In Wireless Sensor Networks (WSNs), relay sensor nodes can aggregate data from edge sensor node into a summary information before sending to the sink. Due to the vast number of sensor nodes in a distributed edge computing (DEC) network, these relay sensor nodes may receive a high number of aggregation requests. This increases the chance of conflicting transmissions, which further leads to unwanted latency. Designing a conflict-free and minimal latency data aggregation schedule remains an open question. Moreover, existing related works have been conducted in traditional WSNs. By leveraging multiple antennas, the Multiple Input Multiple Output (MIMO) and cooperative MIMO called virtual MIMO (V-MIMO) enable broadband wireless communication, thereby improving the performance of WSNs. However, compared with traditional WSNs, MIMO and V-MIMO introduce distinct interference models requiring careful consideration. The work proposes a solution to an NP-hard problem, addressing three challenges: (i) interference; (ii) latency; and (iii) dynamic changes in network topology. Firstly, to counter interference, we propose a model where multiple nodes can simultaneously send data to the same parent by connecting different antennas. Secondly, to minimize latency, we propose a novel distributed heuristic data aggregation scheduling method, which intertwines the construction of an optimal data aggregation tree and conflict-free scheduling. Finally, to handle dynamic network topology changes, we propose lightweight adaptive strategies that do not increase data aggregation latency. Simulation results and theoretical analysis demonstrate superior performance in reducing data aggregation latency. When compared with state-of-the-art solutions, our proposed method decreases data aggregation latency by at least 2.6× on average.

Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. STM-Graph integrates diverse spatial mapping methods, urban features from OpenStreetMap, multiple GNN models, comprehensive visualization tools, and a graphical user interface (GUI) suitable for professional and non-professional users. This modular and extensible framework facilitates rapid experimentation and benchmarking. It allows integration of new mapping methods and custom models, making it a valuable resource for researchers and practitioners in urban computing. The source code of the framework and GUI are available at: https://github.com/Ahghaffari/stm_graph and https://github.com/tuminguyen/stm_graph_gui.

Federated learning has emerged as a privacy-preserving technique for collaborative model training across heterogeneously distributed silos. Yet, its reliance on a single central server introduces potential bottlenecks and risks of single-point failure. Decentralizing the server, often referred to as decentralized learning, addresses this problem by distributing the server’s role across nodes within the network. One drawback regarding this pure decentralization is it introduces communication inefficiencies, which arise from increased message exchanges in large-scale setups. However, existing proposed solutions often fail to simulate the real-world distributed and decentralized environment in their experiments, leading to unreliable performance evaluations and limited applicability in practice. Recognizing the lack from prior works, this work investigates the correlation between model size and network latency, a critical factor in optimizing decentralized learning communication. We propose a graph-based gossiping mechanism, where specifically, minimum spanning tree and graph coloring are used to optimize network structure and scheduling for efficient communication across various network topologies and message capacities. Our approach configures and manages subnetworks on real physical routers and devices and closely models real-world distributed setups. Experimental results demonstrate that our method significantly improves communication, compatible with different topologies and data sizes, reducing bandwidth and transfer time by up to circa 8 and 4.4 times, respectively, compared to naive flooding broadcasting methods.

Artificial intelligence (AI) is increasingly transforming the healthcare sector by enabling advanced automated decision-making. However, implementing Machine Learning (ML) systems in medical imaging applications presents significant challenges, such as ensuring consistent results across different environments, maintaining comprehensive records of experimental processes, and establishing reliable methods for model comparison and evaluation. These issues become more evident as medical AI shifts towards a hybrid Cloud-Edge Continuum (CEC), adding complexity to workload orchestration and performance monitoring across diverse infrastructures. To address these challenges, this work introduces a modular Edge Micro Data Centre (EMDC) architecture that supports a scalable experiment tracking framework designed for AI model deployments in healthcare. The approach extends MLflow with a domain-specific module for medical image segmentation, incorporating several clinically relevant performance metrics. The EMDC architecture enables distributed processing and performance monitoring within a hybrid CEC environment. By aligning MLOps principles with CEC infrastructure, the proposed system facilitates reliable and scalable AI integration into the healthcare sector.

Object detection models often struggle with class imbalance, where rare categories appear significantly less frequently than common ones. Existing sampling-based rebalancing strategies, such as Repeat Factor Sampling (RFS) and Instance-Aware Repeat Factor Sampling (IRFS), mitigate this issue by adjusting sample frequencies based on image and instance counts. However, these methods are based on linear adjustments, which limit their effectiveness in long-tailed distributions. This work introduces Exponentially Weighted Instance-Aware Repeat Factor Sampling (E-IRFS), an extension of IRFS that applies exponential scaling to better differentiate between rare and frequent classes. E-IRFS adjusts sampling probabilities using an exponential function applied to the geometric mean of image and instance frequencies, ensuring a more adaptive rebalancing strategy. We evaluate E-IRFS on a dataset derived from the Fireman-UAV-RGBT Dataset and four additional public datasets, using YOLOv11 object detection models to identify fire, smoke, people and lakes in emergency scenarios. The results show that E-IRFS improves detection performance by 22% over the baseline and outperforms RFS and IRFS, particularly for rare categories. The analysis also highlights that E-IRFS has a stronger effect on lightweight models with limited capacity, as these models rely more on data sampling strategies to address class imbalance. The findings demonstrate that E-IRFS improves rare object detection in resource-constrained environments, making it a suitable solution for real-time applications such as UAV-based emergency monitoring. The code is available at: https://github.com/futurians/E-IRFS.

The distributed architecture and massive connectivity of 5G networks create significant security vulnerabilities that are challenging to address with centralized monitoring due to data privacy restrictions. Furthermore, the traffic data in such environments is characterized by extreme class imbalance, where critical but rare attacks are vastly outnumbered by benign traffic (with observed ratios exceeding 1:200). This paper introduces FedDTKG, a novel federated learning framework designed to provide robust, privacy-preserving intrusion detection under these challenging conditions. The framework features two key innovations: (1) a Temporal-Aware Self-Adaptive Graph Attention Network (TASA-GAT) that explicitly models the temporal dynamics and relational structure of network flows and (2) an Adaptive Synthetic Focal Loss (ASFL) that counters class imbalance by dynamically tuning its focus and incorporating a feature-level variance regularization term to improve minority class representation. We conduct a comprehensive evaluation on a non-IID distribution of 5G traffic data. In a centralized setting, FedDTKG achieves a state-of-the-art F1-macro score of 0.8756, significantly outperforming traditional ML and standard GNN baselines that fail to detect minority classes. In the federated setting, FedDTKG maintains a high F1-macro of 0.7546, whereas conventional GNNs fail completely, demonstrating our model’s resilience to statistical heterogeneity. The findings validate FedDTKG as an effective and practical solution for building collaborative, privacy-first security systems in real-world 5G edge networks.

Federated learning has become increasingly popular due to its ability to bring together multiple learners, enhance model generalizability, and promote knowledge exchange. Such systems inherently rely on the bedrock of security, trust, and fairness among training workers to ensure a conducive learning environment. However, this collaborative landscape has encoun-tered the challenge of free riders, individuals who join the systems to gain benefits without making any substantial contributions. This can negatively impact learning outcomes, fairness, sustain-ability, and trust in a collaborative system. In this paper, we first present a novel stake-based incentive mechanism aimed at promoting active participation among contributors, and con-currently maximizing the reward for clients with consideration of free rider presence in the system. Second, we propose an efficient method for identifying free riders in federated learning based on log analysis. Our method delegates the detection of free riders to training workers and the identification to the aggregator, rather than relying solely on the aggregator. We simulate potential deceptive strategies employed by free riders and assess the extent of our method's coverage across these scenarios. The experimental results conducted on different free rider ratios demonstrate the versatility and applicability of our approach in detecting these clients within the federated learning paradigm.

Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary. In this article, we propose a number of novel extensions to PACK, a recent capacitated partitional spatial clustering method which uses an optimization algorithm that is based on linear programming tasks. These extensions relate to the relocation and location preference of cluster centers, outliers, and non-spatial attributes, and they can be considered jointly. In the context of edge server placement, these improve the spatial location of servers while considering, for example, application placement on the servers in response to spatial application usage patterns. We demonstrate the usefulness of an extended version of PACK with an example with simulated data, as well as a real world example in edge server placement for a city region with various different setups. These setups are evaluated with summary statistics about spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved by 53% at best while simultaneously the proximity degraded only by 18%. The extensions provide valuable means for including non-spatial information in the cluster analysis, and to attain better overall proximity and similarity.

Smart spaces, physical spaces that are integrated with sensor-enabled IoT devices, are a powerful paradigm for optimizing the operations of the space and improving its quality for the occupants. Managing the applications and services running in the space is a complex task as the operations of the devices and services are dependent on the physical characteristics of the space, the occupants of the space, and the technologies that are being integrated. Digital twinning, the combination of physical representations with a virtual counterpart, is a potential technology for facilitating the management of smart space devices and services. While digital twins are increasingly adopted in industry, their use in everyday environments remains low due to difficulties in creating and linking the virtual representation with the physical environment. In this paper, we propose our vision for the adoption of digital twinning as a pathway to improve the functions of smart spaces. We derive a generic reference architecture that comprises four layers, covering the physical space, the sensing infrastructure, the network interfaces, and the underlying computational infrastructure. Next, we identify and address key requirements for the uptake of digital twins in smart space and assess their benefits using the ascendancy model of business analytics. Finally, to demonstrate the practicality of digital twinning, we present a proof-of-concept digital twin for the TellUs smart space at the University of Oulu in Finland and use it to highlight the potential benefits of different ascendancy levels.

In the age of the Internet of Things (IoT) and the expanding computing continuum, it’s crucial to manage and share resources at the edges of networks. This position paper presents a new concept known as ’semantic slicing’. This approach harnesses the power of artificial intelligence (AI), wireless networks, edge computing, and sensing technologies to enable novel applications, optimize resource allocation, and streamline data processing and decision-making across complex systems spanning the computing continuum. Semantic slicing applies a deep understanding of the data and specific application requirements to intelligently allocate resources and distribute processing tasks in the computing continuum. This strategy allows for the creation of systems that are not only more efficient and responsive, but also better equipped to adapt to a variety of applications and services.

Unmanned Aerial Vehicles (UAVs) equipped with air quality sensors offer a powerful solution for increasing the spatial and temporal resolution of air quality data, searching and detecting emission sources, and monitoring emissions from fixed and mobile sources. Despite the numerous advantages of using UAVs, their use, however, presents several challenges that limit their broader adoption. For example, UAVs require efficient algorithms and components to minimize power consumption, the overall payload used on UAVs needs to be small to ensure optimal portability which poses limitations on the sensors that can be integrated with UAVs, and there is a need for specialized algorithms, e.g., for identifying and locating air pollution sources. Currently, most solutions for UAV-based air quality monitoring focus on specific challenges or demonstrating the potential of using UAVs, and there is a lack of comprehensive overview of the research field and its open challenges. In this paper, we contribute a systematic review of UAV-based air quality monitoring, highlighting and analyzing technical solutions and challenges, and identifying open challenges with the aim of providing a research roadmap for the path forward.

The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location–allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.

In this paper, we describe how the microservices paradigm can be used to design and implement distributed edge services for Internet of Things applications. As a case study, traditionally monolithic user mobility analysis service is developed, with distributed and extendable microservices, for the standardized ETSI MEC system reference architecture. In each of the edge system three tiers, microservices implement the service logic with components for movement trace analysis, movement prediction and visualization of the results. The distributed service is implemented with Docker containers and evaluated on real-world settings with low capacity edge servers and real user mobility data. The results show that the edge promise of low latency can be met in such as implementation. The integration of a software development technology with a standardized edge system provides solid background for further development.

Prevalent weather prediction methods are based on sensor data, collected by satellites and a sparse grid of stationary weather stations. Various initiatives improve the prediction models by including additional data sources such as mobile weather sensors, mobile phones, and wireless sensor networks (WSN) of, for example, smart homes. The underlying computing paradigm is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable. When the spatial and temporal density of weather sensor data grows, the required data transmission capacities and computational resources become unfeasible. We identify the challenges posed by spatial distribution of a weather prediction model, and suggest solutions for those challenges. We propose EDISON: an edge-native interpolation approach based on AI methods, distributed horizontally on edge servers. Finally, we demonstrate EDISON with a simple, simulated setup.