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1. Real-Time Physical Threat Detection on Edge Data Using Online Learning

   https://doi.org/10.1109/MCE.2023.3256641  

   Khakurel, Utsab; Rawat, Danda B. (March 2023, IEEE Consumer Electronics Magazine)

   Sensor-powered devices offer safe global connections; cloud scalability and flexibility, and new business value driven by data. The constraints that have historically obstructed major innovations in technology can be addressed by advancements in Artificial Intelligence (AI) and Machine Learning (ML), cloud, quantum computing, and the ubiquitous availability of data. Edge AI (Edge Artificial Intelligence) refers to the deployment of AI applications on the edge device near the data source rather than in a cloud computing environment. Although edge data has been utilized to make inferences in real-time through predictive models, real-time machine learning has not yet been fully adopted. Real-time machine learning utilizes real-time data to learn on the go, which helps in faster and more accurate real-time predictions and eliminates the need to store data eradicating privacy issues. In this article, we present the practical prospect of developing a physical threat detection system using real-time edge data from security cameras/sensors to improve the accuracy, efficiency, reliability, security, and privacy of the real-time inference model. 

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2. Delay-aware Cellular Traffic Scheduling with Deep Reinforcement Learning

   https://doi.org/10.1109/GLOBECOM42002.2020.9322560  

   Zhang, Ticao; Shen, Shuyi; Mao, Shiwen; Chang, Gee-Kung (December 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference)

   null (Ed.)

   Abstract: Radio access network (RAN) in 5G is expected to satisfy the stringent delay requirements of a variety of applications. The packet scheduler plays an important role by allocating spectrum resources to user equipments (UEs) at each transmit time interval (TTI). In this paper, we show that optimal scheduling is a challenging combinatorial optimization problem, which is hard to solve within the channel coherence time with conventional optimization methods. Rule-based scheduling methods, on the other hand, are hard to adapt to the time-varying wireless channel conditions and various data request patterns of UEs. Recently, integrating artificial intelligence (AI) into wireless networks has drawn great interest from both academia and industry. In this paper, we incorporate deep reinforcement learning (DRL) into the design of cellular packet scheduling. A delay-aware cell traffic scheduling algorithm is developed to map the observed system state to scheduling decision. Due to the huge state space, a recurrent neural network (RNN) is utilized to approximate the optimal action-policy function. Different from conventional rule-based scheduling methods, the proposed scheme can learn from the interactions with the environment and adaptively choosing the best scheduling decision at each TTI. Simulation results show that the DRL-based packet scheduling can achieve the lowest average delay compared with several conventional approaches. Meanwhile, the UEs' average queue lengths can also be significantly reduced. The developed method also exhibits great potential in real-time scheduling in delay-sensitive scenarios. 

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3. Virtual Function Placement and Traffic Steering over 5G Multi-Technology Networks

   https://doi.org/10.1109/NETSOFT.2018.8460097  

   Akhtar, Nabeel; Matta, Ibrahim; Raza, Ali; Goratti, Leonardo; Braun, Torsten; Esposito, Flavio (June 2018, 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft))

   Next-generation mobile networks (5G and beyond) are expected to provide higher data rates and ultra-low latency in support of demanding applications, such as virtual and augmented reality, robots and drones, etc. To meet these stringent requirements, edge computing constitutes a central piece of the solution architecture wherein functional components of an application can be deployed over the edge network so as to reduce bandwidth demand over the core network while providing ultra-low latency communication to users. In this paper, we investigate the joint optimal placement of virtual service chains consisting of virtual application functions (components) and the steering of traffic through them, over a 5G multi-technology edge network model consisting of both Ethernet and mmWave links. This problem is NP-hard. We provide a comprehensive “microscopic" binary integer program to model the system, along with a heuristic that is one order of magnitude faster than solving the corresponding binary integer program. Extensive evaluations demonstrate the benefits of managing virtual service chains (by distributing them over the edge network) compared to a baseline “middlebox" approach in terms of overall admissible virtual capacity. We observe significant gains when deploying mmWave links that complement the Ethernet physical infrastructure. Moreover, most of the gains are attributed to only 30% of these mmWave links. 

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4. Do Larger (More Accurate) Deep Neural Network Models Help in Edge-assisted Augmented Reality?

   https://doi.org/10.1145/3472727.3472807  

   Meng, Jiayi; Kong, Zhaoning; Xu, Qiang; Hu, Y. Charlie (August 2021, NAI'21: Proceedings of the ACM SIGCOMM 2021 Workshop on Network-Application Integration)

   Edge-assisted Augmented Reality (AR) which offloads computeintensive Deep Neural Network (DNN)-based AR tasks to edge servers faces an important design challenge: how to pick the DNN model out of many choices proposed for each AR task for offloading. For each AR task, e.g., depth estimation, many DNN-based models have been proposed over time that vary in accuracy and complexity. In general, more accurate models are also more complex; they are larger and have longer inference time. Thus choosing a larger model in offloading can provide higher accuracy for the offloaded frames but also incur longer turnaround time, during which the AR app has to reuse the estimation result from the last offloaded frame, which can lead to lower average accuracy. In this paper, we experimentally study this design tradeoff using depth estimation as a case study. We design optimal offloading schedule and further consider the impact of numerous factors such as on-device fast tracking, frame downsizing and available network bandwidth. Our results show that for edge-assisted monocular depth estimation, with proper frame downsizing and fast tracking, compared to small models, the improved accuracy of large models can offset its longer turnaround time to provide higher average estimation accuracy across frames under both LTE and 5G mmWave. 

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5. Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

   https://doi.org/10.1145/3527155  

   Matsubara, Yoshitomo; Levorato, Marco; Restuccia, Francesco (May 2023, ACM Computing Surveys)

   Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others. However, continuously executing the entire DNN on mobile devices can quickly deplete their battery. Although task offloading to cloud/edge servers may decrease the mobile device’s computational burden, erratic patterns in channel quality, network, and edge server load can lead to a significant delay in task execution. Recently, approaches based on split computing (SC) have been proposed, where the DNN is split into a head and a tail model, executed respectively on the mobile device and on the edge server. Ultimately, this may reduce bandwidth usage as well as energy consumption. Another approach, called early exiting (EE), trains models to embed multiple “exits” earlier in the architecture, each providing increasingly higher target accuracy. Therefore, the tradeoff between accuracy and delay can be tuned according to the current conditions or application demands. In this article, we provide a comprehensive survey of the state of the art in SC and EE strategies by presenting a comparison of the most relevant approaches. We conclude the article by providing a set of compelling research challenges. 

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