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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-toend 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.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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 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 λ, packet size, and the value of Bit Error Rate (BER) increase. INDEX TERMS Vehicular network, IEEE 802.11p, steganographic channel, non-ideal transmission channel, BER.

Vehicular Ad Hoc Networks (VANETs) have been developed to improve the safety, comfort and efficiency of driving on the road. The IEEE 1609.4 is a standard intended to support multi-channel in VANETs. These channels include one control channel for safety applications and six service channels for service applications. However, there is still no comprehensive analysis for the average delay and system throughput of IEEE 1609.4 MAC in VANETs considering error-prone channel under non-saturated conditions. In this paper, we propose an analytical models based on 1-D and 2-D Markov chain to evaluate the performance analysis of IEEE 1609.4 MAC in the presence of error-prone channels. Besides, freezing of the back-off timer is taken into consideration to provide an accurate estimation of access to the channel. The simulation results have been carried out to validate the analytical results of our model. The results show that the performance of our model outperforms the existing model in terms of packet delivery ratio and average delay of safety packets over CCH, and system throughput of service packets over SCHs.

Recently, interest in the field of Vehicular Ad-hoc Networks (VANETs) has grown among research community to improve traffic safety and efficiency on the roads. Despite the many advantages, the transmission range in vehicular network remains one of the major challenges due to the unique characteristics of VANETs such as various communication environments, highly dynamic topology, high node mobility and traffic density. The network would suffer from a broadcast-storm in high vehicular density when a fixed transmission range in VANET is used, while in sparse vehicular density the network could be disconnected frequently. In this paper, we evaluated the impact of different transmission ranges and number of flows formed between vehicles in a highway scenario using AODV as routing protocol. In order to validate the simulation of VANET, traffic and network simulators (SUMO & NS-2) have been used. The performance was evaluated in terms of packet delivery ratio and end-to-end delay. The simulation results have shown that better performance was achieved in term of higher PDR and lower end-to-end delay for less than 500 meters transmission range. On the contrary, the PDR started to decrease and end-to-end delay increased when the transmission range exceeded 500 meters. The performance degraded as the number of flows increased.

Abstract. Reliable and efficient data broadcasting is essential in vehicular networks to provide safety-critical and commercial service messages on the road. There is still no comprehensive analysis of IEEE 802.11p based MAC that portrays the presence of buffer memory in vehicular networks. Besides, most of the analytical works do not fulfill some of the IEEE 802.11p specifications, such as short retry limit and back-off timer freezing. This paper proposes a 1-D and 2-D Markov model to analyze mathematically IEEE 802.11p based MAC for safety and non-safety messages respectively. The work presented in this paper takes into account the traffic arrival along with the first-order buffer memory and freezing of the back-off timer as well, to utilize the channel efficiently and provide higher accuracy in estimation of channel access, yielding more precise results of the system throughput for non-safety messages and lower delay for safety messages. Furthermore, back-off stages with a short retry limit were applied for non-safety messages in order to meet the IEEE 802.11p specifications, guaranteeing that no packet is served indefinitely, avoiding the overestimation of system throughput. A simulation was carried out to validate the analytical results of our model.

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