A Game-Theoretic Analysis for Complementary and Substitutable IoT Services Delivery With Externalities
Bibliographic record
Abstract
The Internet of Things (IoT) connects mobile and wireless devices, and enables the IoT service providers to deliver IoT services to the mobile users in various applications, e.g., transportation and communications. In this paper, the problem of IoT service delivery management is studied with the consideration of substitutability, complementarity, and externalities of delivering IoT services due to the diversity of different IoT components in mobile systems. The substitutable IoT services have similar functionalities to serve IoT users, and the IoT users can switch to buy service from any IoT service provider. The complementary IoT services have different functionalities to serve IoT users, and the IoT users may request a bundle of IoT services from multiple IoT service providers as their IoT services can be integrated. Externalities represent the situation in which IoT users in the same system can affect the utilities of each other due to the connections and interference among the IoT users, which leads to the presence of network effect and congestion effect. To analyze the impact of these factors on the performance of IoT systems, a multi-leader multi-follower Stackelberg game model is introduced. Therein, the IoT service providers and IoT users make their strategic decisions in terms of pricing and service requests, respectively, toward their individual objectives in a distributed manner. A closed-form equilibrium solution is derived analytically through backward induction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".