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Discontinuous reception (DRX) is a key technology for reducing the energy consumption of industrial Internet of Things (IIoT) devices. Specifically, DRX allows the devices to operate in a low-power mode when no data reception is scheduled, and its effectiveness depends on the proper configuration of the DRX parameters. In this paper, we characterize the DRX process departing from a semi-Markov chain modeling. We detail two ways to set DRX parameters to minimize the device power consumption while meeting a mean delay constraint. The first method exhaustively searches for the optimal configuration. In contrast, the second method uses a low-complexity metaheuristic to find a sub-optimal configuration, thus considering ideal and practical DRX configurations. Notably, within the DRX parameters, the inactivity timer (IT) is a caution time that specifies how long a device remains active after the last information exchange. Traditionally, a device implementing DRX will restart the IT after each data reception as a precedent to a low-power mode. The usual approach lies in restarting the IT whenever new data is received during this cautious period, which might sometimes needlessly extend the active time. Herein, we propose a more efficient method in which the transmit base station (BS) explicitly indicates restarting the timer through the control channel only when appropriate. The decision is taken based on the BS's knowledge about its buffer status. We consider Poisson and bursty traffic models, which are typical in IIoT setups, and verify the suitability of our proposal for reducing the energy consumption of the devices without significantly compromising the communication latency through extensive numerical simulations. Specifically, energy-saving gains of up to 30% can be obtained regardless of the arrival rate and delay constraints., Comment: IEEE Transactions on Industrial Informatics (2024)

Direct-to-Satellite IoT (DtS-IoT) has the potential to support multiple verticals, including agriculture, industry, smart cities, and environmental disaster prevention. This work introduces two novel DtS-IoT schemes using power domain NonOrthogonal Multiple Access (NOMA) in the uplink with either fixed (FTP) or controlled (CTP) transmit power. We consider that the IoT devices use LoRa technology to transmit data packets to the satellite in orbit, equipped with a Successive Interference Cancellation (SIC)-enabled gateway. We also assume the IoT devices are empowered with a predictor of the satellite orbit. Using real geographic location and trajectory data, we evaluate the performance of the average number of successfully decoded transmissions, goodput (bytes/lap), and energy consumption (bytes/Joule) as a function of the number of network devices. Numerical results show the trade-off between goodput and energy efficiency for both proposed schemes. Comparing FTP and CTP with regular ALOHA for 100 (600) devices, we find goodput improvements of 65% (29%) and 52% (101%), respectively. Notably, CTP effectively leverages transmission opportunities as the network size increases, outperforming the other strategies. Moreover, CTP shows the best performance in energy efficiency compared to FTP and ALOHA., Comment: 9 pages, 8 figures

The Internet-of-Things (IoT) is rapidly expanding, connecting numerous devices and becoming integral to our daily lives. As this occurs, ensuring efficient traffic management becomes crucial. Effective IoT traffic management requires modeling and predicting intrincate machine-type communication (MTC) dynamics, for which machine-learning (ML) techniques are certainly appealing. However, obtaining comprehensive and high-quality datasets, along with accessible platforms for reproducing ML-based predictions, continues to impede the research progress. In this paper, we aim to fill this gap by characterizing the Smart Campus MTC dataset provided by the University of Oulu. Specifically, we perform a comprehensive statistical analysis of the MTC traffic utilizing goodness-of-fit tests, including well-established tests such as Kolmogorov-Smirnov, Anderson-Darling, chi-squared, and root mean square error. The analysis centers on examining and evaluating three models that accurately represent the two most significant MTC traffic types: periodic updating and event-driven, which are also identified from the dataset. The results demonstrate that the models accurately characterize the traffic patterns. The Poisson point process model exhibits the best fit for event-driven patterns with errors below 11%, while the quasi-periodic model fits accurately the periodic updating traffic with errors below 7%., Comment: SSRN:4655476

Industrial Internet of Things (IIoT) applications involve real-time monitoring, detection, and data analysis. This is challenged by the intermittent activity of IIoT devices (IIoTDs) and their limited battery capacity. Indeed, the former issue makes resource scheduling and random access difficult, while the latter constrains IIoTDs' lifetime and efficient operation. In this paper, we address interconnected aspects of these issues. Specifically, we focus on extending the battery life of IIoTDs sensing events/alarms by minimizing the number of unnecessary transmissions. Note that when multiple devices access the channel simultaneously, there are collisions, potentially leading to retransmissions, thus reducing energy efficiency. We propose a threshold-based transmission-decision policy based on the sensing quality and the network spatial deployment. We optimize the transmission thresholds using several approaches such as successive convex approximation, block coordinate descent methods, Voronoi diagrams, explainable machine learning, and algorithms based on natural selection and social behavior. Besides, we propose a new approach that reformulates the optimization problem as a $Q$-learning solution to promote adaptability to system dynamics. Through numerical evaluation, we demonstrate significant performance enhancements in complex IIoT environments, thus validating the practicality and effectiveness of the proposed solutions. We show that Q-learning performs the best, while the block coordinate descending method incurs the worst performance. Additionally, we compare the proposed methods with a benchmark assigning the same threshold to all the devices for transmission decision. Compared to the benchmark, up to 94\% and 60\% reduction in power consumption are achieved in low-density and high-density scenarios, respectively.

This paper presents an approach for energy-neutral Internet of Things (IoT) scenarios where the IoT devices (IoTDs) rely entirely on their energy harvesting capabilities to sustain operation. We use a Markov chain to represent the operation and transmission states of the IoTDs, a modulated Poisson process to model their energy harvesting process, and a discrete-time Markov chain to model their battery state. The aim is to efficiently manage the duty cycling of the IoTDs, so as to prolong their battery life and reduce instances of low-energy availability. We propose a duty-cycling management based on K- nearest neighbors, aiming to strike a trade-off between energy efficiency and detection accuracy. This is done by incorporating spatial and temporal correlations among IoTDs' activity, as well as their energy harvesting capabilities. We also allow the base station to wake up specific IoTDs if more information about an event is needed upon initial detection. Our proposed scheme shows significant improvements in energy savings and performance, with up to 11 times lower misdetection probability and 50\% lower energy consumption for high-density scenarios compared to a random duty cycling benchmark.

Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-of-the-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least., Comment: IEEE Wireless Communications Letters Print ISSN: 2162-2337 Online ISSN: 2162-2345

Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)- based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability

The Internet of Things (IoT) comprises an increasing number of low-power and low-cost devices that autonomously interact with the surrounding environment. As a consequence of their popularity, future IoT deployments will be massive, which demands energy-efficient systems to extend their lifetime and improve the user experience. Radio frequency wireless energy transfer has the potential of powering massive IoT networks, thus eliminating the need for frequent battery replacement by using the so-called power beacons (PBs). In this paper, we provide a framework for minimizing the sum transmit power of the PBs using devices' positions information and their current battery state. Our strategy aims to reduce the PBs' power consumption and to mitigate the possible impact of the electromagnetic radiation on human health. We also present analytical insights for the case of very distant clusters and evaluate their applicability. Numerical results show that our proposed framework reduces the outage probability as the number of PBs and/or the energy demands increase., Comment: 7 pages, 6 figures, Submitted to "The International Workshop on Very Large Internet of Things (2021)"

Radio frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multi-antenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this paper, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as $0.85\sqrt{M}$, where $M$ is the number of PB's antenna elements. Moreover, a rotation-specific power control mechanism was proposed to i) fairly optimize the WET process if devices' positioning information is available, and/or ii) to avoid hazards to human health in terms of specific absorption rate (SAR). We show that RAB performance even approaches quickly (or surpasses, for scenarios with sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology., Comment: 12 pages, 10 figures. Accepted for publication in IEEE IoT Journal

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet of Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio frequency transmitters called power beacons (PBs). In this paper, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs., Comment: 11 pages and 15 figures, there are corrections compared to the published one in IEEE

LoRaWAN enables massive connectivity for Internet-of-Things applications. Many published works employ stochastic geometry to derive outage models of LoRaWAN over fading channels assuming fixed transmit power and distance-based spreading factor (SF) allocation. However, in practice, LoRaWAN employs the Adaptive Data Rate (ADR) mechanism, which dynamically adjusts SF and transmit power of nodes based on channel state. The community addressed the performance of ADR using simulations, but analytical models have not been introduced. In this letter, we seek to close this gap. We build over an analytical LoRaWAN model to consider the performance of steady-state ADR-enabled LoRaWAN. We derive outage expressions and an optimization procedure to maximize the number of users under reliability constraints. Results show that power allocation reduces interference and improves network capacity while reducing average power., Comment: 4 pages, 4 figures, accepted for publication in IEEE Wireless Communications Letters

Wireless Energy Transfer (WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze Channel State Information (CSI)-free multi-antenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AA-SS (AA-IS), where all antennas transmit the same (independent) signal(s), and SA, where just one antenna transmits at a time such that all antennas are utilized during the coherence block. We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides the greatest average energy, but also the greatest energy dispersion, and the gains depend critically on the mean phase shifts between the antenna elements. We find that consecutive antennas must be $\pi$ phase-shifted for optimum average energy performance under AA-SS. Our numerical results evidenced that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or number of antennas is not always beneficial under such scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS and/or large number of antennas., Comment: 16 pags, 10 figs, 1 table, submitted to IEEE JSAC 2020

Low Power Wide Area Networks (LPWAN) are wireless connectivity solutions for Internet-of-Things (IoT) applications, including industrial automation. Among the several LPWAN technologies, LoRaWAN has been extensively addressed by the research community and the industry. However, the reliability and scalability of LoRaWAN are still uncertain. One of the techniques to increase the reliability of LoRaWAN is message replication, which exploits time diversity. This paper proposes a novel hybrid coded message replication scheme that interleaves simple repetition and a recently proposed coded replication method. We analyze the optimization of the proposed scheme under minimum reliability requirements and show that it enhances the network performance without requiring additional transmit power compared to the competing replication techniques., Comment: 9 pages, 4 figures, to be published in IEEE Transactions on Industrial Informatics

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet of Things (IoT) devices enabled by the upcoming sixth generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this paper. Candidates enablers such as energy beamforming (EB), distributed antenna systems (DAS), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultra-low power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions towards realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail., Comment: 19 pags, 15 figures, 5 tables, submitted to IEEE Internet of Things Journal

We consider a two-phase Wireless-Powered Communication Network under Nakagami-m fading, where a wireless energy transfer process first powers a sensor node that then uses such energy to transmit its data in the wireless information transmission phase. We explore a fixed transmit rate scheme designed to cope with the reliability and delay constraints of the system while attaining closed-form approximations for the optimum wireless energy transfer and wireless information transmission blocklength. Then, a more-elaborate rate control strategy exploiting the readily available battery charge information is proposed and the results evidence its outstanding performance when compared with the fixed transmit rate, for which no battery charge information is available. It even reaches an average rate performance close to that of an ideal scheme requiring full Channel State Information at transmitter side. Numerical results show the positive impact of a greater number of antennas at the destination, and evidence that the greater the reliability constraints, the smaller the message sizes on average, and the smaller the optimum information blocklengths. Finally, we corroborate the appropriateness of using the asymptotic blocklength formulation as an approximation of the non-asymptotic finite blocklength results., Comment: 14 pages, 8 figures, accepted for publication in IEEE TWC

Smart Utility Networks (SUN) rely on the Wireless-SUN (Wi-SUN) specification for years. Recently practitioners and researchers have considered Low-Power Wide-Area Networks (LPWAN) like LoRaWAN for SUN applications. With distinct technologies deployed in the same area and sharing unlicensed bands, one can expect these networks to interfere with one another. This paper builds over a LoRaWAN model to optimize network parameters while accounting for inter-technology interference. Our analytic model accounts for the interference LoRaWAN receives from IEEE 802.15.4g networks, which forms the bottom layers of Wi-SUN systems. We derive closed-form equations for the expected reliability of LoRaWAN in such scenarios. We set the model parameters with data from real measurements of the interplay among the technologies. Finally, we propose two optimization algorithms to determine the best LoRaWAN configurations, given a targeted minimum reliability level. The algorithms maximize either communication range or the number of users given constraints on the minimum number of users, minimum communication range, and minimum reliability. We validate the models and algorithms through numerical analysis and simulations. The proposed methods are useful tools for planning interference-limited networks with requirements of minimum reliability., Comment: 11 pages, 6 figures, published on IEEE Access

Wireless Energy Transfer (WET) is emerging as a potential solution for powering small energy-efficient devices. We propose strategies that use multiple antennas at a power station, which wirelessly charges a large set of single-antenna devices. Proposed strategies operate without Channel State Information (CSI) and we attain the distribution and main statistics of the harvested energy under Rician fading channels with sensitivity and saturation energy harvesting (EH) impairments. A switching antenna strategy, where a single antenna with full power transmits at a time, provides the most predictable energy source, and it is particularly suitable for powering sensor nodes with highly sensitive EH hardware operating under non-LOS (NLOS) conditions; while other WET schemes perform alike or better in terms of the average harvested energy. Under NLOS switching antennas is the best, while when LOS increases transmitting simultaneously with equal power in all antennas is the most beneficial. Moreover, spatial correlation is not beneficial unless the power station transmits simultaneously through all antennas, raising a trade-off between average and variance of the harvested energy since both metrics increase with the spatial correlation. Moreover, the performance gap between CSI-free and CSI-based strategies decreases quickly as the number of devices increases., Comment: 18 pages, 10 figures, accepted for publication in IEEE TCOM