A context sensitive security framework for Enterprise multimedia placement in fog computing environment

Fog computing: A taxonomy, systematic review, current trends and research challenges

“…The suggested network uses techniques such the Ipv6 based classification framework of chronic ailments related to cascaded deep-learning, cloud-fog scheduling of resources & allotment related to time threshold. The preliminary results show that the recommended framework of chronic ailments has more accuracy in finding the [106] Research & Development Edge to Fog, Shared Fog Network-driven Smart City Gill et al [67] Industry Standalone Event-driven Multimedia Jha et al [107] Research & Development Edge to Edge Simulator Healthcare, Smart Building, Capacity Planning for Road Side Units Pravin et al [109] Research & Development Fog to Cloud Event-driven Healthcare Rathee et al [110] Research & Development Shared Fog, Fog to Edge Other Support to Multiple Applications Maatoug et al [111] Industry Shared Fog, Fog to Cloud Event-driven Energy Maheswaran et al [99] Research & Development Shared Fog Simulator Transportation Yousefpour et al [72] Research & Development IoT-Fog-Cloud Simulator Support to Multiple Applications Adhikari et al [82] Research & Development Shared Fog Event-driven Support to Multiple Applications Alraddady et al [97] Academia Fog to Cloud Event-driven Other Gu et al [66] Research & Development Fog to Cloud, Shared Fog Network-driven Other Hu et al [112] Research & Development Fog to Cloud Event-driven Healthcare Javaid et al [113] Industry Fog to Cloud Event-driven Smart City Dsouza et al [63] Research & Development Standalone Other Other Ahmad et al [114] Research & Development Fog to Cloud, Edge to Fog Other Healthcare Tuli et al [88] Research & Development Shared Fog Simulator Healthcare Aujla et al [115] Research & Development Edge to Fog Semi-simulated Healthcare Habibi et al [116] Industry Fog to Cloud Semi-simulated Transportation Yigitoglu et al [69] Industry Fog to Cloud Event-driven Other Krishnan et al [117] Research & Development Shared Fog Semi-simulated Other Xu et al [75] Industry Shared Fog Event-driven Transportation Wang et al [87] Research & Development Edge to Fog Network-driven Other Li et al [118] Research & Development Standalone Network-driven Multiple applications Zhao and Chao [119] Industry Shared Fog Other Smart City Biswas…”

“…Gill et al [67] presented a framework based on security and context requirements that had to choose the suitable fog computing environment for the placement of multi-media documents, images, video, and audio files. In this work, the evaluation of file type classification, an explicit security requirement, context parameters, and final allocation decisions is done using the deep neural network [68].…”

“…Provisioning Suryadevara et al [100] Low Sarkar et al [104] Low Jiang et al [105] Average Haj Qasem et al [106] High Gill et al [67] Average Jha et al [107] Low Hießl et al [108] Average Pravin et al [109] Average Rathee et al [110] Low Maatoug et al [111] Average Maheswaran et al [99] Average Yousefpour et al [72] High Adhikari et al [82] Low Vanneste et al [85] Low Alraddady et al [97] Average Gu et al [66] Low Hu et al [112] Low Javaid et al [113] High Dsouza et al [63] Low Ahmad et al [114] Average Tuli et al [88] Low Aujla et al [115] Low Habibi et al [116] Average Yigitoglu et al [69] Low Krishnan et al [117] Low Xu et al [75] Low Wang et al [87] Average Li et al [118] Average Zhao and Chao [119] Low Guardo et al [120] High Bagula et al [121] High Rakshith et al [71] Average Islam et al [93] High Sood and Mahajan [122] Low Barik et al [123] Low Pinto et al [81] Low Malic et al [98] Low Wu et al [95] Average Al-Khafajiy et al [124] Average Wei et al [125] High Bonadio et al [126] Average Cheng et al…”

A deep neural network based context-aware smart epidemic surveillance in smart cities

“…Deep neural networks (DNNs) would be best fit for computing large and complex input types. These networks can have as many as hidden layers and once trained, would produce results with precision in lesser time (Gill et al ., 2020). These DNNs may be combination of various neural networks (NNs), such as long short term memory (LSTM) (Hochreiter and Schmidhuber, 1997), convolution neural network (CNN) (Kulkarni et al , 2015), recurrent neural network (RNN) (Caterini and Chang, 2018), etc.…”