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1. iCAAP: information-Centric network Architecture for Application-specific Prioritization in Smart Grid

   https://doi.org/10.1109/ISGT49243.2021.9372162  

   James, Anju K. ; Torres, George ; Shrestha, Sharad ; Tourani, Reza ; Misra, Satyajayant ( February 2021 , 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT))

   null (Ed.)

   The smart grid is equipped with bi-directional information flow between its devices, aiming at automation, improved stability, resilience, and robust security. However, enabling effective and reliable communication in a smart grid is a challenging task. The majority of the proposed networking architectures fall short in addressing the key aspects of smart grid communication, including device heterogeneity, protocols and standards interoperability, and particularly application qualityof-service (QoS) requirements. In this paper, we propose iCAAP, an information-centric, QoSaware network architecture that aims to satisfy the low latency, high bandwidth, and high reliability requirements of smart grid communications. In iCAAP, we categorize smart grid traffic (emanating from diverse applications) into three priority classes to enable preferential treatment of traffic flows. Our simulation results demonstrate the higher scalability of iCAAP in satisfying the stringent requirements of high priority traffic compared to the state-of-the-art. 

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2. Security Vulnerabilities of Smart Meters in Smart Grid

   https://doi.org/10.1109/IECON.2019.8926992  

   Gui, Yutian ; Siddiqui, Ali Shuja ; Tamore, Suyash Mohan ; Saqib, Fareena ( October 2019 , Security Vulnerabilities of Smart Meters in Smart Grid)

   Integration of complex and high-speed electronic components in the state of art electric power system enhances the need for improved security infrastructure and resilience against invasive and non-invasive attacks on the smart grid. A modern smart grid system integrates a variety of instruments and standards to achieve cost-effective and time-effective energy measurement and management. As the fundamental component in the smart grid, the smart meter supports real-time monitoring, automatic control, and high-speed communication along with power consumption recording. However, the wide use of smart meters also increases privacy and security concerns. In this paper, we demonstrate the vulnerability of side-channel attacks on secure communication in smart grids for software-based and hardware-based implementations. 

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3. QoS-Aware Priority-Based Task Offloading for Deep Learning Services at the Edge

   https://doi.org/10.1109/CCNC49033.2022.9700676  

   Hosseinzadeh, Minoo ; Wachal, Andrew ; Khamfroush, Hana ; Lucani, Daniel E. ( January 2022 , 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC))

   Emerging Edge Computing (EC) technology has shown promise for many delay-sensitive Deep Learning (DL) based applications of smart cities in terms of improved Quality-of-Service (QoS). EC requires judicious decisions which jointly consider the limited capacity of the edge servers and provided QoS of DL-dependent services. In a smart city environment, tasks may have varying priorities in terms of when and how to serve them; thus, priorities of the tasks have to be considered when making resource management decisions. In this paper, we focus on finding optimal offloading decisions in a three-tier user-edge-cloud architecture while considering different priority classes for the DL-based services and making a trade-off between a task’s completion time and the provided accuracy by the DL-based service. We cast the optimization problem as an Integer Linear Program (ILP) where the objective is to maximize a function called gain of system (GoS) defined based on provided QoS and priority of the tasks. We prove the problem is NP-hard. We then propose an efficient offloading algorithm, called PGUS, that is shown to achieve near-optimal results in terms of the provided GoS. Finally, we compare our proposed algorithm, PGUS, with heuristics and a state-of-the-art algorithm, called GUS, using both numerical analysis and real-world implementation. Our results show that PGUS outperforms GUS by a factor of 45% in average in terms of serving the top 25% higher priority classes of the tasks while still keeping the overall percentage of the dropped tasks minimal and the overall gain of system maximized. 

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4. Self-Organizing mm Wave Networks: A Power Allocation Scheme Based on Machine Learning

   https://doi.org/10.1109/GSMM.2018.8439323  

   Amiri, Roohollah ; Mehrpouyan, Hani ( June 2018 , 2018 11th Global Symposium on Millimeter Waves (GSMM))

   Millimeter-wave (mmWave) communication is anticipated to provide significant throughout gains in urban scenarios. To this end, network densification is a necessity to meet the high traffic volume generated by smart phones, tablets, and sensory devices while overcoming large pathloss and high blockages at mmWaves frequencies. These denser networks are created with users deploying small mm Wave base stations (BSs) in a plug-and-play fashion. Although, this deployment method provides the required density, the amorphous deployment of BSs needs distributed management. To address this difficulty, we propose a self-organizing method to allocate power to mm Wave BSs in an ultra dense network. The proposed method consists of two parts: clustering using fast local clustering and power allocation via Q-learning. The important features of the proposed method are its scalability and self-organizing capabilities, which are both important features of 5G. Our simulations demonstrate that the introduced method, provides required quality of service (QoS) for all the users independent of the size of the network. 

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5. On Modeling and Optimizing LTE/Wi-Fi Coexistence with Prioritized Traffic Classes

   https://doi.org/10.1109/DySPAN.2018.8610465  

   Hirzallah, Mohammed ; Xiao, Yong ; Krunz, Marwan ( October 2018 , 2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   The dramatic growth in demand for mobile data service has prompted mobile network operators (MNOs) to explore new spectrum resources in unlicensed bands. MNOs have been recently allowed to extend LTE-based service called LTE-LAA over 5 GHz U-NII bands, currently occupied by Wi-Fi. To support applications with diverse QoS requirements, both LTE and Wi-Fi technologies introduce multiple priority classes with different channel contention parameters for accessing unlicensed bands. How these different priority classes affect the interplay between coexisting LTE and Wi-Fi technologies is still relatively under-explored. In this paper, we develop a simple and efficient framework that helps MNOs assess the fair coexistence between MNOs and Wi-Fi operators with prioritized channel access under the multi-channel setting. We derive an approximated closed-form solution for each MNO to pre-evaluate the probability of successful transmission (PST), average contention delay, and average throughput when adopting different priority classes to serve different traffics. MNOs and Wi-Fi operators can fit our model using measurements collected offline and/or online, and use it to further optimize their systems’ throughput and latency. Our results reveal that PSTs computed with our approximated closed-form model approach those collected from system-level simulations with around 95% accuracy under scenarios of dense network deployment density and high traffic intensity. 

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