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1. Throwing MUD into the FOG: Defending IoT and Fog by expanding MUD to Fog network

   Andalibi, Vafa ; Kim, DongInn ; Camp, Jean ( July 2021 , HotEdge: A Usenix ATC Workshop)

   null (Ed.)

   Manufacturer Usage Description (MUD) is a proposed IETF standard enabling local area networks (LAN) to automatically configure their access control when adding a new IoT device based on the recommendations provided for that device by the manufacturer. MUD has been proposed as an isolation-based defensive mechanism with a focus on devices in the home, where there is no dedicated network administrator. In this paper, we describe the efficacy of MUD for a generic IoT device under different threat scenarios in the context of the Fog. We propose a method to use rate limiting to prevent end devices from participating in denial of service attacks (DDoS), including against the Fog itself. We illustrate our assumptions by providing a possible real world example and describe the benefits for MUD in the Fog for various stakeholders. 

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2. Fat, oil, and grease (FOG) deposits yield higher methane than FOG in anaerobic co-digestion with waste activated sludge

   https://doi.org/10.1016/j.jenvman.2020.110708  

   Hao, Jiahou ; de los Reyes III, Francis L. ; He, Xia ( August 2020 , Journal of Environmental Management)
3. Driving in the fog: Latency measurement, modeling, and optimization of LTE-based fog computing for smart vehicles

   Xiao, Yong ; Krunz, Marwan ; Volos, Haris ; Bando, Takashi ( June 2019 , IEEE SECON 2019 Conference)

   Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and opti- mizing the queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the end-to-end service latency. This motivates the work in this paper, where we report a five- month urban measurement study of the wireless access latency between a connected vehicle and a fog computing system sup- ported by commercially available multi-operator LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE operators that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that the tolerable latency threshold for each supported type of service can be guaranteed. AdaptiveFog has been implemented on a smart phone app, running on a moving vehicle. The app periodically measures the round-trip time between the vehicle and fog/cloud servers. An empirical spatial statistic model is established to characterize the spatial variation of the latency across the main driving routes of the city. To quantify the perfor- mance difference between different LTE networks, we introduce the weighted Kantorovich-Rubinstein (K-R) distance. An optimal policy is derived for the vehicle to dynamically switch between LTE operators’ networks while driving. Extensive analysis and simulation are performed based on our latency measurement dataset. Our results show that AdaptiveFog achieves around 30% and 50% improvement in the confidence level of fog and cloud latency, respectively. 

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4. Interplay between topography, fog and vegetation in the central South Arabian mountains revealed using a novel Landsat fog detection technique

   https://doi.org/10.1002/rse2.151  

   Ball, Lawrence ; Tzanopoulos, Joseph ; Pettorelli, ed., Nathalie ; Kuemmerle, ed., Tobias ( March 2020 , Remote Sensing in Ecology and Conservation)
5. Energy Efficient Data Forwarding Scheme in Fog-Based Ubiquitous System With Deadline Constraints

   https://doi.org/10.1109/TNSM.2019.2937165  

   Saraswat, Surbhi ; Gupta, Hari Prabhat ; Dutta, Tanima ; Das, Sajal K. ( March 2020 , IEEE Transactions on Network and Service Management)