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Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of…
Serverless computing has attracted a broad range of applications due to its ease of use and resource elasticity. However, developing serverless applications often poses a dilemma -- relying on general-purpose serverless platforms can fall…
Low Earth orbit (LEO) satellites increasingly carry compute hardware capable of on-board processing, yet each satellite generates roughly two orders of magnitude more data than it can downlink per orbit. This mismatch forces operators to…
Satellite-based onboard data processing is crucial for time-sensitive applications requiring timely and efficient rapid response. Advances in edge artificial intelligence are shifting computational power from ground-based centers to…
With the Single-Instance Multi-Tenancy (SIMT) model for composite Software-as-a-Service (SaaS) applications, a single composite application instance can host multiple tenants, yielding the benefits of better service and resource…
This paper proposes a novel split learning architecture designed to exploit the cyclical movement of Low Earth Orbit (LEO) satellites in non-terrestrial networks (NTNs). Although existing research focuses on offloading tasks to the NTN…
In this work, we consider the resource allocation problem for task offloading from Internet of Medical Things (IoMT) devices, to a non-terrestrial network. The architecture considers clusters of IoMT devices that offload their tasks to a…
Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit. However, these constellations often rely on bent-pipe architecture,…
As one of the most promising hotspots in the 6G era, space remote sensing information networks play a key and irreplaceable role in areas such as emergency response and scientific research, and are expected to foster remote sensing data…
We propose integrating the edge-computing paradigm into the multi-robot collaborative scheduling to maximize resource utilization for complex collaborative tasks, which many robots must perform together. Examples include collaborative…
Fog computing is an emerging distributed computing model for the Internet of Things (IoT). It extends computing and caching functions to the edge of wireless networks. Uncrewed Aerial Vehicles (UAVs) provide adequate support for fog…
Many earth science applications require data at both high spatial and temporal resolution for effective monitoring of various ecosystem resources. Due to practical limitations in sensor design, there is often a trade-off in different…
Computing at the edge is increasingly important since a massive amount of data is generated. This poses challenges in transporting all that data to the remote data centers and cloud, where they can be processed and analyzed. On the other…
Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresponding surge in space-generated data. As this trend continues, transmitting large volumes of data…
Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at…
Thanks to the latest advances in containerization, the serverless edge computing model is becoming close to reality. Serverless at the edge is expected to enable low latency applications with fast autoscaling mechanisms, all running on…
Earth observation analytics have the potential to transform many sectors. However, due to limited ground connections, it currently takes hours to days to download and analyze Earth observation data, diminishing the value of data for…
The current trend in end-user devices' advancements in computing and communication capabilities makes edge computing an attractive solution to pave the way for the coveted ultra-low latency services. The success of the edge computing…
Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain challenges at large scales. Developers must manually search through thousands of model-variants -- versions of already-trained models that…
The effective use of computer vision and machine learning for on-orbit applications has been hampered by limited computing capabilities, and therefore limited performance. While embedded systems utilizing ARM processors have been shown to…