Biblio
FastChain is a simulator built in NS-3 which simulates the networked battlefield scenario with military applications, connecting tankers, soldiers and drones to form Internet-of-Battlefield-Things (IoBT). Computing, storage and communication resources in IoBT are limited during certain situations in IoBT. Under these circumstances, these resources should be carefully combined to handle the task to accomplish the mission. FastChain simulator uses Sharding approach to provide an efficient solution to combine resources of IoBT devices by identifying the correct and the best set of IoBT devices for a given scenario. Then, the set of IoBT devices for a given scenario collaborate together for sharding enabled Blockchain technology. Interested researchers, policy makers and developers can download and use the FastChain simulator to design, develop and evaluate blockchain enabled IoBT scenarios that helps make robust and trustworthy informed decisions in mission-critical IoBT environment.
Today, as surveillance systems are widely used for indoor and outdoor monitoring applications, there is a growing interest in real-time generation detection and there are many different applications for real-time generation detection and analysis. Two-dimensional videos; It is used in multimedia content-based indexing, information acquisition, visual surveillance and distributed cross-camera surveillance systems, human tracking, traffic monitoring and similar applications. It is of great importance for the development of systems for national security by following a moving target within the scope of military applications. In this research, a more efficient solution is proposed in addition to the existing methods. Therefore, we present YOLO, a new approach to object detection for military applications.
In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.
Ensuring security in the military applications of IoT is a big challenge. The main reasons for this state of affairs is that the sensor nodes of the network are usually mobile, use wireless links, have a small processing power and have a little energy resources. The paper presents the solution for cryptographic protection of transmission between sensor nodes in the data link layer and for cryptographic protection of data stored in the sensor node resources. For this purpose, the Trusted Platform Module (TPM) was used. The proposed solution makes it possible to build secure and fault tolerant sensor network. The following aspects were presented in the paper: the model of such a network, applied security solutions, analysis of the security in the network and selected investigation results of such a network were presented.