Joint Traffic Routing and Virtualized Security Function Activation in Wireless Multihop Networks
Bibliographic record
Abstract
Decentralized security function deployment (activation) is an important solution to achieve reliable and secure operations of wireless networks. In this paper, we consider the joint problem of security function activation and traffic routing in a wireless multihop network with the aim of minimizing total energy consumption of all nodes in the network while meeting the security requirements of the users. Firstly, we analyze the tradeoff of energy consumption between preventing security damages with protection functions and remedying it with recovery functions. The analysis takes the uncertainty of attacks and network capacity availability into account. Secondly, if the protection function is to be activated, the activation has to be performed along the route of traffic flows, which means that the traffic routing should also be jointly optimized. Therefore, we formulate this problem as a stochastic optimization model. To obtain the optimal solution, we apply a distributed algorithm, i.e., accelerated distributed augmented Lagrangian algorithm which is ensured to converge. Furthermore, we analytically demonstrate that the solution obtained by the accelerated distributed augmented Lagrangian algorithm is optimal to our stochastic optimization model. Based on the setting from real experiments, the performance evaluation reveals that the optimal solution depends largely on the energy budget and energy consumption of the nodes for transferring traffic and running security functions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".