Publications

End-to-End Latency Analysis and Optimal Block Size of Proof-of-Work Blockchain Applications

Published in arxiv, 2022

Abstract: Due to the increasing interest in blockchain technology for fostering secure, auditable, decentralized applications, a set of challenges associated with this technology need to be addressed. In this letter, we focus on the delay associated with Proof-of-Work (PoW)-based blockchain networks, whereby participants validate the new information to be appended to a distributed ledger via consensus to confirm transactions. We propose a novel end-to-end latency model based on batch-service queuing theory that characterizes timers and forks for the first time. Furthermore, we derive an estimation of optimum block size analytically. Endorsed by simulation results, we show that the optimal block size approximation is a consistent method that leads to close-to-optimal performance by significantly reducing the overheads associated with blockchain applications.

Recommended citation: Wilhelmi, F., Barrachina-Muñoz, S., & Dini, P. (2022). End-to-End Latency Analysis and Optimal Block Size of Proof-of-Work Blockchain Applications. arXiv preprint arXiv:2202.01497. https://arxiv.org/pdf/2202.01497.pdf

Stateless Reinforcement Learning for Multi-Agent Systems: the Case of Spectrum Allocation in Dynamic Channel Bonding WLANs

Published in IEEE 2021 Wireless Days (WD), 2021

Abstract: Spectrum allocation in the form of primary channel and bandwidth selection is a key factor for dynamic channel bonding (DCB) wireless local area networks (WLANs). To cope with varying environments, where networks change their configurations on their own, the wireless community is looking towards solutions aided by machine learning (ML), and especially reinforcement learning (RL) given its trial-and-error approach. However, strong assumptions are normally made to let complex RL models converge to near-optimal solutions. Our goal with this paper is two-fold: justify in a comprehensible way why RL should be the approach for wireless networks problems like decentralized spectrum allocation, and call into question whether the use of complex RL algorithms helps the quest of rapid learning in realistic scenarios. We derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of extensive or meaningless RL states.

Recommended citation: Barrachina-Muñoz, S., Chiumento, A., & Bellalta, B. (2021). Stateless Reinforcement Learning for Multi-Agent Systems: the Case of Spectrum Allocation in Dynamic Channel Bonding WLANs. IEEE 2021 Wireless Days (WD).

Wi-Fi Channel Bonding: An All-Channel System and Experimental Study From Urban Hotspots to a Sold-Out Stadium

Published in IEEE/ACM Transactions on Networking, 2021

Abstract: In this paper, we present WACA, the first system to simultaneously measure all 24 Wi-Fi channels that allow channel bonding at 5 GHz with microsecond scale granularity. With WACA, we perform a first-of-its-kind measurement study in areas including urban hotspots, residential neighborhoods, universities, and even a game in Futbol Club Barcelona’s Camp Nou, a sold-out stadium with 98,000 fans and 12,000 simultaneous Wi-Fi connections. We study channel bonding in this environment, and our experimental findings reveal the underpinning factors controlling throughput gain, including channel bonding policy and spectrum occupancy statistics. We then show the significance of the gathered dataset for finding insights, which would not be possible otherwise, given that simple channel occupancy models severely underestimate the available gains. Likewise, we characterize the risks of channel bonding due to other BSS’s, including their missed transmission opportunities and potential collisions due to imperfect sensing of bonded transmissions. We explore 802.11ax which imposes constraints on bonded channels to avoid fragmentation and defines different modes that can trade implementation complexity for throughput. Lastly, we show that the stadium, while seemingly too highly occupied for channel bonding gains, has transient gaps yielding impressive gains.

Recommended citation: Barrachina-Muñoz, S., Bellalta, B., & Knightly, E. (2020, September). Wi-Fi All-Channel Analyzer. IEEE/ACM Transactions on Networking.

Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs

Published in IEEE/ACM Transactions on Networking, 2021

Abstract: While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection. Unfortunately, in uncoordinated high-density deployments where multiple basic service sets (BSSs) may potentially overlap, hand-crafted spectrum management techniques perform poorly given the complex hidden/exposed nodes interactions. To cope with such challenging Wi-Fi environments, in this paper, we first identify machine learning (ML) approaches applicable to the problem at hand and justify why model-free RL suits it the most. We then design a complete RL framework and call into question whether the use of complex RL algorithms helps the quest for rapid learning in realistic scenarios. Through extensive simulations, we derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of broad and/or meaningless states. In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning.

Recommended citation: Barrachina-Muñoz, S., Chiumento, A., & Bellalta, B. (2021). Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs.IEEE Access..

Spatial Reuse in IEEE 802.11ax WLANs

Published in Computer Communications, 2021

Abstract: Dealing with massively crowded scenarios is one of the most ambitious goals of next-generation wireless networks. With this goal in mind, the IEEE 802.11ax amendment includes, among other techniques, the Spatial Reuse (SR) operation. The SR operation encompasses a set of unprecedented techniques that are expected to significantly boost Wireless Local Area Networks (WLANs) performance in dense environments. In particular, the main objective of the SR operation is to maximize the utilization of the medium by increasing the number of parallel transmissions. Nevertheless, due to the novelty of the operation, its performance gains remain largely unknown. In this paper, we first provide a gentle tutorial of the SR operation included in the IEEE 802.11ax. Then, we analytically model SR and delve into the new kinds of MAC-level interactions among network devices. Finally, we provide a simulation-driven analysis to showcase the potential of SR in various deployments, comprising different network densities and traffic loads. Our results show that the SR operation can significantly improve the medium utilization, especially in scenarios under high interference conditions. Moreover, our results demonstrate the non-intrusive design characteristic of SR, which allows enhancing the number of simultaneous transmissions with a low impact on the environment. We conclude the paper by giving some thoughts on the main challenges and limitations of the IEEE 802.11ax SR operation, including research gaps and future directions.

Recommended citation: Wilhelmi, F., Barrachina-Muñoz, S., Cano, C., Selinis, I., & Bellalta, B. (2021). Spatial Reuse in IEEE 802.11ax WLANs. Computer Communications 170, 65-83. https://arxiv.org/abs/1907.04141

Wi-Fi All-Channel Analyzer (Runner up, best paper award)

Published in Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (WINTECH), 2020

Abstract: In this paper, we present WACA, the first system to simultaneously measure the energy in all 24 Wi-Fi channels that allow channel bonding at 5 GHz with microsecond scale granularity. With WACA, we perform a first-of-its-kind measurement campaign in areas including urban hotspots, residential neighborhoods, universities, and a sold-out stadium with 98,000 fans and 12,000 simultaneous Wi-Fi connections. The gathered dataset is a unique asset to find insights otherwise not possible in the context of multi-channel technologies like Wi-Fi. To show its potential, we compare the performance of contiguous and non-contiguous channel bonding using a trace-driven framework. We show that while non-contiguous outperforms contiguous channel bonding’s throughput, occasionally bigger by a factor of 5, their average throughputs are similar.

Recommended citation: Barrachina-Muñoz, S., Bellalta, B., & Knightly, E. (2020, September). Wi-Fi All-Channel Analyzer. In Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (pp. 72-79).

A Flexible Machine Learning-Aware Architecture for Future WLANs

Published in IEEE Communications Magazine, 2020

Abstract: Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on the ITU’s architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.

Recommended citation: Wilhelmi, F., Barrachina-Munoz, S., Bellalta, B., Cano, C., Jonsson, A., & Ram, V. (2020). A Flexible Machine-Learning-Aware Architecture for Future WLANs. IEEE Communications Magazine, 58(3), 25-31. https://ieeexplore.ieee.org/abstract/document/9040258/

On the Performance of the Spatial Reuse Operation in IEEE 802.11 ax WLANs

Published in IEEE Conference on Standards for Communications and Networking (CSCN), 2019

Abstract: The Spatial Reuse (SR) operation included in the IEEE 802.11ax-2020 (11ax) amendment aims at increasing the number of parallel transmissions in an Overlapping Basic Service Set (OBSS). However, many unknowns exist about the performance gains that can be achieved through SR. In this paper, we provide a brief introduction to the SR operation described in the IEEE 802.11ax (draft D4.0). Then, a simulation-based implementation is provided in order to explore the performance gains of the SR operation. Our results show the potential of using SR in different scenarios covering multiple network densities and traffic loads. In particular, we observe significant performance gains when a WLAN applies SR with respect to the default configuration. Interestingly, the highest improvements are observed in the most pessimistic situations in terms of network density and traffic load.

Recommended citation: Wilhelmi, F., Barrachina-Muñoz, S., & Bellalta, B. (2019, October). On the Performance of the Spatial Reuse Operation in IEEE 802.11 ax WLANs. In 2019 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/abstract/document/8931315/

Online Primary Channel Selection for Dynamic Channel Bonding in High-Density WLANs

Published in Wireless Days, 2019

Abstract: In order to dynamically adapt the transmission bandwidth in wireless local area networks (WLANs), dynamic channel bonding (DCB) was introduced in IEEE 802.11n. It has been extended since then, and it is expected to be a key element in IEEE 802.11ax and future amendments such as IEEE 802.11be. While DCB is proven to be a compelling mechanism by itself, its performance is deeply tied to the primary channel selection, especially in high-density (HD) deployments, where multiple nodes contend for the spectrum. Traditionally, this primary channel selection relied on picking the most free one without any further consideration. In this letter, in contrast, we propose dynamic-wise (DyWi), a light-weight, decentralized, online primary channel selection algorithm for DCB that improves the expected WLAN throughput by considering not only the occupancy of the target primary channel but also the activity of the secondary channels. Even when assuming important delays due to primary channel switching, simulation results show a significant improvement both in terms of average delay and throughput.

Recommended citation: Barrachina-Muñoz, S., Wilhelmi, F., & Bellalta, B. (2019). Online Primary Channel Selection for Dynamic Channel Bonding in High-Density WLANs. IEEE Wireless Communications Letters, 9(2), 258-262. https://ieeexplore.ieee.org/abstract/document/8894073

Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs

Published in Wireless Days, 2019

Abstract: Komondor is a wireless network simulator for next-generation wireless local area networks (WLANs). The simulator has been conceived as an accessible (ready-to-use) open source tool for research on wireless networks and academia. An important advantage of Komondor over other well-known wireless simulators lies in its high event processing rate, which is furnished by the simplification of the core operation. This allows outperforming the execution time of other simulators like ns-3, thus supporting large-scale scenarios with a huge number of nodes. In this paper, we provide insights into the Komondor simulator and overview its main features, development stages and use cases. The operation of Komondor is validated in a variety of scenarios against different tools: the ns-3 simulator and two analytical tools based on Continuous Time Markov Networks (CTMNs) and the Bianchi’s DCF model. Results show that Komondor captures the IEEE 802.11 operation very similarly to ns-3. Finally, we discuss the potential of Komondor for simulating complex environments – even with machine learning support – in next-generation WLANs by easily developing new user-defined modules of code.

Recommended citation: Barrachina-Muñoz, S., Wilhelmi, F., Selinis, I., & Bellalta, B. (2019, April). Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs. In 2019 Wireless Days (WD) (pp. 1-8). IEEE. https://ieeexplore.ieee.org/abstract/document/8734225

To overlap or not to overlap: Enabling channel bonding in high-density WLANs

Published in Computer Networks (Elsevier), 2019

Abstract: Wireless local area networks (WLANs) are the most popular kind of wireless Internet connection because of their simplicity of deployment and operation. As a result, the number of devices accessing the Internet through WLANs such as laptops, smartphones, or wearables, is increasing drastically at the same time that applications’ throughput requirements do. To cope with these challenges, channel bonding (CB) techniques are used for enabling higher data rates by transmitting in wider channels, thus increasing spectrum efficiency. However, important issues like higher potential co-channel and adjacent channel interference arise when bonding channels. This may harm the performance of the carrier sense multiple access (CSMA) protocol because of recurrent backoff freezing while making nodes more sensitive to hidden node effects. In this paper, we address the following point at issue: is it convenient for high-density (HD) WLANs to use wider channels and potentially overlap in the spectrum? First, we highlight key aspects of DCB in toy scenarios through a continuous time Markov network (CTMN) model. Then, by means of extensive simulations covering a wide range of traffic loads and access point (AP) densities, we show that dynamic channel bonding (DCB) - which adapts the channel bandwidth on a per-packet transmission - significantly outperforms traditional single-channel on average. Nevertheless, results also corroborate that DCB is more prone to generate unfair situations where WLANs may starve. Contrary to most of the current thoughts pushing towards non-overlapping channels in HD deployments, we highlight the benefits of allocating channels as wider as possible to WLANs altogether with implementing adaptive access policies to cope with the unfairness situations that may appear.

Recommended citation: Barrachina-Muñoz, S., Wilhelmi, F., & Bellalta, B. (2019). To overlap or not to overlap: Enabling channel bonding in high-density WLANs. Computer Networks, 152, 40-53. https://www.sciencedirect.com/science/article/abs/pii/S1389128618309745

Combining Software Defined Networks and Machine Learning to enable Self Organizing WLANs

Published in 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2019

Abstract: Next generation of wireless local area networks (WLANs) will operate in dense, chaotic and highly dynamic scenarios that in a significant number of cases may result in a low user experience due to uncontrolled high interference levels. Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide WLANs with new capabilities to deal with users’ demands, while achieving greater levels of efficiency and flexibility in those complex scenarios. On top of SDN, the use of machine learning (ML) techniques may improve network resource usage and management by identifying feasible configurations through learning. ML techniques can drive WLANs to reach optimal working points by means of parameter adjustment, in order to cope with different network requirements and policies, as well as with the dynamic conditions. In this paper we overview the work done in SDN for WLANs, as well as the pioneering works considering ML for WLAN optimization. Finally, in order to demonstrate the potential of ML techniques in combination with SDN to improve the network operation, we evaluate different use cases for intelligent-based spatial reuse and dynamic channel bonding operation in WLANs using Multi-Armed Bandits.

Recommended citation: López-Raventós, Á., Wilhelmi, F., Barrachina--Muñoz, S., & Bellalta, B. (2019). Combining Software Defined Networks and Machine Learning to enable Self Organizing WLANs. In 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1-8). IEEE. https://ieeexplore.ieee.org/abstract/document/8923569/

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

Published in IEEE 5th World Forum on Internet of Things (WF-IoT), 2019

Abstract: 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.

Recommended citation: Barrachina-Muñoz, S., Adame, T., Bel, A., & Bellalta, B. (2019, April). Towards energy efficient LPWANs through learning-based multi-hop routing. In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) (pp. 644-649). IEEE. https://ieeexplore.ieee.org/abstract/document/8767193/

Dynamic Channel Bonding in Spatially Distributed High-Density WLANs

Published in IEEE Transactions on Mobile Computing, 2019

Abstract: In this paper, we discuss the effects on throughput and fairness of dynamic channel bonding (DCB) in spatially distributed high-density wireless local area networks (WLANs). First, we present an analytical framework based on continuous-time Markov networks (CTMNs) for depicting the behavior of different DCB policies in spatially distributed scenarios, where nodes are not required to be within the carrier sense range of each other. Then, we assess the performance of DCB in high-density IEEE 802.11ac/ax WLANs by means of simulations. We show that there may be critical interrelations among nodes in the spatial domain-even if they are located outside the carrier sense range of each other-in a chain reaction manner. Results also reveal that, while always selecting the widest available channel normally maximizes the individual long-term throughput, it often generates unfair situations where other WLANs starve. Moreover, we show that there are scenarios where DCB with stochastic channel width selection improves the latter approach both in terms of individual throughput and fairness. It follows that there is not a unique optimal DCB policy for every case. Instead, smarter bandwidth adaptation is required in the challenging scenarios of next-generation WLANs.

Recommended citation: Barrachina-Muñoz, S., Wilhelmi, F., & Bellalta, B. (2019). Dynamic channel bonding in spatially distributed high-density WLANs. IEEE Transactions on Mobile Computing, 19(4), 821-835. https://ieeexplore.ieee.org/abstract/document/8642923

Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs

Published in Journal of Network and Computer Applications (JNCA), 2018

Abstract: Spatial Reuse (SR) has recently gained attention for performance maximization in IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for next-generation WLANs, since many deployments are characterized by being uncoordinated by nature. However, the potential of decentralized mechanisms is limited by the significant lack of knowledge with respect to the overall wireless environment. To shed some light on this subject, we show the main considerations and possibilities of applying online learning to address the SR problem in uncoordinated WLANs. In particular, we provide a solution based on Multi-Armed Bandits (MABs) whereby independent WLANs dynamically adjust their frequency channel, transmit power and sensitivity threshold. To that purpose, we provide two different strategies, which refer to selfish and environment-aware learning. While the former stands for pure individual behavior, the second one aims to consider the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment. Through these two strategies we delve into practical issues of applying MABs in wireless networks, such as convergence guarantees or adversarial effects. Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs, showing that substantial improvements on network performance can be achieved regarding throughput and fairness.

Recommended citation: Wilhelmi, F., Barrachina-Muñoz, S., Bellalta, B., Cano, C., Jonsson, A., & Neu, G. (2019). Potential and pitfalls of multi-armed bandits for decentralized spatial reuse in WLANs. Journal of Network and Computer Applications, 127, 26-42. https://www.sciencedirect.com/science/article/pii/S1084804518303655

HARE: Supporting efficient uplink multi-hop communications in self-organizing LPWANs

Published in Sensors, 2018

Abstract: The emergence of low-power wide area networks (LPWANs) as a new agent in the Internet of Things (IoT) will result in the incorporation into the digital world of low-automated processes from a wide variety of sectors. The single-hop conception of typical LPWAN deployments, though simple and robust, overlooks the self-organization capabilities of network devices, suffers from lack of scalability in crowded scenarios, and pays little attention to energy consumption. Aimed to take the most out of devices’ capabilities, the HARE protocol stack is proposed in this paper as a new LPWAN technology flexible enough to adopt uplink multi-hop communications when proving energetically more efficient. In this way, results from a real testbed show energy savings of up to 15% when using a multi-hop approach while keeping the same network reliability. System’s self-organizing capability and resilience have been also validated after performing numerous iterations of the association mechanism and deliberately switching off network devices.

Recommended citation: Adame Vázquez, T., Barrachina-Muñoz, S., Bellalta, B., & Bel, A. (2018). HARE: Supporting efficient uplink multi-hop communications in self-organizing LPWANs. Sensors, 18(1), 115. https://www.mdpi.com/1424-8220/18/1/115

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

Published in Ad-hoc Networks (Elsevier), 2017

Abstract: 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 that enhance Spatial Reuse (SR). In particular, we concentrate in Reinforcement Learning (RL), and more specifically, in Multi-Armed Bandits (MABs), to allow networks to modify both their transmission power and channel based on their experienced throughput. In this work, we study the exploration-exploitation trade-off by means of the ε-greedy, EXP3, UCB and Thompson sampling action-selection strategies. Our results show that optimal proportional fairness can be achieved, even if no information about neighboring networks is available to the learners and WNs operate selfishly. However, there is high temporal variability in the throughput experienced by the individual networks, specially for ε-greedy and EXP3. 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 this variability is reduced using UCB and Thompson sampling, which are parameter-free policies that perform exploration according to the reward distribution of each action.

Recommended citation: Wilhelmi, F., Cano, C., Neu, G., Bellalta, B., Jonsson, A., & Barrachina-Muñoz, S. (2019). Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits. Ad Hoc Networks 88 (2019): 129-141. https://www.sciencedirect.com/science/article/pii/S1570870518302646?casa_token=_3NaFYTPWgoAAAAA:7Z0DkV6IOJ3ITNi1uOoarip1kjU07-DKEdBMaYqoGHhDrqRoGLHQjHAOeaj9ETVoXNYrtXUx0w

Learning optimal routing for the uplink in LPWANs using similarity-enhanced e-greedy

Published in IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017

Abstract: 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 routing 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 e-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.

Recommended citation: Barrachina-Muñoz, S., & Bellalta, B. (2017, October). Learning optimal routing for the uplink in LPWANs using similarity-enhanced e-greedy. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1-5). IEEE. https://ieeexplore.ieee.org/abstract/document/8292373/

Multi-hop communication in the uplink for LPWANs

Published in Computer Networks (Elsevier), 2017

Abstract: Low-Power Wide Area Networks (LPWANs) have arisen as a promising communication technology for supporting Internet of Things (IoT) services due to their low power operation, wide coverage range, low cost and scalability. However, most LPWAN solutions like SIGFOX™or LoRaWAN™rely on star topology networks, where stations (STAs) transmit directly to the gateway (GW), which often leads to rapid battery depletion in STAs located far from it. In this work, we analyze the impact on LPWANs energy consumption of multi-hop communication in the uplink, allowing STAs to transmit data packets in lower power levels and higher data rates to closer parent STAs, reducing their energy consumption consequently. To that aim, we introduce the Distance-Ring Exponential Stations Generator (DRESG) framework,1 designed to evaluate the performance of the so-called optimal-hop routing model, which establishes optimal routing connections in terms of energy efficiency, aiming to balance the consumption among all the STAs in the network. Results show that enabling such multi-hop connections entails higher network lifetimes, reducing significantly the bottleneck consumption in LPWANs with up to thousands of STAs. These results lead to foresee multi-hop communication in the uplink as a promising routing alternative for extending the lifetime of LPWAN deployments.

Recommended citation: Barrachina-Muñoz, S., Bellalta, B., Adame, T., & Bel, A. (2017). Multi-hop communication in the uplink for LPWANs. Computer Networks, 123, 153-168. https://www.sciencedirect.com/science/article/pii/S1389128617302207

GOAT: A Tool for Planning Wireless Sensor Networks

Published in International Workshop on Multiple Access Communications (MACOM), 2015

Abstract: This paper presents GOAT, a software tool that has been developed to study the effect of different Medium Access Control (MAC) and routing protocols on the energy consumption in Wireless Sensor Networks (WSNs). GOAT is a graphical network analysis tool that allows designing WSNs, calculating the energy consumption and overall lifetime in thoroughly configurable WSN scenarios. The aim of the GOAT tool is to obtain a knowledge of the behaviour of WSNs in terms of nodes connectivity and energy consumption prior to the WSN deployment in a real environment.

Recommended citation: Barrachina, S., Adame, T., Bel, A., & Bellalta, B. (2015, September). GOAT: A Tool for Planning Wireless Sensor Networks. In International Workshop on Multiple Access Communications (pp. 147-158). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-23440-3_12

Sergio Barrachina-Muñoz

Publications