The aim of this critical review is to show the applicability of recycled fine aggregates (RFA) in concrete regarding technical performance, environmental impact, energy consumption and cost. It is not possible to judge the performance of concrete by considering one dimension. Thus, this study focussed on the fresh and hardened (e.g., mechanical and durability) properties and environmental and economic life cycle assessment of concrete. Most literature investigated showed that any addition of recycled fine aggregates from construction and demolition waste as a replacement for natural fine aggregates proves detrimental to the functional properties (quality) of the resulting concrete. However, the incorporation of recycled fine aggregates in concrete was proven to enhance the environmental and economic performance. In this study, an extensive literature review based multi criteria decision making analysis framework was made to evaluate the effect of RFA on functional, environmental, and economic parameters of concrete. The results show that sustainability of RFA based concrete is very sensitive to transportation distances. Several scenarios for the transportation distances of natural and recycled fine aggregates and their results show that only if the transportation distance of the natural aggregates is more than double that of RFA, e the RFA based concrete alternatives would be considered as more sustainable.

Effective management of radio resources and service quality assurance are two of the essential aspects to furnish high-quality service in Long Term Evolution (LTE) networks. Despite the base station involving severalingeniousscheduling schemes for resource allocation, the intended outcome might be influenced by the interference, especially in heterogeneous scenarios, where many kinds of small cells can be deployed under the coverage of macrocell area. To develop the network of small cells, it is essential to take into account such boundaries, in particular, mobility, interference and resources scheduling a strategy which assist getting a higher spectral efficiency in anticipate small cells. Another challenge with small cellular network deployment is further analyzing the impact of power control techniques in the uplink direction for the network performance. With that being said, this article investigates the problem of interference in LTE-advanced heterogeneous networks. The proposed scheme allows mitigation inter-cell interference through fractional self-powered control performed at each femtocell user. This study analyzes a scheme with optimum power value that provides a compromise between the served uplink signal within unwanted interference plus noise ratio to enhance spectral efficiency in terms of throughput. In particular, the maximum transmit power for user equipment in uplink direction should be reviewed for small cells as a major contributor to the interference. The simulation results showed that the proposed fractional power control approach can outperform the traditional power control employed as a full compensation mode in small cell uplinks.

The high-speed development of Fifth Generation technologies announces a new era for the internet of things (IoT), distinguished by high-rate connectivity, speed, and low latency. However, this advancement also opens doors to major security challenges and expands the attack surface. Existing general IoT surveys do not systematically analyze Fifth Generation-enabled IoT concerns, which creates a clear need for a focused synthesis of machine learning and deep learning (DL) defenses tailored to Fifth Generation-IoT constraints and threat models. To address this gap, this survey conducts a preferred reporting items for systematic reviews and meta-analyses-guided analysis of recent studies published in the past 5 years, extracting methodologies, results, datasets, metrics, tools, and reported limitations to answer explicit research questions about which approaches work, under which conditions, and with what deployment implications for Fifth Generation IoT threat detection and mitigation. The results show that DL families and hybrid deep models dominate intrusion, anomaly, and malicious traffic detection, while research overemphasizes denial-of-service attacks relative to Replay, Ransomware, Sybil, Man-in-the-Middle, and Phishing attacks. The recommendations, which come from comparative evidence across datasets, attack categories, and model performance limitations, emphasize the need for more diverse and realistic Fifth Generation IoT datasets as well as for understudied learning paradigms, such as continual learning, federated learning, meta-learning, and self-supervised learning. These insights highlight clear research directions toward adaptive, privacy-preserving, and generalizable intrusion detection in Fifth Generation-IoT systems.

In our everyday lives, the IoT is everywhere. They are used for the monitoring and documentation of environmental improvements, fire safety and even other useful roles in our homes, hospitals and the outdoors. IoT-enabled devices that are linked to the internet transmit and receive a large amount of essential data over the network. This provides an opportunity for attackers to infiltrate IoT networks and obtain sensitive data. However, the risk of a loss of privacy and security could outweigh any of these benefits. Many tests have been carried out in order to solve these concerns and find a safer way to minimize or remove the effect of IoT technologies on privacy and security practices in order to protect them. The issue with IoT devices is that they have small output modules, making it impossible to adapt current protection methods to them. This constraint necessitates the presentation of lightweight algorithms that enable IoT devices. In this article, investigated the context and identify different safety, protection, and approaches for securing components of IoT-based ecosystems and systems, as well as evolving security solutions. In addition, several proposed algorithms and authentication methods in IoT were discussed in order to avoid various types of attacks while keeping the limitations of the IoT framework in mind. Also discuss some hardware security in IoT devices.

In this paper, we consider a power control scheme for co-channel deployment of femtocells in the umbrella of macrocell LTE. The severe interference in the downlink from the femtocells should be controlled to guarantee the Quality of Service (QoS) required by the macrocell users. We used clustering method for grouping femtocells according to their geographical localizations. For this scenario, we proposed a hybrid scheme that combines both centralized and distributed schemes to minimize the total power consumption at femtocells, while guaranteeing the QoS of their users. Simulation results showed that our proposed power scheme can allocate power efficiently on the resource blocks in femtocells while obtaining a small performance reduction in femtocell compared to the fully distributed scheme. As well, our method minimized the signaling overhead compared to the fully centralized power allocation scheme.