Cloud-based cost-efficient application and service provisioning in virtualized wireless sensor networks
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are becoming ubiquitous and are used in diverse applications domains. Traditional deployments of WSNs are domain-specific, with applications usually embedded in the WSN, precluding the re-use of the infrastructure by other applications. This can lead to redundant deployments. Now with the advent of IoT, this approach is less and less viable. A potential solution lies in the sharing of a same WSN by multiple applications and services, to allow resource- and cost-efficiency. In this dissertation, three architectural solutions are proposed for this purpose. The first solution consists of two parts: the first part is a novel multilayer WSN virtualization architecture that allows the provisioning of multiple applications and services over the same WSN deployment. The second part of this contribution is the extended architecture that allows virtualized WSN infrastructure to interact with a WSN Platform-as-a-Service (PaaS) at a higher level of abstraction. Both these solutions are implemented and evaluated using two scenario-based proof-of-concept prototypes using Java SunSpot kit. The second architectural solution is a novel data annotation architecture for the provisioning of semantic applications in virtualized WSNs. It is capable of providing in-network, distributed, real-time annotation of raw sensor data and uses overlays as the cornerstone. This architecture is implemented and evaluated using Java SunSpot, AdvanticSys kits and Google App Engine. The third architectural solution is the enhancement to the data annotation architecture on two fronts. One is a heuristic-based genetic algorithm used for the selection of capable nodes for storing the base ontology. The second front is the extension to the proposed architecture to support ontology creation, distribution and management. The simulation results of the algorithm are presented and the ontology management extension is implemented and evaluated using a proof-of-concept prototype using Java SunSpot kit. As another contribution, an extensive state-of-the-art review is presented that introduces the basics of WSN virtualization and motivates its pertinence with carefully selected scenarios. This contribution substantially improves current state-of-the-art reviews in terms of the scope, motivation, details, and future research issues
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".