- 0.5 CEU / 5 PDH credits
- Launched 2018
- 5 courses
- 5 hours
Course Description
With the proliferation of Internet of Things (IoT) and the burgeoning of 4G/5G network, we have seen the dawning of the IoE (Internet of Everything) era. Huge volumes of data generated by things are immersed in our daily life, and hundreds of applications will be deployed at the edge to consume these data.
Cloud computing as a big data processing platform is not efficient enough to support these applications:
- Available computing capacity in the centralized cloud cannot keep up with the explosive computational needs of massive data being generated
- Users get frustrated with longer latency caused by the data movement between the edge and the cloud
- Data owners in the edge have legitimate privacy and security concerns
- Edge devices have energy constraints
These issues in the centralized big data processing era have helped launch a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Leveraging the power of cloud computing, edge computing has the potential to address the limitation of computing capability, the concerns of response time requirement, bandwidth cost saving, data safety and privacy, as well as battery life constraints.
In this dynamic program from IEEE you’ll learn what edge computing is, and then explore several practical applications, ranging from cloud offloading to smart home and city, as well as collaborative edge. You will also discover several challenges and opportunities in the field of edge computing.
Course Objectives
- Understand Edge Computing and the different ways Edge Computing can deliver value
- Examine how Edge Computing is used in real-world applications
- Major technical and research challenges for edge computing
- Security challenges brought by Edge Computing
- Basic tools and software applications that are in use by providers and developers of edge computing resources
Authors and Instructors
Songqing Chen
Full professor of computer science, George Mason University
Songqing Chen is currently a full professor of computer science at George Mason University. His research interests mainly focus on design, analysis, and implementation of algorithms and experimental systems in the distributed and networking environment, particularly in the areas of Internet content delivery systems, Internet measurement and modeling, mobile cloud, and edge computing, network and system security, and distributed systems. He is a recipient of the US NSF CAREER Award and the AFOSR YIP Award. He is a senior member of IEEE and a senior member of ACM.
Quan Zhang
Data Engineer at Salesforce.com
Quan Zhang received his Ph.D. degree in the Department of Computer Science at Wayne State University in 2018, and his M.S. degree in Computer Science at Wayne State University in 2016. Now he is a Data Engineer at Salesforce.com. His research interests include Cloud Computing, Edge Computing, Real-time Streaming Processing, and Energy-efficient Systems.
Mahadev (Satya) Satyanarayanan
the Carnegie Group Professor of Computer Science, Carnegie Mellon University
Mahadev Satyanarayanan (Satya) is the Carnegie Group Professor of Computer Science at Carnegie Mellon University. His multi-decade research career has focused on the challenges of performance scalability, availability and trust in information systems that reach from the cloud to the mobile edge of the Internet. In the course of this work, he has pioneered many advances in distributed systems, mobile computing, pervasive computing, and the Internet of Things (IoT). Most recently, his seminal 2009 publication, The Case for VM-based Cloudlets in Mobile Computing, has inspired many technical efforts worldwide at the intersection of mobile computing, cloud computing, and IoT and has led to the emergence of Edge Computing (also known as Fog Computing).
Jie Cao
Assistant Professor in the School of Information Security and Applied Computing, Eastern Michigan University
Jie Cao is an Assistant Professor in the School of Information Security and Applied Computing, Eastern Michigan University. His research area is connected health and Edge computing. Especially, his recent work focuses on data management of IoT systems, including data quality, semantic annotation, data privacy, and security. His research has been published in prestigious venues, including IEEE Internet of Things Journal and ICDCS, winning HealthCom Best Student Paper Award and an IEEE Best Paper Award.
Kewei Sha
Assistant Professor of Computer Science and Associate Director of Cyber Security Institute at University of Houston - Clear Lake (UHCL)
Kewei Sha is an Assistant Professor of Computer Science and Associate Director of Cyber Security Institute at University of Houston – Clear Lake (UHCL). Before he moved to UHCL, he was the Department Chair and Associate Professor in the Department of Software Engineering at Oklahoma City University. His research interests include Internet of Things, Cyber-Physical Systems, Edge Computing, Network Security and Privacy, and Data Management and Analytics. His research has been supported by NSF and UHCL. Dr. Sha has served as guest Editors at several prestigious technical journals, and organizing committee members of many conferences, including the TPC Chair of IEEE ICCCN 2015. He is also a reviewer for numerous top journals like many IEEE transactions. He is a Senior member of ACM.
