Wildfires pose a significant threat to ecosystems, human lives, and infrastructure worldwide. Traditional wildfire detection and risk assessment methods often suffer from limitations such as delayed detection and low confidence in certain regions. In this paper, we propose a novel computational system based on machine learning for wildfire risk assessment using data collected by drones. The system can integrate various sensors to capture spatiotemporal data on environmental factors such as temperature, humidity, and vegetation. By leveraging high-resolution data collected through autonomous drone missions, our system enhances wildfire risk estimation and enables proactive mission planning. Although the system is mainly designed to address wildfire monitoring using drone-collected data, it can be easily adapted to other environmental monitoring applications and other sources of data. We demonstrate the effectiveness of our approach through a comprehensive evaluation and validation process in both simulated and real-world environments. Our work contributes to advancing wildfire monitoring capabilities, improving early detection, and mitigating the impact of wildfires on communities and the environment.

Drones can play a game-changing role in reducing both cost and time in the context of last-mile deliveries. This paper addresses the last-mile delivery problem from a complex system viewpoint, where the collective performance of the drones is investigated. We consider a last-mile delivery system with a tradable permit model (TPM) for airspace use. Typically, in other research works regarding lastmile delivery drones, a fully cooperative centralized scenario is contemplated. In our approach, due to the TPM, the agents (i.e. drones) need to compete for airspace permits in a distributed manner. We simulate the system and evaluate how different parameters, such as the arrival rate and airspace dimensions, impact the system behavior in terms of the cost and time needed by the drones to acquire flight permits, and the airspace utilization. We use a simplified simulation model, where the agents’ strategies are naïve, and the drones’ flight dynamics are not accounted for. Nevertheless, the simulation’s level of detail is adequate for capturing interesting properties from the agents’ collective behavior, as our results support. The obtained results show that the system’s performance is satisfactory, even with naïve agents and under high traffic conditions. Moreover, a real-world implementation of our competitive decentralized approach would lead to advantages, such as fast permit transactions, simple computational infrastructures, and error resilience.

According to recent studies, unmanned aerial vehicles can play a game-changing part in cost reduction and speed increase to address the last-mile delivery problem and attend to emergencies. Last-mile delivery services are getting more and more relevant, especially when in times when social distance is required. However, a systemic view of how to provide a feasible solution to enable this application as a vertical market in the urban context is still inexistent. Given this scenario, our paper contributes by proposing a cyber-physical system roadmap applicable to last-mile delivery drones. The proposed CPS guidelines are based on the system of systems to enable an enhanced operation towards smart cities' governance. This paper also discusses topics from air space control and reservation to communication infrastructure and decentralized control supported on a blockchain.

One of the most important phases regarding software project management is planning. on this phase, efforts and resources estimation must be taken into account considering human, materials, infrastructure, time, and budget. Generally, it depends on technical, environmental, human and political factors. It also can affect software final cost and development effort. The estimation tackled on this work was based on use case points (UCP) effort estimation technique, introduced by Gustav Karner in 1993. This paper considered both the best and the worst scenarios for developing a software product. It helped to verify and reveal which are the activities that may cause project lateness. The major contribution of this work was to provide acceptable software estimation for avionics system development and case study integration for an unmanned aircraft system (UAS) project. The UCP effort estimation was applied to just one UAS named preliminary testing monitoring station (PTMS). That system was considered as a key part of the UAS project. The strategy to get accurate estimate was the utilization of a well-defined software process, where documentation was continuously improved. This work has adopted rational unified process (RUP), Unified Modeling Language (UML), and object-orientation (00). RUP has a specific artifact for use case models and the estimation was accomplished by taking information from this artifact. Another contribution of this work was the development of a software tool based upon the UCP technique. At the end, this paper reports significant estimate results to help project managers taking important decisions throughout the project development life-cycle.

The tradeoff between analyzability and expressiveness is a key factor when choosing a suitable dataflow model of computation (MoC) for designing, modeling, and simulating applications considering a formal base. A large number of techniques and analysis tools exist for static dataflow models, such as synchronous dataflow. However, they cannot express the dynamic behavior required for more dynamic applications in signal streaming or to model runtime reconfigurable systems. On the other hand, dynamic dataflow models like Kahn process networks sacrifice analyzability for expressiveness. Scenario-aware dataflow (SADF) is an excellent tradeoff providing sufficient expressiveness for dynamic systems, while still giving access to powerful analysis methods. In spite of an increasing interest in SADF methods, there is a lack of formally-defined functional models for describing and simulating SADF systems. This article overcomes the current situation by introducing a functional model for the SADF MoC, as well as a set of abstract operations for simulating it. We present the first modeling and simulation tool for SADF so far, implemented as an open source library in the functional framework ForSyDe. We demonstrate the capabilities of the functional model through a comprehensive tutorial-style example of a RISC processor described as an SADF application, and a traditional streaming application where we model an MPEG-4 simple profile decoder. We also present a couple of alternative approaches for functionally modeling SADF on different languages and paradigms. One of such approaches is used in a performance comparison with our functional model using the MPEG-4 simple profile decoder as a test case. As a result, our proposed model presented a good tradeoff between execution time and implementation succinctness. Finally, we discuss the potential of our formal model as a frontend for formal system design flows regarding dynamic applications.

Real-time embedded systems are present in various application domains such as automotive, aeronautical, space, and telecommunications. Avionics systems (i.e., aviation electronics) represent a specialized class for the aerospace branch. It is a fact that avionics are getting more and more complex considering functionality and design and also using an increased number of digital computer resources. Besides the safety-critical aspect, performance and power consumption have also to be taken into consideration for new designs. Therefore, it is needed to adopt new system capabilities like runtime reconfiguration. In this context, modern system-on-chip (SoC) composed by heterogeneous hardware (i.e., microcontroller and field-programmable gate arrays - FPGA) can figure as an alternative solution. Then, reconfiguration is allowed to take place in runtime, which makes FPGA-based devices interesting for future avionics systems design. Considering this scenario, we introduce in this paper a runtime reconfiguration design implemented using a SoC. Basically, two different areas comprise the SoC hardware architecture: a hard processor area and a reconfigurable area. The former has the authority to manage which configuration will be programmed/used in a given time. The developed design enables both full and partial reconfiguration. In the research work results, we show how different kinds of configuration bitstream modes (i.e., and/or, scrub, scrub clear/set) and data compression impact the system's performance and power consumption. For instance, one full reconfiguration bitstream with no data compression takes ~29 ms to complete with average power consumption of ~42 mW. On the other hand, a given partial reconfiguration (and/or mode) takes ~2.73 ms to complete with ~22 mW average power consumption. Finally, our proposed design opens up the possibility to optimize a system towards adaptability to comply with real-time constraints changing regarding performance and power consumption.