The Narrowband Internet of Things (NB-IoT) has been introduced in the 3rd Generation Partnership Project (3GPP) Rel-13 with the aim to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. With the exponentially increasing number of connected wireless devices in the order of 100 billion, it has become crucial that researchers develop efficient resource management techniques to meet the 5th Generation (5G) quality of service (QoS) requirements. Recently, several research challenges including the low modulation data rates, energy-expensive channel coding techniques, and the fast-growing number of connected devices have been identified as some of the main issues encountered in the design and deployment of NB-IoT systems. In addition, several techniques have emerged in the literature to resolve some of these challenges of NB-IoT systems. However, the research activities towards the enhancement of the NB-IoT resource management are yet to continue in the next half-decade before, high data rates, energy-efficient, and scalable NB-IoT specifications can be released for the standardisation and commercialisation. Considering the limited number of existing surveys of such technical enhancement approaches in a broader perspective (i.e. energy efficiency, data rate performance and scalability) and also the non-existence, to the best of our knowledge, of a comparative survey, this paper seeks to elaborate, describe and compare the performances of such resources management approaches. Of the multiple NB-IoT resources, the focus of this paper is on the data rate, energy efficiency, and scalability enhancement schemes that have been proposed for the last three years. The contribution of the paper lies in the analysis, synthesis, comparison and summarised alignments of some of the major existing schemes towards identifying challenges faced by the NB-IoT development. Finally, this work seeks to identify research challenges, open questions, and opportunities that could arise from the existing techniques analysed.

The growth of Internet of Things (IoT) devices continues to experience an exponential rise due to their vast applications in various industries. However, sustaining their operational lifetime is a major challenge due to critical factors such as the need for frequent recharging of energy buffers. The recent convergence of green energy harvesting (EH) and IoT technologies has proven to be a potential solution to tackle this challenge. However, the intermittent characteristics of green energy sources and wireless fading channels pose another challenge, as the quality of service in IoT networks revolves around the available energy and channel conditions. The traditional optimization strategies based on non-causal knowledge about these random quantities are deemed unsuitable for the realization of an autonomous IoT network operation. To combat this challenge, various algorithms from the field of machine learning (ML) have been proposed as potential solutions. Therefore, in this article, we aim to investigate the applications of ML algorithms in EH IoT networks. To achieve this, first, we provide an overview of ML categories commonly adopted in IoT networks. Secondly, due to the peculiarity of EH IoT networks, we provide an extensive description of the MLcategories widely explored in this domain. This includes reinforcement learning, deep learning, and deep reinforcement learning. Thirdly, we present a review of studies where the applications of the aforementioned MLalgorithms are demonstrated. Further, we identify challenges that are likely to impact the implementation of these algorithms. In conclusion, we highlight unexplored and emerging areas for potential future research.

Artificial intelligence (AI) has become integral to predictive maintenance (PdM) in renewable energy systems (RES), enabling the detection of faults, forecasting of degradation, and optimization of performance. However, existing reviews are fragmented, focusing either on specific energy domains or algorithmic families without a unified framework that connects AI methods to real-world deployment. This paper presents a novel, cross-domain synthesis for solar, wind, hydro, and hybrid systems. Its originality lies in a dual-axis classification framework that maps AI models to their functional roles while accounting for the data realities of different energy infrastructures. Unlike prior studies, this review integrates data characteristics into the comparative analysis, revealing how data constraints shape model selection, scalability, and reliability. By bridging methodological rigor with operational feasibility, this paper establishes a foundation for adaptive, transparent, and scalable AI integration in RES. The findings offer actionable insights for researchers, engineers, and policymakers seeking to advance intelligent asset management in the context of global energy transition.

Most of the current research work on the Narrowband Internet of Things (NB-IoT) is focused on enhancing its network coverage. Many of the existing NB-IoT channel coding techniques are based on repeating transmission data and control signals as a way of enhancing the network’s reliability and therefore, enabling long-distance transmissions. Although most of these efforts are made at the expense of reducing the energy consumption of the NB-IoT network, they do not always consider the channel conditions. Therefore, this work proposes a novel NB-IoT Energy-Efficient Adaptive Channel Coding (EEACC) scheme. The EEACC approach is a two-dimensional (2D) approach which not only selects an appropriate channel coding scheme based on the estimated channel conditions (dynamically classified as bad, medium or good from initial based on a periodically assessed BLER performance outcome) but also minimizes the transmission repetition number under a pre-assessed probability of successful transmission (based on the ratio of previous successful transmissions over the total number of transmissions). This results in creating a single mixed gradient based on which a higher or lower Modulating Coding Scheme (MCS) is selected on each transmission. It is aimed at enhancing the overall energy efficiency of the network by dynamically selecting the appropriate Modulation Coding Scheme (MCS) number and efficiently minimizing the transmission repetition number. Link-level simulations are performed under different channel conditions (good, medium, or bad) considerations to assess the performance of the proposed up-link adaptation technique for NB-IoT. The obtained results demonstrate that the proposed technique outperforms the existing Narrowband Link Adaptation (NBLA) as well as the traditional repetition schemes in terms of the achieved energy efficiency as well as network reliability, latency, and scalability.

Although the Narrowband Internet of Things (NB-IoT) is tightly connected and integrated with cellular networks such as the Long Term Evolution (LTE), it remains an independent radio interface technology. Therefore, it is important to understand its specific requirements by providing a systematic mathematical model of its physical (PHY) layer. This article begins by systematically and numerically describing the PHY layer of the NB- IoT technology, its applications and the context of its deployment. The article then presents a mathematical modelling of the NB- IoT operations mode as this is key for modelling its PHY layer. This is followed by a clear and concise mathematical modelling of the NB-IoT down-link (DL) by elucidating the fundamental differences with the ones of the LTE. This modelling study goes further by providing a comprehensive model and analysis of the data rate against energy efficiency dilemma as an identified problem for the PHY design of the NB- IoT. This analysis is performed theoretically but also tested by means of computer simulation. The obtained results demonstrate that the contribution of the NB- IoT to the LTE in terms of energy consumption is less significant in the inband deployment compared to the guard band and standalone bands. However, the in-band deployment is expected to reduce the LTE's channel capacity more than the other deployment modes. In terms of network scalability, the obtained results further show that the data rate and energy efficiency of the network are reduced affected by the increase in the number of devices within a cell. These modelled network performances of the NB-IoT sufficiently demonstrate that in order to enable a licensed Massive IoT network, there is a need for developing novel PHY layer techniques (modulation, channel coding etc.) capable to efficiently scale the NB-IoT network while maintaining acceptable data rate and low energy consumption.

Network coding consists of intelligently aggregating data packets by means of binary or linear combinations. Recently, network coding has been proposed as a complementary solution for energy efficient multi-hop routing in Wireless Sensor Networks (WSNs). This is because network coding, through the aggregation of packets, considerably reduces the number of transmissions throughout the network. Although numerous network coding techniques for energy efficient routing have been developed in the literature, not much is known about a single survey article reporting on such energy efficient network coding within multi-hop WSNs. As a result, this paper addresses this gap by first classifying and discussing the recent developed energy efficient network coding techniques. The paper then identifies and explains open research opportunities based on analysis of merits of such techniques. This survey aims at providing the reader with a brief and concise idea on the current state-of-art research on network coding mainly focusing on its applications for energy efficient WSNs