More Web Proxy on the site http://driver.im/

research-article

A Survey on IoT Big Data: Current Status, 13 V’s Challenges, and Future Directions

Authors:

Inderveer Chana,

Siobhán ClarkeAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 6

Article No.: 131, Pages 1 - 59

https://doi.org/10.1145/3419634

Published: 06 December 2020 Publication History

Abstract

Driven by the core technologies, i.e., sensor-based autonomous data acquisition and the cloud-based big data analysis, IoT automates the actuation of data-driven intelligent actions on the connected objects. This automation enables numerous useful real-life use-cases, such as smart transport, smart living, smart cities, and so on. However, recent industry surveys reflect that data-related challenges are responsible for slower growth of IoT in recent years. For this reason, this article presents a systematic and comprehensive survey on IoT Big Data (IoTBD) with the aim to identify the uncharted challenges for IoTBD. This article analyzes the state-of-the-art academic works in IoT and big data management across various domains and proposes a taxonomy for IoTBD management. Then, the survey explores the IoT portfolio of major cloud vendors and provides a classification of vendor services for the integration of IoT and IoTBD on their cloud platforms. After that, the survey identifies the IoTBD challenges in terms of 13 V’s challenges and envisions IoTBD as “Big Data 2.0.” Then the survey provides comprehensive analysis of recent works that address IoTBD challenges by highlighting their strengths and weaknesses to assess the recent trends and future research directions. Finally, the survey concludes with discussion on open research issues for IoTBD.

References

[1]

Ejaz Ahmed, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Imran Khan, et al. 2017. The role of big data analytics in Internet of Things. Comput. Netw. 129 (2017), 459--471.

Digital Library

[2]

N. A. M. Alduais, J. Abdullah, A. Jamil, and L. Audah. 2016. An efficient data collection and dissemination for IOT-based WSN. In Proceedings of the IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON’16). IEEE, 1--6.

[3]

Muhammad Salek Ali, Massimo Vecchio, Miguel Pincheira, Koustabh Dolui, et al. 2018. Applications of blockchains in the Internet of Things: A comprehensive survey. IEEE Commun. Surveys Tutor. 21, 2 (2018), 1676--1717.

[4]

LoRa Alliance. [n.d.]. LoRa Alliance. Retrieved from https://lora-alliance.org/.

[5]

Android. [n.d.]. Android Things. Retrieved from https://developer.android.com/things/get-started/.

[6]

Sobia Arshad, Muhammad Awais Azam, Mubashir Husain Rehmani, and Jonathan Loo. 2018. Recent advances in information-centric networking-based Internet of Things (ICN-IoT). IEEE Internet Things J. 6, 2 (2018), 2128--2158.

[7]

Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010. The internet of things: A survey. Comput. Netw. 54, 15 (2010), 2787--2805.

Digital Library

[8]

Luigi Atzori, Antonio Iera, Giacomo Morabito, and Michele Nitti. 2012. The social internet of things (siot)--when social networks meet the internet of things: Concept, architecture and network characterization. Comput. Netw. 56, 16 (2012), 3594--3608.

Digital Library

[9]

AWS. [n.d.]. AWS IoT Button. Retrieved from https://aws.amazon.com/iotbutton/.

[10]

AWS. [n.d.]. AWS IoT Core. Retrieved from https://aws.amazon.com/iot-core/.

[11]

AWS. [n.d.]. AWS IoT Greengrass. Retrieved from https://aws.amazon.com/greengrass/.

[12]

AWS. [n.d.]. Rachio Case Study. Retrieved from https://aws.amazon.com/solutions/case-studies/rachio.

[13]

Microsoft Azure. [n.d.]. Azure IoT Central. Retrieved from https://azure.microsoft.com/en-us/services/iot-central/.

[14]

Microsoft Azure. [n.d.]. Azure IoT Edge. Retrieved from https://azure.microsoft.com/en-us/services/iot-edge.

[15]

Microsoft Azure. [n.d.]. Azure IoT solution accelerators. Retrieved from https://azure.microsoft.com/en-us/features/iot-accelerators/.

[16]

Maggi Bansal, Inderveer Chana, and Siobhán Clarke. 2017. Enablement of IoT-based context-aware smart home with fog computing. J. Cases Info. Technol. 19, 4 (2017), 1--12.

Digital Library

[17]

Harald Bauer and Jan Patel, Mark Viera. 2014. The Internet of Things: Sizing up the opportunity. Retrieved from https://www.mckinsey.com/industries/semiconductors/our-insights/the-internet-of-things-sizing-up-the-opportunity.

[18]

Maria Bermudez-Edo, Tarek Elsaleh, Payam Barnaghi, and Kerry Taylor. 2016. IoT-lite: A lightweight semantic model for the Internet of Things. In Proceedings of the International IEEE Conferences on Ubiquitous Intelligence 8 Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress. IEEE, 90--97.

[19]

Luiz F. Bittencourt, Javier Diaz-Montes, Rajkumar Buyya, Omer F. Rana, and Manish Parashar. 2017. Mobility-aware application scheduling in fog computing. IEEE Cloud Comput. 4, 2 (2017), 26--35.

[20]

Chiara Bodei, Pierpaolo Degano, Gian-Luigi Ferrari, and Letterio Galletta. 2016. Tracing where IoT data are collected and aggregated. Retrieved from https://arXiv:1610.08419.

[21]

Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the internet of things. In Proceedings of the 1st Edition of the MCC Workshop on Mobile Cloud Computing. 13--16.

Digital Library

[22]

Hongming Cai, Yizhi Gu, Athanasios V. Vasilakos, Boyi Xu, and Jun Zhou. 2016. Model-driven development patterns for mobile services in cloud of things. IEEE Trans. Cloud Comput. 6, 3 (2016), 771--784.

[23]

Hongming Cai, Boyi Xu, Lihong Jiang, and Athanasios V. Vasilakos. 2016. IoT-based big data storage systems in cloud computing: Perspectives and challenges. IEEE Internet Things J. 4, 1 (2016), 75--87.

[24]

Quyet H. Cao, Imran Khan, Reza Farahbakhsh, Giyyarpuram Madhusudan, et al. 2016. A trust model for data sharing in smart cities. In Proceedings of the IEEE International Conference on Communications (ICC’16). IEEE, 1--7.

[25]

Andrea Capponi, Claudio Fiandrino, Dzmitry Kliazovich, and Pascal Bouvry. 2017. Energy efficient data collection in opportunistic mobile crowdsensing architectures for smart cities. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM’17). IEEE, 307--312.

[26]

Sravani Challa, Ashok Kumar Das, Prosanta Gope, Neeraj Kumar, et al. 2020. Design and analysis of authenticated key agreement scheme in cloud-assisted cyber--physical systems. Future Gen. Comput. Syst. 108 (2020), 1267--1286.

[27]

Min Chen, Yiming Miao, Yixue Hao, and Kai Hwang. 2017. Narrow band internet of things. IEEE Access 5 (2017), 20557--20577.

[28]

Xing Chen, Aipeng Li, Wenzhong Guo, et al. 2015. Runtime model-based approach to IoT application development. Front. Comput. Sci. 9, 4 (2015), 540--553.

Digital Library

[29]

Michael Chui, Vasanth Ganesan, and Mark Patel. 2017. Taking the Pulse of Enterprise IoT. Technical Report. McKinsey 8 Company. Retrieved from https://www.mckinsey.com/featured-insights/internet-of-things/our-insights/taking-the-pulse-of-enterprise-iot.

[30]

Google Cloud. [n.d.]. Ather Energy: Driving the future of mobility in India with BigQuery and Cloud IoT Core. Retrieved from https://cloud.google.com/customers/ather-energy.

[31]

Google Cloud. [n.d.]. Deep Sky Vineyard: Pairing wine with the IoT. Retrieved from https://cloud.google.com/customers/deep-sky-vineyard/.

[32]

Google Cloud. [n.d.]. Energyworx: Building an energy data management solution using Google Cloud Platform. Retrieved from https://cloud.google.com/customers/energyworx.

[33]

Google Cloud. [n.d.]. Sky: Scaling for success with Sky Q diagnostics. Retrieved from https://cloud.google.com/customers/sky-uk/.

[34]

Google Cloud. 2018. LG CNS uses Google Cloud IoT and Edge TPU. Retrieved from https://www.youtube.com/watch?v=GoWtSeDc-Lc.

[35]

Juan A Colmenares, Reza Dorrigiv, and Daniel G. Waddington. 2017. A single-node datastore for high-velocity multidimensional sensor data. In Proceedings of the IEEE International Conference on Big Data (Big Data’17). IEEE, 445--452.

[36]

Michael Compton, Payam Barnaghi, Luis Bermudez, RaúL GarcíA-Castro, et al. 2012. The SSN ontology of the W3C semantic sensor network incubator group. J. Web Semant. 17 (2012), 25--32.

[37]

Hong-Ning Dai, Raymond Chi-Wing Wong, Hao Wang, Zibin Zheng, and Athanasios V. Vasilakos. 2019. Big data analytics for large-scale wireless networks: Challenges and opportunities. ACM Comput. Surveys 52, 5 (2019), 1--36.

Digital Library

[38]

DB. [n.d.]. Digital Twin and DB IoT Cloud. Retrieved from https://iotcloud.deutschebahn.com/en/.

[39]

David del Rio Astorga, Manuel F. Dolz, Javier Fernández, and J. Daniel García. 2018. Paving the way towards high-level parallel pattern interfaces for data stream processing. Future Gen. Comput. Syst. 87 (2018), 228--241.

[40]

Statista Research Department. 2016. Internet of Things—Number of Connected Devices Worldwide 2015-2025. Technical Report. Statista. Retrieved from https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/.

[41]

Wassim Derguech, Eanna Bruke, and Edward Curry. 2014. An autonomic approach to real-time predictive analytics using open data and internet of things. In Proceedings of the IEEE 11th International Conference on Ubiquitous Intelligence and Computing and IEEE 11th International Conference on Autonomic and Trusted Computing and IEEE 14th International Conference on Scalable Computing and Communications and Its Associated Workshops. IEEE, 204--211.

[42]

Manuel Díaz, Cristian Martín, and Bartolomé Rubio. 2015. Lambda-CoAP: An Internet of things and cloud computing integration based on the lambda architecture and CoAP. In Proceedings of the International Conference on Collaborative Computing: Networking, Applications and Worksharing. Springer, 195--206.

[43]

Manuel Díaz, Cristian Martín, and Bartolomé Rubio. 2016. State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J. Netw. Comput. Appl. 67 (2016), 99--117.

Digital Library

[44]

Almudena Díaz-Zayas, Cesar A. García-Pérez, Alvaro M. Recio-Pérez, and Pedro Merino. 2016. 3GPP standards to deliver LTE connectivity for IoT. In Proceedings of the IEEE 1st International Conference on Internet-of-Things Design and Implementation (IoTDI’16). IEEE, 283--288.

[45]

Ngoc-Thanh Dinh and Younghan Kim. 2018. An energy efficient integration model for sensor cloud systems. IEEE Access 7 (2018), 3018--3030.

[46]

D. M. C. Dissanayake and K. P. N. Jayasena. 2017. A cloud platform for big IoT data analytics by combining batch and stream processing technologies. In Proceedings of the National Information Technology Conference (NITC’17). IEEE, 40--45.

[47]

Miao Du, Kun Wang, Yuanfang Chen, Xiaoyan Wang, and Yanfei Sun. 2018. Big data privacy preserving in multi-access edge computing for heterogeneous Internet of Things. IEEE Commun. Mag. 56, 8 (2018), 62--67.

Digital Library

[48]

Tarek Elgamal, Atul Sandur, Phuong Nguyen, Klara Nahrstedt, and Gul Agha. 2018. Droplet: Distributed operator placement for iot applications spanning edge and cloud resources. In Proceedings of the IEEE 11th International Conference on Cloud Computing (CLOUD’18). IEEE, 1--8.

[49]

Dave Evans. 2011. The Internet of Things: How the Next Evolution of the Internet Is Changing Everything. Technical Report. Cisco. Retrieved from https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf.

[50]

Kuan Fan, Zijian Bao, Mingxi Liu, Athanasios V. Vasilakos, and Wenbo Shi. 2020. Dredas: Decentralized, reliable and efficient remote outsourced data auditing scheme with blockchain smart contract for industrial IoT. Future Gen. Comput. Syst. 110 (2020), 665--674.

[51]

Nazli Farajidavar, Sefki Kolozali, and Payam Barnaghi. 2017. A deep multi-view learning framework for city event extraction from twitter data streams. Retrieved from https://arXiv:1705.09975.

[52]

Alfonso Garcia-de Prado, G. O. Ortiz, Juan Boubeta-Puig, and David Corral-Plaza. 2018. Air4People: A smart air quality monitoring and context-aware notification system. J. Universal Comput. Sci. 24, 7 (2018), 846--863.

[53]

Mouzhi Ge, Hind Bangui, and Barbora Buhnova. 2018. Big data for internet of things: A survey. Future Gen. Comput. Syst. 87 (2018), 601--614.

[54]

Ammar Gharaibeh, Mohammad A Salahuddin, Sayed Jahed Hussini, Abdallah Khreishah, et al. 2017. Smart cities: A survey on data management, security, and enabling technologies. IEEE Commun. Surveys Tutor. 19, 4 (2017), 2456--2501.

[55]

Google. [n.d.]. Bringing intelligence to the edge with Cloud IoT. Retrieved from https://cloud.google.com/blog/products/gcp/bringing-intelligence-edge-cloud-iot.

[56]

Google. [n.d.]. Edge TPU. Retrieved from https://cloud.google.com/edge-tpu/.

[57]

Google. [n.d.]. Google Cloud IoT. Retrieved from https://cloud.google.com/solutions/iot.

[58]

Jianchao Han and Jing Dong. 2007. Perspectives of granular computing in Software Engineering. In Proceedings of the IEEE International Conference on Granular Computing (GRC’07). IEEE, 66--66.

Digital Library

[59]

Ibrahim Abaker Targio Hashem, Victor Chang, Nor Badrul Anuar, Kayode Adewole, et al. 2016. The role of big data in smart city. Int. J. Info. Manage. 36, 5 (2016), 748--758.[60]Ibrahim Abaker Targio Hashem, Ibrar Yaqoob, Nor Badrul Anuar, Salimah Mokhtar, et al. 2015. The rise of “big data” on cloud computing: Review and open research issues. Info. Syst. 47 (2015), 98--115.

Digital Library

[60]

Michael Haupt. 2016. “Data is the New Oil”—A Ludicrous Proposition. Retrieved from https://medium.com/project-2030/data-is-the-new-oil-a-ludicrous-proposition-1d91bba4f294.

[61]

Ying He, F. Richard Yu, Nan Zhao, Victor C. M. Leung, and Hongxi Yin. 2017. Software-defined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach. IEEE Commun. Mag. 55, 12 (2017), 31--37.

Digital Library

[62]

Hugo Hromic, Danh Le Phuoc, Martin Serrano, Aleksandar Antonić, et al. 2015. Real time analysis of sensor data for the internet of things by means of clustering and event processing. In Proceedings of the IEEE International Conference on Communications (ICC’15). IEEE, 685--691.

[63]

Jia Hu, Hui Lin, Xuancheng Guo, and Ji Yang. 2018. DTCS: An integrated strategy for enhancing data trustworthiness in mobile crowdsourcing. IEEE Internet Things J. 5, 6 (2018), 4663--4671.

[64]

Mingfeng Huang, Anfeng Liu, Neal N. Xiong, Tian Wang, and Athanasios V. Vasilakos. 2020. An effective service-oriented networking management architecture for 5G-enabled internet of things. Comput. Netw. 173 (2020), 107--208.

[65]

Emir Husni, Galuh Boy Hertantyo, Daniel Wahyu Wicaksono, et al. 2016. Applied Internet of Things (IoT): Car monitoring system using IBM BlueMix. In Proceedings of the International Seminar on Intelligent Technology and Its Applications (ISITIA’16). IEEE, 417--422.

[66]

IBM. [n.d.]. Watson Internet of Things. Retrieved from https://www.ibm.com/internet-of-things/solutions/iot-platform/watson-iot-platform.

[67]

S. M. Riazul Islam, Daehan Kwak, M. D. Humaun Kabir, Mahmud Hossain, and Kyung-Sup Kwak. 2015. The internet of things for health care: A comprehensive survey. IEEE Access 3 (2015), 678--708.

[68]

Jaejin Jang, Im Y. Jung, and Jong Hyuk Park. 2018. An effective handling of secure data stream in IoT. Appl. Soft Comput. 68 (2018), 811--820.

Digital Library

[69]

Prem Prakash Jayaraman, Charith Perera, Dimitrios Georgakopoulos, Schahram Dustdar, et al. 2017. Analytics-as-a-service in a multi-cloud environment through semantically enabled hierarchical data processing. Softw.: Pract. Exper. 47, 8 (2017), 1139--1156.

Digital Library

[70]

Yuna Jeong, Hyuntae Joo, Gyeonghwan Hong, Dongkun Shin, and Sungkil Lee. 2015. AVIoT: Web-based interactive authoring and visualization of indoor internet of things. IEEE Trans. Consum. Electron. 61, 3 (2015), 295--301.

Digital Library

[71]

Lihong Jiang, Li Da Xu, Hongming Cai, Zuhai Jiang, et al. 2014. An IoT-oriented data storage framework in cloud computing platform. IEEE Trans. Industr. Info. 10, 2 (2014), 1443--1451.

[72]

Young Jin Jung, Yang Koo Lee, Dong Gyu Lee, Yongmi Lee, et al. 2011. Design of sensor data processing steps in an air pollution monitoring system. Sensors 11, 12 (2011), 11235--11250.

[73]

Abdulkadir Karaagac, Niels Verbeeck, and Jeroen Hoebeke. 2019. The integration of LwM2M and OPC UA: An interoperability approach for industrial IoT. In Proceedings of the IEEE 5th World Forum on Internet of Things (WF-IoT’19). IEEE, 313--318.

[74]

Mahdi Kasmi, Faouzi Bahloul, and Haykel Tkitek. 2016. Smart home based on Internet of Things and cloud computing. In Proceedings of the 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’16). IEEE, 82--86.

[75]

Maqbool Khan, Xiaotong Wu, Xiaolong Xu, and Wanchun Dou. 2017. Big data challenges and opportunities in the hype of Industry 4.0. In Proceedings of the IEEE International Conference on Communications (ICC’17). IEEE, 1--6.

[76]

Sefki Kolozali, Maria Bermudez-Edo, Daniel Puschmann, Frieder Ganz, and Payam Barnaghi. 2014. A knowledge-based approach for real-time iot data stream annotation and processing. In Proceedings of the IEEE International Conference on Internet of Things (iThings’14), and IEEE Green Computing and Communications (GreenCom’14) and IEEE Cyber, Physical and Social Computing (CPSCom’14). IEEE, 215--222.

Digital Library

[77]

Sachin Kumar, Prayag Tiwari, and Mikhail Zymbler. 2019. Internet of Things is a revolutionary approach for future technology enhancement: A review. J. Big Data 6, 1 (2019), 111.

[78]

Douglas Laney. 2001. 3D Data Management: Controlling Data Volume, Velocity, and Variety. Technical Report. META Group. Retrieved from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.

[79]

Chao-Hsien Lee, Zheng-Lin Wu, Yun-Ting Chiu, and Vi-Shu Chen. 2019. Heterogeneous industrial IoT integration for manufacturing production. In Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS’19). IEEE, 1--2.

[80]

He Li, Kaoru Ota, and Mianxiong Dong. 2018. Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Netw. 32, 1 (2018), 96--101.

[81]

Tingli Li, Yang Liu, Ye Tian, Shuo Shen, and Wei Mao. 2012. A storage solution for massive IoT data based on NoSQL. In Proceedings of the IEEE International Conference on Green Computing and Communications. IEEE, 50--57.

Digital Library

[82]

Chiehyeon Lim, Kwang-Jae Kim, and Paul P. Maglio. 2018. Smart cities with big data: Reference models, challenges, and considerations. Cities 82 (2018), 86--99.

[83]

Bing Lin, Wenzhong Guo, Naixue Xiong, Guolong Chen, et al. 2016. A pretreatment workflow scheduling approach for big data applications in multicloud environments. IEEE Trans. Netw. Serv. Manage. 13, 3 (2016), 581--594.

Digital Library

[84]

Chao Lin, Debiao He, Xinyi Huang, Kim-Kwang Raymond Choo, and Athanasios V. Vasilakos. 2018. BSeIn: A blockchain-based secure mutual authentication with fine-grained access control system for industry 4.0. J. Netw. Comput. Appl. 116 (2018), 42--52.

[85]

Jiaheng Lu and Irena Holubová. 2019. Multi-model databases: A new journey to handle the variety of data. ACM Comput. Surveys 52, 3 (2019), 1--38.

Digital Library

[86]

Knud Lueth. 2014. IoT Market—Forecasts at a glance. Retrieved from https://iot-analytics.com/iot-market-forecasts-overview/.

[87]

Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2014. Traffic flow prediction with big data: A deep learning approach. IEEE Trans. Intell. Transport. Syst. 16, 2 (2014), 865--873.

[88]

Youzhong Ma, Jia Rao, Weisong Hu, Xiaofeng Meng, et al. 2012. An efficient index for massive IOT data in cloud environment. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2129--2133.

Digital Library

[89]

Altti Ilari Maarala, Xiang Su, and Jukka Riekki. 2016. Semantic reasoning for context-aware Internet of Things applications. IEEE Internet Things J. 4, 2 (2016), 461--473.

[90]

Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, et al. 2018. Machine learning for Internet of Things data analysis: A survey. Dig. Commun. Netw. 4, 3 (2018), 161--175.

[91]

Luca Mainetti, Luigi Patrono, and Antonio Vilei. 2011. Evolution of wireless sensor networks towards the internet of things: A survey. In Proceedings of the 19th International Conference on Software, Telecommunications and Computer Networks. IEEE, 1--6.

[92]

James Manyika, Richard Dobbs, Michael Chui, Jacques Bughin, et al. 2015. The Internet of Things: Mapping the Value Beyond the Hype. Technical Report. McKinsey 8 Company.

[93]

Mohsen Marjani, Fariza Nasaruddin, Abdullah Gani, Ahmad Karim, et al. 2017. Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access 5 (2017), 5247--5261.

[94]

Vasileios A. Memos, Kostas E. Psannis, Yutaka Ishibashi, Byung-Gyu Kim, and Brij B. Gupta. 2018. An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework. Future Gen. Comput. Syst. 83 (2018), 619--628.

Digital Library

[95]

Microsoft. [n.d.]. Windows for Internet of Things. Retrieved from https://developer.microsoft.com/en-us/windows/iot.

[96]

Assaad Moawad, Thomas Hartmann, Francois Fouquet, Gregory Nain, et al. 2015. Beyond discrete modeling: A continuous and efficient model for iot. In Proceedings of the ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS’15). IEEE, 90--99.

[97]

Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, and Mohsen Guizani. 2018. Deep learning for IoT big data and streaming analytics: A survey. IEEE Commun. Surveys Tutor. 20, 4 (2018), 2923--2960.

Digital Library

[98]

Felix Mohr, Marcel Wever, and Eyke Hüllermeier. 2018. Automated machine learning service composition. Retrieved from https://arXiv:1809.00486.

[99]

Bidyut Mukherjee, Songjie Wang, Wenyi Lu, Roshan Lal Neupane, et al. 2018. Flexible IoT security middleware for end-to-end cloud--fog communication. Future Gen. Comput. Syst. 87 (2018), 688--703.

[100]

Rubén Mulero, Aitor Almeida, Gorka Azkune, Patricia Abril-Jiménez, et al. 2018. An IoT-aware approach for elderly friendly cities. IEEE Access 6 (2018), 7941--7957.

[101]

Amy Nordrum. 2016. Popular Internet of Things Forecast of 50 Billion Devices by 2020 is Outdated. Technical Report. IEEE Tech Talk. Retrieved from https://spectrum.ieee.org/tech-talk/telecom/internet/popular-internet-of-things-forecast-of-50-billion-devices-by-2020-is-outdated.

[102]

Yunhe Pan, Yun Tian, Xiaolong Liu, Dedao Gu, and Gang Hua. 2016. Urban big data and the development of city intelligence. Engineering 2, 2 (2016), 171--178.

[103]

Mark Patel, Jason Shangkuan, and Christopher Thomas. 2017. What’s New with the Internet of Things? Technical Report. McKinsey 8 Company. Retrieved from https://www.mckinsey.com/industries/semiconductors/our-insights/whats-new-with-the-internet-of-things.

[104]

Limei Peng, Ahmad R. Dhaini, and Pin-Han Ho. 2018. Toward integrated Cloud--Fog networks for efficient IoT provisioning: Key challenges and solutions. Future Gen. Comput. Syst. 88 (2018), 606--613.

[105]

Juan Luis Pérez and David Carrera. 2015. Performance characterization of the servioticy api: An IoT-as-a-service data management platform. In Proceedings of the IEEE First International Conference on Big Data Computing Service and Applications. IEEE, 62--71.

Digital Library

[106]

Roland Petrasch and Roman Hentschke. 2016. Cloud storage hub: Data management for IoT and industry 4.0 applications: Towards a consistent enterprise information management system. In Proceedings of the Management and Innovation Technology International Conference (MITicon). IEEE, MIT--108.

[107]

Pilaiwan Phupattanasilp and Sheau-Ru Tong. 2019. Augmented reality in the integrative Internet of Things (AR-IoT): Application for precision farming. Sustainability 11, 9 (2019), 2658.

[108]

Giuseppe Piro, Ilaria Cianci, Luigi Alfredo Grieco, Gennaro Boggia, and Pietro Camarda. 2014. Information centric services in smart cities. J. Syst. Softw. 88 (2014), 169--188.

[109]

Andreas P. Plageras, Kostas E. Psannis, Christos Stergiou, Haoxiang Wang, and Brij B. Gupta. 2018. Efficient IoT-based sensor BIG Data collection--processing and analysis in smart buildings. Future Gen. Comput. Syst. 82 (2018), 349--357.

[110]

Kostas E. Psannis, Christos Stergiou, and Brij Bhooshan Gupta. 2018. Advanced media-based smart big data on intelligent cloud systems. IEEE Trans. Sustain. Comput. 4, 1 (2018), 77--87.

[111]

Hazem M Raafat, M Shamim Hossain, Ehab Essa, Samirand Elmougy, et al. 2017. Fog intelligence for real-time IoT sensor data analytics. IEEE Access 5 (2017), 24062--24069.

[112]

M. Mazhar Rathore, Awais Ahmad, Anand Paul, and Seungmin Rho. 2016. Urban planning and building smart cities based on the internet of things using big data analytics. Comput. Netw. 101 (2016), 63--80.

Digital Library

[113]

M. Mazhar Rathore, Anand Paul, Won-Hwa Hong, HyunCheo Seo, Imtiaz Awan, et al. 2018. Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data. Sustain. Cities Soc. 40 (2018), 600--610.

[114]

Partha Pratim Ray. 2016. A survey of IoT cloud platforms. Future Comput. Info. J. 1, 1–2 (2016), 35--46.

[115]

Cisco News Release. 2017. Cisco Survey Reveals Close to Three-Fourths of IoT Projects Are Failing. Retrieved from https://newsroom.cisco.com/press-release-content?articleId=1847422.

[116]

Cisco News Release. 2017. Cisco Survey Reveals Divide Between IoT Value and Trust. Retrieved from https://newsroom.cisco.com/press-release-content?type=webcontent8articleId=1900060.

[117]

Ericsson Press Release. 2010. CEO to shareholders: 50 billion connections 2020. Retrieved from https://www.ericsson.com/en/press-releases/2010/4/ceo-to-shareholders-50-billion-connections-2020.

[118]

Gartner Press Release. 2013. Gartner Says the Internet of Things Installed Base Will Grow to 26 Billion Units By 2020. Retrieved from http://www.gartner.com/newsroom/id/2636073.

[119]

Gartner Press Release. 2015. Gartner Says 6.4 Billion Connected “Things” Will Be in Use in 2016, Up 30 Percent From 2015. Retrieved from https://www.gartner.com/newsroom/id/3165317.

[120]

Dumitru Roman, Jacek Kopeckỳ, Tomas Vitvar, John Domingue, and Dieter Fensel. 2015. WSMO-Lite and hRESTS: Lightweight semantic annotations for Web services and RESTful APIs. J. Web Semant. 31 (2015), 39--58.

Digital Library

[121]

Sandeep Singh Sandha, Mohammad Kachuee, and Sajad Darabi. 2017. Complex event processing of health data in real-time to predict heart failure risk and stress. Retrieved from https://arXiv:1707.04364.

[122]

Sunny Sanyal and Puning Zhang. 2018. Improving quality of data: IoT data aggregation using device to device communications. IEEE Access 6 (2018), 67830--67840.

[123]

Nicolas Seydoux, Khalil Drira, Nathalie Hernandez, and Thierry Monteil. 2016. IoT-O, a core-domain IoT ontology to represent connected devices networks. In Proceedings of the European Knowledge Acquisition Workshop. Springer, 561--576.

Digital Library

[124]

Shayan Shams, Sayan Goswami, Kisung Lee, Seungwon Yang, and Seung-Jong Park. 2018. Towards distributed cyberinfrastructure for smart cities using big data and deep learning technologies. In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS’18). IEEE, 1276--1283.

[125]

Shree Krishna Sharma and Xianbin Wang. 2017. Live data analytics with collaborative edge and cloud processing in wireless IoT networks. IEEE Access 5 (2017), 4621--4635.

[126]

Zhengguo Sheng, Shusen Yang, Yifan Yu, Athanasios V. Vasilakos, et al. 2013. A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Commun. 20, 6 (2013), 91--98.

[127]

Sigfox. [n.d.]. Sigfox. Retrieved from https://www.sigfox.com/en.

[128]

Dilpreet Singh and Chandan K. Reddy. 2015. A survey on platforms for big data analytics. J. Big Data 2, 1 (2015), 8.

[129]

Eugene Siow, Thanassis Tiropanis, and Wendy Hall. 2018. Analytics for the internet of things: A survey. ACM Comput. Surveys 51, 4 (2018), 1--36.

Digital Library

[130]

Tianyi Song, Ruinian Li, Bo Mei, Jiguo Yu, et al. 2017. A privacy preserving communication protocol for IoT applications in smart homes. IEEE Internet Things J. 4, 6 (2017), 1844--1852.

[131]

Christos Stergiou, Kostas E. Psannis, Brij B. Gupta, and Yutaka Ishibashi. 2018. Security, privacy 8 efficiency of sustainable cloud computing for big data 8 IoT. Sustain. Comput.: Info. Syst. 19 (2018), 174--184.

[132]

Christos Stergiou, Kostas E. Psannis, Byung-Gyu Kim, and Brij Gupta. 2018. Secure integration of IoT and cloud computing. Future Gen. Comput. Syst. 78 (2018), 964--975.

[133]

Marco Stolpe. 2016. The internet of things: Opportunities and challenges for distributed data analysis. ACM SIGKDD Explor. Newslett. 18, 1 (2016), 15--34.

Digital Library

[134]

Microsoft Customer Stories. [n.d.]. 365mc. Retrieved from https://customers.microsoft.com/en-us/story/726558-365mc-azure-iot-suite-machine-learning-korea-en.

[135]

Microsoft Customer Stories. [n.d.]. Bridgestone. Retrieved from https://customers.microsoft.com/en-us/story/724108-bridgestone-japan-azure-iot-chemicals-agrochemicals-en-jp.

[136]

Microsoft Customer Stories. [n.d.]. Buhler. Retrieved from https://customers.microsoft.com/en-us/story/buhlergroup-azure-machine-learning-iot-edge-switzerland.

[137]

Microsoft Customer Stories. [n.d.]. Dubai World Trade Centre. Retrieved from https://customers.microsoft.com/en-us/story/dubaiworldtradecentre.

[138]

Microsoft Customer Stories. [n.d.]. Rolls-Royce and Microsoft collaborate to create new digital capabilities. Retrieved from https://customers.microsoft.com/en-us/story/rollsroycestory.

[139]

AWS Case Study. [n.d.]. Bayer Crop Science. Retrieved from https://aws.amazon.com/solutions/case-studies/bayer-cropscience/.

[140]

AWS Case Study. [n.d.]. iRobot. Retrieved from https://aws.amazon.com/solutions/case-studies/irobot-iot/.

[141]

AWS Case Study. [n.d.]. Miovision. Retrieved from https://aws.amazon.com/solutions/case-studies/miovision/.

[142]

IBM Case Study. [n.d.]. GreenCom Networks. Retrieved from https://www.ibm.com/case-studies/greencom-networks.

[143]

IBM Case Study. [n.d.]. HeartBit. Retrieved from https://www.ibm.com/case-studies/heartbit-cloud-fitness-wearable.

[144]

IBM Case Study. [n.d.]. Jakarta Smart City. Retrieved from https://www.ibm.com/case-studies/jakartasmartcity.

[145]

IBM Case Study. [n.d.]. Kone Corp.Retrieved from https://www.ibm.com/case-studies/kone-corp.

[146]

IBM Case Study. [n.d.]. Shenzhen China Star Optoelectronics Technology Co., Ltd. Retrieved from https://www.ibm.com/case-studies/csot-watson-iot-visual-inspection.

[147]

Yunchuan Sun, Houbing Song, Antonio J. Jara, and Rongfang Bie. 2016. Internet of things and big data analytics for smart and connected communities. IEEE Access 4 (2016), 766--773.

[148]

Jie Tang, Dawei Sun, Shaoshan Liu, and Jean-Luc Gaudiot. 2017. Enabling deep learning on IoT devices. Computer 50, 10 (2017), 92--96.

Digital Library

[149]

William Tärneberg, Vishal Chandrasekaran, and Marty Humphrey. 2016. Experiences creating a framework for smart traffic control using aws iot. In Proceedings of the IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC’16). IEEE, 63--69.

Digital Library

[150]

Clive Thompson. 2009. 25 Ideas for 2010: Digital Forgetting. Retrieved from https://www.wired.co.uk/article/25-ideas-for-2010-digital-forgetting.

[151]

Sergio Trilles, Òscar Belmonte, Sven Schade, and Joaquìn Huerta. 2017. A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data. Int. J. Dig. Earth 10, 1 (2017), 103--120.

[152]

Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao, and Athanasios V. Vasilakos. 2015. Big data analytics: A survey. J. Big Data 2, 1 (2015), 1--32.

[153]

Chun-Wei Tsai, Chin-Feng Lai, Ming-Chao Chiang, and Laurence T. Yang. 2013. Data mining for internet of things: A survey. IEEE Commun. Surveys Tutor. 16, 1 (2013), 77--97.

[154]

Arijit Ukil, Soma Bandyopadhyay, and Arpan Pal. 2015. Iot data compression: Sensor-agnostic approach. In Proceedings of the Data Compression Conference. IEEE, 303--312.

Digital Library

[155]

Muhammad Usman, Mian Ahmad Jan, Xiangjian He, and Jinjun Chen. 2019. A survey on big multimedia data processing and management in smart cities. ACM Comput. Surveys 52, 3 (2019), 1--29.

Digital Library

[156]

Jiafu Wan, Jianqi Liu, Zehui Shao, Athanasios V. Vasilakos, et al. 2016. Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors 16, 1 (2016), 88.

[157]

Huaqun Wang, Zhiwei Wang, and Josep Domingo-Ferrer. 2018. Anonymous and secure aggregation scheme in fog-based public cloud computing. Future Gen. Comput. Syst. 78 (2018), 712--719.

Digital Library

[158]

Wei Wang and Dong Guo. 2012. Towards unified heterogeneous event processing for the Internet of Things. In Proceedings of the 3rd IEEE International Conference on the Internet of Things. IEEE, 84--91.

[159]

Mohammad Wazid, Ashok Kumar Das, Vivekananda Bhat, and Athanasios V. Vasilakos. 2020. LAM-CIoT: Lightweight authentication mechanism in cloud-based IoT environment. J. Netw. Comput. Appl. 150 (2020), 102496.

Digital Library

[160]

Wei Wei, Houbing Song, Wei Li, Peiyi Shen, and Athanasios Vasilakos. 2017. Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network. Info. Sci. 408 (2017), 100--114.

[161]

Yongmei Wei, Fengmin Chen, and David Chen Jia Sheng. 2017. Expanstor: Multiple cloud storage with dynamic data distribution. In Proceedings of the IEEE 7th International Symposium on Cloud and Service Computing (SC2’17). IEEE, 85--90.

[162]

Gary White, Christian Cabrera, Andrei Palade, and Siobhán Clarke. 2018. Augmented reality in iot. In International Conference on Service-Oriented Computing. Springer, 149--160.

[163]

Putu Wiramaswara Widya, Yoga Yustiawan, and Joonho Kwon. 2018. A oneM2M-based query engine for Internet of Things (IoT) data streams. Sensors 18, 10 (2018), 3253.

[164]

Shanshan Wu, Liang Bao, Zisheng Zhu, Fan Yi, and Weizhao Chen. 2017. Storage and retrieval of massive heterogeneous IoT data based on hybrid storage. In Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD’17). IEEE, 2982--2987.

[165]

Xiaomin Xu, Sheng Huang, Yaoliang Chen, Kevin Browny, et al. 2014. TSAaaS: Time series analytics as a service on IoT. In Proceedings of the IEEE International Conference on Web Services. IEEE, 249--256.

Digital Library

[166]

Diana C. Yacchirema, David Sarabia-Jácome, Carlos E. Palau, and Manuel Esteve. 2018. A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access 6 (2018), 35988--36001.

[167]

Yoji Yamato, Hiroki Kumazaki, and Yoshifumi Fukumoto. 2016. Proposal of lambda architecture adoption for real time predictive maintenance. In Proceedings of the 4th International Symposium on Computing and Networking (CANDAR’16). IEEE, 713--715.

[168]

Zheng Yan, Peng Zhang, and Athanasios V. Vasilakos. 2016. A security and trust framework for virtualized networks and software-defined networking. Secur. Commun. Netw. 9, 16 (2016), 3059--3069.

Digital Library

[169]

Chi Yang, Deepak Puthal, Saraju P. Mohanty, and Elias Kougianos. 2017. Big-sensing-data curation for the cloud is coming: A promise of scalable cloud-data-center mitigation for next-generation IoT and wireless sensor networks. IEEE Consumer Electron. Mag. 6, 4 (2017), 48--56.

[170]

Yang Yang, Xianghan Zheng, Victor Chang, and Chunming Tang. 2017. Semantic keyword searchable proxy re-encryption for postquantum secure cloud storage. Concurr. Comput.: Pract. Exper. 29, 19 (2017), e4211.

[171]

Yang Yang, Xianghan Zheng, Wenzhong Guo, Ximeng Liu, and Victor Chang. 2018. Privacy-preserving fusion of IoT and big data for e-health. Future Gen. Comput. Syst. 86 (2018), 1437--1455.

Digital Library

[172]

Yang Yang, Xianghan Zheng, and Chunming Tang. 2017. Lightweight distributed secure data management system for health internet of things. J. Netw. Comput. Appl. 89 (2017), 26--37.

Digital Library

[173]

Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Abdullah Gani, Salimah Mokhtar, et al. 2016. Big data: From beginning to future. Int. J. Info. Manage. 36, 6 (2016), 1231--1247.

Digital Library

[174]

Abdulsalam Yassine, Shailendra Singh, M. Shamim Hossain, and Ghulam Muhammad. 2019. IoT big data analytics for smart homes with fog and cloud computing. Future Gen. Comput. Syst. 91 (2019), 563--573.

Digital Library

[175]

Liang Yu, Wei Wu, Xiaohui Li, Guangxia Li, et al. 2015. iVizTRANS: Interactive visual learning for home and work place detection from massive public transportation data. In Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST’15). IEEE, 49--56.

[176]

Tianqi Yu, Xianbin Wang, and Abdallah Shami. 2017. Recursive principal component analysis-based data outlier detection and sensor data aggregation in IoT systems. IEEE Internet Things J. 4, 6 (2017), 2207--2216.

[177]

Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, and Michele Zorzi. 2014. Internet of things for smart cities. IEEE Internet Things J. 1, 1 (2014), 22--32.

[178]

Ivana Podnar Žarko, Krešimir Pripužić, Martin Serrano, and Manfred Hauswirth. 2014. Iot data management methods and optimisation algorithms for mobile publish/subscribe services in cloud environments. In Proceedings of the European Conference on Networks and Communications (EuCNC’14). IEEE, 1--5.

[179]

Feixiong Zhang, Chenren Xu, Yanyong Zhang, K. K. Ramakrishnan, et al. 2015. Edgebuffer: Caching and prefetching content at the edge in the mobilityfirst future internet architecture. In 2015 IEEE 16th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, 1--9.

[180]

Rongyue Zheng, Jianlin Jiang, Xiaohan Hao, Wei Ren, et al. 2019. bcBIM: A blockchain-based big data model for BIM modification audit and provenance in mobile cloud. Math. Prob. Eng. 2019 (2019).

[181]

Yanxu Zheng, Sutharshan Rajasegarar, and Christopher Leckie. 2015. Parking availability prediction for sensor-enabled car parks in smart cities. In Proceedings of the IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP’15). IEEE, 1--6.

[182]

Jun Zhou, Zhenfu Cao, Xiaolei Dong, and Athanasios V. Vasilakos. 2017. Security and privacy for cloud-based IoT: Challenges. IEEE Commun. Mag. 55, 1 (2017), 26--33.

Digital Library

[183]

Jin Zhou, Liang Hu, Feng Wang, Huimin Lu, and Kuo Zhao. 2013. An efficient multidimensional fusion algorithm for IoT data based on partitioning. Tsinghua Sci. Technol. 18, 4 (2013), 369--378.

Cited By

Troupiotis-Kapeliaris AKastrisios CZissis D(2025)Vessel Trajectory Data Mining: A ReviewIEEE Access10.1109/ACCESS.2025.352595213(4827-4856)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3525952
Minelli LSchreiber JSausen PSausen ADe Campos M(2024)Smart Grids data characterization: a revisionCuadernos de Educación y Desarrollo10.55905/cuadv16n2-04216:2(e3357)Online publication date: 16-Feb-2024
https://doi.org/10.55905/cuadv16n2-042
Sargent CKoohang ASvanadze S(2024)Users' Concerns and the Internet of Things (IoT) Risk BeliefsJournal of Global Information Management10.4018/JGIM.35921032:1(1-19)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.4018/JGIM.359210
Show More Cited By

Index Terms

A Survey on IoT Big Data: Current Status, 13 V’s Challenges, and Future Directions

Recommendations

From IoT big data to IoT big services
SAC '17: Proceedings of the Symposium on Applied Computing

The large-scale deployments of Internet of Things (IoT) systems have introduced several new challenges in terms of processing their data. The massive amount of IoT-generated data requires design solutions to speed up data processing, scale up with the ...
A Brief Survey on Big Data in Healthcare

This article presents a brief introduction to big data and big data analytics and also their roles in the healthcare system. A definite range of scientific researches about big data analytics in the healthcare system have been reviewed. The definition ...
A survey on blockchain for big data: Approaches, opportunities, and future directions
Abstract
Big data has generated strong interest in various scientific and engineering domains over the last few years. Despite many advantages and applications, there are many challenges in big data to be tackled for better quality of service, ...
Highlights
- An overview of the application of blockchain for big data analytics is presented.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 6

Invited Tutorial and Regular Papers

November 2021

803 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3441629

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2020

Accepted: 01 August 2020

Revised: 01 August 2020

Received: 01 September 2019

Published in CSUR Volume 53, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

63
Total Citations
View Citations
2,520
Total Downloads

Downloads (Last 12 months)378
Downloads (Last 6 weeks)37

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Troupiotis-Kapeliaris AKastrisios CZissis D(2025)Vessel Trajectory Data Mining: A ReviewIEEE Access10.1109/ACCESS.2025.352595213(4827-4856)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3525952
Minelli LSchreiber JSausen PSausen ADe Campos M(2024)Smart Grids data characterization: a revisionCuadernos de Educación y Desarrollo10.55905/cuadv16n2-04216:2(e3357)Online publication date: 16-Feb-2024
https://doi.org/10.55905/cuadv16n2-042
Sargent CKoohang ASvanadze S(2024)Users' Concerns and the Internet of Things (IoT) Risk BeliefsJournal of Global Information Management10.4018/JGIM.35921032:1(1-19)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.4018/JGIM.359210
Si ZWang JZhao PWang XSong J(2024)EADC: An Efficient Anonymous Data Collection Scheme with Blockchain in Internet of ThingsSensors10.3390/s2422716224:22(7162)Online publication date: 7-Nov-2024
https://doi.org/10.3390/s24227162
Tosi DKokaj RRoccetti M(2024)15 years of Big Data: a systematic literature reviewJournal of Big Data10.1186/s40537-024-00914-911:1Online publication date: 14-May-2024
https://doi.org/10.1186/s40537-024-00914-9
Ren ZLi XMiao YZhu MYuan SDeng RLuo BLiao XXu JKirda ELie D(2024)PIC-BI: Practical and Intelligent Combinatorial Batch Identification for UAV assisted IoT NetworksProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670303(3645-3658)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3670303
Pioli Lde Macedo DCosta DDantas M(2024)Intelligent Edge-powered Data Reduction: A Systematic Literature ReviewACM Computing Surveys10.1145/365633856:9(1-39)Online publication date: 4-Apr-2024
https://dl.acm.org/doi/10.1145/3656338
Xiong PTegegn MSarin JPal SRubin J(2024)It Is All about Data: A Survey on the Effects of Data on Adversarial RobustnessACM Computing Surveys10.1145/362781756:7(1-41)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3627817
Zeng XZhang S(2024)CStream: Parallel Data Stream Compression on Multicore Edge DevicesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338686236:11(5889-5904)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3386862
Gong BGuo CGuo CGuo CSun YWaqas MChen S(2024)SLIM: A Secure and Lightweight Multi-Authority Attribute-Based Signcryption Scheme for IoTIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.333156619(1299-1312)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3331566
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents