ArticleScientific reports2025
Optimizing resource allocation for IoT applications in the edge cloud continuum using hybrid metaheuristic algorithms.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The rise of Internet of Things (IoT) applications has heightened the need for efficient resource allocation across the edge-cloud continuum, combining low-latency edge nodes with high powered cloud servers. Optimizing this resource allocation is challenging, especially when balancing cost and task completion time (makespan). Traditional algorithms often fall short in these dynamic environments, leading to suboptimal solutions. This paper introduces a hybrid Flower Pollination Algorithm and Tabu Search (FPA-TS) algorithm to address IoT-specific requirements for multi objective optimization. The enhanced FPA incorporates adaptive probability based on solution diversity and dynamic levy flight control for effective global search, while Tabu Search contributes memory guided local refinement to further minimize makespan and costs. Extensive simulations with IoT scenarios show that the hybrid FPA-TS outperforms existing algorithms, offering a robust approach to optimizing resource allocation for IoT-driven applications in the edge-cloud continuum.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.