As technology advances, cities are getting smarter. Smart mobility is the key element in smart cities and Autonomous Driving (AV) are an essential part of smart mobility. However, the vulnerability of unmanned vehicles can also affect the value of life and human safety. In this paper, we provide a comprehensive analysis of 3D Point-Cloud (3DPC) processing and learning in terms of development, advancement, and performance for the AV system. 3DPC has recently attracted growing interest due to its extensive applications, such as autonomous driving, computer vision, and robotics. Light Detection and Ranging Sensors (LiDAR) is one of the most significant sensors in AV, which collects 3DPC that can accurately capture the outer surfaces of scenes and objects. Learning and processing tools in the 3DPC are essential for creating maps, perceptions, and localization devices in AV. The intention behind 3DPC learning and practical processing tools is to be considered the most essential modules to create, locate, and perceive maps in an AV system. The goal of the study is to know ``what has been tested in AV system so far and what is necessary to make it safer and more practical in AV system.'' We also provide insights into the necessary open problems that are required to be resolved in the future.

Unmanned aerial vehicle (UAV) has recently gained significant attention due to their efficient structures, cost-effectiveness, easy availability, and tendency to form an ad hoc wireless mobile network. IoT-enabled UAV is a new research domain that uses location tracking with the advancement of aerial technology. In this context, the importance of 3D aerial networks is attracting a lot of attention recently. It has various applications related to information processing, communication, and location-based services. Location identification of wireless nodes is a challenging job and of extreme importance. In this study, we introduced a novel technique for finding indoor and open-air three-dimensional (3D) areas of nodes by measuring the signal strength. The mathematical formulation is based on a path loss model and decision tree machine learning classifier. We constructed 2D and 3D models to gather more accurate information on the nodes. Simulation findings demonstrate that the proposed machine learning-based model excels in nodes location estimation, the actual and estimated distance of different nodes, and calculation of received signal strength in aerial ad hoc networks. In addition, the decision tree constructs an offline phase control in the flying vehicle’s location to enhance the time complexity along with experimental accuracy.

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV’s residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal.

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.

Robot-based platforms and processes have integrated the security and efficiency of data into a comprehensive range for domains, such as manufacturing, industrial, logistical, agricultural, healthcare, and internet services. Smart cyberattacks have been on the rise, specifically targeting corporate, industrial robotic systems. These attacks execute once the internet of things, internet, and organization integration is implemented with the industrial units. We implemented security criteria-based indices for cyber-physical systems (CPS) with industrial components and embedded sensors that process the information logs and processes. We proposed an attack tree-based secure framework that does not include every CPS device but takes into consideration the critical exploitable vulnerabilities to execute the attacks. We categorized each physical device and integrated sensors based on logs and information in a sensor indices device library. This research simulated real-time exploitation of vulnerabilities on CPS robotic systems using the proposed framework in form of a two-phased process. This validates the enhanced data security output of the integrated sensor and physical nodes with the intelligent monitor and controller system health monitor during real-time cyberattacks This research simulated common cyberattacks on cyber-physical controller servers based on cross-site scripting and telnet pivoting. The authors gathered known and unknown vulnerabilities and exploited them with a tree-based attack algorithm. The authors calculated the average time for cyberattackers with different skills when trying to compromise CPS devices and systems.