Integrated Sensing, Communication, and Computation for IoV: Challenges and Opportunities
Bibliographic record
Abstract
The trend of coordinated development of intelligent and connected vehicles is driving the continuous expansion of application scenarios and service functions for the Internet of Vehicles (IoV). The IoV has evolved from basic in-vehicle information services to intelligent networking, enhancing perception and decision-making. It is now developing towards coordinated vehicle-infrastructure control with moderate sensing and decision-making. However, the traditional separation of sensing, communication, and computation functions—and even their pairwise integrations, such as Integrated Sensing and Communication (ISAC), Integrated Communication and Computation (ICC), and Integrated Sensing and Computation (ISC)—create systemic inefficiencies like data redundancy and resource imbalance, which pose a critical bottleneck to the advancement of IoV. The unique characteristics of the IoV, particularly its mission objectives centered on safety and sensing, and the highly dynamic, resource-constrained network environment—compound these systemic inefficiencies, necessitating a dedicated investigation into an IoV-specific ISCC paradigm. Motivated by this, this paper provides the first in-depth, IoV-centric survey of the ISCC paradigm. First, we comprehensively review the development trajectory of the IoV, and based on this evolutionary path, define and categorize the development of ISCC into three distinct phases—Collaborative, Fusion, and Integrated—to clarify its integration process. Second, we analyze the limitations of partial integration paradigms (ISAC, ICC, ISC) and then establish a comprehensive taxonomy of ISCC implementation pathways, encompassing both physical-layer signal integration and network-level task-oriented resource management. Third, we survey the preliminary applications and research on the ISCC in the IoV and related fields. Finally, we outline the challenges and potential solutions to facilitate the realization of the ISCC in the IoV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".