content updates, various fixes

site/data/papers/IC-2024-digest.yaml

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+venue: IC
+year: 2024
+selected:
+  - title: "Revisiting Edge AI: Opportunities and Challenges"
+    authors:
+    - Tobias Meuser
+    - Lauri Lovén
+    - Monowar Bhuyan
+    - Shishir G. Patil
+    - Schahram Dustdar
+    - Atakan Aral
+    - Suzan Bayhan
+    - Christian Becker 0001
+    - Eyal de Lara
+    - Aaron Yi Ding
+    - Janick Edinger
+    - James Gross
+    - Nitinder Mohan
+    - Andy D. Pimentel
+    - Etienne Rivière
+    - Henning Schulzrinne
+    - Pieter Simoens
+    - Gürkan Solmaz
+    - Michael Welzl
+    tldr: "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."
+
+  - title: "On Causality in Distributed Continuum Systems"
+    authors:
+    - Víctor Casamayor-Pujol
+    - Boris Sedlak
+    - Praveen Kumar Donta
+    - Schahram Dustdar
+    tldr: "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."
+
+  - title: "Beyond Von Neumann in the Computing Continuum: Architectures, Applications, and Future Directions"
+    authors:
+    - Dragi Kimovski
+    - Nishant Saurabh
+    - Matthijs Jansen
+    - Atakan Aral
+    - Auday Al-Dulaimy
+    - André B. Bondi
+    - Antonino Galletta
+    - Alessandro V. Papadopoulos
+    - Alexandru Iosup
+    - Radu Prodan
+    tldr: "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."
+
+  - title: "ARASEC: Adaptive Resource Allocation and Model Training for Serverless Edge-Cloud Computing"
+    authors:
+    - Dewant Katare
+    - Eduard Marin
+    - Nicolas Kourtellis
+    - Marijn Janssen
+    - Aaron Yi Ding
+    tldr: "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."
+
+  - title: "WebAssembly at the Edge: Benchmarking a Serverless Platform for Private Edge Cloud Systems"
+    authors:
+    - Giuseppe De Palma
+    - Saverio Giallorenzo
+    - Jacopo Mauro
+    - Matteo Trentin
+    - Gianluigi Zavattaro
+    tldr: "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."
+
+  - title: "HeROsim: An Allocation and Scheduling Simulator for Evaluating Serverless Orchestration Policies"
+    authors:
+    - Vincent Lannurien
+    - Laurent d'Orazio
+    - Olivier Barais
+    - Stéphane Paquelet
+    - Jalil Boukhobza
+    tldr: "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."
+
+  - title: "Hierarchical Network Data Analytics Framework for 6G Network Automation: Design and Implementation"
+    authors:
+    - Youbin Jeon
+    - Sangheon Pack
+    tldr: "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."
+
+  - title: "Digital-Twin-Driven End-to-End Network Slicing Toward 6G"
+    authors:
+    - Mahnoor Yaqoob
+    - Ramona Trestian
+    - Mallik Tatipamula
+    - Huan Xuan Nguyen
+    tldr: "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."
+
+  - title: "The Internet of Things in the Era of Generative AI: Vision and Challenges"
+    authors:
+    - Xin Wang 0120
+    - Zhongwei Wan
+    - Arvin Hekmati
+    - Mingyu Zong
+    - Samiul Alam
+    - Mi Zhang 0002
+    - Bhaskar Krishnamachari
+    tldr: "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."
+
+  - title: "Distributed Federated Deep Learning in Clustered Internet of Things Wireless Networks With Data Similarity-Based Client Participation"
+    authors:
+    - Evangelia Fragkou 0001
+    - Eleftheria Chini
+    - Maria Papadopoulou 0008
+    - Dimitrios K. Papakostas
+    - Dimitrios Katsaros 0001
+    - Schahram Dustdar
+    tldr: "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."