RESUMO

Simultaneous localization and mapping (SLAM) refers to techniques for autonomously constructing a map of an unknown environment while, at the same time, locating the robot in this map. RatSLAM, a prevalent method, is based on the navigation system found in rodent brains. It has served as a base algorithm for other bioinspired approaches, and its implementation has been extended to incorporate new features. This work proposes xRatSLAM: an extensible, parallel, open-source framework applicable for developing and testing new RatSLAM variations. Tests were carried out to evaluate and validate the proposed framework, allowing the comparison of xRatSLAM with OpenRatSLAM and assessing the impact of replacing framework components. The results provide evidence that the maps produced by xRatSLAM are similar to those produced by OpenRatSLAM when they are fed with the same input parameters, which is a positive result, and that implemented modules can be easily changed without impacting other parts of the framework.

Assuntos

RESUMO

Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system's overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room's entrances and exits, which are critical for the security monitoring of patients in healthcare facilities.

Assuntos

RESUMO

In recent years, advanced threats against Cyber-Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To face these challenges, this paper proposes a novel error-robust multidimensional technique for DDoS attack detection. By applying the well-known Higher Order Singular Value Decomposition (HOSVD), initially, the average value of the common features among instances is filtered out from the dataset. Next, the filtered data are forwarded to machine learning classification algorithms in which traffic information is classified as a legitimate or a DDoS attack. In terms of results, the proposed scheme outperforms traditional low-rank approximation techniques, presenting an accuracy of 98.94%, detection rate of 97.70% and false alarm rate of 4.35% for a dataset corruption level of 30% with a random forest algorithm applied for classification. In addition, for error-free conditions, it is found that the proposed approach outperforms other related works, showing accuracy, detection rate and false alarm rate of 99.87%, 99.86% and 0.16%, respectively, for the gradient boosting classifier.

RESUMO

An important area in precision agriculture is related to the efficient use of chemicals applied onto fields. Efforts have been made to diminish their use, aiming at cost reduction and fewer chemical residues in the final agricultural products. The use of unmanned aerial vehicles (UAVs) presents itself as an attractive and cheap alternative for spraying pesticides and fertilizers compared to conventional mass spraying performed by ordinary manned aircraft. Besides being cheaper than manned aircraft, small UAVs are capable of performing fine-grained instead of the mass spraying. Observing this improved method, this paper reports the design of an embedded real-time UAV spraying control system supported by onboard image processing. The proposal uses a normalized difference vegetation index (NDVI) algorithm to detect the exact locations in which the chemicals are needed. Using this information, the automated spraying control system performs punctual applications while the UAV navigates over the crops. The system architecture is designed to run on low-cost hardware, which demands an efficient NDVI algorithm. The experiments were conducted using Raspberry Pi 3 as the embedded hardware. First, experiments in a laboratory were conducted in which the algorithm was proved to be correct and efficient. Then, field tests in real conditions were conducted for validation purposes. These validation tests were performed in an agronomic research station with the Raspberry hardware integrated into a UAV flying over a field of crops. The average CPU usage was about 20% while memory consumption was about 70 MB for high definition images, with 4% CPU usage and 20.3 MB RAM being observed for low-resolution images. The average current measured to execute the proposed algorithm was 0.11 A. The obtained results prove that the proposed solution is efficient in terms of processing and energy consumption when used in embedded hardware and provides measurements which are coherent with the commercial GreenSeeker equipment.

RESUMO

With the emerging Internet of Things (IoT) technology becoming reality, a number of applications are being proposed. Several of these applications are highly dependent on wireless sensor networks (WSN) to acquire data from the surrounding environment. In order to be really useful for most of applications, the acquired data must be coherent in terms of the time in which they are acquired, which implies that the entire sensor network presents a certain level of time synchronization. Moreover, to efficiently exchange and forward data, many communication protocols used in WSN rely also on time synchronization among the sensor nodes. Observing the importance in complying with this need for time synchronization, this work focuses on the second synchronization problem, proposing, implementing and testing a time synchronization service for low-power WSN using low frequency real-time clocks in each node. To implement this service, three algorithms based on different strategies are proposed: one based on an auto-correction approach, the second based on a prediction mechanism, while the third uses an analytical correction mechanism. Their goal is the same, i.e., to make the clocks of the sensor nodes converge as quickly as possible and then to keep them most similar as possible. This goal comes along with the requirement to keep low energy consumption. Differently from other works in the literature, the proposal here is independent of any specific protocol, i.e., it may be adapted to be used in different protocols. Moreover, it explores the minimum number of synchronization messages by means of a smart clock update strategy, allowing the trade-off between the desired level of synchronization and the associated energy consumption. Experimental results, which includes data acquired from simulations and testbed deployments, provide evidence of the success in meeting this goal, as well as providing means to compare these three approaches considering the best synchronization results and their costs in terms of energy consumption.