In this study, we introduce a surveillance model for multi domain IoT environment, which is supported by reinforced barriers with collision-avoidance using heterogeneous smart UAVs. Formally, we define a problem whose goal is minimizing the maximum movement of smart UAVs on condition that collision-avoidance among UAVs is assured when they fly between initial positions and specific spots in order to build reinforced barrier in multi domain. After ILP formulation of the defined problem, a novel approach is proposed to solve the problem. Also, we analyze the performance of the developed scheme through extensive simulations with various scenarios.

Thanks to recent enormous advance of mobile and vehicular technology, it is highly expected that Unmanned Aerial Vehicles (UAVs) are used for various applications in smart cities as well as the number of flying UAVs in the sky is increasing explosively. However, such a proliferation of UAVs accompanies additional critical issues to be considered for efficient, secure use of UAVs. One of important issues should be a privacy of people. When the UAVs fly to perform specific objectives, minimizing movements of UAVs is an important issue to minimize mission completion time and to maximize the network lifetime of UAVs. To do so, one intuitive solution is that UAVs may pass through private area of citizens whereas people do not want any penetration into own area without a permission. For those situations, we may take into account a compromise so that people can decide whether they give a differential, temporal permission for each UAV to access their areas depending on specific benefits by use of UAVs or emergent situation for public safety. In this study, we introduce a framework for privacy preserving movements of UAVs with differential UAVs' permissions given by citizens, which is called as UDiPP (UAVs' Differential Privacy Preserving movements). Then, using ILP, we formally define a problem whose objective is to minimize total movements of UAVs with preserving privacy of citizens. To solve the problem, we propose a novel approach with a creation of UDiPP graph and then evaluate its performance through extensive simulations.

A Maritime Transportation System (MTS) accomplishes diverse tasks including efficient route, information management, transportation stations. In particular, security and surveillance should be one of critical topics in IoT-supported MTS. In this paper, we introduce a secure framework that creates differential security barriers for virtual emotion detection in IoT-assisted maritime transportation systems. We formally define a problem whose goal is to maximize the number of differential security barriers by mobile robots and UAVs. To solve the problem, we propose two different schemes to provide an improved security detection in priority area so that the proposed methods create the maximum number of security barriers to fit with discriminative security levels in MTS subareas. Then, we analyze their performances through various settings and scenarios with detailed discussions.

Most intelligent systems focused on how to improve performance including accuracy, processing speed with a massive number of data sets and those performance-biased intelligent systems, Red AI systems, have been applied to digital twin in smart cities. On the other hand, it is highly reasonable to consider Green AI features covering environmental, economic, social costs for advanced digital twin services. In this paper, we propose eco-friendly low resource security surveillance toward Green AI-enabled digital twin service, which provides eco-friendly security by the active participation of low resource devices. And, we formally define a problem whose objective is to maximize the participation of low source or reusable devices such that reusable surveillance borders are created within security district. Also, a dense sub-district with low resource devices priority completion scheme is proposed to resolve the problem. Then, the devised method is performed by expanded simulations and the achieved result is evaluated with demonstrated discussions.

As an original property of human beings, human emotion recognition has gained a lot of interests of many researchers. The recent emotion recognition scheme using wireless signal is considered as one of emerging techniques toward intelligent smart cities. It is highly reasonable to form virtual emotion barrier that can detect human emotion when people move from one side to another side through at least one device in the barrier using wireless signal. Also, it is critical that the emotion recognition delay by virtual emotion barrier with possible two-way detection must be minimized to provide emotion-based services to citizen in timely manner. In this article, we introduce a virtual emotion detection model with two-way enabled delay bound, which pursues to provide timely emotion-based IoT services in advanced smart cities. Also, we formally define a problem whose objective is to generate two-way enabled virtual emotion barriers such that the virtual emotion detection maximum delay of those barriers should be minimized. Then, we propose a novel Two-Way-Enabled-Border-Slab scheme and evaluate its performance through extensive simulations with various settings and scenarios. Furthermore, relevant to the proposed system, we discuss possible research issues, challenges and future works.

In this paper, we aim to envision 6G convergent terrestrial and non-terrestrial infrastructure of virtual emotion and epidemic prevention with two differential perspectives: Green AI and Red AI, where Green AI focuses on efficiency and reduction, and Red AI additionally pursues accuracy. By fitting with each perspective, we introduce promising key applications using smart devices, autonomous UAVs, mobile robots and subsequently suggest critical future research directions and opportunities toward new frontiers in intelligent terrestrial and non-terrestrial vehicular networks.