicate with other network systems, where the large-scale IoT applications are broken. Therefore, how to optimize the robustness of large-scale IoT topology is a challenge to maintain maximum communication ability even if some device nodes fail, which draws researchers' attention. Nowadays, methods such as heuristic algorithms and learning mechanisms are proposed to enhance the reliability of IoT topology. These methods can efficiently improve the robustness of IoT applications against cyberattacks and prolong the network lifetime even if part of the topology fails. However, these methods sacrifice huge computing resources to get disproportionately robust performance gains, and the larger the IoT topology scale, the more obvious this phenomenon is. Indeed, in the real world, IoT applications cannot spend so much time executing to produce a suboptimal result. We need a fast robustness optimization method for large-scale IoT applications. To address the problem, in this paper, we propose a Lightweight Topology Optimization Strategy for the IoT based on network motif (LITOS), where network motifs are significant repetition patterns widely spread in the network topology. Through the topology analysis, we found that the IoT topology has community characteristics, and we designed an asynchronous community detection algorithm based on a network motif to decompose the large-scale complex IoT topology into lightweight local network topologies. The devices with multiple roles in the communities are grouped together as a "super community". Then, utilizing CPU multi-core computing resources, we present a deep deterministic reinforcement learning mechanism (DDRL) to asynchronously optimize each local lightweight IoT topology, which can reduce the overall optimization time and improve the robustness of the network topology. Furthermore, we developed a novel robustness metric based on network motifs to measure dynamic changes in IoT topology, where network motifs can reveal hidden functional mechanisms and provide researchers with new perspectives on network topology. The new robustness metric has a better effect of guiding the topology towards more reliability. In terms of experimental results, compared with other state-of-the-art optimization algorithms, the running time of the LITOS is 1 — 2 orders of magnitude lower than that of the ROCKS and ROSE algorithms and is about 10% lower than these algorithms in robustness improvement, which can greatly improve the optimizing efficiency for large-scale IoT topologies. © 2023 Science Press. All rights reserved. |
| Author Keywords | asynchronous community detection; deep reinforcement learning; dense network topology; Internet of Things; lightweight topology robustness optimization; network motif |
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