TY - JOUR
T1 - Robust networking
T2 - Dynamic topology evolution learning for internet of things
AU - Chen, Ning
AU - Qiu, Tie
AU - Daneshmand, Mahmoud
AU - Wu, Dapeng Oliver
N1 - Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2021/8
Y1 - 2021/8
N2 - The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.
AB - The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.
KW - Dynamic topology
KW - Evolution learning
KW - Internet of Things
KW - Robustness optimization
UR - http://www.scopus.com/inward/record.url?scp=85122615252&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122615252&partnerID=8YFLogxK
U2 - 10.1145/3446937
DO - 10.1145/3446937
M3 - Article
AN - SCOPUS:85122615252
SN - 1550-4859
VL - 17
JO - ACM Transactions on Sensor Networks
JF - ACM Transactions on Sensor Networks
IS - 3
M1 - 3446937
ER -