Instant messaging represents the most popular digital service worldwide, and WhatsApp is the most used app of this kind. By studying WhatsApp traffic, we can gain major insight into both human behavior and network infrastructure needs. This work presents a unique analysis of mobile networks using WhatsApp uplink traffic. We apply information theory metrics such as Shannon Permutation Entropy, Statistical Complexity, and the Causality Complexity-Entropy Plane to understand specific network patterns, usage behaviors, and areas where users are more likely to engage in online conversations. We also demonstrate how these metrics can be used as features for machine learning techniques such as the K-means algorithm, showing they can be used to identify regions with similar patterns in the WhatsApp network traffic.

Smart agriculture increasingly depends on advanced sensing and data analysis to optimize crop yields and support sustainable pest management. This work introduces an innovative approach to characterizing insect chemical communication, focusing on the Spodoptera genus, through the application of Information Theory Quantifiers (ITQs) and causality planes (Complexity–Entropy and Fisher–Shannon). Data were obtained from Electroantennography (GC-EAG) experiments, which record bioelectrical insect responses to volatile compounds via integrated sensors and microelectrodes. The methodology incorporates signal processing techniques such as ordinal patterns, sliding windows, and pre-processing stages to address the complexity and noise of time-series data. Results reveal that ITQs effectively capture distinct transitions from stochastic resting states to more organized active responses, highlighting their ability to detect and characterize complex chemical stimuli. These findings underscore the potential of ITQs as robust tools for identifying bioactive compounds, with direct applications in pest management and intelligent sensing within smart agriculture.

Accurate trajectory prediction is a fundamental component for the development of intelligent transportation systems and autonomous vehicles. In this work, we propose a novel trajectory prediction approach based on the Temporal Fusion Transformer (TFT) architecture, designed initially for multi-horizon time series forecasting. Our model integrates spatio-temporal patterns, vehicle dynamics, and static contextual information to anticipate future positions with high precision. We adapted the TFT to handle multimodal sequential data, leveraging temporal encodings and attention mechanisms to capture complex interactions and nonlinear trends in vehicle movement. From the results obtained on the I-80 dataset, comparing state-of-the-art models like TrajectoFormer, we observed that the proposed model achieved improvements of up to 5.51% in RMSE and up to 8.39% in ADE for medium and long-term prediction horizons. These results highlight the model’s ability to capture more complex motion patterns and provide more accurate predictions over extended time horizons.

The integration of multiple distributed ledgers in Intelligent Transportation Systems (ITS) introduces challenges for scalable and interoperable authentication. Traditional schemes, which rely heavily on Public Key Infrastructure (PKI), face limitations related to certificate management and key escrow. To address these issues, we propose a federated authentication system based on certificateless public key cryptography (CL-PKC) to enable seamless cross-domain and cross-chain authentication without relying on traditional certificates. The proposed approach is designed to operate at the edge, where authentication is performed close to the user to reduce latency and support mobility. Leveraging the CL-PKC scheme, each user independently generates and manages their own cryptographic keys. Simulation results show reduced credential generation time, lower network usage, and improved latency under heavy and cross-domain conditions.

The design of effective mobility-aware systems requires a deep understanding of how different user groups navigate and interact within urban environments, enabling the development of services and infrastructures that adapt to dynamic movement patterns. Tourist mobility patterns, in particular, offer valuable insights into the spatial and social dynamics that influence system design decisions for smart cities, recommendation engines, and urban planning applications. This work presents a comprehensive approach to modeling urban mobility networks by examining tourist behavior through network analysis techniques. Using Rome as a case study, we leverage a large dataset of Foursquare check-ins collected over 18 months to construct a co-visitation graph where nodes represent points of interest and edges reflect shared visits by the same users. Through network analysis of Rome’s tourist mobility, our work identifies clear structural differences between visitor types: long-term visitors form cohesive, highly clustered mobility networks, while short-term visitors create more fragmented interaction patterns. Key points of interest (especially transportation hubs and cultural landmarks) serve as essential connectors shaping network flow, following preferential attachment dynamics. Mobility-aware systems play a crucial role in leveraging such insights to design adaptive, data-driven services that respond to varying mobility behaviors, ultimately enhancing urban efficiency and user experience.

Newborns with health complications frequently need to be treated in specialized units called Neonatal Intensive Care Units (NICUs). These environments require efficient monitoring and analysis. However, many factors can influence treatment phases, including sound sources and noise levels. Inadequate acoustic conditions and infrastructure can damage babies' health. Our work proposes a valuable method to enable proper monitoring and feedback to medical staff through correctly classifying the main hospital sounds. We performed sound classification in NICUs using Convolutional and Long Short-Term Memory (LSTM) Neural Networks. We focus on three audio classes: cry, human talks, and alerts from hospital machines (beep sounds). The results include extracting relevant sound features and comparing classifiers considering the main NICU sound classes. The CNN and LSTM approaches performed cry sound classification with a precision of 83.5 % and 84.0 %, respectively. To the alerts and talks, the LSTM approach increased CNN recall by 5.8% and 5.6%.

This study aims to optimize vehicular user association to base stations in a mobile network. We propose an efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity. We evaluate this solution using real-world base station locations from São Paulo, Brazil, and the SUMO mobility simulator. We compare our approach against a state of the art solution which uses route prediction, maintaining or surpassing the provided quality of service with the same number of handover operations. Additionally, the proposed solution reduces the execution time by more than 80% compared to an exact method, while achieving optimal solutions.

Smart grids present risks when exchanging valuable data between their systems; theft or alteration of this data could violate consumer privacy. Mainly, the non-technical losses (NTL) occur by illegal connections, meter problems (installation delays or wrong readings), dirty, defective, or mismatched meters, very low estimates of adequate consumption, faulty connections, and missing customers. According to a recent study, utilities lose ${\$}$89.3 billion annually through NTL. We present an energy fraud characterization study based on Information Theory Quantifiers (ITQ) to mitigate this challenge. First, we convert the user’s energy consumption time series into a Bandt-Pompe (BP) probability distribution function using a sliding window. The second step is to extract the ITQ used by the technology. We then apply each metric to the Probability Density Function (PDF) and map the layers to characterize their behavior. Our results show that users with normal and abnormal energy consumption can be distinguished using only Information Theory Quantifiers by considering the range of values for each metric.

This work proposes a centrality-based approach to identify data offloading points in a VSN. The solution presents a scheme to select vehicles used as aggregation points to collect and aggregate other vehicles’ data before uploading it to processing stations. We evaluate the proposed solution in a realistic simulation scenario derived from data traffic containing more than 700,000 individual car trips for 24 hours. We compare our approach with both a reservation-based algorithm and the optimal solution. Our results indicate an upload cost reduction of 30.92% using the centrality-based algorithm and improving the aggregation rate by up to 10.45% when considering the centralized scenario.

Energy disaggregation allows identifying individual consumption of different appliances using only the aggregated signal measured from a single point. This work proposes a neural network trained with wavelets reduced data to perform energy disaggregation. Besides the disaggregation, usually a binary answer by identifying the appliance activation moment, we are interested in estimating the appliance’s consumption value. We consider the U.K.-DALE dataset to perform our experiments, containing data from different appliances of five houses from England. Using our strategy, compared with another well-established work, we achieved improvements per appliance of 11.4% (estimated accuracy) in the disaggregation process and 27.8% ($F_1$-score) in the appliance’s consumption value. Our main contribution was to identify satisfactorily that the coefficients of approximation of the wavelet transform are enough to estimate the individual consumption of household appliances.

Network traffic plays a critical role in network planning and control. The researchers assume that traffic from Ethernet and other IP-related networks have a self-similar nature: high-variability and long-term correlations. Many studies try to model these characteristics for simulation and further optimization. One of the most straightforward approaches to model these characteristics is to consider ON/OFF sources (packet-train), where ON- and OFF-periods are i.i.d., generated with random heavy-tailed distributions. Using information theory quantifiers, in particular the Causality Complexity-Entropy Plane, we show that heavy-tailed distributions do not capture most of the network traffic dynamics. They only reproduce the stochastic dynamics of traffic, which accounts for one of the smallest parts of it. We conduct this study by observing the Abilene dataset, fitting the log-normal and log-logistic distributions, and evaluating them onto Causality Complexity-Entropy Plane in comparison with 1/f-noise, which is one of the most observed long-term correlated noises in nature stochastic processes. Also, to enhance our illustrated results, we use the k-nearest-neighbors (kNN) to classify the real and generated traffic according to the results obtained. Finally, our results show that the dynamics of real network traffic does not match the fitting curves generally presented for synthetic traffic generators.

Vehicular sensing network consists of a promising remote sensing paradigm, in which a variety of new applications will be possible through the processing of data periodically collected by vehicles. However, the acquisition of a large amount of sensing data and the increasing demand for traffic cellular requires mechanisms to decongest the cellular network infrastructure. Thus, this work uses a centrality measure to propose an offloading scheme in which certain vehicles will collect data from their neighbors and transmit it in the cellular uplink. The presented article models the problem as a minimum d-hop dominating set and create a greedy algorithm to solve it. The evaluation considers a realistic dataset to evaluate the proposed approach. When compared with the non-offloading scheme, it shows a cost reduction of up to 82.09% in the best-case and 13.45% in the worst-case.

Summary The middleware solutions for General‐Purpose Distributed Computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi‐cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Cá&Lá (JCL), a distributed‐shared‐memory and task‐oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single‐board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.

Summary Even with the considerable advances in the development of middleware solutions, there is still a substantial gap in Internet of Things (IoT) and high‐performance computing (HPC) integration. It is not possible to expose services such as processing, storage, sensing, security, context awareness, and actuating in a unified manner with the existing middleware solutions. The consequence is the utilization of several solutions with their particularities, thus requiring different skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce the gap between IoT and HPC technologies, we present the JavaCá&Lá (JCL), a middleware used to help the implementation of distributed user‐applications classified as IoT‐HPC. This ubiquity is possible because JCL incorporates (1) a single application programming interface to program different device categories; (2) the support for different programming models; (3) the interoperability of sensing, processing, storage, and actuating services; (4) the integration with MQTT technology; and (5) security, context awareness, and actions services introduced through JCL application programming interface. Experimental evaluations demonstrated that JCL scales when doing the IoT‐HPC services. Additionally, we identify that customized JCL deployments become an alternative when Java‐Android and vice‐versa code conversion is necessary. The MQTT brokers usually are faster than JCL HashMap sensing storage, but they do not perform distributed, so they cannot handle a huge amount of sensing data. Finally, a short example for monitoring moving objects exemplifies JCL facilities for IoT‐HPC development.

This paper presents JSensor, a parallel general purpose simulator which enables huge simulations of Wireless Sensor Networks applications. Its main advantages are: i) to have a simple API with few classes to be extended, allowing easy prototyping and validation of WSNs applications and protocols; ii) to enable transparent and reproducible simulations, regardless of the number of threads of the parallel kernel; and iii) to scale over multi-core computer architectures, allowing simulations of more realistic applications. JSensor is a parallel event-driven simulator which executes according to event timers. The simulation elements, nodes, application, and events, can send messages, process task or move around the simulated environment. The mentioned environment follows a grid structure of extensible spatial cells. The results demonstrated that JSensor scales well, precisely it achieved a speedup of 7.45 with 16 threads in a machine with 16 cores (eight physical and eight virtual cores), and comparative evaluations versus OMNeT++ showed that the presented solution could be 43 times faster.

This work presents a sampling solution applied to intermittently connected delay tolerant wireless sensor networks (ICDT-WSNs). In such networks, when the storage capacity of a node is limited compared to the amount of data to collect, it is common to apply a packet drop strategy based on network layer parameters only. However, such strategies are not suitable for monitoring applications of WSNs where data quality is essential. Alternatively, we propose a data-aware drop strategy which applies a sampling algorithm over the collected data in order to reduce the amount of stored data while keeping an overall data quality to represent the monitored area. In order to evaluate our solution, we executed and compared it with drop strategies in literature. We modeled and simulated scenarios where different events occur. The results show that the sampling algorithm is, approximately, twice better than common drop strategies in all evaluated scenarios.

This work presents a probabilistic constructive heuristic to design the roadside infrastructure for information dissemination in vehicular networks. We formulate the problem as a Probabilistic Maximum Coverage Problem (PMCP) and we use them to maximize the number of vehicles in contact with the infrastructure. We compare our approach to a non-probabilistic MCP in simulated urban areas considering Manhattan-style topology with variable traffic conditions. The results reveal that our approach (Probabilistic MCP) increases the number of contacts between vehicles and dissemination points, optimizes the allocation of dissemination points, distributes the dissemination points in a layout that better fits the traffic flow and provides more regularity in the number of contacts experienced by vehicles.

A wireless sensor network can be used to collect and process environmental data, which is often of multivariate nature. This work proposes a multivariate sampling algorithm based on component analysis techniques in wireless sensor networks. To improve the sampling, the algorithm uses component analysis techniques to rank the data. Once ranked, the most representative data is retained. Simulation results show that our technique reduces the data keeping its representativeness. In addition, the energy consumption and delay to deliver the data on the network are reduced.

Broadcast data dissemination is a fundamental building block for many applications in Vehicular Ad hoc Networks. In the literature, there are solutions to deal with data dissemination in urban environments, but they solely focus on either intermittently connected topologies or well-connected topologies. However, depending on the time of day or the geographical location in a city, the network topologies can change dramatically. Hence, protocols proposed to operate under these networks should be able to adapt themselves to the traffic condition at hand. To tackle this problem, we propose U-HyDi, a broadcast data dissemination protocol suited for urban scenarios with zero infrastructure support. By using solely one-hop neighbor information, U-HyDi can seamless operate under intermittently connected networks by applying store-carry-forward techniques to deliver messages even when there is no end-to-end path. Moreover, under well-connected networks, U-HyDi employs a combination of sender-based and receiver-based broadcast suppression techniques to avoid excessive contention at the link layer. Simulation results show that U-HyDi has a low overhead and a low delivery delay. Furthermore, U-HyDi is able to deliver messages to almost all vehicles in a given region of interest.

Envisioned applications for VANETs will rely extensively on the exchange of broadcast messages to deliver data to vehicles located in a region of interest. Many data dissemination protocols have been proposed in the literature to suppress this need. Surprisingly, most of them were designed to operate exclusively under dense or sparse networks. However, it is reasonable to assume that diverse traffic conditions will coexist in realistic scenarios. Therefore, data dissemination protocols for VANETs should be designed to perceive the traffic condition at hand and adapt accordingly. With this in mind, in this paper we propose HyDiAck, a data dissemination protocol for urban VANETs that relies exclusively on local one-hop neighbor information to deliver messages under dense and sparse networks. In dense scenarios, HyDiAck selects vehicles inside a forwarding zone to rebroadcast messages to further vehicles. Moreover, the protocol employs implicit acknowledgements to guarantee robustness in message delivery under sparse scenarios. When compared to two related protocols - UV-CAST and slotted-1-persistence - simulation results for both Manhattan grid and real city street scenarios show that HyDiAck decreases both the latency to disseminate messages and the network overhead, and also guarantees message delivery to all vehicles in the region of interest.

Video dissemination to a group of vehicles is one of the many fundamental services envisioned for Vehicular Ad hoc Networks. For this purpose, in this paper we describe VoV, a video dissemination protocol that operates under extreme traffic conditions. Contrary to most existing approaches that focus exclusively on always-connected networks and tackle the broadcast storm problem inherent to them, VoV is designed to operate under any kind of traffic condition. We propose a new geographic-based broadcast suppression mechanism that gives higher priority to broadcast to vehicles inside especial forwarding zones. Furthermore, vehicles store and carry received messages in a local buffer in order to forward them to vehicles that were not covered by the first dissemination process, probably as a result of collisions or intermittent disconnections. Finally, VoV employs a rate control mechanism that sets the pace at which messages must be transmitted in an attempt to avoid channel overloading and to overcome the synchronization effects introduced by the channel hopping mechanism employed by IEEE 802.11p. When compared to two well-known solutions -- UV-CAST and AID -- we show that our proposal is more efficient in terms of message delivery, delay and overhead.

In wireless sensor networks, energy consumption is generally associated with the amount of sent data once communication is the activity of the network that consumes more energy. This work proposes an algorithm based on “Principal Component Analysis” to perform multivariate data reduction. It is considered air quality monitoring scenario as case study. The results show that, using the proposed technique, we can reduce the data sent preserving its representativeness. Moreover, we show that the energy consumption and delay are reduced proportionally to the amount of reduced data.

Reprogramming is an important and challenging problem in wireless sensor networks because it is often necessary to in-network sensor processing. Thus, over-the-air programming is a fundamental service that relies upon reliable broadcast for efficient distribution. In this work we use small world features to improve the over-the-air programming. The small world based protocol takes into account the communication workflow of sensor networks to create shortcuts toward the sink, thus improving the reprogramming process. The endpoints of these shortcuts are nodes with more powerful hardware, resulting in a heterogeneous wireless sensor network. We then evaluate the behavior of the small world based protocol regarding the number of transmitted messages, energy consumption and time to reconfigure the network.

A wireless sensor network (WSN) is energy constrained, and the extension of its lifetime is one of the most important issues in its design. Usually, a WSN collects a large amount of data from the environment. In contrast to the conventional remote sensing - based on satellites that collect large images, sound files, or specific scientific data - sensor networks tend to generate a large amount of sequential small and tuple- oriented data from several nodes, which constitutes data streams. In this work, we propose and evaluate two algorithms based on data stream, which use sampling and sketch techniques, to reduce data traffic in a WSN and, consequently, decrease the delay and energy consumption. Specifically, the sampling solution, provides a sample of only log n items to represent the original data of n elements. Despite of the reduction, the sampling solution keeps a good data quality. Simulation results reveal the efficiency of the proposed methods by extending the network lifetime and reducing the delay without loosing data representativeness. Such a technique can be very useful to design energy-efficient and time-constrained sensor networks if the application is not so dependent on the data precision or the network operates in an exception situation (e.g., there are few resources remaining or there is an urgent situation).

This work presents a sampling data stream algorithm for wireless sensor networks (WSNs). The proposed algorithm is based on sampling techniques applied to data histograms created from original data streams acquired by sensor nodes. As a result, the algorithm provides a sample of only log n items to represent the original data of n elements. We show that by using our algorithm, we can save energy and reduce delay in WSN applications in different scenarios while keeping a good data quality.

This work presents the design of real-time applications for wireless sensor networks (WSNs) by using an algorithm based on data stream to process the sensor data. The proposed algorithm is based on sampling techniques applied to data histograms created from original data streams acquired by sensor nodes. As a result, the algorithm provides a sample of log n items to represent the original data stream of n elements. In this work, we show how to use the data reduction algorithm in real-time WSN design.