arXiv subjects

Sergio Barrachina-Muñoz

Publications and source records attributed to Sergio Barrachina-Muñoz.

21 records · Page 2Linked to original sources

arXiv2018

Towards Energy Efficient LPWANs through Learning-based Multi-hop Routing

Low-power wide area networks (LPWANs) have been identified as one of the top emerging wireless technologies due to their autonomy and wide range of applications. Yet, the limited energy resources of battery-powered sensor nodes is a top constraint, especially in single-hop topologies, where nodes located far from the base station must conduct uplink (UL) communications in high power levels. On this point, multi-hop routings in the UL are starting to gain attention due to their capability of reducing energy consumption by enabling transmissions to closer hops. Nonetheless, a priori identifying energy efficient multi-hop routings is not trivial due to the unpredictable factors affecting the communication links in large LPWAN areas. In this paper, we propose epsilon multi-hop (EMH), a simple reinforcement learning (RL) algorithm based on epsilon-greedy to enable reliable and low consumption LPWAN multi-hop topologies. Results from a real testbed show that multi-hop topologies based on EMH achieve significant energy savings with respect to the default single-hop approach, which are accentuated as the network operation progresses.

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arXiv2018

Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits

Next-generation wireless deployments are characterized by being dense and uncoordinated, which often leads to inefficient use of resources and poor performance. To solve this, we envision the utilization of completely decentralized mechanisms to enable Spatial Reuse (SR). In particular, we focus on dynamic channel selection and Transmission Power Control (TPC). We rely on Reinforcement Learning (RL), and more specifically on Multi-Armed Bandits (MABs), to allow networks to learn their best configuration. In this work, we study the exploration-exploitation trade-off by means of the $\varepsilon$-greedy, EXP3, UCB and Thompson sampling action-selection, and compare their performance. In addition, we study the implications of selecting actions simultaneously in an adversarial setting (i.e., concurrently), and compare it with a sequential approach. Our results show that optimal proportional fairness can be achieved, even when no information about neighboring networks is available to the learners and Wireless Networks (WNs) operate selfishly. However, there is high temporal variability in the throughput experienced by the individual networks, specially for $\varepsilon$-greedy and EXP3. These strategies, contrary to UCB and Thompson sampling, base their operation on the absolute experienced reward, rather than on its distribution. We identify the cause of this variability to be the adversarial setting of our setup in which the set of most played actions provide intermittent good/poor performance depending on the neighboring decisions. We also show that learning sequentially, even if using a selfish strategy, contributes to minimize this variability. The sequential approach is therefore shown to effectively deal with the challenges posed by the adversarial settings that are typically found in decentralized WNs.

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arXiv2017

Learning Optimal Routing for the Uplink in LPWANs Using Similarity-enhanced epsilon-greedy

Despite being a relatively new communication technology, Low-Power Wide Area Networks (LPWANs) have shown their suitability to empower a major part of Internet of Things applications. Nonetheless, most LPWAN solutions are built on star topology (or single-hop) networks, often causing lifetime shortening in stations located far from the gateway. In this respect, recent studies show that multi-hop routing for uplink communications can reduce LPWANs' energy consumption significantly. However, it is a troublesome task to identify such energetically optimal routings through trial-and-error brute-force approaches because of time and, especially, energy consumption constraints. In this work we show the benefits of facing this exploration/exploitation problem by running centralized variations of the multi-arm bandit's epsilon-greedy, a well-known online decision-making method that combines best known action selection and knowledge expansion. Important energy savings are achieved when proper randomness parameters are set, which are often improved when conveniently applying similarity, a concept introduced in this work that allows harnessing the gathered knowledge by sporadically selecting unexplored routing combinations akin to the best known one.

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