Internet-of-Things (IoT) Shortest Path Algorithms and Communication Case Studies for Maintaining Connectivity in Harsh Environements
Bibliographic record
Abstract
Research on the shortest path in networks to maintain connectivity in the Internet of Things (IoT) remains an important issue for determining minimal routes, especially in terms of time and distance, between two devices at distinct points (i.e., nodes) of the network. Many constraints exist for IoT smart devices for transmitting a large amount of information and data, such as limited resources, energy, and time consumption, as well as the potential for overwhelmed communication traffic. Several algorithms were designed and implemented to address these problems that can be simulated and considered as information message passing. The search space is often modeled by a graph, where each node corresponds to a location of a smart device, and the edges represent the paths or links that carry messages, while the absence of a path between two nodes designates a communication breakdown or obstacle. Existing pathfinding algorithms are incorporated in applications, such as Google Maps, rescue people, video games, online packet routing, and rescue applications used in harsh environments. For these latter scenarios, the infrastructure for various technologies of communication becomes vulnerable and dysfunctional, so maintaining connectivity and finding the shortest path becomes a priority. Our goal is to remedy this problem by taking advantage of modernized peer-to-peer wireless technologies, such as Wi-Fi Direct, which can be improved through autonomous wireless technology kits like Lopy 4 of Pycom, and through two alternatives of moving devices (nodes) or service drones. This paper investigates several shortest path algorithms and identifies three case studies to maintain connectivity in harsh environments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".