Minicursos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos

A. E. Eiben, J. E. Smith, et al. Introduction to evolutionary computing, volume 53. Springer, 2003.

A. Elkhodary, N. Esfahani, and S. Malek. Fusion: A framework for engineering self-tuning self-adaptive software systems. In Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE '10, pages 7-16, New York, NY, USA, 2010. ACM.

A. J. Ramirez, A. C. Jensen, B. H. Cheng, and D. B. Knoester. Automatically exploring how uncertainty impacts behavior of dynamically adaptive systems. In Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering, pages 568-571. IEEE Computer Society, 2011.

A. M. Del Esposte, F. Kon, F. M. Costa, and N. Lago. Interscity: A scalable microservice-based open source platform for smart cities. In SMARTGREENS, volume 1, pages 35-46, 2017.

Adjih, C., Baccelli, E., Fleury, E., Harter, G., Mitton, N., Noel, T., Pissard-Gibollet, R., Saint-Marcel, F., Schreiner, G., Vandaele, J., and Watteyne, T. (2015). FIT IoTLAB: A Large Scale Open Experimental IoT Testbed. In Anais do IEEE World Forum on Internet of Things, Milan, Italy.

Afanasyev, A., Jiang, X., Yu, Y., Tan, J., Xia, Y., Mankin, A., and Zhang, L. (2017). Ndns: A dns-like name service for ndn. In 2017 26th International Conference on Computer Communication and Networks (ICCCN), pages 1-9. IEEE.

Afanasyev, A., Shi, J., Zhang, B., Zhang, L., Moiseenko, I., Yu, Y., Shang,W., Li, Y., Mastorakis, S., Huang, Y., Abraham, J. P., Newberry, E., DiBenedetto, S., Fan, C., Papadopoulos, C., Pesavento, D., Grassi, G., Pau, G., Zhang, H., Song, T., Yuan, H., Abraham, H. B., Crowley, P., Amin, S. O., Lehman, V., Chowdhury, M., and Wang, L. (2018). Nfd developer's guide. Technical Report NDN-0021, NDN.

Afek, Y., Bremler-Barr, A., Hay, D., Goldschmidt, R., Shafir, L., Avraham, G., and Shalev, A. (2020). NFV-based IoT Security for Home Networks using MUD. In Anais do NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, pages 1-9.

Akkaya, A., Genc, G., and Tugcu, T. (2014). Hla based architecture for molecular communication simulation. Simulation Modelling Practice and Theory, 42:163-177.

Akyildiz, I. F. (2012). Nanonetworks: A new frontier in communications. In Proceedings of the 18th annual international conference on Mobile computing and networking, pages 1-2. ACM.

Akyildiz, I. F. and Jornet, J. M. (2010). Electromagnetic wireless nanosensor networks. Nano Communication Networks, 1(1):3-19.

Akyildiz, I. F., Pierobon, M., and Balasubramaniam, S. (2019). Moving forward with molecular communication: From theory to human health applications [point of view]. Proceedings of the IEEE, 107(5):858-865.

Akyildiz, I. F., Pierobon, M., Balasubramaniam, S., and Koucheryavy, Y. (2015). The internet of bio-nano things. IEEE Communications Magazine, 53(3):32-40.

Al-Rubaie, M. and Chang, J. M. (2016). Reconstruction attacks against mobile-based continuous authentication systems in the cloud. IEEE Transactions on Information Forensics and Security, 11(12):2648-2663.

Al-Shaboti, M., Welch, I., Chen, A., and Mahmood, M. A. (2018). Towards Secure Smart Home IoT: Manufacturer and User Network Access Control Framework. In Anais da 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pages 892-899.

Al-Shawabka, A., Restuccia, F., D'Oro, S., Jian, T., Costa Rendon, B., Soltani, N., Dy, J., Ioannidis, S., Chowdhury, K., and Melodia, T. (2020). Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting. In IEEE INFOCOM 2020, pages 646-655.

Al-Turjman, F. and Baali, I. (2019). Machine learning for wearable iot-based applications: A survey. Transactions on Emerging Telecommunications Technologies, page e3635.

Alam, I., Sharif, K., Li, F., Latif, Z., Karim, M. M., Biswas, S., Nour, B., and Wang, Y. (2020). A Survey of Network Virtualization Techniques for Internet of Things Using SDN and NFV. ACM Computing Surveys, 53(2):1-40.

Amadeo, M., Campolo, C., Quevedo, J., Corujo, D., Molinaro, A., Iera, A., Aguiar, R. L., and Vasilakos, A. V. (2016). Information-centric networking for the internet of things: challenges and opportunities. IEEE Network, 30(2):92-100.

Analysts, G. I. (2020). Wearable sensors - global market trajectory and analytics. Technical report, Global Industry Analysts, Inc.

Aono, Y., Hayashi, T., Trieu Phong, L., and Wang, L. (2016). Scalable and secure logistic regression via homomorphic encryption. In Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, pages 142-144.

Araújo, F. R. C. (2018). Cache colaborativo e distribuído como suporte à mobilidade de produtores em redes sem fio de dados nomeados. Dissertação (Mestrado em Ciência da Computação), Universidade Federal da Bahia, Instituto de Matemática, Programa de Pós-Graduação em Ciência da Computação, Salvador.

Araújo, F. R. C. and Sampaio, L. N. (2017). Mobilidade em ndn: Consumidores versus produtores. In Anais do XIII Workshop de Redes P2P, Dinâmicas, Sociais e Orientadas a Conteúdo, pages 8-13. SBC.

Araújo, F. R. C., de Sousa, A. M., and Sampaio, L. N. (2018). Armazenamento oportunista em redes de dados nomeados sem fio como suporte à mobilidade de produtores. In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.

Araújo, F. R. C., de Sousa, A. M., and Sampaio, L. N. (2019). Scanmob: An opportunistic caching strategy to support producer mobility in named data wireless networking. Computer Networks, 156:62-74.

Araújo, F. R. C., de Sousa, A. M., and Sampaio, L. N. (2019). Uma estratégia de encaminhamento eficiente para redes de veículos aéreos não tripulados de dados nomeados. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 876-889. SBC.

Araujo, G. B. and Sampaio, L. N. (2021). An intelligent edge-traffic routing architecture for vehicular data-mule service. IEEE Latin America Transactions, 19(11):1976-1984.

Araujo, G. B., Peixoto, M. L. M., and Sampaio, L. N. (2021). Ndn4ivc: A framework for simulating and testing applications in vehicular named-data networking.

Asaf, K., Rehman, R. A., and Kim, B.-S. (2020). Blockchain technology in named data networks: A detailed survey. Journal of Network and Computer Applications, 171:102840.

Ashibani, Y. and Mahmoud, Q. H. (2017). Cyber physical systems security: Analysis, challenges and solutions. Computers & Security, 68:81-97.

Azgin, A., Ravindran, R., and Wang, G. (2016). pit/less: Stateless forwarding in content centric networks. In 2016 IEEE Global Communications Conference (GLOBECOM), pages 1-7.

B. H. C. e. a. Cheng. Software Engineering for Self-Adaptive Systems: A Research Roadmap, pages 1-26. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009.

B. Porter and R. Rodrigues Filho. Distributed emergent software: Assembling, perceiving and learning systems at scale. In 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), pages 127-136. IEEE, 2019.

B. Porter and R. Rodrigues Filho. Losing control: The case for emergent software using autonomous perception, assembly and learning. In Proc. of the 10th IEEE International Conf. on Self-Adaptive and Self-Organizing Systems, 2016.

B. Porter, M. Grieves, R. Rodrigues Filho, and D. Leslie. REX: A development platform and online learning approach for runtime emergent software systems. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation. USENIX, 2016.

B. Porter. Runtime modularity in complex structures: A component model for fine grained runtime adaptation. In Component-Based Software Engineering, pages 26-32. ACM, June 2014.

Baccelli, E., Mehlis, C., Hahm, O., Schmidt, T. C., and Wählisch, M. (2014). Information centric networking in the iot: Experiments with ndn in the wild. In Proceedings of the 1st ACM Conference on Information-Centric Networking, ACM-ICN '14, page 77-86. ACM.

Bagaa, M., Taleb, T., Bernabe, J. B., and Skarmeta, A. (2020). A Machine Learning Security Framework for Iot Systems. IEEE Access, 8:114066-114077.

Balasubramaniam, S. and Lio, P. (2013). Multi-hop conjugation based bacteria nanonetworks. IEEE Transactions on NanoBioscience, 12(1):47-59.

Barbone, A. S., Meftah, M., Markiewicz, K., and Dellimore, K. (2019). Beyond wearables and implantables: a scoping review of insertable medical devices. Biomedical Physics & Engineering Express, 5(6):062002.

Barka, E., Kerrache, C. A., Hussain, R., Lagraa, N., Lakas, A., and Bouk, S. H. (2018). A trusted lightweight communication strategy for flying named data networking. Sensors, 18(8).

Baron, K. G., Duffecy, J., Berendsen, M. A., Mason, I. C., Lattie, E. G., and Manalo, N. C. (2018). Feeling validated yet? a scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep medicine reviews, 40:151-159.

Barros, M. T., Balasubramaniam, S., and Jennings, B. (2015). Comparative end-to-end analysis of ca 2+-signaling-based molecular communication in biological tissues. IEEE Transactions on Communications, 63(12):5128-5142.

Batista, D. M., Goldman, A., Hirata, R., Kon, F., Costa, F. M., and Endler, M. (2016). InterSCity: Addressing Future Internet research challenges for Smart Cities. In Anais da 7th International Conference on the Network of the Future (NOF), pages 1-6.

Benzel, T. (2011). The Science of Cyber Security Experimentation: The DETER Project. In Anais da 27th Annual Computer Security Applications Conference, ACSAC '11, page 137-148, New York, NY, USA. Association for Computing Machinery.

Blalock, D., Ortiz, J. J. G., Frankle, J., and Guttag, J. (2020). What is the state of neural network pruning? arXiv preprint arXiv:2003.03033.

Blasco, J., Chen, T. M., Patil, H. K., and Wolff, D. (2019). Wearables security and privacy. In Mission-Oriented Sensor Networks and Systems: Art and Science, pages 351-380. Springer.

Borges, L. F., Barros, M. T., and Nogueira, M. (2021). Toward reliable intra-body molecular communication: An error control perspective. IEEE Communications Magazine, 59(5):114-120.

Boudi, A., Farris, I., Bagaa, M., and taleb, T. (2019). Assessing Lightweight Virtualization for Security-as-a-Service at the Network Edge. IEICE Transactions on Communications, E102.B(5):970-977.

Bremler-Barr, A., Harchol, Y., Hay, D., and Koral, Y. (2014). Deep Packet Inspection as a Service. In Anais da 10th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT '14), page 271-282. Association for Computing Machinery.

Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., and Shi, W. (2018). Federated learning of predictive models from federated electronic health records. International journal of medical informatics, 112:59-67.

Brito, I. V. S., Sampaio, L., and Zhang, L. (2020). (Poster) Towards a distance vector routing protocol for named data networking. In Named Data Networking Community Meeting (NDNcomm) 2020.

Bülbül, N. S. and Fischer, M. (2020). SDN/NFV-based DDoS Mitigation via Pushback. In Anais da ICC 2020 - 2020 IEEE International Conference on Communications (ICC), pages 1-6.

C. Lu, Y. Lu, T. F. Abdelzaher, J. A. Stankovic, and S. H. Son. Feedback control architecture and design methodology for service delay guarantees in web servers. IEEE Transactions on Parallel and Distributed Systems, 17(9):1014-1027, 2006.

C. Müller-Schloer and S. Tomforde. Organic Computing-Technical Systems for Survival in the Real World. Springer, 2017.

Cao, L., Sharma, P., Fahmy, S., and Saxena, V. (2015). NFV-VITAL: A Framework for Characterizing the Performance of Virtual Network Functions. In Anais da 2015 IEEE Conference on Network Function Virtualization and Software Defined Network (NFVSDN), pages 93-99.

Castro, M. and Liskov, B. (1999). Practical byzantine fault tolerance. In Proceedings of the Third Symposium on Operating Systems Design and Implementation, OSDI '99, page 173-186. USENIX Association.

Chahibi, Y., Pierobon, M., Sang, O. S., and Akyildiz, I. F. (2013). A molecular communication system model for particulate drug delivery systems. IEEE Transactions on Biomedical Engineering, 60(12):3468-3483.

Chandra, T. D. and Toueg, S. (1996). Unreliable failure detectors for reliable distributed systems. Journal of the ACM (JACM), 43(2):225-267.

Chandy, K. M. and Lamport, L. (1985). Distributed snapshots: Determining global states of distributed systems. ACM Transactions on Computer Systems (TOCS), 3(1):63-75.

CheckPoint (2020). Keeping the gate locked on your IoT devices: Vulnerabilities found on Amazon's Alexa. Disponível em: https://research.checkpoint.com/2020/amazons-alexa-hacked. Acessado em 14 de Junho de 2021.

Cheng, Y., Wang, D., Zhou, P., and Zhang, T. (2017). A survey of model compression and acceleration for deep neural networks. arXiv preprint ar-Xiv:1710.09282.

Chi, Z., Li, Y., Sun, H., Yao, Y., and Zhu, T. (2019). Concurrent Cross-Technology Communication among Heterogeneous IoT Devices. IEEE/ACM TON, 27(3):932-947.

Chude-Okonkwo, U. A., Malekian, R., and Maharaj, B. T. (2016). Biologically inspired bio-cyber interface architecture and model for internet of bio-nanothings applications. IEEE Trans. on Communications, 64(8):3444-3455.

Chude-Okonkwo, U. A., Malekian, R., Maharaj, B. T., and Vasilakos, A. V. (2017). Molecular communication and nanonetwork for targeted drug delivery: A survey. IEEE Communications Surveys & Tutorials, 19(4):3046-3096.

Cisco (2020). Cisco annual internet report (2018-2023) white paper. Disponível em: https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html. Último acesso em: 30 de maio de 2021.

Classen, J., Wegemer, D., Patras, P., Spink, T., and Hollick, M. (2018). Anatomy of a vulnerable fitness tracking system: Dissecting the fitbit cloud, app, and firmware. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1):5.

Coelho, K., Damião, D., Noubir, G., Borges, A., Nogueira, M., and Nacif, J. (2019). Cryptographic algorithms in wearable communications: An empirical analysis. IEEE Communications Letters, 23(11):1931-1934.

Conti, M., Gangwal, A., Hassan, M., Lal, C., and Losiouk, E. (2020). The road ahead for networking: A survey on icn-ip coexistence solutions. IEEE Communications Surveys Tutorials, 22(3):2104-2129.

Cristian, F. and Schmuck, F. (1995). Agreeing on processor group membership in timed asynchronous distributed systems. Report CSE95-428, UC San Diego.

D. A. Berry and B. Fristedt. Bandit problems: sequential allocation of experiments (monographs on statistics and applied probability). London: Chapman and Hall, 5(71-87):7-7, 1985.

D. Al-Jumeily, A. Hussain, and P. Fergus. Using adaptive neural networks to provide self-healing autonomic software. International Journal of Space-Based and Situated Computing, 5(3):129-140, 2015.

D. Costa and A. Hertz. Ants can colour graphs. Journal of the operational research society, 48(3):295-305, 1997.

D. Fisch, M. Janicke, B. Sick, and C. Muller-Schloer. Quantitative emergence-a refined approach based on divergence measures. In Self-Adaptive and Self-Organizing Systems (SASO), 2010 4th IEEE International Conference on, pages 94-103. IEEE, 2010.

D. Kim and S. Park. Reinforcement learning-based dynamic adaptation planning method for architecture-based self-managed software. In Software Engineering for Adaptive and Self-Managing Systems, 2009. SEAMS'09. ICSE Workshop on, pages 76-85. IEEE, 2009.

D. Merkle, M. Middendorf, and H. Schmeck. Ant colony optimization for resourceconstrained project scheduling. IEEE transactions on evolutionary computation, 6(4):333-346, 2002.

Da Costa, M. R., Kibis, O., and Portnoi, M. (2009). Carbon nanotubes as a basis for terahertz emitters and detectors. Microelectronics Journal, 40(4-5):776-778.

da Silva, M. d. V. D., Rocha, A., Gomes, R. L., and Nogueira, M. (2021). Lightweight Data Compression for Low Energy Consumption in Industrial Internet of Things. In Anais da IEEE 18th Annual Consumer Communications Networking Conference (CCNC).

Datta, T., Apthorpe, N., and Feamster, N. (2018). A developerfriendly library for smart home IoT privacy-preserving traffic obfuscation. In Proceedings of the Workshop on IoT Security and Privacy.

De Amicis, A., De Sanctis, S., Di Cristofaro, S., Franchini, V., Lista, F., Regalbuto, E., Giovenale, E., Gallerano, G. P., Nenzi, P., Bei, R., et al. (2015). Biological effects of in vitro thz radiation exposure in human foetal fibroblasts. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 793:150-160.

De Benedictis, M. and Lioy, A. (2019). A Proposal for Trust Monitoring in a Network Functions Virtualisation Infrastructure. In Anais da 2019 IEEE Conference on Network Softwarization (NetSoft).

de Sousa, A. M., Araújo, F. R. C., and Sampaio, L. N. (2018a). A link-stability-based interest-forwarding strategy for vehicular named data networks. IEEE Internet Computing, 22(3):16-26.

de Sousa, A. M., Araújo, F. R. C., and Sampaio, L. N. (2018b). Encaminhamento seletivo de interesses em redes veiculares de dados nomeados baseado no tempo de vida do enlace. In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.

de Sousa, A. M., Araújo, F. R. C., Greve, F. G. P., and Sampaio, L. N. (2019). Um arcabouço para validação de conteúdo em redes de dados nomeados baseado em blockchain. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 155-168. SBC.

Deogirikar, J. and Vidhate, A. (2017). Security Attacks in IoT: A Survey. In Anais da International Conference on IoT in Social, Mobile, Analytics and Cloud, pages 32-37.

Dhanvijay, M. M. and Patil, S. C. (2019). Internet of things: A survey of enabling technologies in healthcare and its applications. Computer Networks, 153:113-131.

Ding, D., Han, Q.-L., Xiang, Y., Ge, X., and Zhang, X.-M. (2018). A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing, 275:1674 - 1683.

Ding, H., Shu, X., Jin, Y., Fan, T., and Zhang, H. (2019). Recent advances in nanomaterial-enabled acoustic devices for audible sound generation and detection. Nanoscale, 11(13):5839-5860.

Doku, R., Rawat, D. B., Garuba, M., and Njilla, L. (2020). Fusion of named data networking and blockchain for resilient internet-of-battlefield-things. In 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), pages 1-6.

Dolev, D., Dwork, C., and Stockmeyer, L. (1987). On the minimal synchronism needed for distributed consensus. Journal of the ACM, 34(1):77-97.

Dong, S., Li, Z., Tang, D., Chen, J., Sun, M., and Zhang, K. (2019). Your smart home can't keep a secret: Towards automated fingerprinting of iot traffic with neural networks. ArXiv, abs/1909.00104.

Duan, M., Liu, D., Chen, X., Tan, Y., Ren, J., Qiao, L., and Liang, L. (2019). Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications. Proceedings - 2019 IEEE International Conference on Computer Design, ICCD 2019, (Iccd):246-254.

E. Bruneton, T. Coupaye, M. Leclercq, V. Quema, and J.-B. Stefani. An open component model and its support in java. In Component-Based Software Engineering, volume 3054, pages 7-22. Springer Berlin Heidelberg, 2004.

E. Cakar, S. Tomforde, and C. Müller-Schloer. A role-based imitation algorithm for the optimisation in dynamic fitness landscapes. In Swarm Intelligence (SIS), 2011 IEEE Symposium on, pages 1-8. IEEE, 2011.

E. Yuan, S. Malek, B. Schmerl, D. Garlan, and J. Gennari. Architecture-based selfprotecting software systems. In Proceedings of the 9th international ACM Sigsoft conference on Quality of software architectures, pages 33-42, 2013.

Elsken, T., Metzen, J. H., Hutter, F., et al. (2019). Neural architecture search: A survey. J. Mach. Learn. Res., 20(55):1-21.

ETSI (2014a). Network Functions Virtualisation (NFV): NFV Performance & Portability Best Practises. Disponível em: https://www.etsi.org/deliver/etsi_gs/NFV-PER/001_099/001/01.01.01_60/gs_NFV-PER001v010101p.pdf. Acessado em 13 de Maio de 2021.

ETSI (2014b). Network Functions Virtualisation (NFV): Service Quality Metrics. Disponível em: https://www.etsi.org/deliver/etsi_gs/NFV-INF/001_099/010/01.01.01_60/gs_NFV-INF010v010101p.pdf. Acessado em 13 de Maio de 2021.

Eugster, P. T., Felber, P. A., Guerraoui, R., and Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2):114-131.

Fang, C., Yao, H., Wang, Z., Wu, W., Jin, X., and Yu, F. R. (2018). A survey of mobile information-centric networking: Research issues and challenges. IEEE Communications Surveys Tutorials, 20(3):2353-2371.

Farris, I., Bernabe, J. B., Toumi, N., Garcia-Carrillo, D., Taleb, T., Skarmeta, A., and Sahlin, B. (2017). Towards provisioning of SDN/NFV-based security enablers for integrated protection of IoT systems. In Anais da 2017 IEEE Conference on Standards for Communications and Networking (CSCN), pages 169-174.

Farris, I., Taleb, T., Khettab, Y., and Song, J. (2019). A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems. IEEE COMST, 21(1):812-837.

FBI (2015 (Accessed January 10, 2020)). Internet of Things Poses Opportunities for Cyber Crime.

Felicetti, L., Assaf, S. S., Femminella, M., Reali, G., Alarcon, E., and Sole-Pareta, J. (2018). The molecular communications markup language (molcomml). Nano communication networks, 16:12-25.

Felicetti, L., Femminella, M., and Reali, G. (2012). A simulation tool for nanoscale biological networks. Nano Communication Networks, 3(1):2-18.

Ferro, E. and Potorti, F. (2005). Bluetooth and wi-fi wireless protocols: a survey and a comparison. IEEE Wireless Communications, 12(1):12-26.

Fisher, M. J., Lynch, N., and Paterson, M. S. (1985). Impossibility of distributed consensus with one faulty process. Journal of the ACM, 32(2):374-382.

Foerster, J., Assael, I. A., de Freitas, N., and Whiteson, S. (2016). Learning to communicate with deep multi-agent reinforcement learning. In Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc.

Fotiou, N. and Polyzos, G. C. (2016). Decentralized namebased security for content distribution using blockchains. In Computer Communications Workshops (INFOCOM WKSHPS), 2016 IEEE Conference on, pages 415-420. IEEE.

Fouad, H., Hashem, M., and Youssef, A. E. (2020). A nanobiosensors model with optimized bio-cyber communication system based on internet of bio-nano things for thrombosis prediction. Journal of Nanoparticle Resc., 22:1-17.

Frankle, J. and Carbin, M. (2019). The lottery ticket hypothesis: Finding sparse, trainable neural networks. In International Conference on Learning Representations.

Fredrikson, M., Jha, S., and Ristenpart, T. (2015). Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pages 1322-1333.

G. Blair. Complex distributed systems: The need for fresh perspectives. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pages 1410-1421. IEEE, 2018.

G. Liao, K. Datta, and T. L. Willke. Gunther: Search-based auto-tuning of mapreduce. In Euro-Par 2013 Parallel Processing, pages 406-419. Springer, 2013.

Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., and Kiriya, D. (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature, 529:509-527.

Gao, Y., Peng, Y., Xie, F., Zhao, W., Wang, D., Han, X., Lu, T., and Li, Z. (2013). Analysis of security threats and vulnerability for cyber-physical systems. In Anais da 2013 3rd International Conference on Computer Science and Network Technology, pages 50-55.

Gawande, A., Clark, J., Coomes, D., and Wang, L. (2019). Decentralized and secure multimedia sharing application over named data networking. In Proceedings of the 6th ACM Conference on Information-Centric Networking, ICN '19, page 19-29. ACM.

Gember-Jacobson, A., Viswanathan, R., Prakash, C., Grandl, R., Khalid, J., Das, S., and Akella, A. (2014). OpenNF: Enabling Innovation in Network Function Control. In Anais da 2014 ACM Conference on SIGCOMM, pages 163-174.

Gerla, M., Lee, E.-K., Pau, G., and Lee, U. (2014). Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. In 2014 IEEE world forum on internet of things (WF-IoT), pages 241-246. IEEE.

Giaretta, A., Dragoni, N., and Massacci, F. (2019). IoT Security Configurability with Security-by-Contract. Sensors, 19(19).

Grassi, G., Pesavento, D., Pau, G., Vuyyuru, R., Wakikawa, R., and Zhang, L. (2014). Vanet via named data networking. In 2014 IEEE conference on computer communications workshops (INFOCOM WKSHPS), pages 410-415. IEEE.

Grounds, M. and Kudenko, D. (2005). Combining reinforcement learning with symbolic planning. In Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning, pages 75-86. Springer.

Guizani, N. and Ghafoor, A. (2020). A Network Function Virtualization System for Detecting Malware in Large IoT Based Networks. IEEE Journal on Selected Areas in Communications, 38(6):1218-1228.

Gul, E., Atakan, B., and Akan, O. B. (2010). Nanons: A nanoscale network simulator framework for molecular communications. Nano Communication Networks, 1(2):138-156.

Guo, Y. (2018). A survey on methods and theories of quantized neural networks. arXiv preprint arXiv:1808.04752.

Gupta, L., Salman, T., Zolanvari, M., Erbad, A., and Jain, R. (2019). Fault and Performance Management in Multi-Cloud Virtual Network Services using AI: A Tutorial and a Case Study. Computer Networks, 165:106950.

H. Schmeck. Organic computing-a new vision for distributed embedded systems. In Object-Oriented Real-Time Distributed Computing, 2005. ISORC 2005. Eighth IEEE International Symposium on, pages 201-203. IEEE, 2005.

Hafeez, I., Antikainen, M., Ding, A., and Tarkoma, S. (2020). Iotkeeper: Detecting malicious IoT network activity using online traffic analysis at the edge. IEEE Transactions on Network and Service Management, 17:45-59.

Hafeez, I., Ding, A. Y., Suomalainen, L., Kirichenko, A., and Tarkoma, S. (2016). Securebox: Toward Safer and Smarter IoT Networks. In Anais do 2016 ACM Workshop on Cloud-Assisted Networking, pages 55-60.

Han, S., Pool, J., Tran, J., and Dally, W. J. (2015). Learning both weights and connections for efficient neural networks. arXiv preprint ar-Xiv:1506.02626.

Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., and Sikdar, B. (2019). A survey on iot security: application areas, security threats, and solution architectures. IEEE Access, 7:82721-82743.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778.

Hines, M. L. and Carnevale, N. T. (2001). Neuron: a tool for neuroscientists. The neuroscientist, 7(2):123-135.

Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., and Donoghue, J. P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099):164-171.

Hogg, T. and Freitas Jr, R. A. (2012). Acoustic communication for medical nanorobots. Nano Communication Networks, 3(2):83-102.

Hollemans, M. (2017). Google's mobilenets on the iphone. [Online; posted 14-June-2017].

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

Hsieh, K., Phanishayee, A., Mutlu, O., and Gibbons, P. B. (2019). The non-iid data quagmire of decentralized machine learning. Arxiv preprint ar-Xiv:1910.00189.

Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. arXiv preprint arXiv:1602.07360.

IBM (2019 (Accessed January 18, 2020)). Cost of a Data Breach Report.

IDC (2020). IoT Growth Demands Rethink of Long-Term Storage Strategies, says IDC. Disponível em: https://www.idc.com/getdoc.jsp?containerId=prAP46737220. Acessado em 12 de Junho de 2021.

IDTechEX (2021). Wearable technology market by 2028. https://www.gartner.com/en/newsroom/press-releases/2021-01-11-gartner-forecasts-global-spending-on-wearable-devices-to-total-81-5-billion-in-2021. Accessed: 2021-26-05.

IETF (1998). RFC 2330: Framework for IP Performance Metrics. Disponível em: https://datatracker.ietf.org/doc/html/rfc2330. Acessado em 13 de Maio de 2021.

IETF (2011). RFC 6390: Guidelines for Considering New Performance Metric Development. Disponível em: https://datatracker.ietf.org/doc/html/rfc6390. Acessado em 13 de Maio de 2021.

IETF (2017a). Benchmarking Methodology for Virtualization Network Performance. Disponível em: https://tools.ietf.org/id/draft-huang-bmwg-virtual-network-performance-03.html. Acessado em 13 de Maio de 2021.

IETF (2017b). RFC 8172: Considerations for Benchmarking Virtual Network Functions and Their Infrastructure. Disponível em: https://datatracker.ietf.org/doc/html/rfc8172. Acessado em 13 de Maio de 2021.

IETF (2020). RFC 8520: Manufacturer Usage Description Specification. Disponível em: https://datatracker.ietf.org/doc/rfc8520. Acessado em 21 de Junho de 2021.

Ioannou, A. and Weber, S. (2016). A survey of caching policies and forwarding mechanisms in information-centric networking. IEEE Communications Surveys Tutorials, 18(4):2847-2886.

J. O. Kephart and D. M. Chess. The vision of autonomic computing. Computer, 36(1):41-50, 2003.

J.-P. Georgé and M. P. Gleizes. Experiments in emergent programming using selforganizing multi-agent systems. In CEEMAS, pages 450-459. Springer, 2005.

Jablonski, N. G. and Chaplin, G. (2010). Human skin pigmentation as an adaptation to uv radiation. Proceedings of the National Academy of Sciences, 107(Supplement 2):8962-8968.

Jacobson, V., Smetters, D. K., Thornton, J. D., Plass, M. F., Briggs, N. H., and Braynard, R. L. (2009). Networking named content. In Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, CoNEXT'09, pages 1-12. ACM.

Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., and Kim, S.-L. (2018). Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. Arxiv preprint arXiv:1811.11479.

Ji, S., Pan, S., Long, G., Li, X., Jiang, J., and Huang, Z. (2019). Learning private neural language modeling with attentive aggregation. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE.

Jia, X., He, J., Shen, L., Chen, J., Wei, Z., Qin, X., Niu, D., Li, Y., and Shi, J. (2019). Gradient redox-responsive and two-stage rocket-mimetic drug delivery system for improved tumor accumulation and safe chemotherapy. Nano letters, 19(12):8690-8700.

Jia, Y., Zhong, F., Alrawais, A., Gong, B., and Cheng, X. (2020). FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks. IEEE Internet of Things Journal, 7(10):9552-9562.

Jiang, Y., Wang, S., Valls, V., Ko, B. J., Lee, W.-H., Leung, K. K., and Tassiulas, L. (2019). Model pruning enables efficient federated learning on edge devices. arXiv preprint arXiv:1909.12326.

Jin, T., Zhang, X., Liu, Y., and Lei, K. (2017). Blockndn: A bitcoin blockchain decentralized system over named data networking. In 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pages 75-80.

K. Jeong and R. Figueiredo. Self-configuring software-defined overlay bypass for seamless inter-and intra-cloud virtual networking. In Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing, pages 153-164. ACM, 2016.

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D'Oliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konecny, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Qi, H., Ramage, D., Raskar, R., Raykova, M., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1-2):1-210.

Kapetanidou, I. A., Hassan, M., Sarros, C.-A., Conti, M., and Tsaoussidis, V. (2020). Reputation-based trust: A robust mechanism for dynamic adaptive streaming over named data networking. In 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE), pages 114-121.

Karimipour, H., Dehghantanha, A., Parizi, R. M., Choo, K.-K. R., and Leung, H. (2019). A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access, 7:80778-80788.

Karrakchou, O., Samaan, N., and Karmouch, A. (2020a). Endn: An enhanced ndn architecture with a p4-programmabie data plane. In Proceedings of the 7th ACM Conference on Information-Centric Networking, ICN '20, page 1-11. ACM.

Karrakchou, O., Samaan, N., and Karmouch, A. (2020b). Fctrees: A front-coded family of compressed tree-based fib structures for ndn routers. IEEE Transactions on Network and Service Management, 17(2):1167-1180.

Kaspersky (2019). Brasil é o segundo país com mais ataques a dispositivos IoT. Disponível em: https://www.kaspersky.com.br/about/press-releases/2019_brasil-e-o-segundo-pais-com-mais-ataques-a-dispositivos-iot. Acessado em 9 de Março de 2021.

Kaspersky (2020). Quase 30% das empresas que usam IoT sofreram incidentes de segurança. Disponível em: https://www.kaspersky.com.br/blog/empresas-iot-seguranca-dicas/14869. Acessado em 9 de Março de 2021.

Khan, R. A. and Pathan, A.-S. K. (2018). The state-of-the-art wireless body area sensor networks: A survey. International Journal of Distributed Sensor Networks, 14(4):1550147718768994.

Khelifi, H., Luo, S., Nour, B., Moungla, H., Faheem, Y., Hussain, R., and Ksentini, A. (2020). Named data networking in vehicular ad hoc networks: State-of-the-art and challenges. IEEE Communications Surveys Tutorials, 22(1):320-351.

Kietzmann, P., Gündogan, C., Schmidt, T. C., Hahm, O., and Wählisch, M. (2017). The need for a name to mac address mapping in ndn: Towards quantifying the resource gain. In Proceedings of the 4th ACM Conference on Information-Centric Networking, ICN '17, page 36-42. ACM.

Kim, H., Yves, P., and Lee, K. (2016). Development of chip-less and wireless neural probe functioning stimulation and reading in a single device. Microelectronic Engineering, 158:118-125.

Kim, T., Koo, T., and Paik, E. (2015). SDN and NFV Benchmarking for Performance and Reliability. In Anais do 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS), pages 600-603.

Kim, Y.-D., Park, E., Yoo, S., Choi, T., Yang, L., and Shin, D. (2015). Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530.

Konecny, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.

Kong, X. T., Luo, H., Huang, G. Q., and Yang, X. (2019). Industrial wearable system: the human-centric empowering technology in industry 4.0. Journal of Intelligent Manufacturing, 30(8):2853-2869.

Koucheryavy, Y., Yastrebova, A., Martins, D. P., and Balasubramaniam, S. (2021). A review on bio-cyber interfaces for intrabody molecular communications systems. arXiv preprint arXiv:2104.14944.

Kretchmar, M. D. and Jacobvitz, D. B. (2002). Observing mother-child relationships across generations: Boundary patterns, attachment, and the transmission of caregiving. Family process, 41(3):351-374.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Image-Net classification with deep convolutional neural networks. Advances in neural information processing systems, 25:1097-1105.

Król, M. and Psaras, I. (2017). Nfaas: Named function as a service. In Proceedings of the 4th ACM Conference on Information-Centric Networking, ICN '17, page 134-144. ACM.

Król, M., Habak, K., Oran, D., Kutscher, D., and Psaras, I. (2018). Rice: Remote method invocation in icn. In Proceedings of the 5th ACM Conference on Information-Centric Networking, ICN '18, page 1-11. ACM.

Kubota, K. J., Chen, J. A., and Little, M. A. (2016). Machine learning for large-scale wearable sensor data in parkinson's disease: Concepts, promises, pitfalls, and futures. Movement disorders, 31(9):1314-1326.

Kuscu, M., Dinc, E., Bilgin, B. A., Ramezani, H., and Akan, O. B. (2019). Transmitter and receiver architectures for molecular communications: A survey on physical design with modulation, coding, and detection techniques. Proceedings of the IEEE, 107(7):1302-1341.

L. Gambardella and G. Taillard. A multiple ant colony system for vehicle routing problems with time windows. [in:] corne d., dorigo mp: New ideas in optimization, 1999.

Laghrissi, A. and Taleb, T. (2019). A Survey on the Placement of Virtual Resources and Virtual Network Functions. IEEE COMST, 21(2):1409-1434.

Lamport, L. (2019). Time, clocks, and the ordering of events in a distributed system. In Concurrency: The Works of Leslie Lamport, page 179-196. ACM.

Lamport, L. and Fischer, M. (1982). Byzantine generals and transaction commit protocols. Technical Report 62, SRI International.

Lamport, L. et al. (2001). Paxos made simple. ACM Sigact News, 32(4):18-25.

Laprie, J.-C. (2008). From dependability to resilience. In IEEE/IFIP Int. Conf. On Dependable Systems and Networks, pages 1-2.

Laprie, J.-C., Randell, B., and Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transaction Dependable Security Computer, 1(1):11-33.

Lee, C. A., Zhang, Z., Tu, Y., Afanasyev, A., and Zhang, L. (2018). Supporting virtual organizations using attribute-based encryption in named data networking. In 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), pages 188-196. IEEE.

Lee, J., Sun, J., Wang, F., Wang, S., Jun, C.-H., and Jiang, X. (2018). Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR medical informatics, 6(2):e20.

Li, A., Sun, J., Wang, B., Duan, L., Li, S., Chen, Y., and Li, H. (2020a). Lotteryfl: Personalized and communication-efficient federated learning with lottery ticket hypothesis on non-iid datasets. arXiv preprint arXiv:2008.03371.

Li, L., Fan, Y., Tse, M., and Lin, K. Y. (2020b). A review of applications in federated learning. Computers and Industrial Engineering, 149(September).

Li, T., Kong, Z., Mastorakis, S., and Zhang, L. (2019a). Distributed dataset synchronization in disruptive networks. In 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pages 428-437. IEEE.

Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020c). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3):50-60.

Li, T., Shang, W., Afanasyev, A., Wang, L., and Zhang, L. (2018). A brief introduction to ndn dataset synchronization (ndn sync). In IEEE Military Communications Conference (MILCOM), pages 612-618.

Li, Y., Zhang, Z., Wang, X., Lu, E., Zhang, D., and Zhang, L. (2019b). A secure sign-on protocol for smart homes over named data networking. IEEE Communications Magazine, 57(7):62-68.

Li, Z., Xu, Y., Zhang, B., Yan, L., and Liu, K. (2019c). Packet forwarding in named data networking requirements and survey of solutions. IEEE Communications Surveys Tutorials, 21(2):1950-1987.

Lim,W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y. C., Yang, Q., Niyato, D., and Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys Tutorials, 22(3):2031-2063.

Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and Zhao, W. (2017). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 4(5):1125-1142.

Liu, G., Quan, W., Cheng, N., Zhang, H., and Yu, S. (2019). Efficient ddos attacks mitigation for stateful forwarding in internet of things. Journal of Network and Computer Applications, 130:1-13.

Llatser, I., Demiray, D., Cabellos-Aparicio, A., Altilar, D. T., and Alarcón, E. (2014). N3sim: Simulation framework for diffusion-based molecular communication nanonetworks. Simulation Modelling Practice and Theory, 42:210-222.

Loscri, V. and Vegni, A. M. (2015). An acoustic communication technique of nanorobot swarms for nanomedicine applications. IEEE transactions on nanobioscience, 14(6):598-607.

Losilla, F., Garcia-Sanchez, A.-J., Garcia-Sanchez, F., Garcia-Haro, J., and Haas, Z. J. (2011). A comprehensive approach to wsn-based its applications: A survey. Sensors, 11(11):10220-10265.

Lynch, N. (1996). Distributed Algorithms. Morgan Kaufmann Publishers, Inc., San Francisco, CA, USA.

M. Amoui, M. Salehie, S. Mirarab, and L. Tahvildari. Adaptive action selection in autonomic software using reinforcement learning. In Autonomic and Autonomous Systems, 2008. ICAS 2008. Fourth International Conference on, pages 175-181. IEEE, 2008.

M. Clarke, G. S. Blair, G. Coulson, and N. Parlavantzas. An efficient component model for the construction of adaptive middleware. In IFIP/ACM International Conference on Distributed Systems Platforms and Open Distributed Processing, pages 160-178. Springer, 2001.

M. Dorigo, M. Birattari, and T. Stutzle. Ant colony optimization. IEEE computational intelligence magazine, 1(4):28-39, 2006.

Madureira, A. L. R., Araújo, F. R. C., Araújo, G. B., and Sampaio, L. N. (2021). Ndn fabric: Where the software-defined networking meets the contentcentric model. IEEE Transactions on Network and Service Management, 18(1):374-387.

Madureira, A. L. R., Araújo, F. R. C., Prates, L. N. B., and Sampaio, L. N. (2020). Ndn-adap: Uma arquitetura para encaminhamento eficiente de pacotes em redes de dados nomeados. In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 812-825. SBC.

Maier, M., Ebrahimzadeh, A., Rostami, S., and Beniiche, A. (2020). The internet of no things: Making the internet disappear and"see the invisible". IEEE Communications Magazine, 58(11):76-82.

Maiti, A., Jadliwala, M., He, J., and Bilogrevic, I. (2015). (smart) watch your taps: Side-channel keystroke inference attacks using smartwatches. In Proceedings of the 2015 ACM Int. Symposium on Wearable Computers, pages 27-30.

Mannes, E. and Maziero, C. (2019). Naming content on the network layer: A security analysis of the information-centric network model. ACM Computing Surveys, 52(3).

Mao, H., Zhang, Z., Xiao, Z., and Gong, Z. (2018). Modelling the dynamic joint policy of teammates with attention multi-agent ddpg. Arxiv preprint arXiv:1811.07029.

Marku, E., Biczok, G., and Boyd, C. (2020). Securing Outsourced VNFs: Challenges, State of the Art, and Future Directions. IEEE Communications Magazine, 58(7):72-77.

Marxer, C. and Tschudin, C. (2017). Schematized access control for data cubes and trees. In Proceedings of the 4th ACM Conference on Information-Centric Networking, ICN '17, page 170-175. ACM.

Massonet, P., Deru, L., Achour, A., Dupont, S., Croisez, L.-M., Levin, A., and Villari, M. (2017). Security in Lightweight Network Function Virtualisation for Federated Cloud and IoT. In Anais da 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud), pages 148-154.

Mastorakis, S., Afanasyev, A., and Zhang, L. (2017). On the evolution of ndnsim: An open-source simulator for ndn experimentation. ACM SIGCOMM Computer Communication Review, 47(3):19-33.

Mastorakis, S., Gusev, P., Afanasyev, A., and Zhang, L. (2018). Real-time data retrieval in named data networking. In 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN), pages 61-66. IEEE.

Mattson, M. P. (2004). Pathways towards and away from alzheimer's disease. Nature, 430(7000):631-639.

Mcguiness, D. T., Selis, V., and Marshall, A. (2019). Molecularbased nano-communication network: A ring topology nano-bots for in-vivo drug delivery systems. IEEE Access, 7:12901-12913.

McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Singh, A. and Zhu, J., editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 1273-1282, Fort Lauderdale, FL, USA. PMLR.

McMahan, H. B., Moore, E., Ramage, D., and Arcas, B. A. Y. (2016a). Federated learning of deep networks using model averaging. ArXiv preprint arXiv:1602.05629, abs/1602.05629.

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2016b). Communication-efficient learning of deep networks from decentralized data. Arxiv preprint arXiv:1602.05629.

Medhat, A. M., Taleb, T., Elmangoush, A., Carella, G. A., Covaci, S., and Magedanz, T. (2017). Service Function Chaining in Next Generation Networks: State of the Art and Research Challenges. IEEE Communications Magazine, 55(2):216-223.

Melis, L., Song, C., Cristofaro, E. D., and Shmatikov, V. (2019). Exploiting unintended feature leakage in collaborative learning. In IEEE Symposium on Security and Privacy (SP), pages 691-706.

Meneghello, F., Calore, M., Zucchetto, D., Polese, M., and Zanella, A. (2019). IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices. IEEE Internet of Things Journal, 6(5):8182-8201.

Miguel, R., Signorello, S., and Ramos, F. M. V. (2018). Named data networking with programmable switches. In 2018 IEEE 26th International Conference on Network Protocols (ICNP), pages 400-405.

Mijumbi, R., Serrat, J., Gorricho, J.-L., Bouten, N., Turck, F. D., and Boutaba, R. (2016). Network Function Virtualization: State-of-the-Art and Research Challenges. IEEE COMST, 18(1):236-262.

Moghaddam, S. K., Buyya, R., and Ramamohanarao, K. (2019). Performance-Aware Management of Cloud Resources: A Taxonomy and Future Directions. ACM Computing Surveys, 52(4).

Moiseenko, I. and Oran, D. (2017). Path switching in content centric and named data networks. In Proceedings of the 4th ACM Conference on Information-Centric Networking, ICN '17, pages 66-76. ACM.

Montero, D. and Serral-Gracià , R. (2016). Offloading Personal Security Applications to the Network Edge: A Mobile User Case Scenario. In Anais da 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), pages 96-101.

Montero, D., Yannuzzi, M., Shaw, A., Jacquin, L., Pastor, A., Serral-Gracia, R., Lioy, A., Risso, F., Basile, C., Sassu, R., Nemirovsky, M., Ciaccia, F., Georgiades, M., Charalambides, S., Kuusijarvi, J., and Bosco, F. (2015). Virtualized Security at the Network Edge: a User-Centric Approach. IEEE Communications Magazine, 53(4):176-186.

Moore, G. E. (1998). Cramming more components onto integrated circuits. Proceedings of the IEEE, 86(1):82-85.

Mori, S. (2018). Secure caching scheme by using blockchain for informationcentric network-based wireless sensor networks. Journal of Signal Processing, 22(3):97-108.

Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., and Srivastava, G. (2021). A survey on security and privacy of federated learning. Future Generation Computer Systems, 115:619-640.

Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., and Jamalipour, A. (2014). Wireless body area networks: A survey. IEEE Communications surveys & tutorials, 16(3):1658-1686.

Muller, R., Le, H.-P., Li, W., Ledochowitsch, P., Gambini, S., Bjorninen, T., Koralek, A., Carmena, J. M., Maharbiz, M. M., Alon, E., et al. (2014). A minimally invasive 64-channel wireless μ ecog implant. IEEE Journal of Solid-State Circuits, 50(1):344-359.

Nadiger, C., Kumar, A., and Abdelhak, S. (2019). Federated reinforcement learning for fast personalization. In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pages 123-127. IEEE.

Naik, N. (2017). Choice of Effective Messaging Protocols for IoT Systems: MQTT, CoAP, AMQP and HTTP. In Anais do 2017 IEEE International Systems Engineering Symposium (ISSE), pages 1-7.

Nakayama, F., Lenz, P., Banou, S., Nogueira, M., Santos, A., and Chowdhury, K. R. (2019). A continuous user authentication system based on galvanic coupling communication for s-health. Wireless Commun. and Mobile Computing, 2019(1):1-11.

Nakayama, F., Lenz, P., LeFloch, A., Beylot, A.-L., Santos, A., and Nogueira, M. (2021). Performance management on multiple communication paths for portable assisted living. IFIP Int. Symposium on Integrated Network Management.

NDN (2021). Ndn testbed status. Disponível em: http://ndndemo.arl.wustl.edu/ndn.html. Último acesso em: 07 de maio de 2021.

Neema, H., Sztipanovits, J., Steinbrink, C., Raub, T., Cornelsen, B., and Lehnhoff, S. (2019). Simulation integration platforms for cyber-physical systems. In Proceedings of the Workshop on Design Automation for CPS and IoT, pages 10-19.

Negra, R., Jemili, I., and Belghith, A. (2016). Wireless body area networks: Applications and technologies. Procedia Computer Science, 83:1274-1281.

Neshenko, N., Bou-Harb, E., Crichigno, J., Kaddoum, G., and Ghani, N. (2019). Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations. IEEE Communications Surveys Tutorials, 21(3):2702-2733.

Nishio, T. and Yonetani, R. (2019). Client selection for federated learning with heterogeneous resources in mobile edge. In ICC 2019-2019 IEEE International Conference on Communications (ICC), pages 1-7. IEEE.

Niu, Z., Zhong, G., and Yu, H. (2021). A review on the attention mechanism of deep learning. Neurocomputing, 452:48-62.

Nogueira, M., dos Santos, A. L., and Pujolle, G. (2009). A survey of survivability in mobile ad hoc networks. IEEE Commun. Surv. Tutorials, 11:66-77.

Nour, B., Khelifi, H., Hussain, R., Mastorakis, S., and Moungla, H. (2021a). Access control mechanisms in named data networks: A comprehensive survey. ACM Computing Surveys (CSUR), 54(3):1-35.

Nour, B., Li, F., Khelifi, H., Moungla, H., and Ksentini, A. (2019a). Coexistence of icn and ip networks: An nfv as a service approach. In 2019 IEEE Global Communications Conference (GLOBECOM), page 1-6. IEEE Press.

Nour, B., Mastorakis, S., Ullah, R., and Stergiou, N. (2021b). Information-centric networking in wireless environments: Security risks and challenges. IEEE Wireless Communications, 28(2):121-127.

Nour, B., Sharif, K., Li, F., Biswas, S., Moungla, H., Guizani, M., and Wang, Y. (2019b). A survey of internet of things communication using icn: A use case perspective. Computer Communications, 142-143:95-123.

Nour, B., Sharif, K., Li, F., Yang, S., Moungla, H., and Wang, Y. (2019c). Icn publisher-subscriber models: Challenges and group-based communication. IEEE Network, 33(6):156-163.

O. Chapelle and L. Li. An empirical evaluation of thompson sampling. Advances in neural information processing systems, 24:2249-2257, 2011.

O. Kouchnarenko and J.-F. Weber. Adapting component-based systems at runtime via policies with temporal patterns. In Formal Aspects of Component Software, pages 234-253. Springer, 2014.

Ometov, A., Shubina, V., Klus, L., Skibinska, J., Saafi, S., Pascacio, P., Flueratoru, L., Gaibor, D. Q., Chukhno, N., Chukhno, O., et al. (2021). A survey on wearable technology: History, state-of-the-art and current challenges. Computer Networks, 193.

Oresko, J. J., Jin, Z., Cheng, J., Huang, S., Sun, Y., Duschl, H., and Cheng, A. C. (2010). A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine, 14(3):734-740.

P. Arcaini, E. Riccobene, and P. Scandurra. Modeling and analyzing mape-k feedback loops for self-adaptation. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pages 13-23. IEEE Press, 2015.

P. Auer. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 3(Nov):397-422, 2002.

P. Grace, D. Hughes, B. Porter, G. S. Blair, G. Coulson, and F. Taiani. Experiences with open overlays: A middleware approach to network heterogeneity. In Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008, Eurosys '08, pages 123-136, New York, NY, USA, 2008. ACM.

P. K. McKinley, B. H. Cheng, A. J. Ramirez, and A. C. Jensen. Applying evolutionary computation to mitigate uncertainty in dynamically-adaptive, high-assurance middleware. Journal of Internet Services and Applications, 3(1):51-58, 2012.

Perez, A. J. and Zeadally, S. (2017). Privacy issues and solutions for consumer wearables. It Professional, 20(4):46-56.

Pfeiffer, M., Senkovskiy, B. V., Haberer, D., Fischer, F. R., Yang, F., Meerholz, K., Ando, Y., Grüneis, A., and Lindfors, K. (2018). Enhanced light-matter interaction of aligned armchair graphene nanoribbons using arrays of plasmonic nanoantennas. 2D Materials, 5(4):045006.

Pinheiro, P. P. (2020). Proteção de Dados Pessoais: Comentários à Lei n. 13.709/2018-LGPD. Saraiva Educação SA.

Pires, S. S., Ribeiro, A. V., de Sousa, A. M., Freitas, A. E. S., and Sampaio, L. N. (2018). On evaluating the influence of user's music listening habits on cache replacement policies. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 00930-00933.

Pires, S., Araújo, F. R. C., Freitas, A. E. S., and Sampaio, L. N. (2019). Análise de perfis de usuários de música e seus impactos no desempenho de polí­ticas de substituição de cache. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 848-861. SBC.

Pires, S., Ziviani, A., and Sampaio, L. (2021). Contextual dimensions for cache replacement schemes in information-centric networks: a systematic review. PeerJ Computer Science, 7:e418.

Piro, G., Grieco, L. A., Boggia, G., and Camarda, P. (2013). Simulating wireless nano sensor networks in the ns-3 platform. In 2013 27th Int. Conference on Advanced Information Networking and Applications Workshops, pages 67-74. IEEE.

Pöhn, D. and Hommel, W. (2021). Proven and modern approaches to identity management. In Advances in Cybersecurity Management, pages 421-443. Springer.

Poongodi, T., Rathee, A., Indrakumari, R., and Suresh, P. (2020). Iot sensing capabilities: sensor deployment and node discovery, wearable sensors, wireless body area network (wban), data acquisition. In Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, pages 127-151. Springer.

Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., De Amicis, R., Pinto, E. B., Eisert, P., Döllner, J., and Vallarino, I. (2015). Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE computer graphics and applications, 35(2):26-40.

Prates, N., Vergutz, A., Macedo, R., Santos, A., and Nogueira, M. (2020). A defense mechanism for timing-based side-channel attacks on IoT traffic (to appear). In IEEE GLOBECOM.

Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen,W., and Ilie-Zudor, E. (2018). Chained anomaly detection models for federated learning: An intrusion detection case study. Applied Sciences, 8(12):2663.

Psaras, I., Ascigil, O., Rene, S., Pavlou, G., Afanasyev, A., and Zhang, L. (2018). Mobile data repositories at the edge. In USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18). USENIX Association.

Qazi, Z. A., Tu, C.-C., Chiang, L., Miao, R., Sekar, V., and Yu, M. (2013). SIMPLE-Fying Middlebox Policy Enforcement Using SDN. SIGCOMM Comput. Commun. Rev., 43(4):27-38.

Qiao, Z., Yu, X., Zhang, J., and Letaief, K. B. (2021). Communicationefficient federated learning with dual-side low-rank compression. arXiv preprint ar-Xiv:2104.12416.

R. Rodrigues Filho and B. Porter. Defining emergent software using continuous self-assembly, perception, and learning. ACM Transactions Autonomic Adaptive Systems, 12(3):16:1-16:25, Sept. 2017.

R. Rodrigues Filho and B. Porter. Experiments with a machine-centric approach to realise distributed emergent software systems. In Proceedings of the 15th International Workshop on Adaptive and Reflective Middleware, pages 1-6, 2016.

R. Rodrigues Filho, M. P. de Sá, B. Porter, and F. M. Costa. Towards emergent microservices for client-tailored design. In Proceedings of the 19th Workshop on Adaptive and Reflexive Middleware, pages 1-6, 2018.

R. Rodrigues Filho. Emergent Software Systems. PhD thesis, Lancaster University, 2018.

R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction. MIT press, 1998.

Rajkumar, R., Lee, I., Sha, L., and Stankovic, J. (2010). Cyberphysical systems: The next computing revolution. In Design Automation Conference, pages 731-736.

Ramani, S. K. and Afanasyev, A. (2020a). Certcoalesce: Efficient certificate pool for ndn-based systems. In Proceedings of the 7th ACM Conference on Information-Centric Networking, ICN '20, page 158-160. ACM.

Ramani, S. K. and Afanasyev, A. (2020b). Rapid establishment of transient trust for ndn-based vehicular networks. In 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1-6. IEEE.

Ramani, S. K., Tourani, R., Torres, G., Misra, S., and Afanasyev, A. (2019). Ndn-abs: Attribute-based signature scheme for named data networking. In Proceedings of the 6th ACM Conference on Information-Centric Networking, ICN '19, page 123-133. ACM.

Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A. (2016). XNOR-Net: ImageNet classification using binary convolutional neural networks. In European conference on computer vision, pages 525-542. Springer.

Resch, B. (2013). People as sensors and collective sensing-contextual observations complementing geo-sensor network measurements. In Progress in locationbased services, pages 391-406. Springer.

Ribeiro, A. V., Sampaio, L. N., and Ziviani, A. (2017). Explorando a afinidade de usuários para descarregamento de dados mais eficiente em redes celulares de pequeno porte. In Anais do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.

Ribeiro, A. V., Sampaio, L. N., and Ziviani, A. (2018). Affinity-based user clustering for efficient edge caching in content-centric cellular networks. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 00474-00479.

Richard Van Noorden, D. C. (2016). World's tiniest machines win chemistry nobel. Nature, 538(7624):152-153.

Rivest, R. L., Adleman, L., Dertouzos, M. L., et al. (1978). On data banks and privacy homomorphisms. Foundations of secure computation, 4(11):169-180.

Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., and Ruiz, M. C. (2018). Operable: an iot-based wearable to improve efficiency and smart worker care services in industry 4.0. Journal of Sensors, 2018.

Rondon, L. B., da Costa, J. B., Filho, G. P. R., Rosário, D., and Villas, L. A. (2020). Degree centrality-based caching discovery protocol for vehicular nameddata networks. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), pages 1-5.

Rosário, D., Zhao, Z., Santos, A., Braun, T., and Cerqueira, E. (2014). A Beaconless Opportunistic Routing based on a Cross-layer Approach for Efficient Video Dissemination in Mobile Multimedia IoT Applications. Computer Communications, 45:21-31.

Rummery, G. A. and Niranjan, M. (1994). On-line Qlearning using connectionist systems, volume 37. University of Cambridge, Department of Engineering Cambridge, UK.

S. Duan, V. Thummala, and S. Babu. Tuning database configuration parameters with ituned. Proceedings of the VLDB Endowment, 2(1):1246-1257, 2009.

S. Götz, T. Kühn, C. Piechnick, G. Püschel, and U. Aßmann. A models@run.time approach for multi-objective self-optimizing software. In Adaptive and Intelligent Systems, pages 100-109. Springer, 2014.

S. J. Russell and P. Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.

S. L. Scott. A modern bayesian look at the multi-armed bandit. Applied Stochastic Models in Business and Industry, 26(6):639-658, 2010.

S. Malek, N. Esfahani, D. Menasce, J. Sousa, and H. Gomaa. Self-architecting software systems (sassy) from qos-annotated activity models. In Principles of Engineering Service Oriented Systems, 2009. PESOS 2009. ICSE Workshop on, pages 62-69, May 2009.

S. Tomforde, B. Sick, and C. Müller-Schloer. Organic computing in the spotlight. arXiv preprint arXiv:1701.08125, 2017.

Saha, S. and Ahmad, T. (2021). Federated transfer learning: concept and applications. Arxiv preprint arXiv:2010.15561.

Saharkhizan, M., Azmoodeh, A., Dehghantanha, A., Choo, K.-K. R., and Parizi, R. M. (2020). An ensemble of deep recurrent neural networks for detecting iot cyber attacks using network traffic. IEEE Internet of Things Journal, 7(9):8852-8859.

Sairam, R., Bhunia, S. S., Thangavelu, V., and Gurusamy, M. (2019). NETRA: Enhancing IoT Security Using NFV-Based Edge Traffic Analysis. IEEE Sensors Journal, 19(12):4660-4671.

Sakkaff, Z., Immaneni, A., and Pierobon, M. (2018). Applying molecular communication theory to estimate information loss in cell signal transduction: An approach based on cancer transcriptomics. In Proceedings of the 5th ACM International Conference on Nanoscale Computing and Communication, pages 1-7.

Samarakoon, S., Bennis, M., Saad,W., and Debbah, M. (2018). Federated learning for ultra-reliable low-latency v2v communications. In 2018 IEEE Global Communications Conference (GLOBECOM), pages 1-7. IEEE.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510-4520.

Santagati, G. E. and Melodia, T. (2014). Opto-ultrasonic communications for wireless intra-body nanonetworks. Nano Communication Networks, 5(1):3 - 14.

Saxena, D., Raychoudhury, V., Suri, N., Becker, C., and Cao, J. (2016). Named data networking: a survey. Computer Science Review, 19:15-55.

Schwengber, B. H., Vergütz, A., Prates, N. G., and Nogueira, M. (2020). A Method Aware of Concept Drift for Online Botnet Detection. In Anais da 2020 IEEE Global Communications Conference (GLOBECOM), pages 1-6.

Sedky, G. and Mougy, A. E. (2018). Bcxp: Blockchain-centric network layer for efficient transaction and block exchange over named data networking. In 2018 IEEE 43rd Conference on Local Computer Networks (LCN), pages 449-452.

Seixas, N. F. S., Ribeiro, A. V., and Sampaio, L. N. (2017). Um modelo de rede centrada na informação resiliente a ataques de negação de serviços por inundação de interesses. In Anais do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.

Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., Hassan, M., and Seneviratne, A. (2017). A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials, 19(4):2573-2620.

Seo, H., Park, J., Oh, S., Bennis, M., and Kim, S.-L. (2020). Federated knowledge distillation. arXiv preprint arXiv:2011.02367.

Serhane, O., Yahyaoui, K., Nour, B., and Moungla, H. (2021). A survey of icn content naming and in-network caching in 5g and beyond networks. IEEE Internet of Things Journal, 8(6):4081-4104.

Shang, W., Yu, Y., Wang, L., Afanasyev, A., and Zhang, L. (2017). A survey of distributed dataset synchronization in named data networking. Technical Report NDN-0053, NDN.

Shannigrahi, S., Fan, C., and Papadopoulos, C. (2017). Request aggregation, caching, and forwarding strategies for improving large climate data distribution with ndn: A case study. In Proceedings of the 4th ACM Conference on Information-Centric Networking, ICN '17, page 54-65. ACM.

Sharma, M., Elmiligi, H., and Gebali, F. (2020). Network Security and Privacy Evaluation Scheme for Cyber Physical Systems (CPS), pages 191-217. Springer International Publishing, Cham.

Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., and Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(12598):1-12.

Shi, J. and Zhang, B. (2012). Ndnlp: A link protocol for ndn. Technical Report NDN-0006, NDN.

Shi, J., Liang, T., Wu, H., Liu, B., and Zhang, B. (2016). Ndn-nic: Namebased filtering on network interface card. In Proceedings of the 3rd ACM Conference on Information-Centric Networking, ACM-ICN '16, pages 40-49. ACM.

Shi, J., Pesavento, D., and Benmohamed, L. (2020). Ndn-dpdk: Ndn forwarding at 100 gbps on commodity hardware. In Proceedings of the 7th ACM Conference on Information-Centric Networking, ICN '20, page 30-40. ACM.

Shokri, R., Stronati, M., Song, C., and Shmatikov, V. (2017). Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP), pages 3-18. IEEE.

Signorello, S., State, R., François, J., and Festor, O. (2016). Ndn.p4: Programming information-centric data-planes. In 2016 IEEE NetSoft Conference and Workshops (NetSoft), pages 384-389.

Simon, S. (2000). Brewer's cap theorem. CS341 Distributed Information Systems, University of Basel (HS2012).

Singh, J., Pasquier, T., Bacon, J., Ko, H., and Eyers, D. (2016). Twenty Security Considerations for Cloud-Supported Internet of Things. IEEE Internet of Things Journal, 3(3):269-284.

Siracusano, G., Salsano, S., Ventre, P., Detti, A., Rashed, O., and Blefari-Melazzi, N. (2018). A framework for experimenting icn over sdn solutions using physical and virtual testbeds. Computer Networks, 134:245-259.

Sobin, C. (2020). A Survey on Architecture, Protocols and Challenges in IoT. Wireless Personal Communications, 112:1383-1429.

Song, H., Rawat, D. B., Jeschke, S., and Brecher, C. (2016). Cyber-Physical Systems: Foundations, Principles and Applications. Academic Press, Inc., USA, 1st edition.

Sowe, S. K., Simmon, E., Zettsu, K., de Vaulx, F., and Bojanova, I. (2016). Cyber-physical-human systems: Putting people in the loop. IT professional, 18(1):10-13.

Stallings, W. (2020). Cryptography and Network Security: Principles and Practice. Pearson, USA, 8th edition.

Stephenson-Brown, A., Acton, A. L., Preece, J. A., Fossey, J. S., and Mendes, P. M. (2015). Selective glycoprotein detection through covalent templating and allosteric click-imprinting. Chemical science, 6(9):5114-5119.

Stoller, S. D. (1997). Leader election in distributed systems with crash failures. Technical Report 481, Computer Science Dept., Indiana University. Revised July 1997.

Sutton, R. S., Barto, A. G., et al. (1998). Introduction to reinforcement learning, volume 135. MIT press Cambridge.

Tahaei, H., Afifi, F., Asemi, A., Zaki, F., and Anuar, N. B. (2020). The rise of traffic classification in IoT networks: A survey. Journal of Network and Computer Applications, 154:102538.

Tamuly, K. (2018). Compression and acceleration of high-dimensional neural networks. [Online; posted 15-September-2018].

Tao, X., Jin, H., Mintken, M., Wolff, N., Wang, Y., Tao, R., Li, Y., Torun, H., Xie, J., Luo, J., et al. (2020). Three-dimensional tetrapodal zno microstructured network based flexible surface acoustic wave device for ultraviolet and respiration monitoring applications. ACS Applied Nano Materials, 3(2):1468-1478.

Tariq, A., Rehman, R. A., and Kim, B. (2020). Forwarding strategies in ndn-based wireless networks: A survey. IEEE Communications Surveys Tutorials, 22(1):68-95.

Tehrani, P. F., Keidel, L., Osterweil, E., Schiller, J. H., Schmidt, T. C., and Wählisch, M. (2019). Ndnssec: Namespace management in ndn with dnssec. In Proceedings of the 6th ACM Conference on Information-Centric Networking, ICN '19, page 171-172. ACM.

Thompson, J., Gusev, P., and Burke, J. (2019). Ndn-cnl: A hierarchical namespace api for named data networking. In Proceedings of the 6th ACM Conference on Information-Centric Networking, ICN '19, page 30-36. ACM.

Thorp, E. O. (1998). The invention of the first wearable computer. In Digest of Papers. Second Int. Symposium on Wearable Computers, pages 4-8. IEEE.

Titova, L. V., Ayesheshim, A. K., Golubov, A., Fogen, D., Rodriguez-Juarez, R., Hegmann, F. A., and Kovalchuk, O. (2013). Intense thz pulses cause h2ax phosphorylation and activate dna damage response in human skin tissue. Biomedical optics express, 4(4):559-568.

Tizvar, R. and Abbaspour, M. (2020). A density-aware probabilistic interest forwarding method for content-centric vehicular networks. Vehicular Communications, 23:100216.

Trimananda, R., Varmarken, J., Markopoulou, A., and Demsky, B. (2020). Packet-level signatures for smart home devices. In Network and Distributed System Security Symposium (NDSS).

U. Richter, M. Mnif, J. Branke, C. Müller-Schloer, and H. Schmeck. Towards a generic observer/controller architecture for organic computing. GI Jahrestagung (1), 93:112-119, 2006.

V. Issarny, A. Bennaceur, and Y.-D. Bromberg. Middleware-layer connector synthesis: Beyond state of the art in middleware interoperability. In International School on Formal Methods for the Design of Computer, Communication and Software Systems, pages 217-255. Springer, 2011.

W. R. Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25(3/4):285-294, 1933.

W. Zhang, T. Wood, and J. Hwang. Netkv: Scalable, self-managing, load balancing as a network function. In Autonomic Computing (ICAC), 2016 IEEE International Conference on, pages 5-14. IEEE, 2016.

W. Zheng, R. Bianchini, and T. D. Nguyen. Massconf: automatic configuration tuning by leveraging user community information. In ACM SIGSOFT Software Engineering Notes, pages 283-288. ACM, 2011.

Waldo, J., Wyant, G., Wollrath, A., and Kendall, S. (1996). A note on distributed computing. In International Workshop on Mobile Object Systems, pages 49-64. Springer.

Wang, J., Luo, J., Zhou, J., and Ran, Y. (2020). A mobility-predictbased forwarding strategy in vehicular named data networks. In GLOBECOM 2020 - 2020 IEEE Global Communications Conference, pages 01-06.

Wang, L., Lehman, V., Hoque, A. M., Zhang, B., Yu, Y., and Zhang, L. (2018). A secure link state routing protocol for ndn. IEEE Access, 6:10470-10482.

Wang, T., Fan, Q., Li, X., Zhang, X., Xiong, Q., Fu, S., and Gao, M. (2021). Drl-sfcp: Adaptive service function chains placement with deep reinforcement learning. In Anais da ICC 2021 - 2021 IEEE International Conference on Communications (ICC).

Wang, X. and Li, Y. (2020). Content delivery based on vehicular cloud. IEEE Transactions on Vehicular Technology, 69(2):2105-2113.

Wang, X.,Wang, X., andWang, D. (2021). Cost-efficient data retrieval based on integration of vc and ndn. IEEE Transactions on Vehicular Technology, 70(1):967-976.

Watkins, C. J. and Dayan, P. (1992). Q-learning. Machine learning, 8(3-4):279-292.

Wei, G., Bogdan, P., and Marculescu, R. (2013). Efficient modeling and simulation of bacteria-based nanonetworks with bnsim. IEEE Journal on Selected Areas in Communications, 31(12):868-878.

Wirdatmadja, S. A., Barros, M. T., Koucheryavy, Y., Jornet, J. M., and Balasubramaniam, S. (2017). Wireless optogenetic nanonetworks for brain stimulation: Device model and charging protocols. IEEE Trans. on NanoBioscience.

Wolf, M. and Serpanos, D. (2017). Safety and security in cyber-physical systems and internet-of-things systems. Proceedings of the IEEE, 106(1):9-20.

Wood, D., Apthorpe, N., and Feamster, N. (2017). Cleartext data transmissions in consumer iot medical devices. In Proceedings of the 2017 Workshop on Internet of Things Security and Privacy, pages 7-12. ACM.

X. Larrucea, I. Santamaria, R. Colomo-Palacios, and C. Ebert. Microservices. IEEE Software, 35(3):96-100, 2018.

Xie, P., Wu, B., and Sun, G. (2019). Bayhenn: combining bayesian deep learning and homomorphic encryption for secure dnn inference. arXiv preprint arXiv:1906.00639.

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., and Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5(1):1-19.

Y. Ding, H. Sun, and K. Hao. A bio-inspired emergent system for intelligent web service composition and management. Knowledge-Based Systems, 20(5):457-465, 2007.

Yaacoub, J.-P. A., Salman, O., Noura, H. N., Kaaniche, N., Chehab, A., and Malli, M. (2020). Cyber-physical systems security: Limitations, issues and future trends. Microprocessors and Microsystems, 77:103-201.

Yang, Q., Liu, Y., Chen, T., and Tong, Y. (2019a). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1-19.

Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., and Yu, H. (2019b). Federated learning, volume 13. Morgan & Claypool Publishers.

Yang, T., Andrew, G., Eichner, H., Sun, H., Li,W., Kong, N., Ramage, D., and Beaufays, F. (2018). Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903.

Yang, W., Zhang, Y., Ye, K., Li, L., and Xu, C.-Z. (2019c). Ffd: a federated learning based method for credit card fraud detection. In International Conference on Big Data, pages 18-32. Springer.

Yang, Y., Wu, L., Yin, G., Li, L., , and Zhao, H. (2017). A Survey on Security and Privacy Issues in Internet-of-Things. IEEE Internet of Things Journal, 4(5):1250-1258.

Yao, A. C. (1982). Protocols for secure computations. In 23rd annual symposium on foundations of computer science (sfcs 1982), pages 160-164. IEEE.

Ye, H., Randall, C. L., Leong, T. G., Slanac, D. A., Call, E. K., and Gracias, D. H. (2007). Remote radio-frequency controlled nanoliter chemistry and chemical delivery on substrates. Angewandte Chemie Int. Edition, 46(26):4991-4994.

Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., and Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98:289-330.

Yu, T., Sekar, V., Seshan, S., Agarwal, Y., and Xu, C. (2015). Handling a Trillion (Unfixable) Flaws on a Billion Devices: Rethinking Network Security for the Internet-of-Things. In Anais do 14th ACM Workshop on Hot Topics in Networks (HotNets-XIV).

Yu, T., Zhang, Z., Ma, X., Moll, P., and Zhang, L. (2021). A pub/sub api for ndn-lite with built-in security. Technical Report NDN-0071, NDN.

Yu, Y., Afanasyev, A., Clark, D., claffy, k., Jacobson, V., and Zhang, L. (2015). Schematizing trust in named data networking. In Proceedings of the 2nd ACM Conference on Information-Centric Networking, ACM-ICN '15, page 177-186. ACM.

Yu, Y., Afanasyev, A., Seedorf, J., Zhang, Z., and Zhang, L. (2017). Ndn delorean: An authentication system for data archives in named data networking. In Proceedings of the 4th ACM Conference on Information-Centric Networking, ICN '17, page 11-21. ACM.

Zarca, A. M., Bagaa, M., Bernal Bernabe, J., Taleb, T., and Skarmeta, A. F. (2020a). Semantic-Aware Security Orchestration in SDN/NFV-Enabled IoT Systems. Sensors, 20(13).

Zarca, A. M., Bernabe, J. B., Farris, I., Khettab, Y., Taleb, T., and Skarmeta, A. (2018). Enhancing IoT security through network softwarization and virtual security appliances. International Journal of Network Management, 28(5):e2038. e2038 nem.2038.

Zarca, A. M., Bernabe, J. B., Skarmeta, A., and Alcaraz Calero, J. M. (2020b). Virtual IoT HoneyNets to Mitigate Cyberattacks in SDN/NFV-Enabled IoT Networks. IEEE Journal on Selected Areas in Communications, 38(6):1262-1277.

Zarca, A. M., Bernabe, J. B., Trapero, R., Rivera, D., Villalobos, J., Skarmeta, A., Bianchi, S., Zafeiropoulos, A., and Gouvas, P. (2019a). Security Management Architecture for NFV/SDN-Aware IoT Systems. IEEE Internet of Things Journal, 6(5):8005-8020.

Zarca, A. M., Garcia-Carrillo, D., Bernal Bernabe, J., Ortiz, J., Marin-Perez, R., and Skarmeta, A. (2019b). Enabling Virtual AAA Management in SDN-Based IoT Networks. Sensors, 19(2).

Zhang, H., Li, Y., Zhang, Z., Afanasyev, A., and Zhang, L. (2018a). Ndn host model. ACM SIGCOMM Computer Communication Review, 48(3):35-41.

Zhang, L. (2019). The role of data repositories in named data networking. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1-5.

Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., Claffy, K., Crowley, P., Papadopoulos, C., Wang, L., and Zhang, B. (2014). Named data networking. SIGCOMM Comput. Commun. Rev., 44(3):66-73.

Zhang, L., Estrin, D., Burke, J., Jacobson, V., Thornton, J. D., Smetters, D. K., Zhang, B., Tsudik, G., Claffy, K., Krioukov, D., Massey, D., Papadopoulos, C., Abdelzaher, T., Wang, L., Crowley, P., and Yeh, E. (2010). Named data networking (ndn) project. Technical Report NDN-0001, NDN.

Zhang, W., Hwang, J., Rajagopalan, S., Ramakrishnan, K. K., and Wood, T. (2016). Performance Management Challenges for Virtual Network Functions. In 2016 IEEE NetSoft Conference and Workshops, pages 20-23.

Zhang, Y., Afanasyev, A., Burke, J., and Zhang, L. (2016). A survey of mobility support in named data networking. In 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 83-88.

Zhang, Y., Xia, Z., Afanasyev, A., and Zhang, L. (2019a). A note on routing scalability in named data networking. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1-6.

Zhang, Y., Xia, Z., Mastorakis, S., and Zhang, L. (2018b). Kite: Producer mobility support in named data networking. In Proceedings of the 5th ACM Conference on Information-Centric Networking, ICN '18, pages 125-136. ACM.

Zhang, Z., Liu, S., King, R., and Zhang, L. (2021). Supporting multiparty signing over named data networking.

Zhang, Z., Vasavada, V., Ma, X., and Zhang, L. (2019b). Dledger: An iot-friendly private distributed ledger system based on dag.

Zhang, Z., Yu, Y., Ramani, S. K., Afanasyev, A., and Zhang, L. (2018c). Nac: Automating access control via named data. In MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), pages 626-633.

Zhang, Z., Yu, Y., Zhang, H., Newberry, E., Mastorakis, S., Li, Y., Afanasyev, A., and Zhang, L. (2018d). An overview of security support in named data networking. IEEE Communications Magazine, 56(11):62-68.

Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V. (2018). Federated learning with non-iid data. arXiv preprint arXiv:1806.00582.

Zheng, G., Zhang, G., Yang,W., Valli, C., Shankaran, R., and Orgun, M. A. (2017). From wannacry to wannadie: Security trade-offs and design for implantable medical devices. In 2017 17th International Symposium on Communications and Information Technologies (ISCIT), pages 1-5. IEEE.

Zhou, L., Guo, H., and Deng, G. (2019). A fog computing based approach to DDoS mitigation in IIoT systems. Computers & Security, 85:51-62.

Zhu, H., Zhang, H., and Jin, Y. (2021). From federated learning to federated neural architecture search: a survey. Complex & Intelligent Systems, 7(2):639-657.

Zhuo, H. H., Feng, W., Lin, Y., Xu, Q., and Yang, Q. (2020). Federated deep reinforcement learning. Arxiv preprint arXiv:1901.08277.