Repositório RCAAP

The increase of the digitalization taking place in various industrial domains is leading developers towards the design and implementation of more and more complex networked control systems (NCS) supported by Wireless Sensor Networks (WSN). This naturally raises new challenges for the current WSN technology, namely in what concerns improved guarantees of technical aspects such as real-time communications together with safe and secure transmissions. Notably, in what concerns security aspects, several cryptographic protocols have been proposed. Since the design of these protocols is usually error-prone, security breaches can still be exposed and MALICIOUSly exploited unless they are rigorously analyzed and verified. In this paper we formally verify, using ProVerif, three cryptographic protocols used in WSN, regarding the security properties of secrecy and authenticity. The security analysis performed in this paper is more robust than the ones performed in related work. Our contributions involve analyzing protocols that were modeled considering an unbounded number of participants and actions, and also the use of a hierarchical system to classify the authenticity results. Our verification shows that the three analyzed protocols guarantee secrecy, but can only provide authenticity in specific scenarios.

To experimentally assess the impact of surface reflection on the received signal strength of a set of short-andmedium-range shore-to-shore links (<200 m) that use antennas installed at two heights, at a few meters above surface (<3 m).

In this paper, a multi-hop airborne system is built based on Bluetooth 5 connected autonomous drones to relay real-time data of Internet of Things (IoT). A new lightweight Onboard Bluetooth Transceiver (OBT) is developed for reliable drone-to-drone and drone-to-ground communications. A graphical user interface is presented to monitor real-time flight trajectory of the drones and end-to-end data delivery. Outdoor experiments are conducted in real world to test autonomous flight control of the drones and received signal strength of the OBT communications.

This book is the fifth volume in the successful book series Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage on the Robot Operating System (ROS), which is currently considered to be the primary development framework for robotics applications, and the latest trends and contributing systems. The content is divided into six parts. Pat I presents for the first time the emerging ROS 2.0 framework, while Part II focuses on multi-robot systems, namely on SLAM and Swarm coordination. Part III provides two chapters on autonomous systems, namely self-driving cars and unmanned aerial systems. In turn, Part IV addresses the contributions of simulation frameworks for ROS. In Part V, two chapters explore robotic manipulators and legged robots. Finally, Part VI presents emerging topics in monocular SLAM and a chapter on fault tolerance systems for ROS. Given its scope, the book will offer a valuable companion for ROS users and developers, helping them deepen their knowledge of ROS capabilities and features.

Advanced driving assistance systems (ADAS) pose stringent requirements to a system’s control and communications, in terms of timeliness and reliability, hence, wireless communications have not been seriously considered a potential candidate for such deployments. However, recent developments in these technologies are supporting unprecedented levels of reliability and predictability. This can enable a new generation of ADAS systems with increased flexibility and the possibility of retrofitting older vehicles. However, to effectively test and validate these systems, there is a need for tools that can support the simulation of these complex communication infrastructures from the control and the networking perspective. This paper introduces a co-simulation framework that enables the simulation of an ADAS application scenario in these two fronts, analyzing the relationship between different vehicle dynamics and the delay required for the system to operate safely, exploring the performance limits of different wireless network configurations.

The Erasmus program is a subprogram of the Lifelong Learning program, exclusive for Higher Education that promotes (among other initiatives), the mobility of students(studies, training or internships). The mobility of students of higher education seeks to improve the quality and development of future professionals, providing a multidisciplinary and multicultural experience. Academic Pharmacy/Pharmacy Technicians. We conducted a descriptive and transversal study on the implementation of the mobility program and analyze the results, which involved applying a survey to students. Since 2009/2010, the Pharmacy Degree at ESTSP has established 7 SMs protocols resulting in an average mobility of 5 students IN and 7 Students OUT. We have also endeavoured in SMp Protocols for extracurricular training with an average of 3 students OUT. The application process is normally open during the year before the mobility period. For most of the students involved, this was a first time opportunity to be in a foreign country and more than 70% choose the mobility program because it is seen as a possibility to improve their curriculum, for personal development or even to pursue employment opportunities abroad. The mobility for teachers is also encouraged. The exchange of experiences and training, acquired during cooperation activities should be an element of continuous dynamics and institutional affirmation. Initiatives such as the ERASMUS Program contribute to the educational and scientific enrichment, and promote international competitiveness among Higher Education Institutions.

Cache memories have a strong impact on the response time of tasks executed on modern computing platforms. For tasks scheduled under fixed-priority preemptive scheduling (FPPS), the worst-case response time (WCRT) analyses that account for cache persistence between jobs along with cache related preemption delays (CRPDs) have been shown to dominate analyses that only consider CRPDs. Yet, the existing approaches that analyze cache persistence in the context of WCRT analysis can only support direct-mapped caches. In this work, we analyze cache persistence in the context of WCRT analysis for set-associative caches. The main contributions of this work are: (i) to propose a solution to find persistent cache blocks (PCBs) of tasks considering set-associative caches, (ii) to present three different approaches to calculate cache persistence reload overheads (CPROs), i.e., the memory overhead due to eviction of PCBs of tasks, under set-associative caches, and (iii) an experimental evaluation showing that our proposed approaches result in up to 22 percentage points higher task set schedulability than the state-of-the-art approaches.

Reactive programming (RP) languages and Synchronous Coordination (SC) languages share the goal of orchestrating the execution of computational tasks, by imposing dependencies on their execution order and controlling how they share data. RP is often implemented as libraries for existing programming languages, lifting operations over values to operations over streams of values, and providing efficient solutions to manage how updates to such streams trigger reactions, i.e., the execution of dependent tasks. SC is often implemented as a standalone formalism to specify existing component-based architectures, used to analyse, verify, transform, or generate code. These two approaches target different audiences, and it is non-trivial to combine the programming style of RP with the expressive power of synchronous languages. This paper proposes a lightweight programming language to describe component-based Architectures for Reactive systems, dubbed ARx, which blends concepts from RP and SC, mainly inspired to the Reo coordination language and its composition operation, and with tailored constructs for reactive programs such as the ones found in ReScala. ARx is enriched with a type system and with algebraic data types, and has a reactive semantics inspired in RP. We provide typical examples from both the RP and SC literature, illustrate how these can be captured by the proposed language, and describe a web-based prototype tool to edit, parse, and type check programs, and to animate their semantics.

Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.

Each fog node interacts with data from multiple end-users in mobile fog computing (MFC) networks. Malicious users can use a variety of programmable wireless devices to launch different modes of smart attacks such as impersonation attack, jamming attack, and eavesdropping attack between fog servers and legitimate users. The existing research in MFC lacks in the contributions of defense of smart attack and also requires in the discussions of subjective decision making by participants. Therefore, we propose a smart attack defense scheme for authorized users in MFC in this paper. First, we construct a static zero-sum game model between smart attackers and legitimate users based on prospect theory. Second, the double Q-learning (DQL) is proposed to restrain the attack motive of smart attackers in the dynamic environment. The proposed DQL method generates the optimum defense choice of legitimate users against smart attacks so that they can efficiently determine whether to use only physical layer security (PLS) to avoid those smart attacks. We use our scheme to contrast with the basic schemes, i.e., Q-learning scheme, the Sarsa scheme, and the greedy strategy. Experiment results prove that the proposed scheme can enhance the utility of legitimate users, restrain the attack motive of smart attackers, and further provide better security protection in the MFC environment.

Digital image recognition has been used in the different aspects of life, mostly in object classification and detections. Monitoring of animal life with image recognition in natural habitats is essential for animal health and production. Currently, Sheep Pain Facial Expression Scale (SPFES) has become the focus of monitoring sheep from facial expression. In contrast, pain level estimation from facial expression is an efficient and reliable mark of animal life. However, the manual assessment is lack of accuracy, time-consuming, and monotonous. Hence, the recent advancement of deep learning in computer vision helps to classify facial expression as fast and accurate. In this paper, we proposed a sheep face dataset and framework that uses transfer learning with fine-tuning for automating the classification of normal (no pain) and abnormal (pain) sheep face images. Current state-of-the-art convolutional neural networks (CNN) based architectures are used to train the sheep face dataset. The data augmentation, L2 regularization, and fine-tuning has been used to prepare the models. The experimental results related to the sheep facial expression dataset achieved 100% training, 99.69% validation, and 100% testing accuracy using the VGG16 model. While employing other pre-trained models, we gained 93.10% to 98.4% accuracy. Thus, it shows that our proposed model is optimal for high-precision classification of normal and abnormal sheep faces and can check on a comprehensive dataset. It can also be used to assist other animal life with high accuracy, save time and expenses.

Cyber-Physical Production Systems (CPPS) are key enablers for industrial and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new revolution towards the Smart Manufacturing oncept and is usually designated as the 4 th Industrial Revolution. Despite the huge interest from the industry to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems. Facing this paradigm and, since CPPS have reached a level of complexity, where the human intervention for operation and control is becoming increasingly difficult, Smart Factories require autonomic methodologies for security management and self-healing. This paper presents an Intrusion Detection System (IDS) approach for CPPS, based on the deterministic Dendritic Cell Algorithm (dDCA). To evaluate the dDCA effectiveness, a testing dataset was generated, by implementing and injecting various attacks on a OPC UA based CPPS testbed. The results show that these attacks can be successfully detected using the dDCA.

Platooning is a promising concept used within the Intelligent Transportation System to increase efficiency and safety of road transportation. It is based on periodically sharing the kinematic status of the platoon members to allow reducing inter-vehicle distances in a safe way. This coordination is automatic and depends heavily on the wireless channel. A common technique to improve the channel properties is to use Time-Division Multiple Access (TDMA) that organizes the access to the wireless medium in slots assigned exclusively to each vehicle. However, while platoons are physical and dynamic, the corresponding dynamic reconfiguration of a logical TDMA frame is non-trivial. In this paper we address this Cyber-Physical problem resorting to the RA-TDMAp protocol to track the dynamics of a platoon, specifically joining, merging and leaving. In our solution, we include an adequate admission control block, to verify whether joining or merging can be accepted, and we present the state-machine that handles the reconfiguration process. We validate our TDMA reconfiguration mechanism with simulations using the Plexe/Vein/OMNeT++ framework. We show the effectiveness of the proposed mechanisms which ensures a synchronized start of the platoon control with the TDMA frame reconfiguration.

Smart Cities will leverage the Internet-of-Things (IoT) paradigm to enable cyber-physical loops over urban processes. Vehicular backhauls contribute to IoT platforms by allowing sensor/actuator nodes near roads to explore opportunistic connections to passing vehicles when other communication backhauls are unavailable. A placement process of nodes that includes vehicular networks as a connectivity backhaul requires estimates of infrastructure-to-vehicle (I2V) wireless service at potential deployment sites. However, carrying out I2V measurement campaigns at all potential locations can be very expensive; so, predictive models are necessary. To this end, qualitative characteristics of a potential site, such as infrastructural points-of-interest (POI) relating to traffic (i.e., traffic lights, crosswalks) and fleet activities (i.e., bus stops, garbage bins) can inform about the vehicles' mobility patterns and quality of the I2V service. In this paper, we show the contribution of POI (and site-specific information) to I2V transfers, leveraging a real-world dataset of geo-referenced I2V WiFi link measurements in urban settings. We present the distributions of throughput with respect to distance per POI class and site, and apply exponential regression to obtain practical throughput/distance models. We then use these models to compare I2V transfer estimation methodologies with different levels of POI-specific data and data resolution. We observe that I2V transfer estimate accuracy can improve from an average over-estimation of 18.3% with respect to measured values, if site or POI-specific information metrics are not used, to 9.3% in case such information is used.

This paper demonstrates that interval type-2 Fuzzy Logic Systems (FLS’s) are suited to Multiprocessor System-on-a-Chip (MPSoC) design modeling because of their ability to handle the uncertainty associated with input parameters. This makes for a scalable modeling process, whereas prior usage of type-1 FLS’s for modeling inhibited scalability. Specifically, the paper proposes a type-2 Takagi Sugeno Kang (TSK) FLS for modeling MPSoCs built around a multicore processor with shared memory. The paper also presents a TSK FLS reconfiguration methodology in order to dynamically reduce the energy consumption of such a multiprocessor. Modeling of MPSoC’s by means of the type-2 TSK FLS was found to be effective in terms of a reduction in the Energy Delay Product when applied to five large-scale numerical processing applications on an MPSoC. In fact, experimental results show a significant reduction (by 87–93% across the applications) in the energy consumption of an MPSoC.

In Unmanned Aerial Vehicle (UAV)-enabled wireless powered sensor networks, a UAV can be employed to charge the ground sensors remotely via Wireless Power Transfer (WPT) and collect the sensory data. This paper focuses on trajectory planning of the UAV for aerial data collection and WPT to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels. Consider network states of battery levels and buffer lengths of the ground sensors, channel conditions, and location of the UAV. A flight trajectory planning optimization is formulated as a Partial Observable Markov Decision Process (POMDP), where the UAV has partial observation of the network states. In practice, the UAV-enabled sensor network contains a large number of network states and actions in POMDP while the up-to-date knowledge of the network states is not available at the UAV. To address these issues, we propose an onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states.

In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.

For assisting data communications in human-unfriendly environments, Unmanned Aerial Vehicles (UAVs) are employed to relay data for ground sensors thanks to UAVs' flexible deployment, high mobility, and line-of-sight communications. In UAV relay networks, energy efficient data relay is critical due to limited battery of the ground sensing devices. In this paper, we propose a butter-aware transmission scheduling optimization to minimize the energy consumption of the ground devices under constraints of butter overflows and energy cost fairness on the ground devices. Moreover, we show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. The performance of the proposed algorithm is evaluated in terms of network sizes, packet arrival rates, and fairness of the energy consumption. Numerical results confirm that the proposed scheduling algorithm reduces the energy consumption of the ground devices in a fair fashion, while the butter overflow constraint holds.

In the design of shore-to-shore and shore-to-vessel wireless links, the impact of the ray reflected on the surface is often neglected. It adds that, in some coastal areas, the geometry of the reflection changes over time due to tides. When choosing an antenna height for an inshore node, often the largest possible height is used, but this approach can lead to signal degradation. The two-ray model is the most fundamental path loss model to account for the contribution of the reflected ray. We carried out experimental measurements at the shores of a freshwater body to verify that the two-ray model can predict the major trends of the path loss experienced by a 2.4 GHz over-water wireless link. We focus on short-to-medium distance links, with antennas installed a few meters above surface. We observed considerable consistency between measurements and model estimates, leading us to conclude that the two-ray model may bring benefits when applied to the network design of over-water links affected by tidal variations, which is our end-goal.

To overcome the limitation of strictly non-preemptive frame transmission in Ethernet networks, the IEEE 802.1Qbu standard was introduced. This standard specifies a one-level frame preemption paradigm wherein, depending on their priority levels, frames are grouped into two categories: namely, the “express frames” and the “preemptable frames”. These two categories are given with the interpretation that (1) only express frames can preempt preemptable frames; and (2) two frames belonging to the same category cannot preempt each other. While this approach partially solves the problem, some preemptable frames can still suffer long blocking periods, irrespective of their individual priority levels. Indeed, there are frames that do not fall into the express frames category, but nevertheless have firm timing requirements that can only be met if they can benefit from preempting lower priority frames. To ameliorate the condition of such frames, we propose a multi-level preemption paradigm. Specifically, we expose the limitations of the one-level preemption approach experimentally; and we present the feasibility and implementation requirements of the multi-level preemption scheme in details.