Sökning: "Resource-constrained edge devices"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Resource-constrained edge devices.

1. 1. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

3. 2. Smart Tracking for Edge-assisted Object Detection : Deep Reinforcement Learning for Multi-objective Optimization of Tracking-based Detection Process

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Shihang Zhou; [2023]
   Nyckelord :Tracking-By-Detection; Deep Reinforcement Learning; Multi-Objective Optimization; Spårning genom detektion; Djup förstärkningsinlärning; Multiobjektiv optimering;

   Sammanfattning : Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. LÄS MER

4. 3. Computation Offloading for Real-Time Applications : Server Time Reservation for Periodic Tasks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Lizzy Tengana Hurtado; [2023]
   Nyckelord :Computation Offloading; Real-Time Applications; Resource Reservation; Beräkningsavlastning; realtidsapplikationer; resursreservation;

   Sammanfattning : Edge computing is a distributed computing paradigm where computing resources are located physically closer to the data source compared to the traditional cloud computing paradigm. Edge computing enables computation offloading from resource-constrained devices to more powerful servers in the edge and cloud. LÄS MER

5. 4. Measuring the responsiveness of WebAssembly in edge network applications

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Remo Scolati; [2023]
   Nyckelord :Cloud Computing; Edge Computing; Internet of Things; WebAssembly; Cloud Computing; Edge Computing; Internet of Things; WebAssembly;

   Sammanfattning : Edge computing facilitates applications of cyber-physical systems that require low latencies by moving compute and storage resources closer to the end application. Whilst the edge network benefits such systems in terms of responsiveness, it increases the systems’ complexity due to edge devices’ often heterogeneous and resource-constrained nature. LÄS MER

7. 5. Edge Compute Offloading Strategies using Heuristic and Reinforcement Learning Techniques.

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Chrysoula Dikonimaki; [2023]
   Nyckelord :Computation offloading; Edge Computing; Heuristics; Multi-armed bandit; Beräkningsavlastning; Heuristics; Kantberäkning; Flerarmad bandit-algoritm;

   Sammanfattning : The emergence of 5G alongside the distributed computing paradigm called Edge computing has prompted a tremendous change in the industry through the opportunity for reducing network latency and energy consumption and providing scalability. Edge computing extends the capabilities of users’ resource-constrained devices by placing data centers at the edge of the network. LÄS MER