10 papers selected.
Revisiting Edge AI: Opportunities and Challenges
Tobias Meuser, Lauri Lovén, Monowar Bhuyan, Shishir G. Patil et al.
TL;DR — A multi-author position paper that revisits the state of edge AI, cataloguing deployment barriers and open research problems across hardware, networking, and software layers.
Why notable — Brings together 19 leading researchers to synthesize the field’s most pressing edge AI challenges, making it an authoritative reference for practitioners and researchers planning edge deployments.
On Causality in Distributed Continuum Systems
Víctor Casamayor-Pujol, Boris Sedlak, Praveen Kumar Donta, Schahram Dustdar
TL;DR — Formalizes causal reasoning across cloud-to-edge continuum systems, providing a conceptual framework for tracking cause-and-effect relationships in highly distributed deployments.
Why notable — Addresses a foundational gap in distributed systems theory that becomes critical when debugging or optimizing multi-tier edge–cloud pipelines.
Beyond Von Neumann in the Computing Continuum: Architectures, Applications, and Future Directions
Dragi Kimovski, Nishant Saurabh, Matthijs Jansen, Atakan Aral et al.
TL;DR — Surveys non-von Neumann architectural paradigms—neuromorphic, in-memory, and dataflow computing—and maps them onto continuum computing use cases spanning edge to cloud.
Why notable — Offers a rare cross-cutting view of how emerging hardware architectures reshape the design space for distributed Internet applications.
ARASEC: Adaptive Resource Allocation and Model Training for Serverless Edge-Cloud Computing
Dewant Katare, Eduard Marin, Nicolas Kourtellis, Marijn Janssen et al.
TL;DR — Proposes ARASEC, a system that jointly optimizes resource allocation and on-device model training for serverless functions deployed across edge and cloud nodes.
Why notable — Demonstrates measurable efficiency gains in a realistic serverless edge-cloud setting, directly informing how operators should provision heterogeneous serverless infrastructure.
WebAssembly at the Edge: Benchmarking a Serverless Platform for Private Edge Cloud Systems
Giuseppe De Palma, Saverio Giallorenzo, Jacopo Mauro, Matteo Trentin et al.
TL;DR — Benchmarks a WebAssembly-based serverless runtime on private edge cloud hardware, measuring cold-start latency, throughput, and isolation overhead compared to container-based alternatives.
Why notable — Provides concrete empirical data that practitioners need when evaluating WebAssembly as a lightweight alternative to Docker for edge serverless deployments.
HeROsim: An Allocation and Scheduling Simulator for Evaluating Serverless Orchestration Policies
Vincent Lannurien, Laurent d’Orazio, Olivier Barais, Stéphane Paquelet et al.
TL;DR — Introduces HeROsim, an open simulator that models serverless function placement and scheduling policies across heterogeneous infrastructure, enabling fair policy comparison without live cluster costs.
Why notable — Fills a practical tooling gap for researchers and platform engineers who need reproducible evaluation environments for serverless orchestration algorithms.
Hierarchical Network Data Analytics Framework for 6G Network Automation: Design and Implementation
Youbin Jeon, Sangheon Pack
TL;DR — Designs and implements a hierarchical analytics framework that aggregates network telemetry at multiple granularities to automate management decisions in 6G deployments.
Why notable — Bridges the gap between 6G vision and practical automation by providing a concrete architecture with implementation details and empirical evaluation.
Digital-Twin-Driven End-to-End Network Slicing Toward 6G
Mahnoor Yaqoob, Ramona Trestian, Mallik Tatipamula, Huan Xuan Nguyen
TL;DR — Proposes a digital-twin framework that continuously models and reconfigures end-to-end network slices, enabling dynamic SLA enforcement across heterogeneous 6G infrastructure.
Why notable — Connects digital twin technology to the operational problem of network slice management, a key requirement for 6G service assurance.
The Internet of Things in the Era of Generative AI: Vision and Challenges
Xin Wang 0120, Zhongwei Wan, Arvin Hekmati, Mingyu Zong et al.
TL;DR — Examines how generative AI models can be integrated into IoT pipelines for data synthesis, anomaly detection, and on-device inference, and identifies the key resource and privacy constraints.
Why notable — Provides a structured research agenda for one of the most active intersections in Internet computing, relevant to both IoT platform designers and ML practitioners.
Distributed Federated Deep Learning in Clustered Internet of Things Wireless Networks With Data Similarity-Based Client Participation
Evangelia Fragkou 0001, Eleftheria Chini, Maria Papadopoulou 0008, Dimitrios K. Papakostas et al.
TL;DR — Proposes a clustered federated learning scheme for wireless IoT networks that selects participating clients based on data similarity, reducing communication overhead and improving model convergence.
Why notable — Addresses a core practical challenge in IoT federated learning—heterogeneous and non-IID data—with an empirically validated participation strategy.