The use of civilian drones in the social aspects of human life is developing; to the extent that UAVs are expected to have a major role in smart cities. Despite the growth of UAV technology, the rules for using them are either not clear or have not yet been legislated. UAV civil liability is the most important area that, despite its benefits, poses social challenges and therefore needs research. Public and cyber security, privacy, personal injury, property damage and reputational damage are all types of responsibilities that can result from UAV performance. This article, written in a descriptive-analytical manner, while examining the conditions for the realization of civil liability, has proved the occurrence of losses in the field of UAV activities and based on the theory of respect, has considered the rule of civil liability applicable and due to the lack of personality for the UAV and also the category of artificial intelligence in the position of determining the person responsible for compensation, has been in search of a human factor.

Human face recognition by drones is necessary for various applications, such as surveillance, search and security. The previous methods for face recognition are highly sensitive to limitations such as height, angle and distance from the face. In this article, a new approach for face detection and identification by deep learning is presented. The proposed method is done in three steps. In the first step, images are zoned with the selective search algorithm. In the second step, a deep network is proposed for box refinement operation to identify the target boxes with high accuracy and speed. Actually, a two-class classification problem is performed by deep learning to locate faces. In the third step, the localized images are trained to the proposed deep network to perform face recognition. In the architecture of the proposed method, the properties of widely used deep networks are used in combination, and a quantitative comparison of the proposed method with new methods in terms of computational complexity shows that training the proposed model requires less execution time than other methods. In addition, the evaluation of the proposed method on the DroneFace dataset shows that for different distance and height from the target, the proposed method has an average face recognition rate of 75. 9 and an average face recognition rate of 84. 6. Therefore, the proposed method has higher accuracy and efficiency than the new methods in this field and can be used for surveillance and security applications.

Understanding the involved environmental elements and variables leads to accuracy, correctness, efficiency of decision making, reducing the number of active human casualties and finally, vulnerability in the battlefield. Utilization of intelligent control systems, command and battlefield awareness equipment has provided many facilities for the Army, so that effective use of these capabilities will allow for greater command of the battlefield. The overall purpose of this study is to explain how drones are used to gather information from battlefields in Nezaja battle squads. In this applied-descriptive research with mixed data analysis approach, 150 participants selected among from 260 commanders, managers and experts in the battle squads of Nezaja regional headquarters, and information and security units of the Nezaja Information and Operations Departments based on the stratified random sampling method. Data were analyzed by SPSS software using t-test and chi-square. The research results show that gathering information through various means using unmanned vehicles can greatly improve the accuracy of the collected information and can be effective in identifying threats and creating security opportunities in a timely manner, but the current capability of the Armed Forces information gathering systems (especially Nezaja) is not enough to respond to all threats and needs to be strengthened or updated.

The Internet of Drones (IoD) is a decentralized network that connects drones to controlled airspace. The connection of drones in these networks is through the Internet of Things. Hence, these networks are vulnerable to all the security and privacy threats that affect IoT networks. In addition, as the application of these networks is highly sensitive in many cases, there are greater potential security threats. The components of these networks work together to identify new and advanced threats. One of the ways to identify new and advanced threats in these networks is distributed machine learning where the data is sent to a central server to learn the general model. This model violates the privacy of network components. It also has a very high level of communication. On the other hand, the central server as the only point of failure may have many problems. In this case, federated learning helps distributed and decentralized networks to share their local model instead of sending their local and secret data. Since the shared models may also disclose some information, we propose a secure and privacy-preserving protocol based on homomorphic encryption. The protocol proposed was for federal learning model and detection of new and advanced threats in the Internet of Drones.

drones are a useful tool and even fun at times. But along with many applications, they will have wide threats. The current research was carried out with the aim of investigating the role of civilian drones in disrupting order and security.The current research method was mixed-exploratory. The research data was compiled in the field and through semi-structured interviews in the qualitative part and the distribution of questionnaires in the quantitative part. The validity of the quantitative part of the questionnaire was confirmed through face validity and its reliability was reported as 0.89. The participants in the qualitative part included 14 experts from Faraja, the Revolutionary Guards, and other security agencies until the theoretical and socio-statistical saturation was reached. They have directly and indirectly used drones in their crimes. In order to analyze the qualitative data using the thematic analysis method, MAXQDA software was used, and SPSS software was used for the descriptive and inferential analysis of the questionnaire data. The interview with experts led to the identification of the functions of civilian drones in disrupting order and security in the form of 38 basic themes, 7 organizing themes, and 2 comprehensive themes. The results showed that UAVs can cause disturbances in order and security through intelligence measures (protection and care, location, covert collection) and operational measures (transfer, combat, and suicide, forest, combat, and intelligence support).

In recent decade, the U.S. government has achieved the technology of remote control automated weapons such as advanced drones which are equipped with modern intelligence facilities, smart missiles and targeted killing programs. This achievement has created new challenging issues in international community. Use of the drones in borders of several Islamic countries like Yemen, Somalia, Afghanistan, Iraq and especially Pakistan, has caused dissatisfaction among the people and the government of these countries. Regarding Pakistan’s, these drones have caused fatalities and injured many civilians and destroyed many buildings. These attacks are mainly illegitimate, violation of international law, the UN charter and the norms of human rights and humanitarian law. This paper discusses the various aspects of the subject, and also analyze the drone attacks. Examines the taken position by international law and Humanitarian law regarding Drones’ strikes? Answers the question whether this attacks can be the subject ICC jurisdiction in terms of aggression and violation of rules of war.

One of the main factors in designing drones and submarines is the appropriate choice of an electrical energy storage device as the energy source of their propulsion systems. This review paper addresses the energy storage technologies, whether battery or fuel cell, applicable in drone and submarine industries. Batteries are widely used in drones due to their lower weight and occupation space and can act as a primary or secondary power supply. Among various chemical compounds, lithium-ion and lithium polymer batteries are the most common battery technologies with a higher energy density. Although the energy density of fuel cells is much higher than that of batteries and increases flight endurance and altitude, they are not yet common in the drone industry due to the high price and volume. This feature of fuel cells has led to their usage as an energy source in the air-independent propulsion technology of submarines. Also, extreme temperature variations for drones can reduce the efficiency and lifetime of batteries and even fuel cells. Battery insulation is provided for temperature drop due to an increase in altitude, and heat management systems are provided for temperature increase due to high current flow.

What are the consequences of expanding the use of armed drones by states and non-state actors on international security? This is a question that has been on the minds of many IR scholars recently. The unique drone capabilities such as eliminating the risk of loss of forces and remotely piloted, deniability, simple technology, surveillance, etc. is introducing it as one of the main option to powering projection. Drones have a direct impact on international security in at least two ways: first by emergence of new international threats, and second by improving the ability of state/non-state actors. In this article, these two levels are analyzed and examined. Also considering the role of armed drones in the security regional of the Middle East, we suggest some strategic points for the defense doctrine of the Islamic Republic of Iran. We conclude that the use of drones should be understood in relation to the military innovation move toward automated and robotic systems. So the role of drones will become more highlight in the future.