IoT-related Attack Platforms
2020
- 13Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage13
- Abstract Views13
Artifact Description
We study the jamming resistant mobile device communication problem under the multiple resource constraint model in 5G networks. Given a set of communication links, assume that the complete channel state information of each link is unknown subject to jamming resistant constraints, but we can estimate it by exploiting the memory along with channel state feedback. Assume time is divided into time-slots. The objective is to select links under the multiple resource constraint model to transmit sequentially to maximize the jamming resistant throughput over an infinite time horizon. Existing work simply assumes a single resource constraint or an even simpler model. To this end, we apply the framework of restless multi-armed bandit and develop a fast and simple approximation algorithm. We prove that the proposed algorithm can achieve good approximation bounds. We evaluate and compare our work with a greedy method adapted from the well known Whittle’s index policy, and show that our algorithm outperfoms the greedy method in terms of average throughput.
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