The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional Internet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.

Introduction/purpose: The term Internet of Things (IoT) usually refers to the collective network of connected devices and the technology that facilitates communication between these devices and the cloud, as well as among these devices. The IoT concept is lately considered and applied as the appropriate in design of systems intended for distribution of data and information between heterogeneous devices with the aim to improve efficiency and effectiveness of business and decision making. The IoT enables energy and supply chain monitoring, production coordination, equipment performance optimization, transportation, public health, and improves workers' safety and health. In addition to smart devices, IoT technology also enables the connection of various sensors as a source of data on various physical phenomena, and, based on the information obtained, it is possible to control the operation of devices, make predictions, make decisions, etc. In this paper, specific areas of the application of the IoT in the defense and security sector are analyzed in order to identify the possibilities of applying modern technologies in raising the defense potential of the state and define the directions of future research in the subject area. Methods: The methods of content analysis of current research were applied, and then, with the deductive method, conclusions were reached about the future directions of the development of IoT technology. Results: A detailed analysis of past and ongoing research in the defense and security sector was carried out, and potential directions of future research into the IoT were given in order to increase the operational capabilities of armed forces. Conclusion: IoT services will certainly contribute to a greater degree of automation and improvement of the quality of military decisions on the battlefield, especially in the conditions of unexpected scenarios in an unpredictable hostile environment, thus facilitating the reduction in both human and material losses in operations.

Application of cooperative multiple sensors for distributed signal sensing and modulation classification offers significant improvement of classification performance, compared to using single sensor, especially in the presence of multipath fading. In this paper, several cooperative AMC schemes with hard and soft decision fusion were analyzed. Classification performances of cooperative AMC schemes are investigated and evaluated in terms of average probability of correct classification, through Monte-Carlo simulations.

Cognitive radio networks (CRN) and wireless sensor networks (WSN) inherently deploy multiple sensors, thus providing an excellent basis for distributed signal sensing and modulation classification. Deploying cooperative multiple sensors offers considerable improvement of automatic modulation classification (AMC) performances, compared to single sensor deployment, especially in the presence of multipath fading. In this paper, different decision fusion methods for AMC with multiple sensors in multipath fading environment were presented and analyzed. Soft decision fusion and joint decision fusion were defined for AMC algorithm using fourth-order cumulants, and comparison with appropriate hard decision fusion methods for same AMC algorithm is performed. Classification performances of derived AMC schemes with fusion of decision, in terms of average probability of correct classification, are investigated and evaluated through Monte-Carlo simulations.

Data fusion methods are proposed for cooperative modulation classification by using multiple sensors in multipath fading channels. Performances of these methods are evaluated, and compared with those of the existing optimal decision fusion method, through Monte Carlo simulations for a realistic application scenario, and evident gains are confirmed.