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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