6G is a next-generation cellular communication technology that builds up on existing 5G networks which are currently rolled out worldwide. Through incorporation of artificial intelligence (AI) and machine learning (ML), the core 5G network is advanced into an intelligent 6G network. The 6G Artificial Intelligence Radio Access Network (AI-RAN) is anticipated to offer advanced features like reduced latency, improved bandwidth, data rates and coverage. Furthermore, AI-RAN is expected to support complex use cases such as extreme connectivity, multi-user communications and dynamic spectrum access. This paper provides a detailed survey and thorough assessment of AI-RAN’s vision and state-of-the-art challenges. We first present a concise introduction to 6G AI-RAN followed by background information on the current 5G RAN and its challenges that must be overcome to implement 6G AI-RAN. The paper then examines trending research issues in AI-RAN i.e. challenges related to spectrum allocation, network architecture, and resource management. We discuss the methods to overcome these challenges which include the adoption of advanced machine learning and edge computing technologies to boost the performance of 6G AI-RAN. We conclude by stating open research directions.

Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation.

In Surveillance Wireless Sensor Networks (SWSN), the nodes are distributed at the borders to detect unauthorized intrusions. In such applications, as the nodes are usually equipped with limited batteries and the network area is harsh to be accessible for humans, energy efficiency is considered as one of the main issues in designing the sensor networks. In the last years, Wireless Rechargeable Sensor Networks (WRSN) provides valuable insights into solving the energy constraint problem in WSNs. In a WRSN, Wireless Mobile Chargers (WMC) are responsible to recharge the batteries of the sensor nodes in order to enhance the operation time of the network. In most of the previous WRSN based schemes, the WMCs recharge the nodes singly and directly, which results in increasing the travel time of WMC and waiting time of sensor nodes. In addition, despite of using WMC, the permanent network lifetime has not been addressed in most of the previous schemes. Therefore, to overcome the limitations of existing literature, this research work focuses on the design and development of Broker-based Nodes Recharging Scheme (BNRS) which significantly contributes to improved lifetime and performance of SWSN. In the proposed scheme, power banks are employed as brokers between sensor nodes and WMC. The main duty of a power bank is to receive energy from WMC and transfer to the sensor nodes. Based on the simulation experiments the proposed BNRS achieves the permanent network operation which results in increasing security in the border of countries.

The trend of using Unmanned Aerial Vehicles (UAVs) in industries is rapidly growing. Nowadays, they are used in many applications, from surveillance to disaster assistance. Almost all applications of UAVs require cameras either to perform specific vision tasks such as facial recognition and vehicle number plate identification or to avoid obstacles in the flight path of the UAV. The most emerging application of UAVs today is to provide security and surveillance, and they are mostly used for vehicle number identification. Typical Automatic Number Plate Recognition (ANPR) uses static high-resolution cameras mounted in specific places to identify the vehicles number plates. Identifying the characters on the number plate becomes a very crucial task when the number plate is at an arbitrary angle to the drone camera. The camera gimble angle, the height of the UAV from the vehicle, and the relative speed of the UAV w.r.t vehicle play a very key role in identifying the vehicle license plate correctly. This study explains how Automatic Number Plate Recognition (ANPR) is performed on real-time images using MATLAB with the help of a UAV to analyze the effect of the above-mentioned key factors. The process is completed in three steps: collecting visual data from the drone, processing that data, and obtaining the recognized number plate.