Terahertz communication is emerging as a future technology to support Terabits per second link with highlighting features as high throughput and negligible latency. However, the unique features of the Terahertz band such as high path loss, scattering and reflection pose new challenges and results in short communication distance. The antenna directionality, in turn, is required to enhance the communication distance and to overcome the high path loss. However, these features in combine negate the use of traditional medium access protocols (MAC). Therefore, novel MAC protocol designs are required to fully exploit their potential benefits including efficient channel access, control message exchange, link establishment, mobility management, and line-of-sight blockage mitigation. An in-depth survey of Terahertz MAC protocols is presented in this paper. The paper highlights the key features of the Terahertz band which should be considered while designing an efficient Terahertz MAC protocol, and the decisions which if taken at Terahertz MAC layer can enhance the network performance. Different Terahertz applications at macro and nano scales are highlighted with design requirements for their MAC protocols. The MAC protocol design issues and considerations are highlighted. Further, the existing MAC protocols are also classified based on network topology, channel access mechanisms, and link establishment strategies as Transmitter and Receiver initiated communication. The open challenges and future research directions on Terahertz MAC protocols are also highlighted.

Detecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions. This paper proposes a novel, yet effective outlier learning algorithm that is based on decomposing the full attributes space into different combinations of subspaces, in which the 3D-vectors, representing the data points per 3D-subspace, are rotated about the geometric median, using Rodrigues rotation formula, to construct the overall outlying score. The proposed approach is parameter-free, requires no distribution assumptions and easy to implement. Extensive experimental study and comparison are conducted on both synthetic and real-world datasets with six popular outlier detection algorithms, each from different category. The comparison is evaluated based on the precision @s, average precision, rank power, AUC ROC and time complexity metrics. The results show that the performance of the proposed method is competitive and promising.

The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of several researchers. Human activities are an important challenge in various fields. There are many great applications in this area, including smart homes, assistive robotics, human–computer interactions, and improvements in protection in several areas such as security, transport, education, and medicine through the control of falling or aiding in medication consumption for elderly people. The advanced enhancement and success of deep learning techniques in various computer vision applications encourage the use of these methods in video processing. The human presentation is an important challenge in the analysis of human behavior through activity. A person in a video sequence can be described by their motion, skeleton, and/or spatial characteristics. In this paper, we present a novel approach to human activity recognition from videos using the Recurrent Neural Network (RNN) for activity classification and the Convolutional Neural Network (CNN) with a new structure of the human skeleton to carry out feature presentation. The aims of this work are to improve the human presentation through the collection of different features and the exploitation of the new RNN structure for activities. The performance of the proposed approach is evaluated by the RGB-D sensor dataset CAD-60. The experimental results show the performance of the proposed approach through the average error rate obtained (4.5%).

Remote monitoring of individuals with special healthcare needs and controlling their living spaces using emerging technologies is a significant focus for researchers from various disciplines, forming a crucial element of future healthcare development. Digitizing the healthcare sector demands expertise and knowledge transfer to create new paradigms and innovative solutions to enhance life quality and reduce healthcare burdens. One of the most promising technologies in this area is the Digital Twin (DT), a virtual replica of the real world with advanced features for data clustering, classification, and forecasting. This paper introduces an innovative context-aware framework for monitoring indoor air quality and human activity, integrating technologies like the Internet of Things (IoT), 6G networks, sensing and localization techniques, Edge Computing, Deep Learning models, and cloud platforms. The multidisciplinary research emphasizes the interaction of the DT concept with its environment and other technologies. The contributions include: establishing an architecture with sensors, gateways, and a DT object on Azure cloud, validated with AI models; linking 6G network sensing and communication capabilities with IoT-based techniques to enhance performance; and developing deep learning models for Human Activity Recognition (HAR) using inertial sensors, achieving a test accuracy of 99.34% and a real-time accuracy of 92.10%.

The evolution toward 5G wireless networks was the result of an exponential growth in data traffic demands and a significant increase in the number of connected devices with stringent quality of services (QoS) requirements. This evolution leads to an increase in the total power consumption and indirectly produces pollution intensive carbon footprints. To overcome 5G challenges in traffic demands and power consumption, Cloud Radio Access Network (C-RAN) architecture is introduced. This paper investigates the energy consumption in 5G C-RAN using switch on/off cell sectors and densification by exploiting Small Cell Remote Radio Heads (SC-RRHs). We develop sector switch on/off algorithms based on reducing the number of underutilized sectors per Macro Cell RRH (MC-RRH). Then, we apply a selective SC-RRHs distribution allowing SC-RRHs to serve users of switched off sectors. Simulations results show that our proposed approaches based on the switch on/off by sector can achieve more than 55% of power saving which makes them more efficient than those based on the switch on/off by cell in terms of power saving.

Nowadays, energy consumption issues has gained particular interest specially in the telecommunication field. The rise of energy consumption leads to a significant increase on CO2 emissions and has indirect drawbacks on environment. Reducing energy consumption in wireless network became a hot topic of research. In this paper, we propose an energy saving approach in heterogeneous networks (HetNet) by switching ON/OFF LTE-Advanced Base Station (BS) and allowing their users according to their SINR (Signal to Interference plus Noise Ratio) values to femto cells or to neighbors eNodeBs (eNBs) by applying the concept of Coordinated Multipoint (CoMP) in order to guarantee Quality of service (QoS).