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1. Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping

   https://doi.org/10.1145/3561972  

   Ali, Ali J. ; Kouroshli, Marziye ; Semenova, Sofiya ; Hashemifar, Zakieh Sadat ; Ko, Steven Y. ; Dantu, Karthik ( January 2023 , ACM Transactions on Embedded Computing Systems)

   Localization in urban environments is becoming increasingly important and used in tools such as ARCore [ 18 ], ARKit [ 34 ] and others. One popular mechanism to achieve accurate indoor localization and a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Furthermore, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading tasks without the large latencies seen when offloading to the cloud. In this article, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 [ 50 ] as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closing, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant, which would allow for the deployment of other end applications that use Visual-SLAM. We perform a detailed performance and resources use (CPU, memory, network, and power) analysis to fully understand the effect of our proposed split architecture. 

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2. AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization

   https://doi.org/10.1109/INFOCOM53939.2023.10229009  

   Chen, Ying ; Inaltekin, Hazer ; Gorlatova, Maria ( May 2023 , Proc. IEEE INFOCOM)

   Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge. This paper presents AdaptSLAM, an edge-assisted visual (V) and visual-inertial (VI) SLAM system that adapts to the available communication and computation resources, based on a theoretically grounded method we developed to select the subset of keyframes (the representative frames) for constructing the best local and global maps in the mobile device and the edge server under resource constraints. We implemented AdaptSLAM to work with the state-of-the-art open-source V-and VI-SLAM ORB-SLAM3 framework, and demonstrated that, under constrained network bandwidth, AdaptSLAM reduces the tracking error by 62% compared to the best baseline method. 

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3. Feature Compression for Rate Constrained Object Detection on the Edge

   Yuan, Zhongzheng ; Samyak Rawlekar ; Siddharth Garg ; Elza Erkip ; Yao Wang ( August 2022 , In MLSys 2023 Workshop on Resource-Constrained Learning in Wireless Networks)

   Recent advances in computer vision has led to a growth of interest in deploying visual analytics model on mobile devices. However, most mobile devices have limited computing power, which prohibits them from running large scale visual analytics neural networks. An emerging approach to solve this problem is to offload the computation of these neural networks to computing resources at an edge server. Efficient computation offloading requires optimizing the trade-off between multiple objectives including compressed data rate, analytics performance, and computation speed. In this work, we consider a “split computation” system to offload a part of the computation of the YOLO object detection model. We propose a learnable feature compression approach to compress the intermediate YOLO features with lightweight computation. We train the feature compression and decompression module together with the YOLO model to optimize the object detection accuracy under a rate constraint. Compared to baseline methods that apply either standard image compression or learned image compression at the mobile and perform image de-compression and YOLO at the edge, the proposed system achieves higher detection accuracy at the low to medium rate range. Furthermore, the proposed system requires substantially lower computation time on the mobile device with CPU only. 

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4. Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives

   Wang, Jianyu ; Pan, Jianli ; Esposito, Flavio ; Calyam, Prasad ; Yang, Zhicheng ; Mohapatra, Prasant. ( October 2018 , ACM computing surveys)

   Mobile devices supporting the "Internet of Things" (IoT), often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality (VR), augmented reality (AR), multimedia delivery and artificial intelligence (AI), which could require broad bandwidth, low response latency and large computational power. Edge cloud or edge computing is an emerging topic and technology that can tackle the deficiency of the currently centralized-only cloud computing model and move the computation and storage resource closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow the mobile devices and the edge cloud to work together smoothly. In this survey paper, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in edge cloud have demonstrated profound potentials for future technology and application development. 

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5. Edge Computing Enabled Cognitive Portable Ground Penetrating Radar

   https://doi.org/10.4108/eai.29-6-2019.2282886  

   Wu, Dalei ; Omwenga, Maxwell ; Liang, Yu ; Yang, Li ; Huston, Dryver ; Xia, Tian ( January 2019 , MOBIMEDIA 2019, June 29-30, Weihai, People's Republic of China)

   With distributed communication, computation, and storage resources close to end users, edge computing has great potentials to support delay-sensitive industrial applications involving intelligent edge devices. Cognitive portable ground penetrating radars (GPRs) are expected to achieve high-quality sensing performance in a variety of industrial environments by operating intelligently and adaptively under varying sensing conditions. Although edge computing makes it very promising to develop cognitive portable GPRs, both strict performance requirement and trade-offs between communication and computation pose significant challenges. This paper presents an edge computing framework for cognitive portable GPRs. Specifically, the system architecture of an EC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the edge server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by edge computing. 

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