Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network, together with target values for the next generation of wireless systems, and discuss protocol, integration, and implementation issues. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.

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-ofthe-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.

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R² = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)², consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R² = 0.96; MSE = 0.33 (µg/L)²; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management.

The impending scarcity of the radio frequency (RF) spectrum has led to a boom in the development of complementary technologies such as visible light communication (VLC). Hybrid systems that complement both technologies have demonstrated numerous advantages in terms of data rate, bandwidth, and security. However, the successful operation of a hybrid VLC/RF network is highly dependent on the network selection process. In this paper, three network selection algorithms are proposed for hybrid VLC/RF systems in an indoor Internet of things (IoT)-home scenario. The proposed algorithms consider the signal-to-noise ratio (SNR), the available capacity of each network, the number of devices connected to each network, and the users’ location within the scenario. Each algorithm analyzes the data load requirements of each mobile terminal and IoT device according to these metrics. Based on this evaluation, the network that offers the best benefit in terms of signal quality, capacity, and throughput is selected. The numerical results show that the VLC network presents higher SNR values, especially for users near its access points. In addition, a higher bandwidth, a higher connection capacity, and an improvement in the quality of service are perceived from these positions. The three proposed selection algorithms presented good overall performance of the hybrid system, with the Analytical Algorithm offering the best performance for connected users, superior to the Sequential Algorithm by an average of 15.9%. The network selection algorithms efficiently allocated the maximum number of users for the VLC network, improving overall system performance and reducing the RF network data load.

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 article 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 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 (ML) approaches, including reinforcement learning (RL) and decision transformers (DTs), 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.