survey

Edge Computing and Sensor-Cloud: Overview, Solutions, and Directions

Authors:

Quan Z. ShengAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 13s

Article No.: 281, Pages 1 - 37

https://doi.org/10.1145/3582270

Published: 13 July 2023 Publication History

Abstract

Sensor-cloud originates from extensive recent applications of wireless sensor networks and cloud computing. To draw a roadmap of the current research activities of the sensor-cloud community, we first investigate the state-of-the-art sensor-cloud literature reviews published since the late 2010s and discovered that these surveys have primarily studied the sensor-cloud in specific aspects, security-enabled solutions, efficient management mechanisms, and architectural challenges. While the existing surveys have reviewed the sensor-cloud from various perspectives, they are inadequate for the three key issues that require urgent attention in the sensor-cloud: reliability, energy, and heterogeneity. To fill this gap, we perform a thorough survey by examining the origins of the sensor-cloud and providing an in-depth and comprehensive discussion of these three key challenges. We summarize initial designs of the new edge-based schemes to address these challenges and identify several open issues and promising future research directions.

References

[1]

Ding Wang, Ping Wang, and Chenyu Wang. 2020. Efficient multi-factor user authentication protocol with forward secrecy for real-time data access in WSNs. ACM Trans. Cyber-Phys. Syst. 4, 3 (May2020), 1–26.

Digital Library

[2]

Tian Wang, Yuzhu Liang, Yi Yang, Guangquan Xu, Hao Peng, Anfeng Liu, and Weijia Jia. 2020. An intelligent edge-computing-based method to counter coupling problems in cyber-physical systems. IEEE Network 34, 3 (2020), 16–22.

[3]

Rana M. Abdul Haseeb-Ur-Rehman, Misbah Liaqat, Azana Hafizah Mohd Aman, Siti Hafizah Ab Hamid, Rana Liaqat Ali, Junaid Shuja, and Muhammad Khurram Khan. 2021. Sensor cloud frameworks: State-of-the-art, taxonomy, and research issues. IEEE Sens. J. 21, 20 (2021), 22347–22370.

[4]

Sunanda Bose, Sumit Kumar Paul, and Nandini Mukherjee. 2021. Predicting spatio-temporal phenomena of mobile resources in sensor cloud infrastructure. ACM Trans. Spatial Algor. Syst. 7, 3 (2021), 1–38.

Digital Library

[5]

Tian Wang, Yuzhu Liang, Weijia Jia, Muhammad Arif, Anfeng Liu, and Mande Xie. 2019. Coupling resource management based on fog computing in smart city systems. J. Netw. Comput. Appl. 135 (2019), 11–19.

Digital Library

[6]

Guangjie Han, Xu Miao, Hao Wang, Mohsen Guizani, and Wenbo Zhang. 2019. CPSLP: A cloud-based scheme for protecting source location privacy in wireless sensor networks using multi-sinks. IEEE Trans. Vehic. Technol. 68, 3 (March2019), 2739–2750.

[7]

Rajendra Kumar Dwivedi, Munish Saran, and Rakesh Kumar. 2019. A survey on security over sensor-cloud. In Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence’19). IEEE, 31–37.

[8]

R. Geetha, A. K. Suntheya, and G. Umarani Srikanth. 2020. Cloud integrated IoT enabled sensor network security: Research issues and solutions. Wireless Pers. Commun. 113, 2 (2020), 747–771.

[9]

Nawaf Almolhis, Abdullah Mujawib Alashjaee, Salahaldeen Duraibi, Fahad Alqahtani, and Ahmed Nour Moussa. 2020. The security issues in IoT-cloud: A review. In Proceedings of the 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA’20). IEEE, 191–196.

[10]

Mohammad Farhan Khan, Rajendra Kumar Dwivedi, and Rakesh Kumar. 2019. Towards power aware data transmission in sensor cloud: A survey. In Proceedings of the International Conference on Computer Networks and Inventive Communication Technologies (ICCNCT’19). Springer, 317–325.

[11]

Jasenka Dizdarević, Francisco Carpio, Admela Jukan, and Xavi Masip-Bruin. 2019. A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration. ACM Comput. Surv. 51, 6 (2019), 1–29.

Digital Library

[12]

Rajendra Kumar Dwivedi, Nikita Kumari, and Rakesh Kumar. 2020. Integration of wireless sensor networks with cloud towards efficient management in IoT: A review. In Proceedings of the Advances in Data and Information Sciences. Springer, 97–107.

[13]

Rajendra Kumar Dwivedi and Rakesh Kumar. 2018. Sensor cloud: Integrating wireless sensor networks with cloud computing. In Proceedings of the 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON’18). IEEE, 1–6.

[14]

Ju Ren, Deyu Zhang, Shiwen He, Yaoxue Zhang, and Tao Li. 2019. A survey on end-edge-cloud orchestrated network computing paradigms: Transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput. Surv. 52, 6 (2019), 1–36.

Digital Library

[15]

Motahareh Nazari Jahantigh, Amir Masoud Rahmani, Nima Jafari Navimirour, and Ali Rezaee. 2020. Integration of internet of things and cloud computing: A systematic survey. IET Commun. 14, 2 (2020), 165–176.

Digital Library

[16]

Yulei Wu. 2020. Cloud-edge orchestration for the internet-of-things: Architecture and ai-powered data processing. IEEE IoT J. 2020 (2020), 1–14.

[17]

Sudip Misra, Subarna Chatterjee, and Mohammad Obaidat. 2017. On theoretical modeling of sensor cloud: A paradigm shift from wireless sensor network. IEEE Syst. J. 11, 2 (June2017), 1084–1093.

[18]

Waqas Ahmad, Aamir Rasool, Abdul Rehman Javed, Thar Baker, and Zunera Jalil. 2021. Cyber security in iot-based cloud computing: A comprehensive survey. Electronics 11, 1 (2021), 1–34.

[19]

Fei Chen, Duming Luo, Tao Xiang, Ping Chen, Junfeng Fan, and Hong-Linh Truong. 2021. IoT cloud security review: A case study approach using emerging consumer-oriented applications. ACM Comput. Surv. 54, 4 (2021), 1–36.

Digital Library

[20]

Xiting Peng, Kaoru Ota, and Mianxiong Dong. 2020. Multi-attribute based double auction towards resource allocation in vehicular fog computing. IEEE IoT J. 7, 4 (2020), 3094–3103.

[21]

Chuan Lin, Guangjie Han, Xingyue Qi, Mohsen Guizani, and Lei Shu. 2020. A distributed mobile fog computing scheme for mobile delay-sensitive applications in SDN-enabled vehicular networks. IEEE Trans. Vehic. Technol. 69, 5 (2020), 5481–5493.

[22]

Renhung Hwang, Yuling Hsueh, and Haowei Chung. 2014. A novel time-obfuscated algorithm for trajectory privacy protection. IEEE Trans. Serv. Comput. 7, 2 (June2014), 126–139.

[23]

Lianyong Qi, Shunmei Meng, Xuyun Zhang, Ruili Wang, Xiaolong Xu, Zhili Zhou, and Wanchun Dou. 2018. An exception handling approach for privacy-preserving service recommendation failure in a cloud environment. Sensors 18, 7 (June2018), 1–11.

[24]

Fabio Antonelli, Vittorio Cortellessa, Marco Gribaudo, Riccardo Pinciroli, Kishor S. Trivedi, and Catia Trubiani. 2020. Analytical modeling of performance indices under epistemic uncertainty applied to cloud computing systems. Fut. Gener. Comput. Syst. 102 (2020), 746–761.

Digital Library

[25]

Azadeh Neiat, Athman Bouguettaya, Timos Sellis, and Sajib Mistry. 2017. Crowdsourced coverage as a service: Two-level composition of sensor cloud services. IEEE Trans. Knowl. Data Eng. 29 (July2017), 1384–1397.

Digital Library

[26]

Chunlin Li A. B., Jingpan Bai A., Ge B. Yuan, and Youlong Luo A. 2020. Heterogeneity-aware elastic provisioning in cloud-assisted edge computing systems. Fut. Gener. Comput. Syst. 112 (2020), 1106–1121.

[27]

Farshid Bijarbooneh, Wei Du, Edith Ngai, Xiaoming Fu, and Jiangchuan Liu. 2016. Cloud-assisted data fusion and sensor selection for internet of things. IEEE IoT J. 3, 3 (June2016), 257–268.

[28]

Hao, Yufu Suling, and Jia Jinxing. 2015. A scalable cloud for internet of things in smart cities. J. Comput. 26, 3 (2015), 1–13.

[29]

Hanbo Yang, Zheng Sun, Gedong Jiang, Fei Zhao, Xufeng Lu, and Xuesong Mei. 2020. Cloud manufacturing-based condition monitoring platform with 5G and standard information model. IEEE IoT J. 8, 8 (2020), 6949–6948.

[30]

S. Subashini and P. Mathiyalagan. 2020. A cross layer design and flower pollination optimization algorithm for secured energy efficient framework in wireless sensor network. Wireless Pers. Commun. 112 (2020), 1601–1628.

Digital Library

[31]

M Amin Yazdi and Marius Politze. 2020. Reverse engineering: The university distributed services. In Proceedings of the Future Technologies Conference (FTC’20). Springer, 223–238.

[32]

Aishwariya Chakraborty, Sudip Misra, and Ayan Mondal. 2020. QoS-aware dynamic cost management scheme for sensors-as-a-service. IEEE Trans. Serv. Comput. 2020 (2020), 1–12.

[33]

Nengxian Liu, Jeng-Shyang Pan, et al. 2019. A bi-population QUasi-affine transformation evolution algorithm for global optimization and its application to dynamic deployment in wireless sensor networks. EURASIP J. Wireless Commun. Netw. 2019, 1 (2019), 175.

[34]

Fatemeh Heravan, Mohammad Moghaddam, and Seyed Seno. 2016. SDN-based scheduling strategy on load balancing of virtual sensor resources in sensor-cloud. In Proceedings of the 8th International Symposium on Telecommunications (IST’16). 666–671.

[35]

R. K. Dwivedi, S. Singh, and R. Kumar2019. Integration of wireless sensor networks with cloud: A review. In Proceedings of the 9th International Conference on Cloud Computing, Data Science and Engineering (Confluence’19). IEEE, 114–119.

[36]

Tian Wang, Yuzhu Liang, Yujie Tian, Md Zakirul Alam Bhuiyan, Anfeng Liu, and A. Taufiq Asyhari. 2021. Solving coupling security problem for sustainable sensor-cloud systems based on fog computing. IEEE Trans. Sust. Comput. 6, 1 (2021), 43–53.

[37]

Salvatore Distefano, Giovanni Merlino, and Antonio Puliafito. 2012. Enabling the cloud of things. In Proceedings of the IEEE International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS’12). IEEE, 858–863.

Digital Library

[38]

Chunsheng Zhu, Victor Leung, Laurence Yang, and Lei Shu. 2013. Collaborative location-based sleep scheduling to integrate wireless sensor networks with mobile cloud computing. In Proceedings of the IEEE International Workshop on Globecom Workshops (GC Wkshps’13). IEEE, 452–457.

[39]

Giancarlo Fortino, Mukaddim Pathan, and Giuseppe Fatta. 2012. Bodycloud: Integration of cloud computing and body sensor networks. In Proceedings of the IEEE International Conference on Cloud Computing Technology and Science (CloudCom’12). IEEE, 851–856.

Digital Library

[40]

Madoka Yuriyama and Takayuki Kushida. 2010. Sensor-cloud infrastructure-physical sensor management with virtualized sensors on cloud computing. In Proceedings of the IEEE International Conference on 13th Network-Based Information Systems (NBiS’10). IEEE, 1–8.

Digital Library

[41]

Mahmoud Abuelela and Stephan Olariu. 2010. Taking VANET to the clouds. In Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia (MoMM’10). ACM, 6–13.

Digital Library

[42]

Ihsan Ali, Abdullah Gani, Ismail Ahmedy, Ibrar Yaqoob, Suleman Khan, and Mohammad Anisi. 2018. Data collection in smart communities using sensor cloud: Recent advances and taxonomy and future research directions. IEEE Commun. Mag. 56, 7 (July2018), 192–197.

[43]

Loyao Yeh, Peiyuu Chiang, Yilang Tsai, and Jiunlong Huang. 2018. Cloud-based fine-grained health information access control framework for lightweight IoT devices with dynamic auditing and attribute revocation. IEEE Trans. Cloud Comput. 6, 2 (April2018), 532–544.

[44]

Subarna Chatterjee, Sudip Misra, and Samee U. Khan. 2019. Optimal data center scheduling for quality of service management in sensor-cloud. IEEE Trans. Cloud Comput. 7, 1 (March2019), 89–101.

[45]

Tamoghna Ojha, Sudip Misra, Narendra Singh Raghuwanshi, and Hitesh Poddar. 2019. DVSP: Dynamic virtual sensor provisioning in sensor–cloud-based internet of things. IEEE IoT J. 6, 3 (2019), 5265–5272.

[46]

Tian Wang, Yang Li, Weiwei Fang, Wenzheng Xu, Junbin Liang, Yewang Chen, and Xuxun Liu. 2018. A comprehensive trustworthy data collection approach in sensor-cloud system. IEEE Trans. Big Data 2018 (March2018), 1–12.

[47]

P. Sangulagi and A. Sutagundar. 2020. Agent based load balancing in sensor cloud. In Proceedings of the International Conference on Inventive Computation Technologies (ICICT’20). IEEE, 342–347.

[48]

Chunsheng Zhu, Xiuhua Li, Victor Leung, Xiping Hu, and Laurence Yang. 2014. Job scheduling for cloud computing integrated with wireless sensor network. In Proceedings of the IEEE International Conference on Cloud Computing Technology and Science (CloudCom’14). IEEE, 62–69.

[49]

Zomaya Albert, Delicato Flavia, Santos Igor, Khan Samee, and Pirmez Luci. 2015. Olympus: The cloud of sensors. IEEE Cloud Comput. 2, 2 (April2015), 48–56.

[50]

Mohammad Aazam and Euinam Huh. 2014. Fog computing and smart gateway based communication for cloud of things. In Proceedings of the IEEE International Conference on Future Internet of Things and Cloud (FiCloud’14). IEEE, 464–470.

Digital Library

[51]

Adam A. Alli and Muhammad Mahbub Alam. 2020. The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications. Internet Things 9 (2020), 1–20.

[52]

Priyanka Rawat, Kamal Deep Singh, Hakima Chaouchi, and Jean Marie Bonnin. 2014. Wireless sensor networks: A survey on recent developments and potential synergies. J. Supercomput. 68, 1 (April2014), 1–48.

Digital Library

[53]

T. Vigneswari et al. 2021. Smart IOT cloud based livestock monitoring system: A survey. Turk. J. Comput. Math. Educ. 12, 10 (2021), 3308–3315.

[54]

Proshikshya Mukherjee, Tanmaya Swain, and Amlan Datta. 2020. Issues of some task scheduling strategies on sensor cloud environment. In Smart Intelligent Computing and Applications Vol. 160. Springer, 651–663.

[55]

Kanegonda Ravi Chythanya, KomuravellySudheer Kumar, Mothe Rajesh, and S. Tharun Reddy. 2020. Sensor cloud: A breakdown information on the utilization of wireless sensor network by means of cloud computing. Test Eng. Manage. 82 (2020), 13945–13954. https://www.researchgate.net/publication/340478642.

[56]

Giancarlo Fortino, Daniele Parisi, Vincenzo Pirrone, and Giuseppe Fatta. 2014. BodyCloud: A SaaS approach for community body sensor networks. Fut. Gener. Comput. Syst. 35 (June2014), 62–79.

[57]

Kalyani Divi and Hong Liu. 2013. Modeling of WBAN and cloud integration for secure and reliable healthcare. In Proceedings of the 8th International Conference on Body Area Networks (BodyNets’13). ICST, 128–131.

Digital Library

[58]

Jiafu Wan, Caifeng Zou, Sana Ullah, Chinfeng Lai, Ming Zhou, and Xiaofei Wang. 2013. Cloud-enabled wireless body area networks for pervasive healthcare. IEEE Netw. 27, 5 (September2013), 56–61.

[59]

Salim Bitam, Abdelhamid Mellouk, and Sherali Zeadally. 2015. VANET-cloud: A generic cloud computing model for vehicular ad hoc networks. IEEE Wireless Commun. 22, 1 (February2015), 96–102.

Digital Library

[60]

Arif Muhammad, Wang Guojun, and Balas Valentina Emilia. 2018. Secure VANETs: Trusted communication scheme between vehicles and infrastructure based on fog computing. Stud. Inf. Contr. 27, 2 (2018), 235–246.

[61]

S. Madria2018. Sensor cloud: Sensing-as-a-service paradigm. In Proceedings of the 19th IEEE International Conference on Mobile Data Management (MDM’18). IEEE, 13–6.

[62]

Peng Xu, Shuanghong He, Wei Wang, Willy Susilo, and Hai Jin. 2018. Lightweight searchable public-key encryption for cloud-assisted wireless sensor networks. IEEE Trans. Industr. Inf. 14, 8 (August2018), 3712–3723.

[63]

R. Pereira, C. Barros, S. Pereira, P. M. Mendes, and C. A. Silva. 2020. A middleware for managing the heterogeneity of data provining from IoT devices in ambient assisted Living environments. In Proceedings of the IEEE Andes Conference (ANDESCON’20). IEEE, 1–6.

[64]

Eun Lee, Hariharasudhan Viswanathan, and Dario Pompili. 2018. Model-based thermal anomaly detection in cloud datacenters using thermal imaging. IEEE Trans. Cloud Comput. 6, 2 (April2018), 330–343.

[65]

R. Bruce Wallace, Frank Horsfall, Rafik Goubran, Ali El-Haraki, and Frank Knoefel. 2019. The challenges of connecting smart home health sensors to cloud analytics. In Proceedings of the IEEE Sensors Applications Symposium (SAS’19). IEEE, 1–5.

Digital Library

[66]

Mohammad Nazmul Alam and Roch H. Glitho. 2018. An infrastructure as a service for the internet of things. In Proceedings of the IEEE 7th International Conference on Cloud Networking (CloudNet’18). IEEE, 1–7.

[67]

Jinfang Jiang, Guangjie Han, Lei Shu, Sammy Chan, and Kun Wang. 2017. A trust cloud model for underwater wireless sensor networks. IEEE Commun. Mag. 55, 3 (March2017), 110–116.

Digital Library

[68]

Chunsheng Zhu, Xiuhua Li, Victor Leung, Laurence Yang, Edith Ngai, and Lei Shu. 2020. Towards pricing for sensor-cloud. IEEE Trans. Cloud Comput. 8, 4 (2020), 1018–1029.

[69]

Sherif Abdelwahab, Bechir Hamdaoui, Mohsen Guizani, and Taieb Znati. 2015. Cloud of things for sensing as a service: Sensing resource discovery and virtualization. In Proceedings of the IEEE International Conference on Global Communications (GLOCOM’15). IEEE, 1–7.

[70]

Huajun Hong, Chingling Fan, Yenchen Lin, and Chenghsin Hsu. 2016. Optimizing cloud-based video crowdsensing. IEEE IoT J. 3, 3 (June2016), 299–313.

[71]

Jianhua Liu, Jiadi Yu, and Shigen Shen. 2018. Energy-efficient two-layer cooperative defense scheme to secure sensor-clouds. IEEE Trans. Inf. Forens. Secur. 13, 2 (February2018), 408–420.

Digital Library

[72]

Prasenjit Maiti, Jaya Shukla, Bibhudatta Sahoo, and Ashok Turuk. 2018. QoS-aware fog nodes placement. In Proceedings of the IEEE International Conference on Recent Advances in Information Technology (RAIT’18). IEEE, 1–6.

[73]

Xu Xiaolong, Zhang Xuyun, gao Honghao, and Xue Yuan. 2019. BeCome: Blockchain-enabled computation offloading for IoT in mobile edge computing. IEEE Trans. Industr. Inf. 16, 6 (2019), 4187–4195.

[74]

Shi Ming Huang, David C. Yen, Ting Jyun Yan, and Yi Ting Yang. 2021. An intelligent mechanism to automatically discover emerging technology trends: Exploring regulatory technology. ACM Trans. Manage. Inf. Syst. 13, 2 (2021), 1–29.

Digital Library

[75]

Z. Fan, W. Yang, and K. Tian. 2019. An edge computing service model based on information-centric networking. In Proceedings of the IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS’19). IEEE, 498–505.

[76]

Mohammad Hassan, Biao Song, and Euinam Huh. 2009. A framework of sensor-cloud integration opportunities and challenges. In Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication (ICUIMC’09). ACM, 618–626.

Digital Library

[77]

Carlo Puliafito, Enzo Mingozzi, Francesco Longo, Antonio Puliafito, and Omer F. Rana. 2019. Fog computing for the internet of things: A survey. ACM Trans. Internet Technol. 19, 2 (2019), 1–41.

Digital Library

[78]

Riccardo Venanzi, Burak Kantarci, Luca Foschini, and Paolo Bellavista. 2018. MQTT-driven sustainable node discovery for internet of things-fog environments. In Proceedings of the IEEE International Conference on Communications (ICC’18). IEEE, 1–6.

[79]

Salma Abdalla Hamad, Quan Z. Sheng, Wei Emma Zhang, and Surya Nepal. 2020. Realizing an internet of secure things: A survey on issues and enabling technologies. IEEE Commun. Surv. Tutor. 22, 2 (2020), 1372–1391.

[80]

Mithun Mukherjee, Lei Shu, and Di Wang. 2018. Survey of fog computing: Fundamental and network applications and research challenges. IEEE Commun. Surv. Tutor. 20, 3 (March2018), 1826–1857.

[81]

Yazdan Ahmad Qadri, Ali Nauman, Yousaf Bin Zikria, Athanasios V. Vasilakos, and Sung Won Kim. 2020. The future of healthcare internet of things: A survey of emerging technologies. IEEE Commun. Surv. Tutor. 22, 2 (2020), 1121–1167.

[82]

C. Liu, J. Hua, and C. Julien. 2019. Scents: Collaborative sensing in proximity iot networks. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops’19). IEEE, 189–195.

[83]

Mung Chiang and Tao Zhang. 2016. Fog and IoT: An overview of research opportunities. IEEE IoT J. 3, 6 (December2016), 854–864.

[84]

Min Chen, Yongfeng Qian, Yixue Hao, Yong Li, and Jeungeun Song. 2018. Data-driven computing and caching in 5G networks: Architecture and delay analysis. IEEE Wireless Commun. 25, 1 (2018), 70–75.

Digital Library

[85]

Amitav Mukherjee. 2018. Fog-aided data reception in next-generation MIMO radio access networks with edge sensing. In Proceedings of the IEEE International Conference on Communications (ICC’18). IEEE, 1–6.

[86]

Jinkyu Kang, Osvaldo Simeone, Joonhyuk Kang, and Shlomo Shitz. 2018. Control-data separation with Decemberentralized edge control in fog-assisted uplink communications. IEEE Trans. Wireless Commun. 17, 6 (June2018), 3686–3696.

Digital Library

[87]

A. L. É. Battisti, D. C. Muchaluat-Saade, and F. C. Delicato. 2020. V-PRISM: An edge-based IoT architecture to virtualize multimedia sensors. In Proceedings of the IEEE 6th World Forum on Internet of Things (WF-IoT’20). IEEE, 1–6.

[88]

Saurav Sthapit, John Thompson, Neil M. Robertson, and James R. Hopgood. 2018. Computational load balancing on the edge in absence of cloud and fog. IEEE Trans. Mobile Comput. 18, 7 (2018), 1499–1512.

[89]

Abebe Diro and Naveen Chilamkurti. 2018. Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Commun. Mag. 56, 9 (September2018), 124–130.

[90]

Peng Yang, Ning Zhang, Shan Zhang, Kan Yang, Li Yu, and Xuemin Shen. 2017. Identifying the most valuable workers in fog-assisted spatial crowdsourcing. IEEE IoT J. 4, 5 (October2017), 1193–1203.

[91]

Cheol-Ho Hong and Blesson Varghese. 2019. Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms. ACM Comput. Surv. 52, 5 (2019), 1–37.

Digital Library

[92]

Tie Qiu, Jiancheng Chi, Xiaobo Zhou, Zhaolong Ning, and Dapeng Oliver Wu. 2020. Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Commun. Surv. Tutor. 22, 4 (2020), 2462–2488.

[93]

Abbas Javed, Hadi Larijani, Ali Ahmadinia, Rohinton Emmanuel, Mike Mannion, and Des Gibson. 2017. Design and implementation of a cloud enabled random neural network-based decentralized smart controller with intelligent sensor nodes for HVAC. IEEE IoT J. 4, 2 (April2017), 393–403.

[94]

Stephanie Challita, Fawaz Paraiso, and Philippe Merle. 2017. Towards formal-based semantic interoperability in multi-clouds: The FCLOUDS framework. In Proceedings of the IEEE International Conference on Cloud Computing (CLOUD’17). IEEE, 710–713.

[95]

Anh LeTun, Hoan N. Mau Quoc, Martin Serrano, Manfred Hauswirth, John Soldatos, Thanasis Papaioannou, and Karl Aberer. 2012. Global sensor modeling and constrained application methods enabling cloud-based open space smart services. In Proceedings of the IEEE Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing (UIC/ATC’12). IEEE, 196–203.

Digital Library

[96]

Chunsheng Zhu, Victor Leung, Kun Wang, Laurence Yang, and Yan Zhang. 2017. Multi-method data delivery for green sensor-cloud. IEEE Commun. Mag. 55, 5 (May2017), 176–182.

Digital Library

[97]

Sevil Ahmed, Andon Topalov, and Nikola Shakev. 2017. A robotized wireless sensor network based on MQTT cloud computing. In Proceedings of the IEEE International Workshop on Electronics and Control and Measurement and Signals and their Application to Mechatronics (ECMSM’17). IEEE, 1–6.

[98]

Yi Xu and Abdelsalam Helal. 2015. Scalable cloud–sensor architecture for the Internet of Things. IEEE IoT J. 3, 3 (2015), 285–298.

[99]

Jiantao Zhou, Yong Xiang, Leoyu Zhang, Fei Chen, Shaoning Pang, and Xiaofeng Liao. 2017. Computation outsourcing meets lossy channel: Secure sparse robustness decoding service in multi-clouds. IEEE Trans. Big Data 4 (June2017), 1–10.

[100]

Antonio Celesti, Maria Fazio, Maurizio Giacobbe, Antonio Puliafito, and Massimo Villari. 2016. Characterizing cloud federation in IoT. In Proceedings of the IEEE International Workshops on Advanced Information Networking and Applications Workshops (WAINA’16). IEEE, 93–98.

[101]

Massimo Ficco, Christian Esposito, Yang Xiang, and Francesco Palmieri. 2017. Pseudo-dynamic testing of realistic edge-fog cloud ecosystems. IEEE Commun. Mag. 55, 11 (November2017), 98–104.

[102]

Zhi Li, Xianwei Zhou, Yanzhu Liu, Haitao Xu, and Li Miao. 2017. A non-cooperative differential game-based security model in fog computing. China Commun. 14, 1 (January2017), 180–189.

[103]

Daewoo Kim, Hyojung Lee, Hyungseok Song, Nakjung Choi, and Yung Yi. 2018. On the economics of fog computing: Inter-play among infrastructure and service providers and users and edge resource owners. In Proceedings of the IEEE International Conference on Communications (ICC’18). IEEE, 1–6.

[104]

Chi Yang, Deepak Puthal, Saraju 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 Consum. Electr. Mag. 6, 4 (October2017), 48–56.

[105]

Arijit Roy, Sudip Misra, and Soumi Nag. 2020. PRIME: An optimal pricing scheme for mobile sensors-as-a-service. IEEE Trans. Mobile Comput. 2020 (2020), 1–12.

[106]

Heng Wang, Liuqing Chen, Min Li, and Pengfei Gong. 2020. Consensus-based clock synchronization in wireless sensor networks with truncated exponential delays. IEEE Trans. Sign. Process. 68 (2020), 1425–1438.

[107]

Pengfei Hu, Huansheng Ning, Tie Qiu, Houbing Song, Yanna Wang, and Xuanxia Yao. 2017. Security and privacy preservation scheme of face identification and resolution framework using fog computing in internet of things. IEEE IoT J. 4, 5 (October2017), 1143–1155.

[108]

Martin Henze, René Hummen, Roman Matzutt, Daniel Catrein, and Klaus Wehrle. 2013. Maintaining user control while storing and processing sensor data in the cloud. Int. J. Grid High Perf. Comput. 5, 4 (December2013), 97–112.

Digital Library

[109]

Martin Henze. 2020. The quest for secure and privacy-preserving cloud-based industrial cooperation. In Proceedings of IEEE Conference on Communications and Network Security (CNS’20). IEEE, 1–5.

[110]

Kai Yang, Yuanming Shi, and Zhi Ding. 2017. Low-rank matrix completion for mobile edge caching in Fog-RAN via Riemannian optimization. In Proceedings of the IEEE Global Communications Conference (GLOBECOM’17). IEEE, 1–6.

Digital Library

[111]

Chunsheng Zhu, Victor Leung, Laurence Yang, Lei Shu, Joel Rodrigues, and Xiuhua Li. 2015. Trust assistance in sensor-cloud. In Proceedings of the IEEE International Conference on Computer Communications Workshops (INFOCOM WKSHPS’15). IEEE, 342–347.

[112]

Liehuang Zhu, Chuan Zhang, Chang Xu, and Kashif Sharif. 2018. RTSense: Providing reliable trust-based crowdsensing services in CVCC. IEEE Netw. 32, 3 (June2018), 20–26.

[113]

Li Feng, Jie Yang, and Huan Zhang. 2017. RT-notification: A novel real-time notification protocol for wireless control in fog computing. Chin. Commun. 14, 11 (December2017), 17–28.

[114]

Chiamu Yu, Chiyuan Chen, and Hanchieh Chao. 2015. Verifiable and privacy-assured and and accurate signal collection for cloud-assisted wireless sensor networks. IEEE Commun. Mag. 53, 8 (August2015), 48–53.

Digital Library

[115]

Sajeeb Saha, Md. Ahsan Habib, Tamal Adhikary, Md. Abdur Razzaque, and Md. Mustafizur Rahman. 2019. Tradeoff between execution speedup and reliability for compute-intensive code offloading in mobile device cloud. Multimedia Syst. 25, 5 (2019), 577–589.

[116]

Stephan Olariu. 2020. A survey of vehicular cloud research: Trends, applications and challenges. IEEE Trans. Intell. Transport. Syst. 21, 6 (2020), 2648–2663.

[117]

Xavi Bruin, Eva Tordera, and Ghazal Tashakor. 2016. Foggy clouds and cloudy fogs: A real need for coordinated management of fog-to-cloud computing systems. IEEE Wireless Commun. 23, 5 (October2016), 120–128.

Digital Library

[118]

Xiaoyu Chen, Lening Wang, and Canran Wang. 2018. Predictive offloading in mobile-fog-cloud enabled cyber-manufacturing systems. In Proceedings of the IEEE Industrial Cyber-Physical Systems (ICPS’18). IEEE, 167–172.

[119]

Luis Neto, Joao Reis, Ricardo Silva, and Gil Goncalves. 2017. Sensor selcomp and a smart component for the industrial sensor cloud of the future. In Proceedings of the IEEE International Conference on Industrial Technology (ICIT’17). IEEE, 1256–1261.

[120]

Jianshan Zhou, Daxin Tian, Yunpeng Wang, Zhengguo Sheng, Xuting Duan, and Victor C. M. Leung. 2019. Reliability-optimal cooperative communication and computing in connected vehicle systems. IEEE Trans. Mobile Comput. 19, 5 (2019), 1216–1232.

[121]

Ahmed Salim Alrawahi, Kevin Lee, and Ahmad Lotfi. 2019. A multiobjective QoS model for trading cloud of things resources. IEEE IoT J. 6, 6 (2019), 9447–9463.

[122]

Kuljeet Kaur, Sahil Garg, Georges Kaddoum, Syed Hassan Ahmed, Francois Gagnon, and Mohammed Atiquzzaman. 2019. Demand-response management using a fleet of electric vehicles: An opportunistic-SDN-based edge-cloud framework for smart grids. IEEE Netw. 33, 5 (2019), 46–53.

Digital Library

[123]

Mohammad Al-Shayeji, Fahad Ebrahim, et al. 2019. A secure and energy-efficient platform for the integration of wireless sensor networks and mobile cloud computing. Comput. Netw. 165, 24 (2019), 1–13.

Digital Library

[124]

Guangjie Han, Jiaxin Du, Chuan Lin, Hongyi Wu, and Mohsen Guizani. 2020. An energy-balanced trust cloud migration scheme for underwater acoustic sensor networks. IEEE Trans. Wireless Commun. 19, 3 (2020), 1636–1649.

Digital Library

[125]

Sherali Zeadally, Faisal Karim Shaikh, Anum Talpur, and Quan Z. Sheng. 2020. Design architectures for energy harvesting in the internet of things. Renew. Sust. Energy Rev. 128 (2020), 1–22. DOI:

[126]

Subarna Chatterjee, Subhadeep Sarkar, and Sudip Misra. 2015. Energy-efficient data transmission in sensor-cloud. In Proceedings of the IEEE International Conference on Applications and Innovations in Mobile Computing (AIMoC’15). IEEE, 68–73.

[127]

Shakena Grace and Sumalatha. 2014. SCA-an energy efficient transmission in sensor cloud. In Proceedings of the IEEE International Conference on Recent Trends in Information Technology (ICRTIT’14). IEEE, 1–5.

[128]

Dapeng Wu, Boran Yang, Honggang Wang, Dalei Wu, and Ruyan Wang. 2016. An energy-efficient data forwarding strategy for heterogeneous WBANs. IEEE Access 4 (September2016), 7251–7261.

[129]

Suman Bhunia, Jayita Pal, and Nandini Mukherjee. 2014. Fuzzy assisted event driven data collection from sensor nodes in sensor-cloud infrastructure. In In Proceedings of the 14th IEEE/ACM International Symposium on Cluster and Cloud and Grid Computing (CCGrid’14). IEEE/ACM, 635–640.

Digital Library

[130]

Tian Wang, Yuzhu Liang, Yilin Zhang, Xi Zheng, Muhammad Arif, Jin Wang, and Qun Jin. 2020. An intelligent dynamic offloading from cloud to edge for smart iot systems with big data. IEEE Trans. Netw. Sci. Eng. 7, 4 (2020), 2598–2607.

[131]

Rashmi Dalvi and Sanjay Madria. 2015. Energy efficient scheduling of fine-granularity tasks in a sensor cloud. In Proceedings of the IEEE International Conference on Database Systems for Advanced Applications (DASFAA’15). IEEE, 498–513.

[132]

Thanh Dinh and Younghan Kim. 2017. Information centric sensor-cloud integration: An efficient model to improve wireless sensor networks lifetime. In Proceedings of the IEEE International Conference on Communications (ICC’17). IEEE, 1–6.

[133]

Zeyuan Yan, Yanyan Tan, Wei Zheng, Lili Meng, and Huaxiang Zhang. 2021. Leader recommend operators selection strategy for a multiobjective evolutionary algorithm based on decomposition. Inf. Sci. 550 (2021), 166–188.

[134]

Werner Kurschl and Wolfgang Beer. 2009. Combining cloud computing and wireless sensor networks. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (iiWAS’09). ACM, 512–518.

Digital Library

[135]

Chunsheng Zhu, Victor Leung, Laurence Yang, Xiping Hu, and Lei Shu. 2015. Collaborative location-based sleep scheduling for wireless sensor networks integrated with mobile cloud computing. IEEE Trans. Comput. 64, 7 (July2015), 1844–1856.

Digital Library

[136]

Navroop Kaur and Sandeep Sood. 2017. An energy-efficient architecture for the internet of things (IoT). IEEE Syst. J. 11, 2 (June2017), 796–805.

[137]

Zheshi Chen, Chunhong Li, and Wenjun Sun. 2020. Bitcoin price prediction using machine learning: An approach to sample dimension engineering. J. Comput. Appl. Math. 365 (2020), 1–13.

Digital Library

[138]

Antonio Rafael Sabino Parmezan, Vinicius M. A. Souza, and Gustavo E. A. P. A. Batista. 2019. Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Inf. Sci. 484 (2019), 302–337.

Digital Library

[139]

Myriam Hernández-Álvarez, Edgar A. Torres Hernández, Sang Guun Yoo, et al. 2019. Stock market data prediction using machine learning techniques. In Proceedings of the International Conference on Information Technology & Systems (ICITS’19). Springer, 539–547.

[140]

Chapoulade Elodie, Talon Aurélie, Chateauneuf Alaa, Breul Pierre, Hermand Guillaume, and Leconte Marc. 2020. Sensors position optimization for monitoring the convergence of radioactive waste storage tunnel. Nucl. Eng. Des. 367 (2020), 1–10.

[141]

Subarna Chatterjee and Sudip Misra. 2015. Optimal composition of a virtual sensor for efficient virtualization within sensor-cloud. In Proceedings of the IEEE International Conference on Communications (ICC’15). IEEE, 448–453.

[142]

Changsheng You, Kaibin Huang, and Hyukjin Chae. 2016. Energy efficient mobile cloud computing powered by wireless energy transfer. IEEE J. Select. Areas Commun. 34, 5 (May2016), 1757–1771.

Digital Library

[143]

Jiming Chen, Songyuan Li, Shuo Chen, Shibo He, and Zhiguo Shi. 2017. Q-charge: A quadcopter-based wireless charging platform for large-scale sensing applications. IEEE Netw. 31, 6 (November2017), 56–61.

Digital Library

[144]

Yisheng Zhao, Victor Leung, Chunsheng Zhu, Hui Gao, Zhonghui Chen, and Hong Ji. 2017. Energy-efficient sub-carrier and power allocation in cloud-based cellular network with ambient RF energy harvesting. IEEE Access 5 (February2017), 1340–1352.

[145]

Tian Wang, Yuzhu Liang, Yaxin Mei, Muhammad Arif, and Chunsheng Zhu. 2018. High-accuracy localization for indoor group users based on extended Kalman filter. Int. J. Distrib. Sens. Netw. 14, 11 (2018), 1–10.

[146]

Ahmed Latif, Bassem Atty, and Shamim Hossain. 2018. Secure quantum steganography protocol for fog cloud Internet of Things. IEEE Access 6 (March2018), 10332–10340.

[147]

Maria Fazio and Antonio Puliafito. 2015. Cloud4sens: A cloud-based architecture for sensor controlling and monitoring. IEEE Commun. Mag. 53, 3 (March2015), 41–47.

Digital Library

[148]

Azadeh Neiat, Athman Bouguettaya, Timos Sellis, and Hai Dong. 2014. Spatio-temporal composition of sensor cloud services. In Proceedings of the IEEE International Conference on Web Services (ICWS’14). IEEE, 241–248.

Digital Library

[149]

Mohamed Guezguez, Slim Rekhis, and Noureddine Boudriga. 2017. A sensor cloud for the provision of secure and QoS-aware healthcare services. Arab. J. Sci. Eng. 2 (November2017), 1–24.

[150]

Abbas Javed, Hadi Larijani, Ali Ahmadinia, and Des Gibson. 2017. Smart random neural network controller for HVAC using cloud computing technology. IEEE Trans. Industr. Inf. 13, 1 (February2017), 351–360.

[151]

Zhihua Xia, Xinhui Wang, Liangao Zhang, Zhan Qin, Xingming Sun, and Kui Ren. 2016. A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans. Inf. Forens. Secur. 11, 11 (November2016), 2594–2608.

Digital Library

[152]

Ismail Butun, Salvatore D. Morgera, and Ravi Sankar. 2014. A survey of intrusion detection systems in wireless sensor networks. IEEE Commun. Surv. Tutor. 16, 1 (May2014), 266–282.

[153]

Nam Giang, Rodger Lea, Michael Blackstock, and Victor Leung. 2018. Fog at the edge: Experiences building an edge computing platform. In Proceedings of the IEEE International Conference on Edge Computing (EDGE’18). IEEE, 9–16.

[154]

Subarna Chatterjee, Ranjana Ladia, and Sudip Misra. 2017. Dynamic optimal pricing for heterogeneous service-oriented architecture of sensor-cloud infrastructure. IEEE Trans. Serv. Comput. 10, 2 (March2017), 203–216.

[155]

Qi Qi, Jianxin Liao, Jingyu Wang, Qi Li, and Yufei Cao. 2016. Dynamic resource orchestration for multi-Task application in heterogeneous mobile cloud computing. In Proceedings of the Computer Communications Workshops (INFOCOM WKSHPS’16). IEEE, 221–226.

[156]

Peijin Cong, Junlong Zhou, Liying Li, Kun Cao, and Keqin Li. 2020. A survey of hierarchical energy optimization for mobile edge computing: A perspective from end devices to the cloud. ACM Comput. Surv. 53, 2 (2020), 1–44.

Digital Library

[157]

TiTiNguyen, VuNguyenHa, LongBaoLe, and Robert Schober. 2020. Joint data compression and computation offloading in hierarchical fog-cloud systems. IEEE Trans. Wireless Commun. 19, 1 (2020), 293–309.

[158]

Tiansheng Huang, Weiwei Lin, Chennian Xiong, Rui Pan, and Jingxuan Huang. 2020. An ant colony optimization-based multiobjective service replicas placement strategy for fog computing. IEEE Trans. Cybernet. 2020 (2020), 1–14.

[159]

Jianhua He, Jian Wei, Kai Chen, Zuoyin Tang, Yi Zhou, and Yan Zhang. 2018. Multitier fog computing with large-scale iot data analytics for smart cities. IEEE IoT J. 5, 2 (April2018), 677–686.

[160]

Mohammad Moghaddam and Alberto Garcia. 2018. A fog-based internet of energy architecture for transactive energy management systems. IEEE IoT J. 5, 2 (April2018), 1055–1069.

[161]

Ouns Bouachir, Moayad Aloqaily, Lewis Tesng, and Azzedine Boukerche. 2020. Blockchain and fog computing for cyber-physical systems: Case of smart industry. Computer 53, 9 (2020), 36–45.

[162]

Zhenyu Zhou, Haijun Liao, Xiaoyan Wang, Shahid Mumtaz, and Jonathan Rodriguez. 2020. When vehicular fog computing meets autonomous driving: Computational resource management and task offloading. IEEE Netw. 34, 6 (2020), 1–7.

Digital Library

[163]

Yong Yu, Manho Au, Giuseppe Ateniese, Xinyi Huang, Yuanshun Dai, Willy Susilo, and Geyong Min. 2017. Identity-based remote data integrity checking with perfect data privacy preserving for cloud storage. IEEE Trans. Inf. Forens. Secur. 12, 4 (October2017), 767–778.

Digital Library

[164]

Tian Wang, Jiyuan Zhou, Minzhe Huang, MD Zakirul Alam Bhuiyan, Anfeng Liu, Wenzheng Xu, and Mande Xie. 2018. Fog-based storage technology to fight with cyber threat. Fut. Gener. Comput. Syst. 83 (2018), 208–218.

[165]

Fei Chen, Fengming Meng, Tao Xiang, Hua Dai, Jianqiang Li, and Jing Qin. 2020. Towards usable cloud storage auditing. IEEE Trans. Parallel Distrib. Syst. 31, 11 (2020), 2605–2617.

[166]

Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, and Jian Shen. 2021. Privacy-preserving multi-keyword searchable encryption for distributed systems. IEEE Trans. Parallel Distrib. Syst. 32, 3 (2021), 561–574.

[167]

Yi Qian. 2020. 5G wireless communication networks: Challenges in security and privacy. IEEE Wireless Commun. 27, 4 (2020), 2–3.

[168]

Jindan Zhang, Rongxing Lu, Baocang Wang, and Xu An Wang. 2020. Comment on “privacy-preserving public auditing protocol for regenerating-code-based cloud storage.” IEEE Trans. Inf. Forens. Secur. 16 (2020), 1288–1289.

Digital Library

[169]

Ferucio Laurentiu Tiplea and Cristian Hristea. 2021. PUF protected variables: A solution to RFID security and privacy under corruption with temporary state disclosure. IEEE Trans. Inf. Forens. Secur. 16 (2021), 999–1013.

[170]

Wentai Wu, Ligang He, Weiwei Lin, and Rui Mao. 2021. Accelerating federated learning over reliability-agnostic clients in mobile edge computing systems. IEEE Trans. Parallel Distrib. Syst. 32, 7 (2021), 1539–1551.

[171]

Cuixian Lu, Guolong Feng, Yuxin Zheng, Keke Zhang, Han Tan, Galina Dick, and Jens Wickert. 2020. Real-time retrieval of precipitable water vapor from galileo observations by using the MGEX network. IEEE Trans. Geosci. Remote Sens. 58, 7 (2020), 4743–4753.

[172]

Hanguang Luo, Guangjun Wen, and Jian Su. 2020. Lightweight three factor scheme for real-time data access in wireless sensor networks. Wireless Netw. 26, 2 (2020), 955–970.

Digital Library

[173]

Kang Liao, Chunyu Lin, Yao Zhao, and Moncef Gabbouj. 2020. DR-GAN: Automatic radial distortion rectification using conditional GAN in real-time. IEEE Trans. Circ. Syst. Vid. Technol. 30, 3 (2020), 725–733.

Digital Library

[174]

Tian Wang, Jiandian Zeng, Yongxuan Lai, Yiqiao Cai, Hui Tian, Yonghong Chen, and Baowei Wang. 2020. Data collection from WSNs to the cloud based on mobile Fog elements. Fut. Gener. Comput. Syst. 105 (2020), 864–872.

Digital Library

[175]

Iftikhar Ahmad, Samreen Ayaz, Syed Yasser Arafat, Faisal Riaz, and Humaira Jabeen. 2013. QoS routing for real time traffic in mobile ad hoc network. In Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication (ICUIMC’13). 1–6.

Digital Library

[176]

Dabin Kim and Young-Bae Ko. 2015. A novel message broadcasting strategy for reliable content retrieval in multi-hop wireless content centric networks. In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (IMCOM’15). 1–8.

Digital Library

[177]

Zhangbing Zhou, Deng Zhao, Lu Liu, and Patrick Hung. 2018. Energy-aware composition for wireless sensor networks as a service. Fut. Gener. Comput. Syst. 80 (March2018), 299–310.

Digital Library

[178]

Wenwen Gong, Lianyong Qi, and Yanwei Xu. 2018. Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment. Wireless Commun. Mobile Comput. 4 (April2018), 1–8.

Digital Library

[179]

Hui Guo, Lan Lan Rui, and Zhi Peng Gao. 2020. A zone-based content pre-caching strategy in vehicular edge networks. Fut. Gener. Comput. Syst. 106 (2020), 22–33.

Digital Library

[180]

Amelec Viloria, Nelson Alberto Lizardo Zelaya, and Nohora Mercado-Caruzo. 2020. Design of a network with wireless sensor applied to data transmission based on IEEE 802.15.4 standard. Proc. Comput. Sci. 175 (2020), 665–670.

[181]

Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb. 2021. Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Commun. Surv. Tutor. 23, 2 (2021), 1342–1397.

[182]

Jinfang Jiang, Guangjie Han, Lei Shu, Sammy Chan, and Kun Wang. 2017. A trust model based on cloud theory in underwater acoustic sensor networks. IEEE Trans. Industr. Inf. 13, 1 (December2017), 342–350.

[183]

Tian Wang, Guangxue Zhang, Md Zakirul Alam Bhuiyan, Anfeng Liu, Weijia Jia, and Mande Xie. 2020. A novel trust mechanism based on fog computing in sensor–cloud system. Fut. Gener. Comput. Syst. 109 (2020), 573–582.

[184]

David Airehrour, Jairo A. Gutierrez, and Sayan Kumar Ray. 2019. SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things. Fut. Gener. Comput. Syst. 93 (2019), 860–876.

Digital Library

[185]

Qussai Yaseen, Firas AlBalas, Yaser Jararweh, and Mahmoud Ayyoub. 2016. A fog computing based system for selective forwarding detection in mobile wireless sensor networks. In Proceedings of the IEEE International Workshops on Foundations and Applications of Self Systems (FAS-W’16). IEEE, 256–262.

[186]

Sarder Fakhrul Abedin, Md. Golam Rabiul Alam, S. M. Ahsan Kazmi, Nguyen H. Tran, Dusit Niyato, and Choong Seon Hong. 2019. Resource allocation for ultra-reliable and enhanced mobile broadband IoT applications in fog network. IEEE Trans. Commun. 67, 1 (2019), 489–502.

[187]

Xi Zheng, Lei Pan, and Erdem Yilmaz. 2017. Security analysis of modern mission critical android mobile applications. In Proceedings of the Australasian Computer Science Week Multiconference (MOBILESoft’14). ACM, 1–9.

Digital Library

[188]

Sancheng Peng, Guojun Wang, Yongmei Zhou, Cong Wan, Cong Wang, Shui Yu, and Jianwei Niu. 2019. An immunization framework for social networks through big data based influence modeling. IEEE Trans. Depend. Secure Comput. 16, 6 (2019), 984–995.

[189]

Harish Radhappa, Lei Pan, James Xi Zheng, and Sheng Wen. 2018. Practical overview of security issues in wireless sensor network applications. Int. J. Comput. Appl. 40, 4 (2018), 202–213.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 13s

December 2023

1367 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3606252

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University of Sydney, Australia

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Published: 13 July 2023

Online AM: 13 February 2023

Accepted: 09 January 2023

Revised: 02 September 2022

Received: 14 July 2021

Published in CSUR Volume 55, Issue 13s

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