Delivering downlink ACKs to Class A LoRaWAN devices via LEO satellite constellations requires predicting end-to-end propagation delays of 10–500 ms to hit a 1-second receive window, a timing challenge absent from terrestrial schedulers. We present DORSAL, the first Mixed-Integer Linear Programming (MILP) scheduler for downlink delivery in direct-to-satellite LoRaWAN networks. It introduces theDownlink Arrival Receive-window Timing (DART)as the central constraint to jointly optimize gateway selection and packet injection timing across ISL-capable and bent-pipe architectures. Evaluation on a Walker 5×5 polar constellation (25 satellites, 650 km) and a commercial ground station network yields five results: (i) DART infeasibility blocks 73% of bent-pipe deliveries regardless of scheduler quality: DART-unaware approaches collapse from 93% nominal to only 20% real delivery; (ii) ISL routing raises %ACK by +67.4 pp over bent-pipe (91.6% vs. 24.2% atnGS=3 ground stations, ISM band) by eliminating the three-way simultaneous-visibility constraint, with 73% of ACKs delivered via ≥ 2-hop paths; (iii) ISL %ACK is nearly flat acrossnGS∈ {3, 6, 12} ground stations (91.6%–92.7%), showing that a minimal threestation polar network captures the full scheduling benefit of an ISL-equipped constellation; (iv) ISL hardware speed has negligible impact up to 300 ms/hop: %ACK is flat from 0 to 300 ms, then degrades in two discrete steps (−5.3 pp at 500 ms, −2 pp at 700 ms) as successive hop-count tiers lose causal injection feasibility; and (v) the MILP solves to proven optimality within seconds for up to 103devices, confirming that the sparse conflict-graph structure keeps the problem tractable at realistic IoT subscriber counts.

The rapid growth of the Internet of Things (IoT) presents sustainability challenges, including increased maintenance requirements and overall higher energy consumption. This motivates self-sustainable IoT ecosystems based on Energy Harvesting (EH). This paper treats IoT deployments in which IoT devices (IoTDs) rely solely on EH to sense and transmit information about events/alarms to a base station (BS). The objective is to effectively manage the duty cycling of the IoTDs to prolong battery life and maximize the relevant data delivered to the BS. The BS can also selectively wake up specific IoTDs to gather extra information following initial detection. We propose a K-nearest neighbors (KNN)-based duty cycling management to optimize energy efficiency and detection accuracy by considering spatial correlations among IoTDs’ activity and their EH process. We evaluate machine learning approaches, including reinforcement learning (RL) and decision transformers (DT), to maximize information captured from events while managing energy consumption. All three approaches (KNN, RL, and DT) achieve significant energy savings over state-of-the-art methods. Moreover, the RL-based solution approaches the performance of a genie-aided benchmark as the number of IoTDs increases.

Low Earth Orbit (LEO) constellations are pivotal for the global connectivity of Internet of Things (IoT) networks. They facilitate Direct-to-Satellite (DtS) communications for ground nodes and address the absence of terrestrial infrastructure in remote regions. Although LoRa-based solutions provide low-cost connectivity to constrained devices in DtS-IoT networks, scalability remains a significant challenge due to heavy channel congestion associated with large-scale deployments. The LoRa Optimistic Collision Information-based (L-OCI) mechanism has been designed to estimate efficiently the network size of a LoRa-based DtS-IoT deployment. It enables a passing satellite to gauge the potential congestion in the served region by estimating the number of active ground nodes within its coverage area. However, this mechanism was devised to operate assuming a single LEO satellite. This paper introduces the constellation LOCI (CL-OCI), a new methodology for network size estimation designed for LEO satellite constellations. We thoroughly tested CL-OCI using FLoRaSat, a discrete-event satellite constellation simulator with realistic orbital DtS-IoT scenarios. Results indicate a substantial improvement in scalability, doubling the maximum number of nodes CL-OCI can estimate, and a sevenfold reduction in energy wasted due to collided transmissions compared to the baseline single-satellite L-OCI.

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 intricate 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%. • We examine the discrepancy in traffic models utilized in machine learning (ML). • We emphasize the importance of reliable traffic models in reducing ML training costs. • We validate MTC traffic models by characterizing a Smart Campus dataset. • We compare the goodness-of-fit of the proposed models for various MTC scenarios. • We show the suitability of the proposed models for representing MTC traffic patterns.

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 and 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, while 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 as a precedent to a low-power mode. Traditionally, the IT is restarted whenever new data is received, 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 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 our proposal's suitability for reducing the devices' energy consumption without significantly compromising the communication latency. Specifically, energy saving gains up to 30% can be obtained regardless of the arrivals rate and delay constraints.

In this work, we address a novel replication scheme for Internet-of-Things (IoT) low power wide area networks (LPWAN), considering the ability of a gateway to recover, through successive interference cancellation (SIC), superposed signals in the power domain. We show that the introduced mathematical framework matches the Monte Carlo simulations. The proposed scheme outperforms typical transmission schemes, at least from 1 to 2 orders of magnitude, in terms of outage probability. We also show that the scheme fits well with previous replication schemes, outperforming their versions without non-orthogonal replications. Moreover, we show that the proposed scheme can be robust to cases with high SIC imperfection, presenting slight performance losses even in very harsh scenarios, as long as the constraints are met. Finally, we conclude that the proposed scheme can be more energy efficient than previous replication schemes and achieve better reliability in traffic-loaded networks.

The worldwide adoption of the Internet of Things (IoT) depends on the massive deployment of sensor nodes and timely data collection. However, installing the required ground infrastructure in remote or inaccessible areas can be economically unattractive or unfeasible. Cost-effective nanosatellites deployed in low Earth orbits (LEOs) are emerging as an alternative solution: on-board IoT gateways provide access to remote IoT devices, according to direct-to-satellite IoT (DtS-IoT) architectures. One of the main challenges of DtS-IoT is to devise communication protocols that scale to thousands of highly constrained devices served by likewise constrained orbiting gateways. In this article, we tackle this issue by first estimating the (varying) size of the device set underneath the (mobile) nanosatellite footprint. Then, we demonstrate the applicability of the estimation when used to intelligently throttle DtS-IoT access protocols. Since recent works have shown that MAC protocols improve the throughput and energy efficiency of a DtS-IoT network when a network size estimation is available, we present, here, a novel and computationally efficient network size estimator in DtS-IoT: our optimistic collision information (OCI)-based estimator. We evaluate OCI’s effectiveness with extensive simulations of DtS-IoT scenarios. Results show that when using network size estimations, the scalability of a frame-slotted Aloha-based DtS-IoT network is boosted eightfold, serving up to$4 \times 10^{3}$devices, without energy efficiency penalties. We also show the effectiveness of the OCI mechanism given realistic detection ratios and demonstrate its low computational cost implementation, making it a strong candidate for network estimation in DtS-IoT.

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 multiantenna 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 article, 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: 1) fairly optimize the WET process if devices’ positioning information is available and/or 2) avoid hazards to human health in terms of specific absorption rate (SAR), which is an RF exposure metric that quantifies the absorbed power in a unit mass of human tissue. We show that RAB performance even approaches quickly (or surpasses, for scenarios with a 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 (LOS) conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology.

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 the idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being a 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 by up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

Abstract The continuous traffic increase of mobile communication systems has the collateral effect of higher energy consumption, affecting battery lifetime in the user equipment (UE). An effective solution for energy saving is to implement a discontinuous reception (DRX) mode. However, guaranteeing a desired quality of experience (QoE) while simultaneously saving energy is a challenge; but undoubtedly both energy efficiency and the QoE have been essential aspects for the provision of real‐time services, such as voice over Internet protocol (VoIP), voice over LTE, and mobile broadband in 4G networks and beyond. This paper focuses on human voice communications and proposes a Gaussian process regression algorithm that is capable of recognizing patterns of silence and predicts its duration in human conversations, with a prediction error as low as 1.87%. The proposed machine learning mechanism saves energy by switching OFF/ON the radio frequency interface, in order to extend the UE autonomy without harming QoE. Simulation results validate the effectiveness of the proposed mechanism compared with the related literature, showing improvements in energy savings of more than 30% while ensuring a desired QoE level with low computational cost.

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 article. Candidates enablers, such as energy beamforming (EB), distributed antenna systems (DASs), 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 ultralow power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions toward realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

Radio-frequency wireless energy transfer (RF-WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze channel state information (CSI)free multiantenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AASS (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 π-phase shifted for optimum average energy performance under AA-SS. Our numerical results evidence that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or the number of antennas is not always beneficial under such a scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS, and/or a large number of antennas. Finally, AA-SS (SA and AA-IS) is (are) preferable when devices are (are not) clustered in specific spatial directions.

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 article, 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.

Previous transmission techniques for fading channels that exploit signal space diversity (SSD) are based on rotation or precoding of an original signal constellation. In this work, we follow a completely different approach to design full diversity multidimensional codebooks whose implementation has low complexity and requires little storage. The design consists of finding good permutations of the labels of points in a fixed grid. We consider both the uniform and the Gaussian-shaped grids. An ensemble average performance based on an enumerative analysis of the error events is presented, which brings some insight into the problem. Three low-complexity algorithms are then proposed to find good permutations sets. Both analytical and simulation results confirm the good performance of the proposed codebooks, which can be considered as good candidates for practical communications systems.

Low-power wide-area networks (LPWANs) are wireless connectivity solutions for Internet-of-Things 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 article 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.

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. The 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. While switching antennas is the best under NLOS, transmitting simultaneously with equal power in all antennas is the most beneficial as LOS increases. Moreover, spatial correlation is not beneficial unless the power station transmits simultaneously through all antennas, raising a tradeoff 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.

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.

The coexistence of different types technologies supported by the same infrastructure introduces unwanted interference that affects negatively the communication system. In this manuscript, recently proposed Nyquist-I pulses are evaluated in terms of bit error rate (BER) considering first, the effect of cochannel interference (CCI) and later, inter-symbol interference (ISI) and CCI simultaneously, under the effects of time jitter. The results indicate that considering a fixed interference power, as the number of interfering signals increases, the effect of the CCI also increases. Additionally, when the effects of CCI and ISI are considered simultaneously, both the magnitude of the main lobe and the magnitudes of the lateral side-lobes of the impulse response are preponderant in the calculation of the BER.

The next generations of wireless communications are expected to have great demand for security and spectrum efficiency, and the current secrecy solutions may not be enough. In this paper we propose an optimization framework to address the physical layer security in cognitive radio networks when the secondary users employ improper Gaussian signaling. We resort to genetic algorithms to find optimal values of the secondary transmit power and the degree of impropriety, simultaneously. Then, two different problems regarding the system performance are solved: minimizing the secrecy outage probability and maximizing the secondary achievable rate. In both problems we evaluate, besides the secrecy outage probability, the effective secure throughput and the secure energy efficiency of the system as well. The results show that the secondary network using improper signaling outperforms conventional proper signaling in terms of secrecy outage probability and the effective secure throughput, while in terms of the secure energy efficiency, adopting proper signals attains better performance than improper ones.

Location awareness allows to define a concurrent transmission region where a primary and a secondary networks can coexist. We investigate the impact of joint rate and power control on the performance of a cognitive radio ad hoc network overlaying a primary system. The proposed strategies adequately adjust the secondary user transmit power to increase the concurrent transmission probability while respecting a given constraint on the achievable primary rate. Two power control strategies are described: Rate-Efficient Power Control to maximize the secondary capacity, and Energy-Efficient Power Control to minimize the secondary energy consumption. Numerical results show that the proposed schemes expand the concurrent transmission region and that it is possible to increase the average rate and save energy.