The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.

The current number of working mothers has greatly increased. Subsequently, baby care has become a daily challenge for many families. Thus, most parents send their babies to their grandparents' house or to baby care houses. However, the parents cannot continuously monitor their babies' conditions either in normal or abnormal situations. Therefore, an Internet of Things-based Baby Monitoring System (IoT-BBMS) is proposed as an efficient and low-cost IoT-based system for monitoring in real time. We also proposed a new algorithm for our system that plays a key role in providing better baby care while parents are away. In the designed system, Node Micro-Controller Unit (NodeMCU) Controller Board is exploited to gather the data read by the sensors and uploaded via Wi-Fi to the AdaFruit MQTT server. The proposed system exploits sensors to monitor the baby's vital parameters, such as ambient temperature, moisture, and crying. A prototype of the proposed baby cradle has been designed using Nx Siemens software, and a red meranti wood is used as the material for the cradle. The system architecture consists of a baby cradle that will automatically swing using a motor when the baby cries. Parents can also monitor their babies' condition through an external web camera and switch on the lullaby toy located on the baby cradle remotely via the MQTT server to entertain the baby. The proposed system prototype is fabricated and tested to prove its effectiveness in terms of cost and simplicity and to ensure safe operation to enable the baby-parenting anywhere and anytime through the network. Finally, the baby monitoring system is proven to work effectively in monitoring the baby's situation and surrounding conditions according to the prototype.

Vehicular ad-hoc networks (VANETs) have been developed to provide safety-related and commercial service applications on the road. The IEEE 1609.4 is a standard (legacy) designed to support multi-channels in VANETs, namely control channel (CCH) and service channels (SCHs) with fixed alternating CCH and SCH intervals. The CCH is dedicated to broadcast safety and control applications while SCHs are used to transfer service data applications. However, due to the nature of contention-based channel access scheme and the transmission of multiple applications over the CCH during a fixed interval, safety applications performance is degraded during CCH congestion in high network density scenarios. In this paper, we propose an adaptive multi-channel assignment and coordination (AMAC) scheme for the IEEE 802.11p/1609.4 in VANETs which exploits channel access scheduling and channel switching in a novel way. AMAC scheme includes an adaptive execution of the peer-to-peer negotiation phase between service providers and users for SCH resource reservations, and collision-aware packet transmission mechanisms. These two mechanisms alleviate collisions and increase packet delivery ratio (PDR) of safety applications on the CCH. Thereby, the AMAC scheme ensures an efficient and reliable quality of service (QoS) for different traffic flows and improves the time diversity among vehicles based on the traffic conditions. For performance analysis, analytical models are developed based on 1-D and 2-D Markov chain models taking into account an error-prone channels. The probabilities of successful transmission and collisions have been derived to compute PDR, and delay for safety packets in legacy standard and AMAC scheme. Analytical and simulation results indicate that the AMAC scheme reduces the collisions and increases the PDR for safety applications over the CCH compared with the legacy standard. In addition, AMAC scheme outperforms the legacy standard in terms of system throughput of service applications.

Fuzzy Time Series (FTS) models are commonly used in time series forecasting, where they do not require any statistical assumptions on time series data. FTS models can handle data sets with a small number of observations or with uncertainty. This is a general advantage of FTS as compared with other techniques. However, FTS models still have some criticisms, such as the optimal lengths of intervals and the proper weights, which always influence the model accuracy and still have been of many concerns in literature. The work in this paper proposes a novel FTS forecasting model based on a new tree partitioning method (TPM) and Markov chain (MC), called FTSMC-TPM, for determining the optimal partitions of intervals and the proper weights vectors respectively, and this will greatly improve the model accuracy. The efficiency of the FTSMC-TPM model is tested using two types of time series consisting of the air pollution index (API) data, which is collected from Kuala Lumpur, Malaysia and the benchmark data of the yearly enrollments for the University of Alabama. Three statistical criteria have been used for investigating the accuracy of the proposed model. The results indicate that the proposed model outperforms the existing classic and advanced time series models in terms of forecasting accuracy. In addition, the proposed model shows the ability to successfully deal with forecasting problems to obtain higher model accuracy, which is examined in comparison with the existing models to validate its superiority. Hence, this study demonstrates that the proposed model is more suitable for the accurate prediction of air pollution events as well as for forecasting any type of random time series.

This paper proposes a statistical-based channel modelling approach for a drone assisted multi-user coded cooperation (DA-MUCC) for evaluating the performance metrics of a next-generation wireless communication system. The proposed approach may find its applications in smart cities, disaster management and agriculture for building reliable communication links among ground users (GUs)/user equipments (UEs) over the scenarios where an infrastructure-based network (like a Base Station (BS)) is difficult to establish or disrupted. In such scenarios, an air-to-ground (A2G) channel is modelled based on the probabilistic approach of line-of-sight (LoS) and statistical independence of the links. The network performance of the proposed system model is evaluated by closed-form average outage probability and average rate over the Rayleigh and Rician fading channel models. The analytical performance is corroborated by Monte-Carlo simulations and also compared with the existing state-of-the-art approaches. Finally, we derive the probability density function (PDF) for signal-to-noise ratio (SNR) of relay link which is useful under the scenarios where the direct links among GUs are in the deep fade.

Vehicular network is a communication technology designed to provide comfort and improve life safety and driving efficiency on the road. In vehicular network, trustworthy communication is very important as fake applications may lead to disastrous road accidents. Several information hiding methods are used to enable vehicles to communicate secretly or to covertly report a misbehaving vehicle. The work in this paper focuses on a performance analysis based on 2-D Markov chain model for the system throughput of steganographic scheme in relation to the IEEE 802.11p standard. This model studies wireless padding (WiPad) that is used to hide data into the padding of packets at the physical layer of wireless local area networks (WLANs). The analytical study is under non-saturated conditions with non-ideal transmission channel. The study also considers the rate of packet arrival with the first order of buffer memory, back-off timer freezing, back-off phases, and short retry limit to satisfy the IEEE 802.11p specifications. It emphasizes that taking these factors into account are significant in modelling the system throughput of the steganographic channel. These factors typically provide a precise channel access estimation, yield more accurate findings of system throughput, use the channel efficiently, prevent overestimation of saturation throughput, and ensure that no packet is served indefinitely. The model is validated by comparing the numerical and simulation results under different network parameters. Analytical and simulation results stated that the values of the system throughput of the steganographic channel based on data and control frames are low as the vehicles number $n$ , traffic arrival rate $\lambda $ , packet size, and the value of Bit Error Rate (BER) increase.