Consensus-enabled and Value-oriented Collaboration in Distributed IoT Systems: Mechanisms, Design, and Implementation
Bibliographic record
Abstract
The ongoing convergence of Internet of Things (IoT), artificial intelligence and big data analytics has inspired many innovative IoT applications. Enabling these new applications requires accurate and reliable capabilities in data sensing, exchange and processing, which can be best fulfilled by collaborative IoT systems. Nevertheless, the dynamic condition of IoT networks may lead to ever-changing demand and objectives among devices, making it difficult for reliable and efficient collaboration. To overcome these challenges, this thesis develops a new framework on consensus-enabled and value-oriented collaboration, which resolves two critical technical challenges, i.e., low latency consensus creation and value-oriented decision-making, to enable collective mindset, promote collaborative behavior, and eventually enhance situation-aware resource sharing in distributed IoT systems.\nFirst, consensus creates a foundation of collaboration among distributed devices. However, reaching consensus usually involves a time-consuming negotiation process, which may significantly degrades the system real-time performance. To resolve this issue, a smart futures based resource trading scheme is proposed, which implements resource trading in advance by predicting onsite resource supply and demand and signing futures contracts, so as to avoid the latency for conventional onsite negotiation. Apart from consensus creation, collaboration participants also need to make specific decisions, e.g., resource allocation and task scheduling schemes, based on the time-changing situation of their needs and interests. Conventional decision-making mechanisms focus on the optimization of specific system performance, while overlooking how users actually benefit from the improved performance. We address this issue by a proposed concept of value of service (VoS), which characterizes user-perceived value by a value function and enables value-oriented decision-making to optimize comprehensive functional benefits brought to users under fast-changing system situations.\nFinally, the consensus enabled collaboration is implemented in two realistic applications, i.e., 1) a collaborative rendering scheme which opportunistically leverages dynamic IoT resource to offer real-time and high-quality rendering, and 2) a collaborative multi-camera system which offers real-time 3D reconstruction of dynamic scene via optimal viewpoints planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".