This work presents a framework for risk-aware pathfinding in topographic terrains, designed to support realistic and tactical route planning in agent-based simulations. The proposed method enhances the A ∗ algorithm by incorporating topographic elevation and risk costs into its path cost estimation function, enabling the computation of paths that reflect tactical constraints in simulation scenarios. To improve the efficiency of heuristic estimations used in path search, we introduce the use of Deep Neural Networks (DNNs) trained to learn correction factors for heuristic functions. These models adjust distance and risk estimates during the search, outperforming conventional heuristic functions, particularly in complex environments. To reduce the computational overhead typically associated with DNN inferences, we use an Iterative Heuristic Map (IHM), a caching and batching mechanism that minimizes redundant model queries by reusing heuristic estimates for spatially related nodes. The framework introduces the notion of View Point Configurations (VPCs), a method to model multiple risk sources in a digital elevation model (DEM) by simulating the field of view from elevated risk points. VPCs enable the representation of dangerous and safe terrain areas and are used to train and evaluate DNN models. The presented work conducted comprehensive experiments in increasingly complex scenarios, including unitary, mixed, and multiple active VPC configurations. It also analyzes the risk exposure of the paths calculated by the proposed framework, comparing them with those generated by the baseline A ∗ algorithm. The results confirm the effectiveness of the framework in reducing computation costs, enhancing risk-aware DNN generalization, and improving tactical pathfinding in topographic terrains.

IoT platforms often rely on resource-constrained devices with specific requirements for efficient resource utilization. Wireless communication, reduced form factor, and low power consumption are key factors for a successful IoT deployment. In the context of energy requirements, this paper examines the energy consumption of three widely used communication protocols: Constrained Application Protocol (CoAP), Hypertext Transfer Protocol (HTTP), and Message Queue Telemetry Transport (MQTT) in resource-constrained IoT applications. Firstly, existing studies are explored to understand the characteristics, architecture, and performance of the protocols. Secondly, the main aspects of each protocol are highlighted. Then, a study methodology is proposed to evaluate current consumption by comparing the secure and insecure versions of these protocols using a standardized 10-byte payload. The tests are performed under two signal levels (high and low) to assess their effect on energy consumption. The findings of this study indicate that while unencrypted MQTT QoS 0 is the most energy-efficient baseline, the introduction of cryptographic security layers imposes a severe energy penalty. Secure HTTP exhibited exponentially higher energy usage due to the computational overhead of TLS handshakes, whereas Secure MQTT QoS 0 remained the most efficient among the encrypted options. Furthermore, the results demonstrate that adverse signal conditions tend to increase energy consumption, emphasizing the critical trade-off between robust security, communication reliability, and energy autonomy in constrained IoT devices.

The increasing use of Unmanned Aerial Vehicles (UAVs) in applications such as logistics, environmental monitoring, infrastructure inspections, and precision agriculture has brought new safety and operational efficiency challenges. In this context, ensuring collision avoidance in operations with multiple UAVs in shared spaces has become a priority. This work proposes a framework based on Deep Reinforcement Learning (DRL), designed to optimize collaborative trajectory planning and collision avoidance in dynamic environments, which include static and moving obstacles. The approach combines LiDAR sensors with Detection And Avoidance (DAA) algorithms. These systems allow UAVs to identify and avoid obstacles accurately, even in dense urban scenarios and with inaccurate sensors. In addition, the framework promotes collaboration between UAVs through decentralized architectures, allowing greater efficiency in the use of airspace and reducing overlap in shared missions. Performed simulations have shown that the proposed approach provides improvement in movement safety. The results show that the system is adaptable in dynamic scenarios, making it promising for applications in complex airspaces.

As wireless communication continues to advance, the move towards Sixth-Generation (6G) networks has heightened the need for faster data speeds and reliable connections, prompting new approaches to connectivity. In scenarios such as natural disasters, where Ground Base Stations (GBSs) may be compromised, the use of Unmanned Aerial Vehicles (UAVs) has become increasingly important. A promising approach is to deploy low-altitude UAVs equipped with compact Base Stations (BSs) to reestablish essential communication networks and offer temporary coverage. However, identifying the optimal locations for these UAV-BSs presents a complex challenge. This paper proposes an innovative solution using UAVs as base stations (UAV-BSs) and introduces a Mixed-Integer Non-Linear Programming (MINLP) optimization model to position UAV-BSs based on real-time demand and network conditions. Traditional methods struggle with the complexity of UAV-BS deployment, so a novel algorithm combining the JAYA optimization technique is used. Extensive experiments show this approach maximizes network coverage and connectivity while minimizing UAV-BS power consumption , outperforming other methods in placement accuracy, power usage, packet loss , and latency. The algorithm also adapts to varying network conditions, making it a valuable tool for optimizing UAV-BS locations in dynamic environments.

The integration of Software-Defined Networking (SDN) in Flying Ad-Hoc Networks (FANETs) introduces centralized control and programmability, enhancing network flexibility and UAV resource optimization. This study evaluates and compares the performance of SDN-based FANETs using two platforms: ns-3 and Mininet-WiFi. A multi-layer architecture was implemented to analyze network behavior under different scenarios, including single-hop, multihop, multipath, and mesh topologies. Performance metrics such as bandwidth, jitter, round-trip time (RTT), and packet loss were assessed. The results reveal that Mininet-WiFi consistently outperforms ns-3, achieving up to 87% higher bandwidth, 26 times lower packet loss, and significantly reduced jitter and latency. The study highlights key limitations in ns-3, such as excessive packet fragmentation, retransmissions, and high processing overhead due to VXLAN tunneling. Recommendations for improving FANET-SDN emulation, including MTU optimizations, alternative tunneling methods, and enhanced controller configurations, are provided. These findings serve as a foundation for future research on optimizing SDN-based UAV networks for real-world deployments.

This article presents a method for generating a dataset from CAN protocol communication using a test bench based on the IEC 62228-2019 standard. Modern automotive embedded systems feature many electronic control units for engine control, ABS, traction control, and power steering. Ensuring reliability and effectiveness, consistent testing, and data analysis are essential. This article proposes a testing approach to verify and record CAN communication under disturbances, using a redundant channel and a fault injection method with specially developed hardware. Different injected noise values were defined based on prior work. A sequence of tests applying the hardware-in-the-loop approach generates valuable preprocessed datasets. The results show that the proposed method records all CAN communication data, producing around 15 million records. This dataset enables detailed analysis of EFT fault propagation in the CAN network from various vehicle noise sources, providing a foundation for future data-driven diagnostic systems development.

OBJECTIVE: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. METHODS: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. RESULTS: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. CONCLUSION: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Today, In-Vehicle Wireless Sensor Networks (IVWSNs) are being used by car manufacturers because it saves time in the assembling process; saves costs in the harness and after-sales; and makes vehicles lighter, which helps lessen fuel consumption. There is no definition for wireless solution technology for IVWSNs, because each one has its own characteristics, and probably this is one of the reasons for its smooth usage in the automotive industry. A gap identified in wireless sensor networks for the automotive domain is that the related literature focuses only on ordinary cars with a star topology and few of them with mesh topology. This article aims to cover this gap by presenting an experimental study performed on verifying the new Bluetooth 5 technology working in a mesh topology applied to public transportation systems (buses). To perform this evaluation, a setup to emulate an IVWSN was deployed in a working city bus. Measuring the network metrics, the bus was placed in a variety of conditions during its trajectory to determine the influence of the passengers and the whole environment on the data transmission. The results suggest that Bluetooth 5 in a mesh topology is a promising candidate for IVWSNs because it shows the robustness of losing only 0.16% packets in the worst test, as well as its ability to cover a wider range compared to its previous version, indeed a better RSSI and jitter, with lower transmission power, compared to a star topology. The round trip time results can support the analysis for time-critical applications.

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness.

Efficient exploration of unknown environments is a fundamental precondition for modern autonomous mobile robot applications. Aiming to design robust and effective robotic exploration strategies, suitable to complex real-world scenarios, the academic community has increasingly investigated the integration of robotics with reinforcement learning (RL) techniques. This survey provides a comprehensive review of recent research works that use RL to design unknown environment exploration strategies for single and multirobots. The primary purpose of this study is to facilitate future research by compiling and analyzing the current state of works that link these two knowledge domains. This survey summarizes: what are the employed RL algorithms and how they compose the so far proposed mobile robot exploration strategies; how robotic exploration solutions are addressing typical RL problems like the exploration-exploitation dilemma, the curse of dimensionality, reward shaping, and slow learning convergence; and what are the performed experiments and software tools used for learning and testing. Achieved progress is described, and a discussion about remaining limitations and future perspectives is presented.

This paper presents a study about a runtime mechanism to monitor the performance degradation in intra-vehicular networks. The proposed mechanism focuses on the integration of fault modeling in communication protocols, as non-functional requirements (NFR), using aspect-oriented modeling (AOM), to model the performance degradation generated by faults, linking test and design phases of distributed control systems. A case study analyzing the mechanism performance in both CAN and CAN-FD protocols was conducted considering the NFR specification related to fault disturbances. In order to evaluate and simulate the mechanism under real fault scenarios, an active suspension control system was considered as an example of a critical control system and faults were injected using the hardware Vector VH6501 (CAN disturbance interface). The network performance analysis was made based on the software Vector CANoe considering different network busloads and CAN/CAN-FD rates between 1 to 4 Mbps. Results show that the mechanism efficiently detects anomalous events on performance with short response time with busloads up to 30%. The performed experiments show better performance on CAN-FD registering lower average jitter during fault injection in all busloads tested scenarios.

Simultaneous localization and mapping (SLAM) is a fundamental problem in mobile robotics. One solution to this problem is RatSLAM, which is a SLAM algorithm inspired by the navigation system in rodent brains. RatSLAM has a set of parameters that have to be adjusted individually for each environment to generate a suitable map. To date, there is no automatic tuning of these parameters and they are hand-tuned by trial-and-error. The present work proposes an automatic parameter tuning method, which employs the Iterative Closest Point (ICP) algorithm to evaluate the generated maps as well as the Irace algorithm to find the best parameters settings. The process is formulated as an optimization problem, where the objective function takes into account the parameters values, the generated RatSLAM's maps, and a ground truth map. Irace automatically generates a unique combination of RatSLAM parameter set, where each combination generates a map. Then, deviations between the generated and a ground truth map are evaluated with the ICP algorithm. These evaluations are taken into account by Irace to generate new combinations of RatSLAM parameters until this deviation reaches a stop criterion. The proposed methodology was successfully tested on three different scenarios. It was able to automatically estimate parameters to generate experience maps close to the ground truth maps. Furthermore, it showed how the tuned parameters can be shared across different scenarios, which might pave the way to develop similar approaches for other SLAM algorithms that also depend on fine-tuning of their parameters.

Urban mobility is one of the main problems faced by big cities. The electric multiple unit (EMU) is the best alternative since it transports a high volume of people at a low cost. Failures during operation cause delays and inconvenience for passengers and operators. This article deals with failures in DC traction motors and their control systems in a transport company operating urban trains. The study focuses on identifying the main causes and consequences of these failures through a statistical analysis. Initially, the study proposes multidimensional scaling to analyze these observations and concludes with the use of neural networks. The acquired results are useful for actions in traction motors during preventive maintenance and in corrective maintenance.

This paper presents an approach to monitor the performance degradation in CAN networks and the fault effect on time constraints of periodic control tasks. The work proposes the use of a test-based method supported by fault and error injection that helps the engineer to define how these faults degrade the system performance. In addition, a runtime jitter monitoring technique is proposed and applied to a CAN-based vehicular network. The runtime jitter analyses the performance oscillation according to a time window and with a tolerance range defined during previous performance tests. A case study was conducted monitoring a critical control system during error injection and the analysis technique was applied in order to verify the performance oscillation detection. Results show that with a typical CAN rate of 1 Mbps, the runtime jitter detect anomalies in performance during a short time period up to 30% of busload. The experiments show the degradation of 4,2 times on average jitter between 10% and 30% of busload with fault injection. The detection is also possible with higher busload 50% and 80%, but with an increase in the detection time. The present study emphasizes the importance of performance monitoring with the recent advances in automotive electronics.

Simulation systems are important tools for the development of training tasks in different application domains. The use of military simulated problems (MSPs) in simulation systems allows the introduction of challenging decision making situations to military trainees so that they can exercise their skills. To support the implementation of such MSPs in an artillery battery simulator, this work investigates how to compute dynamic changes, such as flooding-driven modifications, into the hierarchical and irregular navigation grid structure expressing the environmental characteristics of large virtual terrain scenarios. The paper describes an efficient mechanism to process these modifications during simulation runtime, without affecting the overall system response time, thus the user interaction with simulations. Experimental results show that the proposed techniques are able to handle time-constrained modifications of large virtual terrain scenarios.

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.

Autonomous exploration aims to develop mechanisms that enable a robot to navigate through an unknown environment, seeking to collect information that is relevant to its objective, with minimal or no human intervention. This article presents an autonomous exploration strategy based on a multimodal continuous utility function and optimization metaheuristic. The problem of autonomous exploration of mobile robots is formulated as an optimization problem, providing data for a Firefly-based algorithm that is able to search for points in the solution space, representing cells on the map being constructed, that best meet the exploration objectives. Simulations in a Robot Operating System environment showed that the proposed approach is able to guide robot through good trajectories, leading to full mapping, without any human intervention.

Important signal processing techniques need that the response of the different elements of a sensor array have specific characteristics. For physical systems this often is not achievable as the array elements’ responses are affected by mutual coupling or other effects. In such cases, it is necessary to apply array interpolation to allow the application of ESPRIT, Forward Backward Averaging (FBA), and Spatial Smoothing (SPS). Array interpolation provides a model or transformation between the true and a desired array response. If the true response of the array becomes more distorted with respect to the desired one or the considered region of the field of view of the array increases, nonlinear approaches become necessary. This work presents two novel methods for sector discretization. An Unscented Transform (UT) based method and a principal component analysis (PCA) based method are discussed. Additionally, two novel nonlinear interpolation methods are developed based on the nonlinear regression schemes Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). These schemes are extended and applied to the array interpolation problem. The performance of the proposed methods is examined using simulated and measured array responses of a physical system used for research on mutual coupling in antenna arrays.

Situation awareness in surveillance systems benefits from high‐quality video streaming service. This is even more important considering military systems, in which delays in image transmission may have a significant impact on the decision‐making process. However, in order to deliver high‐quality video streaming service, the required network infrastructure may be prohibitively complex, or even completely impossible to deploy, if mobile data providers are considered. Moreover, the demand for high network throughput poses extra requirements on the network. Considering this context, this paper addresses the problem of highly mobile networks composed of unmanned aerial vehicles (UAVs) as data providers of a military surveillance system. The proposed approach to tackle the problem is based on a Software Defined Networking (SDN) approach aiming at providing the best routes to deliver the data, enhancing the end‐user quality of experience. An extensive experimental campaign was performed by means of simulations and the acquired results provide solid evidence of the usefulness of this proposal.

Faults in industrial communication protocols represent an important problem for industrial systems as they may lead to severe losses. Observing the need for approaches to deal with this type of problem, this paper studies reliability aspects related to industrial communication protocols and the modeling of possible communication faults. The work proposes a requirements specification based on aspect-oriented concepts to map and model common faults in design phases of embedded systems. A model integrating fault modeling in communication protocols extending the Real-Time From Requirements to Design using Aspects (RT-FRIDA) framework is presented. A case study on vehicular networks was conducted considering the nonfunctional requirements specification of an Active Suspension System. Then, a sequence of fault requirements based in recent studies is modeled and presented to validate the proposed approach. The main contribution of this work is to enable the use of fault modeling in communication protocols and aspect-oriented modeling techniques to mitigate the fault impact in the timing behavior of critical tasks. This study indicates the use of observers' technique modeled to fault diagnosis, so that they can be aggregated into a real-time control system.

Agent behaviors in simulation systems are related to fundamental capabilities of realistically developing (semi) autonomous navigation actions. This is particularly important when dealing with the implementation of Computer Generated Forces (CGFs) for simulation systems in tactical military training applications. Moreover, these systems take into consideration the particularities of the domain-specific simulation tasks and the numerous heterogeneous CGFs inserted on them in order to generate better knowledge and learning experience to simulation system users. Based on these reasons, this paper reviews recurrent navigation problems as to propose a task-oriented and parameterized (semi) autonomous navigation framework to deal with CGF navigation needs in military simulation. Combining global and local navigation techniques, and controlled transition between alternative degrees of navigation autonomy, the framework aims to overcome the challenges of implementing customizable CGF navigation behaviors and, at the same time, to allow interaction with both users and other simulation systems in distributed simulation settings. A case study is presented in which the proposed techniques are analyzed in a domain-specific simulation problem providing evidence of their suitability to address the studied military simulation problems.

Video streaming is an important service provided by surveillance systems to enhance situation awareness. However, in military systems, data acquisition heavily depends on the network infrastructure. In this application domain, units are spread and the distance between the sources of data and the decision makers may be very large. In the case of video streaming, the demand for high network throughput poses some extra requirements on the network. Considering the mobility patterns of the military units and the diversity of the new generations of sensors, especially those used by Unmanned Aerial Vehicles (UAV), the configuration and the management of the network must be so dynamic and so sensitive to data flow parameters that manual configuration is not acceptable. For this reason, the capability of the network to configure itself to offer the necessary Quality of Service is a must. Using principles of Software Defined Networks (SDN), this paper presents an analysis of video streaming for military surveillance in which multiple UAVs are employed as data providers through an SDN-enabled network, with promising results.

Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology's strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.

Wireless Sensor Networks (WSN) are often composed of a wide range of sensor nodes, which may vary greatly in their type of hardware platform, as well as their sensing and mobility capabilities. The ability of a sensor to move is a particularly important feature in dynamic scenarios, since mobile sensors can fill the gap caused by the failures of those that are stationary, and thus extend the lifetime and span of a WSN. However, there remains the problems of intersensory communication in the field when integrating mobile sensors into the Sensor Web in dynamic scenarios since it does not have the necessary interoperability for automatically managing the different types of sensor data and activities involved in such scenarios. This paper tackles this problem by adopting an approach consisting of an enhanced messaging protocol and a dynamic sensor management component. In validating the proposal, two different realistic scenarios were simulated to evaluate the achieved results in terms of interoperability and performance. The results provided evidence that the proposal complies with Sensor Web standards as well as being suitable for near real-time data publication, and is thus able to support applications in dynamic scenarios.

This paper presents a novel cross-layer anycast medium access control (MAC) protocol for wireless sensor networks (WSNs) based on advanced preamble propagation and geographic routing. The protocol aims at reducing the sleep-delay problem, common in asynchronous preamble-based MAC protocols. When a packet is ready to be transmitted, differently from other state-of-the-art protocols, the preamble sequence is propagated in advance for several hops before packet release. So, a simultaneous propagation of signaling and packet is obtained. The number of nodes in the Forwarding Candidate Set (FCS) in our anycast protocol is chosen to settle the average speed of preambles to the velocity of packet propagation. In order to enablethe simultaneous transmissions without collision, a minimum distance between preambles and data packet is preserved. Simulations results showed that our protocol outperforms in latency all other asynchronous MAC protocols appraised, for all evaluated packet sizes. This result was obtained with an energy consumption similar to the other state-of-the-art protocols.

Technological advances in information and communications systems are enabling access to education for millions of students, regardless of their geographical location. In this sense, distance learning schools take advantage of technological tools, such as course management systems (CMS) and their plugins. This work proposes a plugin for the e-learning platform Moodle that automatically sends e-mail messages to the students in order to motivate them to take part in the distance learning courses and, consequently, to reduce the dropout rates. This plugin has been tested in courses promoted by the General Coordination of Distance Education (CGEAD) of the Brazilian National School of Public Administration (ENAP). Based on data from its operation within the real ENAP distance learning environment, the proposed plugin is particularly successful to tackle the enrollment locking cases that compose the student dropout rate and also to motivate course enrolled students to access the virtual learning system more frequently.

Time synchronization is the most fundamental service to many wireless sensor network applications, which count mainly with low-power devices. This paper focuses in proposing and implementing a time synchronization service for low-power wireless sensor networks using low frequency real-time clocks in each node. This work present the design, implementation and test of an adaptive algorithm, making the timing of the clocks convert as quickly as possible and after this conversion, keeping them most similar as possible. The goal is to achieve the best method that ensures right timing and still having low energy consumption. Experimental results provide evidence of the success in meeting this goal.

The current static model used for allocating the spectrum of frequencies and the increasing demand for network resources imposed by modern applications and services may lead to a resources scarcity problem. Dealing with this problem demands optimized resources allocation. An alternative to provide this optimization is by allowing resources sharing among network operators. In this paper, an architecture is presented to encourage network operators to share their underutilized resources. A multilevel broker is proposed to control the resources sharing. This broker dynamically establishes a service level agreement that takes into account the quality of service requirements of resources renters and the cost of the resources. A performance evaluation shows that the implementation of the proposed architectures leads to better allocation of underutilized network resources.

Providing high quality network access is challenging for network operators in the current static model used for allocating the spectrum of frequencies. Dealing with this challenge demands optimized resources allocation. One of the ways of providing this optimization is by allowing resources sharing among network operators which share the same geographical area. To allow and control the sharing of resources in such network scenarios, in this paper, a multilevel broker is presented to allow network operators to share their underutilized resources. This broker dynamically establishes a service level agreement that takes into account the quality of service requirements of the resources renters. A performance evaluation conducted in a scenario composed of multiple LTE-Advanced network operators shows that the implementation of the proposed architectures leads to more efficient allocation of underutilized network resources compared to two algorithms found in the literature.

Wireless sensor networks (WSNs) are currently employed in a vast number of different applications ranging from home automation and health care to military systems. Although their application may vary greatly, WSNs share a common set of characteristics such as a limited energy supply and simple hardware. A common issue related with the application of WSNs is sensor localization, for some types of applications it is important that the sensors know the relative or absolute position of other sensors in the network, such as surveillance of monitoring networks. If sensors are randomly placed they may resort a wide range of methods such as Global Navigation Satellite Systems (GNSS) or received signal strength indicators (RSSI). In this work we present an alternative to relative sensor localization by employed a crossed dipole antenna in the reception and a known polarization in the transmission. The accuracy of the proposed methods is measured trough numerical simulations and results are presented.

Wireless sensor networks are evolving to hybridnetworks in which static and mobile sensor nodes cooperate in order to address challenging requirements imposed by new emerging applications. However, due to the ad hoc nature of the network and especially to resources constraints of the sensor nodes, this cooperation is not trivial, requiring a number of retransmissions thus wasting precious resources. In this paper the use of cooperative multiple input multiple output (MIMO) techniques is proposed to overcome transmission problems, ensuring a reliable and more efficient communication link with less retransmissions. Extensive simulation experiments support the proposal, and the results highlight the benefits in using MIMO to deliver messages from static to mobile nodes in wireless sensor networks.

This work presents a method of coordinating static ground sensors with Unmanned Aerial Vehicles using coordinators. The ground wireless sensors are responsible for the primary detection of an event (e.g. intrusion in the monitored area). After the initial detection, a suitable UAV, equipped with high quality surveillance devices, should be designed inspect the event's area. A method combining a geographic routing with a UAV-CoordinatorPosition function that is responsible to deliver the initial alarm to the selected UAV is presented. In this method, each UAV has a coordinator node on the ground network. This coordinator receive periodic updates of the UAV actual position. When an alarm is issued by any ground sensor, it is forwarded to the appropriated coordinator, and it is then responsible to forward using the geo-routing to the selected UAV. The position of the coordinator is determined by the UAV-CoordinatorPosition function which receives the characteristics of the UAV as input and returns the position of the corresponding coordinator. Results showed the efficiency of the proposed method, reducing by 45.39% the number of hops needed to find the appropriate UAV in comparison with the strategy using a bio-inspired method presented in the literature. In most cases, the UAV could handle at least 65% of the alarms.

How to measure and maintain connectivity is an important issue in ad hoc networks. A special case of such network is Wireless Sensor Networks (WSN), which are often deployed in harsh environments and also susceptible to a number of problems that may negatively affect the connectivity among the nodes. An additional factor that increases the cost of connectivity maintenance in ad hoc networks is when the nodes can move. When it comes to the WSN domain, this aspect is still more problematic, as the often small sensor nodes have in general a limited energy budget, and then should not use too much energy in the management of their connectivity. The goal of this work is to choose a topology for mobile WSN and improve the network connectivity as a whole while considering and influencing the energy consumption among all the nodes in the network. Different network topologies are considered and discussed. After evaluation of the pros and cons of the estimation quality when applied to each studied topology, a clustered hierarchical algorithm was chosen for network deployment. By means of a link estimator and considering different variables, a metric have been defined to estimate the link reliability. As a result, improved network connectivity is reported.

The emerging applications using sensor network technologies constitute a new trend requiring several different devices to work together and partly autonomously. However, the integration and coordination of heterogeneous sensors in these emerging systems is still a challenge, especially when the target application scenario is susceptible to constant changes. The setup and adaptation of these systems are challenging, considering that their nodes are distributed and must respect operational constraints, such as energy consumption. Due to the dynamicity of the scenarios in which they are deployed and the nature of their operations these systems require autonomous decisions that have to be taken without any human operator intervention. This paper presents a reflective middleware that supports heterogeneous sensor networks deployed in dynamic scenarios. This middleware presents specific handling of users’ requirements by representing them as missions that must be accomplished by the network. These missions are then translated to network parameters and activities that are distributed to network nodes by means of the autonomous reasoning that takes into account network nodes’ capabilities and environment conditions. A multi-agent framework is proposed to provide the necessary support for missions’ dissemination and for the required reasoning to allow autonomous behaviour related to mission allocation and network adaptation.

This paper presents simulation results of a setup strategy for wireless sensor networks, based on an agent-oriented middleware. A great problem that has to be tackled in sensor networks is how to setup them to provide the data required from the user with a minimal overhead. Having this goal in mind, the proposed approach abstracts the network setup as missions, which are handled within the network by intelligent agents that disseminate and divide the work related to the missions. Several experiments using this approach are provided, showing its efficiency and the low overhead that it imposes to the network.

This paper presents a comparison among different strategies to coordinate the use of heterogeneous wireless sensors aimed for area surveillance. The heterogeneity among the sensor nodes is related to their sensing and mobility capabilities. The goal of the strategies is to provide coordination among the different nodes, in order to make the wireless sensor network perform its missions with higher efficiency. Strategies combine advantages of bio-inspired and utility-based approaches to coordination. Simulations of scenarios with different characteristics were performed and the results are compared and analyzed.

Model-driven design (MDD) can be perceived in the recent literature as an option to deal with the increasing complexity of the design of distributed embedded real-time systems (DERTS). This paper reports some results of a research project aiming to support a MDD approach, which applies concepts of the aspect-oriented (AO) paradigm in order to improve the treatment of non-functional requirements (NFR) in the design of DERTS. A tool named GenERTiCA, which generates source code from UML diagrams and also weaves aspect adaptations, has been developed to support such MDD/AO approach. This paper presents results regarding the use of GenERTiCA to generate code and implement aspects (from a high-level framework of aspect) for the RT-FemtoJava platform, a RTSJ-based and optimized Java platform for DERTS.

Many modern applications require high-performance platforms to deal with a variety of algorithms requiring massive calculations. Moreover, low-cost powerful hardware (e.g., GPU, PPU) and CPUs with multiple cores have become abundant, and can be combined in heterogeneous architectures. To cope with this, reconfigurable computing is a potential paradigm as it can provide flexibility to explore the computational resources on hybrid and multi-core desktop architectures. The workload can optimally be (re)distributed over heterogeneous cores along the lifecycle of an application, aiming for best performance. As the first step towards a run-time reconfigurable load-balancing framework, application requirements and crosscutting concerns related to timing play an important role for task allocation decisions. In this paper, we present the use of aspect-oriented paradigms to address non-functional application timing constraints in the design phase. The DERAF aspectspsila framework is extended to support reconfiguration requirements; and a strategy for load-balancing is described. In addition, we present preliminary evaluation using an Unmanned Aerial Vehicle (UAV) based surveillance system as case study.

Sensor networks are being applied in several emerging sophisticated applications due to the use of powerful and high-quality sensor nodes, such as radars and visible light cameras. However, these nodes need additional features to optimally benefit from heterogeneous modern computing platforms. Therefore, reconfigurable computing is a potential paradigm for those scenarios as it can provide flexibility to explore the computational resources on that kind of high performance computing system. This paper presents a reconfigurable sensor node allocation support, based on application requirements, provided by a middleware focused on heterogeneous sensor networks. In order to address this concern, an aspect-orientation paradigm and intelligent agents approach is proposed followed by an UAV case study.

This work presents a proposal to use aspect orientation in the analysis and design of distributed embedded real-time systems (DERTS). These systems have several requirements directly related to their main characteristics, the so-called non-functional requirements (NFR), which refer to orthogonal properties, conditions, and restrictions that are spread out over the entire system. Pure object-oriented methods do not address successfully those so called cross-cutting concerns, so new technologies, like aspect orientation, are applied in order to fulfill this gap. The paper presents DERAFistributed embedded real-time aspects framework, an extensible and high-level framework (i.e. implementation-independent) to handle NFR of DERTS at earlier design stages. DERAF combines the use of aspects with RT-UML, aiming to separate the handling of non-functional from functional requirements in the model driven design of DERTS. The paper presents the use of DERAF on a case study of an unmanned air vehicle (UAV)

This paper presents the evaluation of a multithread distributed real-time object-oriented platform. A communication API was developed to increase functionalities of an API that implements the real-time specification for Java standard, extending it to cover embedded distributed applications. Real-time restrictions of the communication are ensured by a time-out mechanism. The API can be adapted to be used with different underlying network and physical mediums. The development focused on restrictive embedded platforms with low performance and small memory. An evaluation in terms of the fulfillment of timing constraints, and memory footprint is presented for a CAN-bus network. The results also demonstrate the timely correctness provided by the communication API running over an RTSJ implementation

This paper presents the TinyCAN, a resource constrained controller area network controller implemented in VHDL language. The core description is fully synthesizable and fits in a FPGA-based platform occupying a maximum of 366 look-up tables. An enhanced error control strategy was elaborated with the aim of producing a more suitable behavior for safety-critical applications. For all that, compatibility with off-the-shelf components was preserved implementation details and comments on design decisions are given. The paper also presents some guidelines for the use of the proposed CAN controller IP for time-triggered architectures.

The embedded real-time development community is investigating different approaches in order to provide modularity and reuse features for system design in this area, as well as a more appropriate mapping technique between requirements and implementation. The Java technology is very promising for this community, mainly after the research efforts on its real-time extension RTSJ-real-time specification for Java. However, there are still some critic factors related to the adoption of Java for real-time applications, and some of them deserve special attention. This paper reports a study of some of these decisive factors, such as the choice of the underlying operating system, the use of a middleware (virtual machine) or a native code, and the use of the Java real-time API (RTSJ).