CCS Concepts

Indoor positioning systems (IPSs) are vital for location-based services in complex spaces such as retail, healthcare, and logistics, but traditional IPSs require intensive, time-consuming offline fingerprinting that limits scalability. This paper proposes OPTIMAPS (Optimized Positioning Technique Integrating Model-based and Pairwise Selection), a novel solution utilizing Bluetooth Low Energy (BLE) technology without the need for pre-deployment RSSI collection or site surveys. OPTIMAPS relies on a log-distance path loss model, with model parameters automatically determined from environmental geometry and signal diversity analysis. The system employs a nearest-neighbor (NN) estimator for fast, robust initialization, followed by Sequential Least Squares Programming (SLSQP) for constrained nonlinear optimization within a restriction circle based on testbed granularity. A key innovation is the use of the Chebyshev metric and mean pairwise distance for RSSI vector discrimination, enhancing accuracy. OPTIMAPS was validated in a 720 m² real-world testbed, using 15 BLE access points and 148 distinct testing locations. The system achieved an average positioning error (APE) of 2.65 meters, competitive with state-of-the-art techniques and superior to other survey-free methods in anchor density-normalized error. Computational complexity scales linearly with environmental size, significantly outperforming metaheuristic optimizers like genetic algorithms, which scale quadratically or worse. Spatial analysis showed SLSQP reduced errors by over 30% in challenging, multipath-rich rooms, with negligible overhead in open spaces. Statistical tests demonstrated OPTIMAPS’ robustness and practical deployability, confirming it as a scalable, accurate, and energy-efficient IPS for diverse, dynamic indoor environments.

This study conducts a comparative analysis of the performance of different programming languages on Android embedded systems, focusing on execution time. Through controlled experiments, we compared traditional and emerging programming languages (Java, Kotlin, C, C++, Python, Golang, and Rust) using a variety of representative algorithms. The results revealed significant disparities among the programming languages, with C and C++ performing significantly better than the others, although in some algorithms Golang approached close in terms of execution time. The native Android application languages, Java and Kotlin, showed intermediate results compared to the others, with very close outcomes between them. Python and Rust had the worst results, with Python's performance being justified by the interpreted nature of the language and the lack of multiprocessing features on Android. Rust was possibly hindered by its inability to efficiently access low-level resources when applied to the Android application layer.

The analysis of memory consumption is important for the stability and performance of Android applications. This work presents a method to extract and visualize memory-related data from Android bug report files, focusing on improving the debugging process for potential memory issues. The proposed method utilizes regular expressions for extracting key memory metrics and presents this data through interactive visualizations, aiding developers in identifying memory performance issues. We demonstrate the use of this visualization approach through an experimental analysis involving a high memory pressure scenario. This scenario allowed us to monitor memory consumption over time and analyze the causes of process terminations under this stress condition. Future work will involve evaluating the method's effectiveness under varying memory pressure scenarios, using the same or similar applications, and integrating advanced learning techniques for log analysis to capture complex patterns related to memory issues, ultimately helping to optimize system performance.

Indoor positioning systems (IPSs) allow the location and tracking of mobile devices in indoor environments where the global positioning system (GPS) does not provide satisfactory results. In model-based IPSs, it is common to use signal propagation models to estimate distances between anchor nodes and mobile devices using the received signal strength indicator (RSSI). However, using fixed parameters in the path loss model to characterize the signal in large-scale scenarios results in the degradation of the positioning accuracy. In this article, we propose the adaptive model (ADAM) positioning system, a model-based IPS that chooses the best anchor nodes to benefit the positioning computation and uses different parameters for the log-distance model to represent the signal in different regions and conditions of the scenario. Then, we estimate a single, more precise position using a data fusion technique. Our proposal does not require training nor prior knowledge of the best parameters for each region. We evaluated the performance of our proposed system in a real-world, large-scale environment using Bluetooth-based mobile devices. Our results clearly show that ADAM can locate mobile devices with an average error of 2.93 m in relation to the real position, which is 23% better than literature-based models using fixed parameters for the entire environment.

Abstract Map2Check is a software verification tool that combines fuzzing, symbolic execution, and inductive invariants. It automatically checks safety properties in C programs by adopting source code instrumentation to monitor data (e.g., memory pointers) from the program’s executions using LLVM compiler infrastructure. For SV-COMP 2020, we extended Map2Check to exploit an iterative deepening approach using LibFuzzer and Klee to check for safety properties. We also use Crab-LLVM to infer program invariants based on reachability analysis. Experimental results show that Map2Check can handle a wide variety of safety properties in several intricate verification tasks from SV-COMP 2020.

Taking into account that education supported by technology is a basic need for the citizen of a world in which the demand for computation and the access to information grow exponentially, this paper highlights the creation of cognitive computing systems that help people to acquire knowledge and to learn effectively within digital education environments. With this goal in mind, this paper summarizes the outcomes of a Systematic Mapping of Literature where the main aim was to identify what pedagogical approaches, methods, techniques, tools, and education activities have been used to recommend learning objects in the context of cognitive computing systems. We analyzed 348 papers from Scopus and Engineering Village digital libraries from which we selected 19 papers for data extraction. In order to increase the confidence of the proposed systematic review we calculated the Kappa Coefficient obtaining that the agreement was substantial (79.47%). From the data extracted we did several analysis. Concerning to pedagogical theories, 47.37% of papers presents a humanist approach and 26.32% a cognitivist approach, which shows one coherence with proposal of cognitive computing based on emotional and language processing. Most papers use natural-language, image or audio processing to detect emotion, attention and interactions to generate a user profile and, thus, to perform educational resource recommendation. Despite almost half papers do not indicate exactly the educational resources recommended, around 36% are textual materials and 45% are related to personalized exercises or interactive/games activities. Furthermore, we identified the spread use of analytical learning and cognitive computing for pedagogical activities recommendation. This paper ends up proposing new approaches and methods to educational recommendation for improving the students performance from interaction data in educational environments.

The automation of computational systems is currently presenting itself as a facilitator in several segments of society, among them is the agricultural sector. As far as irrigation methods are concerned, information technology, especially in embedded systems, can be applied to a large extent on crops and rural properties, offering alternatives to meet the needs of the farmer, such as adequate water supply for the plantations and the dosage of nutrients. The objective of this work is to automate the greenhouse irrigation through the Arduino platform in order to control the flow of water to reduce financial costs. As a result, we obtained a prototype of an automated greenhouse irrigation system that was tested for efficiency and performance. From the results of the tests, it was verified that the developed system met the operating needs for irrigation of the greenhouse and was characterized as a good alternative for the cultivation of the plantations.

This paper depicts a machine learning method for fainting and epileptic seizures automatic recognition. We evaluated five machine learning techniques in order to find out which classification method maximizes the accuracy level and, at the same time, minimizes the computational complexity since the experimental environment has very limited computational resources (processing power). We prototype such method in a wearable device, taking into account F-Score and Accuracy metrics. The experimental evaluation shows that there are no significant difference between KNN, PART, and C4.5. However, KNN has high computational cost when compared to PART and C4.5. PART has low computational cost when compared to C4.5 since it identified less rules.

One of the biggest problems with chronical patients in treatment is medication adherence. Studies indicate that taking drugs out of time influence the patient's treatment decreasing the drugs effect. To minimize this problem, we developed a ubiquitous and intelligent system able to monitor the taking of medicines and to identify whether the patient is meeting the requirements prescribed by the doctor. An architecture provided with a decision system based on rules and trees to evaluate data collected from an intelligent medicine cabinet, sensors and electronic devices available in the patient's home was designed. The system classified the drugs taken pattern and released messages on social networks, SMS, and consumer electronic devices such as TV, smartphone and tablets, without human interference. Its goal is to help keeping the medication on time and helping to decide what to do in case of missing the right time. The algorithms J48, Rep and Random tree, were tested to classify the taking medicine patterns and to chose the right services available. The obtained results are very promising and reached an acceptable accuracy rate.

In this work, we study the impact of data aggregation in WSNs with mobile sink nodes that can move at higher speeds. In these cases, propagated queries cannot be answered by using the sink's position when the query was sent, since the sink node will probably be elsewhere. Therefore, conventional data aggregation schemes may not be appropriate due to this high speed. Thus, we propose and evaluate the performance of three new algorithms for data routing and aggregation in WSNs when the sink is moving at a high speed. These algorithms explore the sink movement to create a routing graph composed of the union paths that intersect the sink's trajectory. At each hop, the sink speed and current network delays are used as metric to guide in-network data aggregation. Our results clearly show significant energy savings and efficient data delivery achieved by the proposed algorithms in different scenarios.

Manaus, the capital city of the state of Amazonas and the sixth Brazilian largest gross domestic product, demands highly qualified hardware and software developers for consumer electronics and telecommunication sectors of its free trading zone. However, what can be viewed as an opportunity for the youth alumni of local institutions of higher education contrasts with the low level of qualification provided by such institutions. As a consequence, most positions either remain opened or are occupied by professionals from other Brazilian regions. Moreover, the issue relies more on soft skills than on hard skills, as the local institutions are aligned with International standard academic curriculum and cover modern technologies. Soft skill is the enter barrier to the job market. English proficiency, communication skill, writing (in both Portuguese and English) skill, among others, are top listed failure reasons in the hiring processes. As one project manager highlighted: “it is much cheaper and quicker for the company to fill in some specific technological gaps, than to leverage a poor English proficiency”. This paper presents a proposal of an involving activity aiming to address several soft skills at the same time, with minimal disturbance in the student daily routine. The concept is based on the impact of immersing the student in a systematic project development experience. Clear communicated project goal, where the students have specific roles, with assigned accountable peers, everything under the umbrella of an Agile-like software development process (derived from SCRUM) are in the core of the activity. This paper details the incremental implementation methodology of the program among four research areas, and 23 projects or project activities, involving 34 students. The results of the two years running program will also be described, including the increase of progress visibility by advisors, the improvement of students communication and writing skills and the qualification on SCRUM software development process tools and documentation. The authors mapped important success factors in achieving relevant improvement on students' profile, and presented a list of recommendations for the next phase of the program, including some obvious one, as the importance of the active participation of advisors, and some less obvious one, like the resistance to the Internet online tool due to daily meeting coinciding with the peak of Internet usage in the University.

Some Inherent Vehicular Ad Hoc Networks (VANETs) characteristics, such as intermittent connectivity, highly dynamic topology, and hard delay constraints, make data communication a challenging task in these networks. Due to such peculiarities, in this work we study the impact of using vehicles predicted locations as a metric for data communication in VANETs. In our proposal, called LPRV (Localization Prediction-based Routing for VANETs), packet forwarding is performed by nodes with predicted future localization closer to the delivery destination, without the need for exchanging additional control message. The proposed algorithm also explores the knowledge of a digital map to limit the scope of message exchanges in the shortest path for vehicles between source and destination. We compared our proposal to both classic Flooding and SIFT (Simple Forwarding over Trajectory) algorithms. Our results clearly demonstrate the efficiency of the proposed solution in different scenarios, especially in terms of delivery rate, number of hops and delay, with a reduced number of message transmissions.

Sink nodes are responsible for aggregating data gathered by a Wireless Sensor Network (WSN) and transmitting them to a monitoring facility. Usually, these are static nodes that serve as gateways to infrastructured networks. In some applications, these sink nodes move around the monitoring area, usually in lower speeds (e.g., robots, dirigibles). In this work, we go further and study WSN scenarios in which sink nodes can move at higher speeds, such as an Unmanned Aerial Vehicle (UAV) or even an airplane. In these cases, propagated queries cannot be answered by using the sink's position when the query was sent, since the sink will be elsewhere due to its high speed. Thus, we propose and evaluate the performance of three algorithms for data query in WSNs when the sink is moving at a high speed. Our results show clearly the need for new algorithms for these scenarios as well as the good performance of our proposed algorithms.

With the intense use of applicative in mobile device, the question "usability" begins to invigorate strongly as a study object, it is being considered a determinant factor of the success of this segment of the mobile computation. This article proposes an improvement way in the usability of the embedded applicative in mobile devices, considering a new method of user interaction. The Mandos interaction method is based on task idea, which consists in a possible operation that could be executed in an applicative by the user and probabilities between task changes. The cited probabilities will be used for construction of the user interface to interact dynamically with the user. To validate the considered method was developed a framework, called Mandos, which propitiates functionalities to the applicative developer that uses Java technology. A case study will be presented where some prototypes of embedded applicative mobile was developed, using Mandos framework for the interaction method evaluation. The evaluation was established in order to identify any relation of usability gain or loss of applicative, having like approvers the own users of the embedded applicative in mobile telephones.

In this paper, we introduce the ezRealtime project, which relies on the Time Petri Net (TPN) formalism and defines a Domain-Specific Modeling (DSM) tool to provide an easy- to-use environment for specifying Embedded Hard Real-Time (EHRT) systems and for synthesizing timely and predictable scheduled C code. Therefore, this paper presents a generative programming method in order to boost code quality and improve substantially developer productivity by making use of automated software synthesis. The ezRealtime tool reads and automatically translates the system's specification to a time Petri net model through composition of building blocks with the purpose of providing a complete model of all tasks in the system. Hence, this model is used to find a feasible schedule by applying a depth-first search algorithm. Finally, the scheduled code is generated by traversing the feasible schedule, and replacing transition's instances by the respective code segments. We also present the application of the proposed method in an expressive case study.

The context of this work is related to embedded hard real-time systems development, more specifically, in the software generation phase. Embedded software has become much harder to design caused by the diversity of requirements and high complexity. Correctness and timeliness verification is an issue to be concerned. Usually, complex embedded real-time systems rely on specialized operating system kernels. However, operating systems may introduce significant overheads in execution time as well as in memory requirement. Software synthesis might be an alternative approach to operating systems usage, since it can generate tailored code for satisfying functional, performance, and resource constraints, and automatically generate runtime support (scheduling, resource management, communication, etc.) customized for each particular specification. However, the dispatcher and timer interrupt handler overheads are often neglect in software synthesis research. This paper provides a formal approach for system's modeling, and such model is adopted for synthesizing a timely and predictable scheduled code taking into account dispatcher and interrupt handler overheads.

This paper presents a methodology for mapping the live sequence chart (LSC) language to an equivalent coloured Petri net (CPN) model LSC is a language for system specification, allowing one to specify what should happen for all execution of the system as well as the modeling of anti-scenarios. However, analysis and verification of system properties are not possible. In order to allow diagnosis of inconsistent specifications, besides simulation, verification and analysis should be considered. Therefore, the proposition of a CPN model for LSC allows verification and analysis of system described in LSC, hence, contributing for increasing designers' confidence on the system development process and reducing risk that may lead to project failure.

Embedded hard real-time systems have stringent timing constraints that must be satisfied for the correct functioning of the system. Hence all tasks must be finished before their deadlines. In addition, there are systems where energy is another constraint that must also be satisfied. In this paper, a pre-runtime scheduling algorithm is presented in order to find schedules satisfying both timing and energy constraints. The proposed approach uses state space exploration for finding pre-runtime schedules. However, the main problem with such methods is the space size, which can exponentially grow. This paper tackles this problem through a depth-first search method for generating a partial timed labeled transition system derived from the time Petri net model.

One of the most intricate problems in the synthesis of real-time systems is the scheduling. We present a formal modeling methodology based on time Petri nets (TPN) and a framework for application-specific scheduler synthesis. Finding a feasible scheduling is not an easy task because this problem, in its general form, is NP-hard. The method proposed finds a scheduling, whether one exists, using state space exploration. The problem with this approach is the space size, which can be very large for medium to large systems. We show how to minimize this problem using behavior restrictions at system modeling, and a partial-order reduction method. Additionally, the algorithm proposed for finding a feasible schedule uses a depth-first search based method. Therefore, states are only generated if strictly necessary. It is verified through real-world experimental results that a schedule is found examining a reduced number of states.