Enabling Intelligence on Multi-Access Edge Networks with Heterogeneous Resources

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The increasing urbanization and growing adoption of the Internet of Things (IoT) mark the dawn of the smart cities era, an era that will heavily rely on the collection and analysis of large amounts of data to improve urban life. Yet, transferring these volumes of distributed data to cloud servers across entire cities via multiple routers and links is still a big challenge from the delay, cost, and security/privacy viewpoints. The analysis of as much of this data as possible must thus be performed close to or even on its generating edge devices (e.g., sensors, smartphones, monitoring cameras, drones, connected vehicles). Being limited in resources, these heterogeneous and wirelessly-connected devices must work together as a multi-access edge network to perform such analytics in a distributed manner. This innovative direction of enabling intelligence over multi-access edge networks calls for revolutionary paradigms that can jointly and dynamically allocate resources and tasks among devices, while considering all their computing/networking heterogeneities and energy constraints, so as to fulfill all the desired analytics with high accuracy and/or within the required timelines. The proposed research program will make a leap towards empowering machine learning and data analytics on networks of heterogeneous edge devices by laying the foundations of the novel paradigm of multi-access edge intelligence (MEI). MEI will establish the design requirements and real-time algorithmic procedures that enable the execution of one or multiple (possibly correlated) learning/analytics tasks on optimized/adaptive clusters of wireless edge devices with heterogeneous communications, processing, and energy capacities. It will also involve the joint optimization of physical (i.e., networking/computing) resources, network formations, and learning models, while accommodating physical heterogeneities, network uncertainties, and mobility considerations, so as to achieve the desired learning/analytics quality indicators for different types of applications. In addition, the program will establish a Canadian MEI research and testing hub at Queen's University that promotes MEI research and validates the developed MEI innovations both in this program and by other Canadian researchers and industries. Training highly qualified personnel is also an important and integral goal of this program. This training will include experiences in network design, edge computing techniques, distributed data analytics, resource optimization, and hardware/software implementations of MEI. Two PhD, two MSc, and two undergraduate students will receive training through this research program. Given the very strong market demand on HQP in the information technology, telecommunications, data analytics, and edge intelligence sectors, the future employment of this program's trained HQP will accelerate disseminating next-generation MEI technologies to the Canadian industry.