Resilient IoT Architectures Over Dynamic Sensor Networks With Adaptive Components
Bibliographic record
Abstract
As competing industries delve into the Internet of Things (IoT), a growing challenge of interoperability and redundant deployments is magnified. Specifically, as we augment more “things” in the IoT fabric, how will these components interact across their heterogeneity, let alone collaborate. In this paper, we address the core issue of component interaction and operation under the IoT umbrella. We present our contribution in the framework of wireless sensor networks (WSNs), as a founding block in the IoT. More importantly, we present a novel paradigm in the design of WSNs, to build a resilient architecture that decouples operational mandates from the nodes. We abstract IoT things as wirelessly interfaced components, which introduce functionality physically decoupled from their devices; boosting resilience, dynamicity, and resource utilization. This approach dissects the study of any IoT nodal capacity to its “connected” components, and empowers dynamic associativity between things to serve varying functional requirements and levels. It also enables reintroducing only the components required to suffice for network operation, or only those needed to meet a new requirement. More importantly, critical resources in the network will be shared within their neighborhoods. Thus network lifetime will relate to functional cliques of dynamic IoT nodes, rather than individual networks. We evaluate the cost effectiveness and resilience of our paradigm via simulations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".