Spring 2020: ECE590 / COMPSCI590 – Edge Computing

Overview: This seminar-format class explores opportunities and challenges associated with edge computing, the diffusion of centralized cloud computing functionality to include resource-constrained systems in physical proximity to the users, such as cloudlets, mobile phones, and smart gateways.

The course surveys recent advances in edge computing and its role in enabling the next generation of the Internet of Things and the smart cities of the future. Students will learn the strengths and the limitations of edge computing systems, and will explore a range of algorithm and system adaptation techniques for developing edge-specific platforms, algorithms, and applications. Students will complete an individual or a team-based research project, theory-oriented or applied.

Lecture times: Mondays, Wednesdays 01:25 PM – 02:40 PM, Hudson Hall 216

Professor office hours: Mondays 03:00 PM – 04:00 PM, Wednesdays 11:00 AM – 12:00 AM, CIEMAS 2471


  • Quizzes: 10%
  • Research paper presentation: 20%
  • Research project: 60%
  • Participation in class discussions: 10%

A brief sampling of topics we will discuss:

  • Evolution of distributed systems: from mainframes to cloud to edge
  • Multi-tier distributed system architectures, in the Internet of Things and in the 5G vision
  • Hardware and software edge computing platforms
  • Taking machine learning out of datacenters:
    • Federated learning
    • Reinforcement learning on the edge
  • Edge computing aiding advanced interactive applications:
    • Edge-supported smart vehicles and drones
    • Augmented and virtual reality
  • Function allocation and resource management in multi-tier fog computing systems
  • Security and privacy in edge computing

Research project information: 

A research project in edge computing is an important component of this class; it corresponds to 60% of the overall course grade.

Research projects can be done individually or in groups of up to three students. Project teams need to be established by January 21st; the project proposal outlining the key ideas of the proposed work and the plan of action needs to be submitted by February 10th.

Projects can be theory-oriented or applied. For applied projects, a range of related equipment is available in the instructor’s I^3T Lab, including:

Students are particularly encouraged to come up with projects that can improve some specific element of Duke University student, staff, or visitor experience. Bonus points will be given for connecting the project to Duke Blue Devils, Duke Lemur Center, or other specific and unique element of life at Duke.

Introductory lectures: 

January 8th: Introduction, syllabus.
January 13th: Multi-tier architectures.
January 15th: How edge helps the IoT.
January 22nd: How edge helps higher-end mobile devices.
January 27th: How edge helps the cloud.
January 29th: Edge applications: AR/VR. ML on the edge.

Invited speakers: Jim Fletcher, Chuck Byers

Research papers covered in this class’s student presentations: 

  • Real-Time Video Analytics: The Killer App for Edge Computing, IEEE Computer, 2017 [ web link ]
  • MUTE: Bringing IoT to Noise Cancellation, ACM SIGCOMM’18 [ web link ]
  • MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints, ACM MobiSys’16 [ web link ]
  • Cachier: Edge-caching for Recognition Applications, IEEE ICDCS’17 [ web link ]
  • Edge Computing for the Internet of Things: A Case Study, IEEE IoT Journal, 2018 [ web link ]
  • Understanding the Mirai Botnet, Usenix Security’17 [ web link ]
  • Vigil: The Design and Implementation of a Wireless Video Surveillance System, ACM MobiCom’15 [ web link ]
  • Supporting Mobile VR in LTE Networks: How Close Are We? ACM SIGMETRICS’18 [ web link ]
  • In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning, IEEE Network’19 [ web link ]
  • DeltaVR: Achieving high-performance mobile VR dynamics through pixel reuse, IEEE IPSN’19 [ web link ]
  • Edge Assisted Real-time Object Detection for Mobile Augmented Reality, ACM MobiCom’19 [ web link ]
  • Distributed Deep Neural Networks over the Cloud, the Edge and End Devices, IEEE ICDCS’17 [ web link ]
  • DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics, IEEE INFOCOM’18 [ web link ]
  • SDN-based edge computing security: Detecting and mitigating flow rule attacks, ACM Symposium on Edge Computing’19 [ web link ]
  • Computing While Charging: Building a Distributed Computing Infrastructure Using Smartphones, ACM CoNext’12 [ web link ]
  • DeepCache: Principled Cache for Mobile Deep Vision, ACM MobiCom’18 [ web link ]
  • A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics, IEEE IoT Journal’19 [ web link ]
  • ICON: Intelligent Container Overlays, ACM HotNets’18 [ web link ]
  • Augmenting Cognition Through Edge Computing, IEEE Computer’19 [ web link ]
  • A New Era for Web AR with Mobile Edge Computing, IEEE Internet Computing’18 [ web link ]
  • An Empirical Study of Latency in an Emerging Class of Edge Computing Applications for Wearable Cognitive Assistance, ACM Symposium on Edge Computing’17 [ web link ]