Agent-Oriented Cooperative Smart Objects: From IoT System Design to Implementation
Bibliographic record
Abstract
The future Internet of Things (IoT) is expected to enable a new and wide range of decentralized systems (from small-scale smart homes to large-scale smart cities) in which “things” are able to sense/actuate, compute, and communicate, and thus play a central and crucial role. The growing importance of such novel networked cyber-physical context demands suitable and effective computing paradigms to fulfill the various requirements of IoT systems engineering. In this paper, we propose to explore an agent-based computing paradigm to support IoT systems analysis, design, and implementation. The synergic meeting of agents with IoT makes it possible to develop smart and dynamic IoT systems of diverse scales. Our agent-oriented approach is specifically based on the agent-based cooperating smart object (ACOSO) methodology and on the related ACOSO middleware: they provide effective agent design and programming models along with efficient tools for the actual construction of an IoT system in terms of a multiagent system. A case study concerning the development of a complex IoT system, namely a Smart University Campus, is described to show the effectiveness and efficiency of the proposed approach.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".