Joint Job Partitioning and Collaborative Computation Offloading for Internet of Things
Bibliographic record
Abstract
Advances in Internet of Things (IoT) bring massive intelligent applications, many of which are computation intensive and time sensitive. With limited resources of IoT devices, mobile computation offloading can be exploited to offload part of the applications to nearby devices that have more powerful computing resources, thereby speeding up the applications and reducing the energy consumption. In this paper, we consider application partitioning and collaborative computation offloading in IoT networks, in order to meet the completion deadline of the applications while minimizing the overall energy consumption. The problem is formulated as a binary integer linear programming problem, which is transformed into a weighted bipartite matching problem and then solved by the centralized Kuhn-Munkres algorithm. To fit the large-scale IoT scenarios, three distributed algorithms are then introduced from different perspectives. The first one is referred to as the noncooperative matching (NCM) algorithm, where each node makes offloading decision based on its own interest in minimizing energy consumption. Afterward, an asynchronous greedy matching (AGM) algorithm is developed by considering the mutual interest of the requestor and collaborator pairs in terms of their energy consumptions. Finally, a maximum differential energy matching (MDEM) algorithm is devised by relaxing the network stability requirement, which can further benefit the energy efficiency for all network nodes. Theoretical analysis and simulation results demonstrate that both the NCM and AGM algorithms guarantee the network stability and improve the energy saving compared with entirely local execution, while the MDEM algorithm can further achieve near-optimal energy consumption at the expense of higher implementation overheads.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".