A Pervasive Collaborative Architectural Model at the Network’s Periphery
Bibliographic record
Abstract
Pervasive collaborative computing within the Internet of Things (IoT) has progressed rapidly over the last decade. Nevertheless, emerging architectural models and their applications still suffer from limited capacity in areas like power, efficient computing, memory, connectivity, latency and bandwidth. Technological development is still in progress in the fields of hardware, software and wireless communications. Their communication is usually done via the Internet and wireless via base stations. However, these models are sometimes subject to connectivity failures and limited coverage. The models that incorporate devices with peer-to-peer (P2P) communication technologies are of great importance, especially in harsh environments. Nevertheless, their power-limited devices are randomly distributed on the periphery where their availability can be limited and arbitrary. Despite these limitations, their capabilities and efficiency are constantly increasing. Accelerating development in these areas can be achieved by improving architectures and technologies of pervasive collaborative computing, which refers to the collaboration of mobile and embedded computing devices. To enhance mobile collaborative computing, especially in the models acting at the network’s periphery, we are interested in modernizing and strengthening connectivity using wireless technologies and P2P communication. Therefore, the main goal of this paper is to enhance and maintain connectivity and improve the performance of these pervasive systems while performing the required and expected services in a challenging environment. This is especially important in catastrophic situations and harsh environments, where connectivity is used to facilitate and enhance rescue operations. Thus, we have established a resilient mobile collaborative architectural model comprising a peripheral autonomous network of pervasive devices that considers the constraints of these resources. By maintaining the connectivity of its devices, this model can operate independently of wireless base stations by taking advantage of emerging P2P connection technologies such as Wi-Fi Direct and those enabled by LoPy4 from Pycom such as LoRa, BLE, Sigfox, Wi-Fi, Radio Wi-Fi and Bluetooth. Likewise, we have designed four algorithms to construct a group of devices, calculate their scores, select a group manager, and exchange inter- and intra-group messages. The experimental study we conducted shows that this model continues to perform efficiently, even in circumstances like the breakdown of wireless connectivity due to an extreme event or congestion from connecting a huge number of devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".