Ning Chen
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Abstract
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.
- Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Weerakoon, and Karl-Erik Årzén. 2020. Five challenges in cloud-enabled intelligence and control. ACM Transactions on Internet Technology (TOIT) 20, 1 (2020), 1–19. Google Scholar
Digital Library
- Hanen Ahmadi and Ridha Bouallegue. 2015. RSSI-based localization in wireless sensor networks using regression tree. In 2015 International Wireless Communications and Mobile Computing Conference (IWCMC’15). 1548–1553.Google ScholarCross Ref
- Ji Hyoung Ahn and TaeJin Lee. 2018. ALLYS: All you can send for energy harvesting networks. IEEE Transactions on Mobile Computing 17, 4 (2018), 775–788.Google ScholarCross Ref
- Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, and Hwee-Pink Tan. 2014. Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys and Tutorials 16, 4 (2014), 1996–2018.Google ScholarCross Ref
- Emilio Ancillotti, Carlo Vallati, Raffaele Bruno, and Enzo Mingozzi. 2017. A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management. Computer Communications 112 (2017), 1–13. Google ScholarDigital Library
- Luis Miguel Antonio and Carlos A. Coello Coello. 2017. Coevolutionary multi-objective evolutionary algorithms: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 22, 6 (2017), 851–865.Google ScholarDigital Library
- Nouha Baccour, Anis Koubâa, Luca Mottola, Marco Antonio Zúñiga, Habib Youssef, Carlo Alberto Boano, and Mário Alves. 2012. Radio link quality estimation in wireless sensor networks: A survey. ACM Transactions on Sensor Networks (TOSN) 8, 4 (2012), 34. Google ScholarDigital Library
- Majid Bahrepour, Nirvana Meratnia, Mannes Poel, Zahra Taghikhaki, and Paul J.M. Havinga. 2010. Distributed event detection in wireless sensor networks for disaster management. In 2010 2nd International Conference on Intelligent Networking and Collaborative Systems (INCOS’10). 507–512. Google ScholarDigital Library
- Albert László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. Science 286, 5439 (1999), 509–512.Google Scholar
- David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba. 2020. Understanding the role of individual units in a deep neural network. Proceedings of the National Academy of Sciences 117, 48 (2020), 30071–30078.Google ScholarCross Ref
- Kriti Bhargava, Stepan Ivanov, Diarmuid McSweeney, and William Donnelly. 2019. Leveraging fog analytics for context-aware sensing in cooperative wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) 15, 2 (2019), 1–35. Google ScholarDigital Library
- Pierre Buesser, Fabio Daolio, and Marco Tomassini. 2011. Optimizing the robustness of scale-free networks with simulated annealing. In International Conference on Adaptive and Natural Computing Algorithms. Springer, 167–176. Google ScholarDigital Library
- Ismail Butun, Patrik Österberg, and Houbing Song. 2020. Security of the Internet of Things: Vulnerabilities, attacks and countermeasures. IEEE Communications Surveys & Tutorials 22, 1 (2020), 616–644.Google ScholarDigital Library
- Dmitrii Chemodanov, Flavio Esposito, Prasad Calyam, and Andrei Sukhov. 2020. REBATE: A repulsive-based traffic engineering protocol for dynamic scale-free networks. Future Generation Computer Systems 108, 7 (2020), 624–635.Google ScholarCross Ref
- Ning Chen, Tie Qiu, Xiaobo Zhou, Keqiu Li, and Mohammed Atiquzzaman. 2019. An intelligent robust networking mechanism for the internet of things. IEEE Communications Magazine 57, 11 (2019), 91–95.Google ScholarDigital Library
- Yajing Chen, Nikolaos Chiotellis, LiXuan Chuo, Carl Pfeiffer, Yao Shi, Ronald G. Dreslinski, Anthony Grbic, Trevor Mudge, David D. Wentzloff, David Blaauw, et al. 2016. Energy-autonomous wireless communication for millimeter-scale Internet-of-Things sensor nodes. IEEE Journal on Selected Areas in Communications 34, 12 (2016), 3962–3977. Google ScholarDigital Library
- Federico Chiariotti, Chiara Pielli, Nicola Laurenti, Andrea Zanella, and Michele Zorzi. 2019. A game-theoretic analysis of energy-depleting jamming attacks with a learning counterstrategy. ACM Transactions on Sensor Networks (TOSN) 16, 1 (2019), 1–25. Google ScholarDigital Library
- Ewan R. Colman and Geoff J. Rodgers. 2013. Complex scale-free networks with tunable power-law exponent and clustering. Physica A: Statistical Mechanics and Its Applications 392, 21 (2013), 5501–5510.Google ScholarCross Ref
- Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 1459–1468. Google ScholarDigital Library
- Hans J. Herrmann, Christian M. Schneider, André A. Moreira, José S. Andrade Jr, and Shlomo Havlin. 2011. Onion-like network topology enhances robustness against malicious attacks. Journal of Statistical Mechanics: Theory and Experiment 2011, 1 (2011), P01027.Google ScholarCross Ref
- Petter Holme, Beom Jun Kim, Chang No Yoon, and Seung Kee Han. 2002. Attack vulnerability of complex networks. Physical Review E 65, 5 (2002), 056109.Google ScholarCross Ref
- Shihong Hu and Guanghui Li. 2020. TMSE: A topology modification strategy to enhance the robustness of scale-free wireless sensor networks. Computer Communications 157, 5 (2020), 53–63.Google ScholarCross Ref
- Swami Iyer, Timothy Killingback, Bala Sundaram, and Zhen Wang. 2013. Attack robustness and centrality of complex networks. PloS One 8, 4 (2013), e59613.Google ScholarCross Ref
- Fatemeh Jalali, Kerry Hinton, Robert Ayre, Tansu Alpcan, and Rodney S. Tucker. 2016. Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications 34, 5 (2016), 1728–1739.Google ScholarDigital Library
- Xingpei Ji, Bo Wang, Dichen Liu, Guo Chen, Fei Tang, Daqian Wei, and Lian Tu. 2016. Improving interdependent networks robustness by adding connectivity links. Physica A: Statistical Mechanics and its Applications 444 (2016), 9–19.Google Scholar
- Xiaoyu Ji, Xinyan Zhou, Miao Xu, Wenyuan Xu, and Yabo Dong. 2020. OPCIO: Optimizing power consumption for embedded devices via GPIO configuration. ACM Transactions on Sensor Networks (TOSN) 16, 2 (2020), 1–28. Google ScholarDigital Library
- Chetan B. Khadse, Madhuri A. Chaudhari, and Vijay B. Borghate. 2017. Electromagnetic compatibility estimator using scaled conjugate gradient backpropagation based artificial neural network. IEEE Transactions on Industrial Informatics 13, 3 (2017), 1036–1045.Google ScholarCross Ref
- Aron Laszka, Benjamin Johnson, and Jens Grossklags. 2018. On the assessment of systematic risk in networked systems. ACM Transactions on Internet Technology (TOIT) 18, 4 (2018), 1–28. Google ScholarDigital Library
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436.Google Scholar
- He Li, Kaoru Ota, and Mianxiong Dong. 2019. Deep reinforcement scheduling for mobile crowdsensing in fog computing. ACM Transactions on Internet Technology (TOIT) 19, 2 (2019), 1–18. Google ScholarDigital Library
- RongHua Li, Jeffrey Xu Yu, Xin Huang, Hong Cheng, and Zechao Shang. 2012. Measuring robustness of complex networks under MVC attack. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, 1512–1516. Google ScholarDigital Library
- Zhiyuan Lin, Tim Althoff, and Jure Leskovec. 2018. I’ll be back: On the multiple lives of users of a mobile activity tracking application. In Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 1501–1511. Google ScholarDigital Library
- Vitor H.P. Louzada, Fabio Daolio, Hans J. Herrmann, and Marco Tomassini. 2013. Smart rewiring for network robustness. Journal of Complex networks 1, 2 (2013), 150–159.Google ScholarCross Ref
- Ning Lv, Chen Chen, Tie Qiu, and Arun Kumar Sangaiah. 2018. Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in SAR images. IEEE Transactions on Industrial Informatics 14, 12 (2018), 5530–5538.Google ScholarCross Ref
- Charles Marx, Flavio Calmon, and Berk Ustun. 2020. Predictive multiplicity in classification. In International Conference on Machine Learning. PMLR, 6765–6774.Google Scholar
- Darshan Vishwasrao Medhane, Arun Kumar Sangaiah, M. Shamim Hossain, Ghulam Muhammad, and Jin Wang. 2020. Blockchain-enabled distributed security framework for next generation IoT: An edge-cloud and software defined network integrated approach. IEEE Internet of Things Journal 7, 7 (2020), 6143–6149.Google ScholarCross Ref
- Jihong Park, Sumudu Samarakoon, Mehdi Bennis, and Mérouane Debbah. 2019. Wireless network intelligence at the edge. Proceedings of IEEE 107, 11 (2019), 2204–2239.Google ScholarCross Ref
- Tie Qiu, Jiancheng Chi, Xiaobo Zhou, Zhaolong Ning, Mohammed Atiquzzaman, and Dapeng Oliver Wu. 2020. Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials 22, 4 (2020), 2462–2488.Google ScholarCross Ref
- T. Qiu, J. Liu, W. Si, and D. O. Wu. 2019. Robustness optimization scheme with multi-population co-evolution for scale-free wireless sensor networks. IEEE/ACM Transactions on Networking 27, 3 (2019), 1028–1042. Google ScholarDigital Library
- Tie Qiu, Aoyang Zhao, Feng Xia, Weisheng Si, and Dapeng Wu. 2017. ROSE: Robustness strategy for scale-free wireless sensor networks. IEEE/ACM Transactions on Networking 25, 5 (2017), 2944–2959. Google ScholarDigital Library
- Lei Rong and Jing Liu. 2018. A heuristic algorithm for enhancing the robustness of scale-free networks based on edge classification. Physica A: Statistical Mechanics and Its Applications 503 (2018), 503–515.Google ScholarCross Ref
- M.S. Roopa, Santosh Pattar, Rajkumar Buyya, Kuppanna Rajuk Venugopal, S.S. Iyengar, and L.M. Patnaik. 2019. Social Internet of Things (SIoT): Foundations, thrust areas, systematic review and future directions. Computer Communications 139, 5 (2019), 32–57.Google ScholarDigital Library
- David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986. Learning representations by back-propagating errors. Nature 323, 6088 (1986), 533.Google ScholarCross Ref
- Arun Kumar Sangaiah, Ali Asghar Rahmani Hosseinabadi, Morteza Babazadeh Shareh, Seyed Yaser Bozorgi Rad, Atekeh Zolfagharian, and Naveen Chilamkurti. 2020. IoT resource allocation and optimization based on heuristic algorithm. Sensors 20, 2 (2020), 539–564.Google ScholarCross Ref
- Arun Kumar Sangaiah, Darshan Vishwasrao Medhane, Tao Han, M. Shamim Hossain, and Ghulam Muhammad. 2019. Enforcing position-based confidentiality with machine learning paradigm through mobile edge computing in real-time industrial informatics. IEEE Transactions on Industrial Informatics 15, 7 (2019), 4189–4196.Google ScholarCross Ref
- Arun Kumar Sangaiah, Mehdi Sadeghilalimi, Ali Asghar Rahmani Hosseinabadi, and Weizhe Zhang. 2019. Energy consumption in point-coverage wireless sensor networks via bat algorithm. IEEE Access 7 (2019), 180258–180269.Google ScholarCross Ref
- Salvatore Scellato, Ilias Leontiadis, Cecilia Mascolo, Prithwish Basu, and Murtaza Zafer. 2013. Evaluating temporal robustness of mobile networks. IEEE Transactions on Mobile Computing 12, 1 (2013), 105–117. Google ScholarDigital Library
- Christian M. Schneider, André A. Moreira, José S. Andrade, Shlomo Havlin, and Hans J. Herrmann. 2011. Mitigation of malicious attacks on networks. Proceedings of the National Academy of Sciences 108, 10 (2011), 3838–3841.Google ScholarCross Ref
- Steven H. Strogatz. 2001. Exploring complex networks. Nature 410, 6825 (2001), 268.Google Scholar
- Jun Wu, Mauricio Barahona, Yuejin Tan, and Hongzhong Deng. 2011. Spectral measure of structural robustness in complex networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 41, 6 (2011), 1244–1252. Google ScholarDigital Library
- Degan Zhang, Guang Li, Ke Zheng, Xuechao Ming, and Zhao-Hua Pan. 2014. An energy-balanced routing method based on forward-aware factor for wireless sensor networks. IEEE Transactions on Industrial Informatics 10, 1 (2014), 766–773.Google ScholarCross Ref
- Jianan Zhang, Eytan Modiano, and David Hay. 2017. Enhancing network robustness via shielding. IEEE/ACM Transactions on Networking 25, 4 (2017), 2209–2222. Google ScholarDigital Library
- Mingxing Zhou and Jing Liu. 2014. A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks. Physica A: Statistical Mechanics and its Applications 410 (2014), 131–143.Google Scholar
- Mingxing Zhou and Jing Liu. 2017. A two-phase multiobjective evolutionary algorithm for enhancing the robustness of scale-free networks against multiple malicious attacks. IEEE Transactions on Cybernetics 47, 2 (2017), 539–552.Google Scholar
Index Terms
- Robust Networking: Dynamic Topology Evolution Learning for Internet of Things
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