Unmanned Aerial Vehicles (UAVs) are improving considerably search and rescue (SAR) operations by providing unprecedented capabilities in dynamic and hazardous environments. This study presents an innovative, collaborative multi-UAV data fusion approach that addresses the critical challenge of locating multiple moving targets within strict time constraints. This approach improves traditional search techniques by incorporating intelligent information sharing, fusion, and coordinated path planning. The core innovation of the algorithm lies in its ability to dynamically and collaboratively predict the geographical zones with the highest probability of needed rescue operations. This enables the group of UAVs to coordinate and optimize their search strategies in real-time. This research offers valuable insights into multi-UAV collaboration through high-fidelity simulations involving more than 600 different scenarios with UAV swarms and moving ground targets. The experimental results indicate that their effectiveness significantly improves as the number of UAVs increases, following a quadratic trend until it reaches a plateau. In particular, the accuracy rate remains above 90%, regardless of the number of UAVs after reaching the plateau. This suggests that while a higher density of UAVs enhances search efficiency, larger UAV swarms yield diminishing returns. Notably, the approach shows superior efficiency in environments with clustered targets, which makes it particularly suitable for disaster response scenarios that involve more concentrated target locations.

The exponential growth of the Internet of Things (IoT) has introduced unprecedented challenges in data processing, privacy preservation, and energy efficiency. Traditional centralized approaches are often unsuitable for IoT environments due to bandwidth limitations, data heterogeneity, and privacy concerns. This study proposes a novel framework combining federated learning (FL) and autoencoders to address these issues in IoT-based image classification tasks. By lever-aging Unmanned Aerial Vehicles (UAVs) as intermediaries for model aggregation and distribution, the framework minimizes communication overhead while maintaining data privacy. Autoencoders are employed for unsupervised feature extraction, enabling effective data representation even in the absence of labeled data. Results demonstrate that, while autoencoders achieve lower classification accuracy compared to supervised approaches, they provide significant advantages in bandwidth efficiency, scalability, and privacy preservation. The integration of UAVs further enhances the system by optimizing communication and enabling model improvement in real-time. This framework offers a flexible and resource-efficient solution for IoT applications, particularly in scenarios where data labeling is impractical or privacy is paramount.

When dealing with large-scale natural disasters such as floods, landslides, hurricanes, or heavy snowfalls, there are often many victims who are trapped in hard-to-reach places, making the time to locate and rescue them critical. In this context, deploying unmanned aerial vehicles (UAVs) alongside a swarm of unmanned ground vehicles (UGVs) has the potential to speed up the Search and Rescue (SAR) missions, as the collaboration between these agents can combine advantages from both of them. The idea is that any simple UAV equipped GPS and communication capabilities can locate besieged and isolated individuals, referred to as Points of interest (POIs), and when it comes across (flies over) a GPS-limited UGV, it guides the UGVs toward the POIs. In this paper, we explore different approaches to Air-to-Ground (A2G) coordination in a number of distinct scenarios among unmanned vehicles, and through simulation compare their efficiency based of parameters and metrics.

Smart wearable devices for patient monitoring rely on batteries as energy-source for capturing vital signs, processing information locally, and transmitting data. The advantages of such solutions are providing mobility to users, connectivity to send data constantly, and low cost. These devices are wireless and must be tiny to be carried comfortably by the users. This fact restricts energy autonomy and requires frequent replacement or recharge of batteries. The highest energy cost is commonly attributed to transmissions in wireless devices, and several studies focused on communication and routing protocols to enhance energy efficiency in such solutions. However, researchers should give more attention to data acquisition of physiological sensors regarding energy efficiency in such solutions. In this preliminary study, we present the effects of a self-adaptive algorithm on the energy consumption of popular wearable physiological sensors. Our prototype is composed of an oximeter and a temperature sensor. Our experiments demonstrate that the self-adaptive procedure can save up to 80% energy consumption regarding the oximeter when monitoring stable patients at low risk and 51% in unstable patients. In addition, the temperature sensor can reach 97% of energy savings in the self-adaptive mode. The sensors’ data acquisition can present a superior energy cost than radio transmissions on such devices. In future work, we will explore the potential benefits of the algorithm in all main activities of our monitoring device.

Wearable sensors utilize small, low-cost, noninvasive, and wireless components. These sensors capture vital signs, allowing the monitoring of patients remotely. In this manner, they are efficient tools to enhance patient care and can be used to monitor vulnerable populations, and keep track of the development of chronic diseases, and the transmission of infectious illnesses – such as during pandemics. However, there are many challenges to monitoring patients using wearables, with massive data generation and battery power consumption being significant constraints. Strategies to reduce data generation should be applied taking into account the patient’s clinical status and health risks. Previous studies took advantage of single early-warning scores (EWS) utilized in infirmaries to detect emergencies, reduce transmissions, and be a reference for self-adaptive features embedded in the devices. Our work proposes the use of composite EWS to infer health deterioration risk, minimize data transmissions and power consumption, and reduce excessive alarms through self-adaptive features based on these scores. We also compare our method with previous studies using real patient data. Further, we propose applying self-adaptive features to sampling, processing, and transmission rates. Our method demonstrated enhanced data reduction, 81% fewer readings than the baseline, significant pruning of the number of alarms, and dynamic and automatic inference of patient risk.

The Internet of Things (IoT) has enabled novel solutions for monitoring patients’ health through wearable sensors in conditions of both non-communicable and infectious diseases. In this paper, we report work in progress involving the development of an IoT-based COVID-19 health monitoring system that can effectively monitor the essential physiological functions of a patient through wireless sensors, thus supporting the early detection of severe cases and the continuous assessment of the patient status. The work provides several main contributions, as it includes: (i) a brief description of the current IoT-based system for remote monitoring of COVID-19 patients; (ii) a description of embedded characteristics of our device, including its contextual functions, early warning score mechanisms and self-adaptive features; and (iii) a description of our preliminary experiment results. Our proposed solution reduced drastically the amount of redundancy in data and still maintain monitoring accuracy. Given the COVID-19 scenarios, in which human resources are extended to the limit and the number of patients in severe conditions is often high, a system that can support IoT-based continuous monitoring are essential to identify changes in clinical status promptly and accurately and can potentially transform the way patients are monitored.

Many applications require the processing of event streams from different sources in combination with large amounts of background knowledge. Semantic CEP is a paradigm designed specifically for that. It extends complex event processing (CEP) with RDF support and uses a network of operators to process RDF streams in combination with RDF knowledge bases. Another popular class of systems designed for a similar purpose are the RDF stream processors (RSPs). These are systems that extend SPARQL (the RDF query language) with stream processing capabilities. Semantic CEP and RSPs have similar purposes but focus on different things. The former focuses on scalability and distributed processing while the latter tend to focus on the intricacies of RDF stream processing per se. In this paper we propose the use of RSP engines as building blocks for Semantic CEP. We present an infrastructure, called DSCEP, that allows the encapsulation of existing RSP engines into CEP-like operators so that these can be seamlessly interconnected in a distributed, decentralized operator network. DSCEP handles the hurdles of such interconnection, such as reliable communication, stream aggregation and slicing, event identification and time-stamping, etc., allowing users to concentrate on the queries. We also discuss in the paper how DSCEP can be used to speedup monolithic SPARQL queries by splitting them into parallel subqueries operating over restricted parts of the knowledge base.

We propose two extensions for a state-of-the-art method of rollback-recovery in distributed CEP (complex event processing). In CEP, an operator network is used to search for patterns in events streams. Sometimes these operators fail and lose their state. Rollback-recovery is a method for dealing with such state losses. The type of rollback-recovery we consider is upstream backup, where the state of a failed operator is recovered by replaying to it the input events that led it to that state. These events are kept in upstream operators' memory buffers, which are trimmed continuously as the downstream operator progresses. The first extension we propose saves memory and speeds up recovery by avoiding to store and retransmit unnecessary events. The second extension makes the base method of upstream backup compatible with data-parallel CEP, allowing that the windows into which operators partition their input be processed in parallel. We evaluated the proposed extensions through experiments that showed a significant reduction in memory usage and recovery time at the expense of a negligible processing overhead during normal operation.

As pervasive computing meets social networks, there is a fast growing research field called pervasive social computing. Applications in this area exploit the richness of information arising out of people using sensor-equipped pervasive devices in their everyday life combined with intense use of different social networking services. We call this set of information pervasive social context. We provide a taxonomy to classify pervasive social context along the dimensions space, time, people, and information source (STiPI) as well as commenting on the type and reason for creating such context. A survey of recent research shows the applicability and usefulness of the taxonomy in classifying and assessing applications and systems in the area of pervasive social computing. Finally, we present some research challenges in this area and illustrate how they affect the systems being surveyed.