The Internet of Things (IoT) is a promising technology which tends to revolutionize and connect the global world via heterogeneous smart devices through seamless connectivity. The current demand for Machine-Type Communications (MTC) has resulted in a variety of communication technologies with diverse service requirements to achieve the modern IoT vision. More recent cellular standards like Long-Term Evolution (LTE) have been introduced for mobile devices but are not well suited for low-power and low data rate devices such as the IoT devices. To address this, there is a number of emerging IoT standards. Fifth Generation (5G) mobile network, in particular, aims to address the limitations of previous cellular standards and be a potential key enabler for future IoT. In this paper, the state-of-the-art of the IoT application requirements along with their associated communication technologies are surveyed. Additionally, the 3rd Generation Partnership Project (3GPP) cellular-based Low-Power Wide Area (LPWA) solutions to support and enable the new service requirements for Massive to Critical IoT use cases are discussed in detail, including Extended Coverage Global System for Mobile Communications for the Internet of Things (EC-GSM-IoT), enhanced Machine-Type Communications (eMTC), and Narrowband-Internet of Things (NB-IoT). Furthermore, 5G New Radio (NR) enhancements for new service requirements and enabling technologies for the IoT are introduced. This paper presents a comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT. The challenges and open research directions pertinent to the deployment of Massive to Critical IoT applications are also presented in coming up with an efficient context-aware congestion control (CACC) mechanism.

Exact state-of-charge estimation is necessary for every application related to energy storage systems to protect the battery from deep discharging and overcharging. This leads to an improvement in discharge efficiency and extends the battery lifecycle. Batteries are a main source of energy and are usually monitored by management systems to achieve optimal use and protection. Coming up with effective methods for battery management systems that can adequately estimate the state-of-charge of batteries has become a great challenge that has been studied in the literature for some time. Hence, this paper analyses the different energy storage technologies, highlighting their merits and demerits. The various estimation methods for state-of-charge are discussed, and their merits and demerits are compared, while possible applications are pointed out. Furthermore, factors affecting the battery state-of-charge and approaches to managing the same are discussed and analysed. The different modelling tools used to carry out simulations for energy storage experiments are analysed and discussed. Additionally, a quantitative comparison of different technical and economic modelling simulators for energy storage applications is presented. Previous research works have been found to lack accuracy under varying conditions and ageing effects; as such, integrating hybrid approaches for enhanced accuracy in state-of-charge estimations is advised. With regards to energy storage technologies, exploring alternative materials for improved energy density, safety and sustainability exists as a huge research gap. The development of effective battery management systems for optimisation and control is yet to be fully exploited. When it comes to state-of-the-art simulators, integrating multiscale models for comprehensive understanding is of utmost importance. Enhancing adaptability across diverse battery chemistries and rigorous validation with real-world data is essential. To sum up the paper, future research directions and a conclusion are given.

The rapid development of smart sensors has enabled the integration of wireless sensor networks with the Internet. The emergence of the Internet of Things represents a significant paradigm shift, with wireless sensor networks remaining essential to its framework. Most smart sensor nodes will serve as foundational components, which can cause network congestion. Congestion is a critical factor affecting the performance of Internet of Things networks. The constrained application protocol, developed by the Internet Engineering Task Force, serves as a lightweight RESTful protocol designed for resource-constrained nodes. Resource-constrained networks often face limitations in hardware and link capabilities, leading to challenges such as packet loss and increased delay. This review examines the constrained application protocol, highlighting its distinct fundamental features that facilitate deployment in constrained Internet of Things environments. This paper provides an overview of wireless sensor networks as foundational elements for Internet of Things applications, followed by a discussion on low power wide area networks and their standardisation as promising technologies for these applications. This paper discusses prominent state-of-the-art methods for congestion control in the constrained application protocol, highlighting several research challenges and future directions. It proposes a three-state Markov model and machine learning-based strategies for congestion control in resource-constrained Internet of Things networks.

Internet of Things (IoT)-enabled smart mobility systems are increasingly proposed as a foundation for efficient, sustainable, and data-driven urban transportation. Yet, existing literature is largely fragmented and descriptive, with limited methodological transparency and cross-layer evaluation. This chapter presents a structured analytical framework for evaluating smart mobility systems built on IoT architectures. Rather than a narrative survey, the chapter adopts a reproducible review methodology with explicit selection criteria and comparison dimensions. A five-layer analytical taxonomy covering sensing, connectivity, compute, intelligence, and governance is introduced and used consistently to classify technologies, architectures, and deployments. Core enabling technologies are examined using role, assumption, trade-off, and limitation analysis, supported by cross-technology evaluation matrices. Representative city and platform implementations are assessed using a standardized layer-mapped case template and comparative scoring rubric to enable cross-case evaluation. Sustainability, socio-economic, governance, and trust impacts are analyzed using mechanism-based and indicator-driven reasoning, with a clear distinction between modeled, pilot, and operational evidence. The chapter concludes with a prioritized challenge-to-research mapping and bounded future directions, providing reusable evaluation artifacts and a decision-oriented framework for researchers, practitioners, and policymakers.