期刊
IEEE-ACM TRANSACTIONS ON NETWORKING
卷 30, 期 1, 页码 368-381出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNET.2021.3113916
关键词
Statistics; Sociology; Topology; Network topology; Robustness; Three-dimensional displays; Measurement; Scale-free Internet of Things; adaptive evolution algorithms; robustness optimization; 3D deployment; self-competition
类别
资金
- National Natural Science Foundation of China [61672131, U2001204]
- National Key Research and Development Program of China [2019YFB1703601]
- Tianjin Science Foundation for Distinguished Young Scholars [20JCJQJC00250]
- Key Research and Development Program of Tianjin [20YFZCGX01150]
The Internet of Things consists of numerous sensing nodes forming a large scale-free network. Optimizing network topology to increase resistance against malicious attacks is complex. Traditional genetic algorithms lack global search ability and may lead to premature convergence, slowing down population evolution. Therefore, an Adaptive Robustness Evolution Algorithm (AREA) with self-competition mechanism is proposed to address this issue.
Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology to increase resistance against malicious attacks is a complex problem, especially on 3-dimension (3D) topological deployment. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, the additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the population's evolution. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. A self-competitive mechanism is used to ensure convergence. We construct a 3D IoT topology that is optimized by AREA. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.
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