RaSEC: An Intelligent Framework for Reliable and Secure Multilevel Edge Computing in Industrial Environments

Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats.

[1]  Xiangjian He,et al.  Error Concealment for Cloud–Based and Scalable Video Coding of HD Videos , 2019, IEEE Transactions on Cloud Computing.

[2]  Xiangjian He,et al.  Frame Interpolation for Cloud-Based Mobile Video Streaming , 2016, IEEE Transactions on Multimedia.

[3]  Xiangjian He,et al.  A Distributed and Anonymous Data Collection Framework Based on Multilevel Edge Computing Architecture , 2020, IEEE Transactions on Industrial Informatics.

[4]  Ju Ren,et al.  Distilling at the Edge: A Local Differential Privacy Obfuscation Framework for IoT Data Analytics , 2018, IEEE Communications Magazine.

[5]  D. PraveenKumar,et al.  Machine learning algorithms for wireless sensor networks: A survey , 2019, Inf. Fusion.

[6]  Xiangjian He,et al.  A Mobile Multimedia Data Collection Scheme for Secured Wireless Multimedia Sensor Networks , 2020, IEEE Transactions on Network Science and Engineering.

[7]  Xiong Li,et al.  Pairing based anonymous and secure key agreement protocol for smart grid edge computing infrastructure , 2018, Future Gener. Comput. Syst..

[8]  Bing Chen,et al.  Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues , 2018, IEEE Access.

[9]  Xiaodong Lin,et al.  Querying in Internet of Things with Privacy Preserving: Challenges, Solutions and Opportunities , 2018, IEEE Network.

[10]  Xiangjian He,et al.  Data Sharing in Secure Multimedia Wireless Sensor Networks , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[11]  Xiangjian He,et al.  A Joint Framework for QoS and QoE for Video Transmission over Wireless Multimedia Sensor Networks , 2018, IEEE Transactions on Mobile Computing.

[12]  Ting He,et al.  Location Privacy in Mobile Edge Clouds: A Chaff-Based Approach , 2017, IEEE Journal on Selected Areas in Communications.

[13]  Nirwan Ansari,et al.  EdgeIoT: Mobile Edge Computing for the Internet of Things , 2016, IEEE Communications Magazine.

[14]  Xiangjian He,et al.  Survey of Error Concealment techniques: Research directions and open issues , 2015, 2015 Picture Coding Symposium (PCS).

[15]  Mohamed Medhat Gaber,et al.  Edge Machine Learning: Enabling Smart Internet of Things Applications , 2018, Big Data Cogn. Comput..

[16]  Xiong Li,et al.  Privacy Preserving Data Aggregation Scheme for Mobile Edge Computing Assisted IoT Applications , 2019, IEEE Internet of Things Journal.

[17]  Xiangjian He,et al.  A Survey on Representation Learning Efforts in Cybersecurity Domain , 2019, ACM Comput. Surv..

[18]  Xiangjian He,et al.  Performance evaluation of High Definition video streaming over Mobile Ad Hoc Networks , 2018, Signal Process..

[19]  Tapani Ristaniemi,et al.  Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era , 2018, IEEE Wireless Communications.

[20]  Salil S. Kanhere,et al.  Privacy-Preserving Collaborative Path Hiding for Participatory Sensing Applications , 2011, 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems.

[21]  Mianxiong Dong,et al.  Eyes in the Dark: Distributed Scene Understanding for Disaster Management , 2017, IEEE Transactions on Parallel and Distributed Systems.

[22]  Md Zakirul Alam Bhuiyan,et al.  A Secure IoT Service Architecture With an Efficient Balance Dynamics Based on Cloud and Edge Computing , 2019, IEEE Internet of Things Journal.

[23]  David Hutchison,et al.  The Extended Cloud: Review and Analysis of Mobile Edge Computing and Fog From a Security and Resilience Perspective , 2017, IEEE Journal on Selected Areas in Communications.

[24]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[25]  Rashid Mehmood,et al.  UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities , 2018, IEEE Access.

[26]  Yan Zhang,et al.  Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing , 2020, IEEE Transactions on Big Data.

[27]  Jemal H. Abawajy,et al.  Identifying cyber threats to mobile-IoT applications in edge computing paradigm , 2018, Future Gener. Comput. Syst..

[28]  Vinod Vokkarane,et al.  A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure , 2018, IEEE Transactions on Services Computing.

[29]  Kim-Kwang Raymond Choo,et al.  Challenges of Connecting Edge and Cloud Computing: A Security and Forensic Perspective , 2017, IEEE Cloud Computing.

[30]  Guihai Chen,et al.  Privacy and Quality Preserving Multimedia Data Aggregation for Participatory Sensing Systems , 2015, IEEE Transactions on Mobile Computing.

[31]  Xinyu Yang,et al.  A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications , 2017, IEEE Internet of Things Journal.

[32]  Mohsen Guizani,et al.  Security in the Internet of Things Supported by Mobile Edge Computing , 2018, IEEE Communications Magazine.