With the rapid development of Industry 5.0, the Industrial Internet of Things (IIoT) plays an increasingly important role as a key information infrastructure supporting data collection, transmission, and decision-making. Federated Learning-enabled IIoT (FL-enabled IIoT) systems deploy artificial intelligence (AI) models at the network edge and utilize model aggregation mechanisms to facilitate efficient data processing and intelligent decision-making. However, anomalies occurring in local nodes can propagate through the aggregation process, leading to model contamination and performance degradation, thereby compromising overall system reliability. To address this issue, this paper proposes a reliability model for FL-enabled IIoT systems. In this model, we systematically describe the entire process of model performance degradation caused by node failures and its impact on system task reliability. This includes the effects of multiple functional failures induced by node faults (i.e., data loss, communication interruption, and computational resource degradation) on model performance, as well as the failure propagation process caused by model contamination and data quality deterioration. Additionally, to evaluate the impact of model performance variations on practical production tasks, a task-oriented reliability metric is proposed. Simulation and experimental results demonstrate that the proposed modeling approach effectively characterizes the model performance degradation process and task reliability under node failure conditions.

Task failures in decentralized Internet of Things (IoT)-edge computing environments not only lead to inefficiencies, increased latency, and resource wastage but can also introduce system instability and cause application malfunctions. These failures may arise due to network disruptions, resource constraints, or inefficient task scheduling, ultimately affecting the overall reliability and performance of IoT-edge systems. This study presents a novel Long Short-Term Memory (LSTM)-based Federated Learning (FL) framework for proactive task failure prediction, ensuring adaptive scheduling and efficient resource utilization. Unlike existing conventional methods, our approach personalizes failure prediction per device, addressing heterogeneous execution characteristics while preserving data privacy. By integrating LSTM with FL, we improve the failure detection accuracy and reduce unnecessary task executions. We first trained all models using Federated Learning (FL) and then conducted a comparative analysis of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and LSTM. Our findings show that LSTM achieves the highest accuracy and F1 score, while CNN excels in recall and energy efficiency. These insights validate the effectiveness of our FL-based failure prediction framework and highlight the advantages of model personalization for dynamic decentralized IoT-edge environments.

Industrial Wireless Sensor Networks (IWSNs) play a critical role in real-time monitoring and data collection in smart factories. However, energy constraints in sensor nodes significantly limit the network lifespan. In addition, traditional simulation methods overlook the impact of industrial noise, reducing the truthfulness of experimental results. To address these challenges, we propose an Automated Guided Vehicle-Integrated Noise-Aware Adaptive Clustering (A-INAC) algorithm. The algorithm incorporates an Industrial Wireless Noise Model (IWNM) to reflect noise characteristics in the factory environment and optimizes the selection of cluster directors to achieve more balanced energy consumption. In addition, a hierarchical transmission strategy leveraging the mobility of AGVs is designed to meet large-scale network transmission needs. Simulation results demonstrate that the A-INAC algorithm can effectively reduce network energy consumption and extend network lifetime by 39% and 118% compared to LEACH and LEACH-C, respectively.

Vehicular Edge Computing (VEC) is a key enabler of real-time intelligence in next-generation transportation systems. However, conventional Federated Learning (FL) in VEC typically depends on static edge-server aggregation, resulting in high communication overhead, increased latency, and poor responsiveness under dynamic mobility. To overcome these challenges, we propose Proximity-Aware Federated Learning (PA-FL), a decentralized framework that integrates vehicle-to-vehicle (V2V) collaboration and edge-assisted synchronization to enhance learning efficiency, scalability, and robustness. PA-FL introduces three core innovations: (i) Collaborative Local Aggregation, where vehicles perform proximity-based model fusion before forwarding updates to the edge, reducing uplink traffic and accelerating convergence; (ii) Adaptive Neighbor Selection, which dynamically filters peers based on spatiotemporal proximity and link stability to ensure context-relevant learning; and (iii) Context-Aware Synchronization, which adjusts aggregation frequency based on vehicular density and mobility to improve energy efficiency and learning consistency. Extensive experiments demonstrate that PA-FL achieves an average accuracy of 87.08% ± 0.49, surpassing state-of-the-art FL baselines by over 13% in accuracy and 11% in F1 score. It reduces task failure rates across all proximity ranges and lowers per-round energy consumption to 0.038 J, achieving a 6× improvement in communication efficiency. Delay per communication round is also reduced to 0.85 seconds, supporting real-time responsiveness. These results validate PA-FL as a resilient and scalable framework for symbiotic FL where vehicles collaboratively learn from local context while contributing to global intelligence in AI-integrated, 6G-enabled vehicular edge environments.

After geohazards occur, conducting rapid and sustainable secondary geohazard monitoring plays a crucial role in reducing secondary geohazard risks. However, geohazard situations vary across different areas and dynamically change with the development of geohazards. Therefore, ensuring timely data collection and the ability to dynamically adjust to changes in geohazards poses significant challenges in geohazard monitoring scenarios. This article proposes a low-latency data collection scheme considering data importance levels (LLDCL), which prioritizes data collection from high-importance sensor nodes (SNs) while still collecting data from lower importance SNs. Given the potential for sudden events in geohazard monitoring scenarios that may require adjustments to the emergency levels of monitoring points, this article introduces a deep reinforcement learning (DRL) algorithm for unmanned aerial vehicles (UAVs) path planning based on weighted age of information (DRL-WAoI). This algorithm enables UAVs to respond quickly to dynamic environments by adjusting their flight paths in real time. Furthermore, considering the limited battery capacity of UAVs, this article establishes a token-based energy trading model between UAVs and the base station (BS) to facilitate UAV recharging. Simulation experiments show that the LLDCL scheme can effectively adapt to the dynamically changing conditions of geohazard monitoring scenarios, providing a viable solution for UAV data collection and transmission.

An important challenge in edge computing service management is maintaining good quality of service and low latency for end-users. Edge services must be hosted near the end user, necessitating sophisticated management of virtualized resources in the edge infrastructure. In the context of edge computing, users' location and mobility patterns are essential components of resource allocation and service transfer. As a result, it is of the utmost importance to assess the effectiveness of the proposed management solutions in a city environment with realistic mobility. The purpose of this study is to investigate the simulation of edge computing and to present an integrated solution environment that makes use of two validated simulators for the modeling of urban mobility and edge computing services.

The emergence of the Internet of Things (IoT) has revolutionized various domains by enabling seamless connectivity and real-time data exchange between connected IoT devices. However, in sparse deployment scenarios where sensor nodes are sparsely distributed, ensuring low data delivery latency becomes a significant challenge. Our research aims to address this issue by utilizing unmanned aerial vehicles (UAVs) to support IoT networks. In the existing UAV-aided IoT systems, all UAVs are required to return to the base station to deliver data, which results in significant data delivery latency. To overcome this limitation, we propose a collaborative data acquisition model that uses air-to-air data relay between UAVs. By leveraging the mobility and agility of UAVs, the proposed system facilitates efficient data relay between sensor nodes and the base station. To further optimize the performance of the system, we present a time-balancing scheduling data acquisition (TSDA) scheme. This scheme combines a centripetal-based relay pairing method for UAVs to achieve seamless data relay and a joint scheduling scheme to minimize the hovering time during data delivery. Through extensive simulations, we demonstrate that the proposed TSDA scheme can achieve lower data delivery latency in sparse deployment scenarios compared to existing data acquisition schemes. In addition, the joint scheduling scheme can significantly reduce the hovering time of UAVs so that the collaborative relaying advantage can be better exploited.

In practical cyber-manufacturing systems (CMS), the node component is the forwarder of information and the provider of services. This dual role makes the whole system have the typical physical-services interaction characteristic, making CMS more vulnerable to cascading failures than general manufacturing systems. In this work, in order to reasonably characterize the cascading process of CMS, we first develop an interdependent network model for CMS from a physical-service networking perspective. On this basis, a realistic cascading failure model for CMS is designed with full consideration of the routing-oriented load distribution characteristics of the physical network and selective load distribution characteristics of the service network. Through extensive experiments, the soundness of the proposed model has been verified and some meaningful findings have been obtained: (1) attacks on the physical network are more likely to trigger cascading failures and may cause more damage; (2) interdependency failures are the main cause of performance degradation in the service network during cascading failures; and (3) isolation failures are the main cause of performance degradation in the physical network during cascading failures. The obtained results can certainly help users to design a more reliable CMS against cascading failures.

The ever-growing aging of the population has emphasized the importance of in-home AAL (Ambient Assisted Living) services for monitoring and improving its well-being and health, especially in the context of care facilities (retirement villages, clinics, senior neighborhood, etc). The paper proposes a novel simulation-driven platform named E-ALPHA (Edge-based Assisted Living Platform for Home cAre) which supports both Edge and Cloud Computing paradigm to develop innovative AAL services in scenarios of different scales. E-ALPHA flexibly combines Edge, Cloud or Edge/Cloud deployments, supports different communication protocols, and fosters the interoperability with other IoT platforms. Moreover, the simulation-based design helps in preliminary assessing (i) the expected performance of the service to be deployed according to the infrastructural characteristics of each specific small, medium and large scenario; and (ii) the most appropriate applications/platform configuration for a real deployment (kind and number of involved devices, Edge- or Cloud-based deployment, required connectivity type, etc). In this direction, two different use cases modeled according to realistic input (coming from past experience involving real testbed) are shown in order to demonstrate the potentials of the proposed simulation-driven AAL platform.

Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge, a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing edge gateway supporting multiradio and multitechnology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness, and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.

Social Internet of Things (SIoT) is a new paradigm where Internet of Things (IoT) merges with social networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem.” In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: 1) perception; 2) transportation; and 3) application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions.

The paper proposes AirGround, a flexible and expandable framework to support the collaboration and the coordination between aerial and terrestrial drones in order to accomplish a common mission in a more effective and fast way. In particular the designed application-level framework allows the dynamic tasks assignment to the different devices involved into the communication process and the distributed leader election according to specific executive parameters and system conditions (i.e., residual energy, computational power, abilities offered by specific on board sensors). The AirGround effectiveness has been evaluated throughout a real testbed to measure the overall system performance in terms of both neighbour discovery and leader election speed by increasing the number of drones in different channel and environmental conditions.

We are currently witnessing the increasing diffusion of new location-aware smartphones applications that make intensive use of positioning information, however, the widespread and pervasive GPS technology is gradually giving way to new or revised less power-hungry communication technologies able to guarantee almost the same accuracy. Within this general context, mainly motivated by the current green communication paradigm, this paper proposes an energy-efficient and cost-effective localization architecture based on the improvement of classical cell-tower schemes coupled with a dynamic fingerprinting update phase in order to face the natural changes in the radio environments. The proposed system architecture has been implemented and tested in a real scenario to measure the performances in terms of accuracy and energy saving that will make it preferable to the traditional GPS-based systems in the next future. The obtained results show the effectiveness of the considered approach that makes possible to estimate the current position of a mobile user with a very small error (≈ 20m).

Nowadays, new communication paradigms such as those related to wireless cognitive networks, can take great advantages from advanced inventory management policies by considering radio resources as an extremely perishable commodity with a short-term life time. Starting from this challenging and multidisciplinary research field, the paper proposes to adapt the NewsVendor model, coming from Logistics, to guarantee an effective bandwidth provisioning for cognitive networks also drawing a comparison with a classical adaptive period inventory management policy. Numerical results, validated throughout simulation campaigns, confirm that the NewsVendor model always outperforms the adaptive period inventory management policy by improving both the total profit and the user satisfaction levels.

In this paper we present the successful implementation of WEVCast (Wireless Eavesdropping Video Casting) [2] a new mechanism to improve the performance of multicast streaming video over wireless networks. It is well known that the standard IEEE 802.11 protocol has no specific mechanism for multicast transmission and it always uses the base transmission rate (i.e., 1/6 Mbps for 802.11 b/g) because it simply implements multicasting using broadcasting. On the contrary, WEVCast system allows network devices to switch in overhearing mode in order to capture video packets; moreover, a simply feedback mechanism, based on RSSI messages periodically sent by the client nodes, has been added and managed by the sender node to dynamically adjust the transmission data rate. A detailed testbed has been implemented to validate our study and the quality of the received video content has been analyzed throughout the BVQM software. Obtained results show that WEVCast is a valid solution to realize a simple and cost-effective wireless hot-spot for multimedia contents delivery.

Nowadays Internet has become a critical part of our daily lives and an affordable Internet access, is a leading engine of economic, cultural, social and political growth. In our opinion, customer cooperation could give a boost to Internet availability and affordability, therefore, we propose an attractive and efficient pricing mechanism to give incentives to wireless users which decide to share their connection resources with others. Thanks to the proposed strategy, a natural expansion of the wireless coverage area can be realized increasing accessibility and reducing connection fees. To take into account the customers' satisfaction, a users' utility function is defined and, to preserve the network efficiency, the network congestion is controlled and prevented. Obtained results, in terms of profits obtained by wireless nodes, overall network utility and customers' satisfaction, show the goodness of the proposed strategy and encourage further studies.

This paper explains the potential role of an integrated satellite-high altitude platform (HAP)-terrestrial system proposing a new multilayered inter HAPs satellite routing algorithm (IHSR) integrated with an efficient admission control scheme in order to guarantee an adequate quality of service to multimedia traffic connections. The performance of the IHSR algorithm is evaluated through intensive simulations comparing it with a previous classical routing scheme under several traffic load scenarios. The obtained results demonstrate that the proposed strategy achieves an improvement in the inter-HAP links utilization managing a greater number of active connections without decrease the offered QoS degree.

3G telecommunications systems use microcellular radio coverage, where a high capacity is requested. The usage of these, so called, hotspot has its advantages, because the smaller the cell the higher the bit rate, but it also carries some disadvantages like the higher interference level and the larger average number of handover. The latter is particularly perceived by vehicular users, because it can cause serious degradations in the provided quality of service. To exploit microcellular coverage advantages and, at the same time, limiting its disadvantages, this paper presents two algorithms which work on users' mobility behaviour profile, in order to estimate their recurrent movements and allocate on the future traversed microcells the requested resources in advance. These algorithms introduce remarkable improvements in the grade of service (C.F. Yu et al., 2002) of vehicular users

The main objective of this work is the proposal of a new connection admission control (CAC) algorithm guaranteeing both a high connection multiplexing level and, in particular, a good quality of service (QoS) for real-time multimedia traffic sources mapped over the service classes of the DVB-RCS standard. In this paper, we suppose a DVB-RCS system architecture using a multi-spot beam geostationary satellite with regenerative payload. We also suppose IP traffic to be carried, in return and forward directions, via DVB/MPEG-2 traffic streams instead of to the obsolete ATM encapsulation. Simulation results show that, even if the real time multimedia connections are more sensitive to the delay jitter, the agreed QoS is always guaranteed, even when the system load increases; moreover, using the proposed algorithm, a high system throughput is obtainable.

This paper focuses its attention on the rate adaptation problem in wireless network with mobile hosts. We define two new quality of service (QoS) indexes. They express the degradation degree of bandwidth and the frequency of bandwidth degradation suffered by data packet flows. These indexes are used to define and propose a new rate adaptation algorithm named degradation degree based fair algorithm (DDFA). The aim of the proposed algorithm is to fairly distribute the effects of QoS degradation among the cell visited by the mobile hosts. The DDFA algorithm has been compared with a fair adaptation algorithm proposed in the literature. The performance evaluation shows that proposed algorithm reduces the QoS degradation of flows without adding control overhead compared to the reference fair algorithm.

The proposal of a centralized connection admission control (CAC) procedure in the network control center of a satellite DVB-RCS system, harmonized with techniques of request scheduling, is the main objective of this work. The behavior of such DVB-RCS system architecture, that uses a multispot beam geostationary satellite with regenerative payload, is deeply analyzed and tested. Simulation results show that the proposed techniques guarantees a good QoS to all supported service classes. Particular attention is offered to RBDC traffic sources because they are more sensitive to the delay jitter and consequently they are harder to manage.

This paper analyses the performances of two types of CAC algorithms proposed for deciding the admission of new connections in a geostationary satellite system. Both algorithms are specifically designed for high latency networks as they are characterized by low processing time on board, simple parameters, and real-time decisions. Tests are performed on the multimedia satellite platform of EuroSkyWay and aim at assessing the robustness of the CAC algorithms in guaranteeing the target quality to heterogeneous applications and in exploiting effectively the satellite bandwidth.

This paper describes a multiple access protocol, which answers the need for an efficient handling of the traffic coming from a wide range of broadband applications over geo-synchronous satellite air-interfaces. We propose a hybrid medium access control scheme, which combines both random access and demand assignment multiple access policies. It is coupled with a connection admission control algorithm, exploiting the advantages of statistical multiplexing. Dynamic management of the frame header allows minimizing the signaling overhead and maximizing the effectiveness of the reservation procedure.

In this paper we propose the use of an in-band signalling technique coupled with a distributed connection admission control and traffic resource management scheme, to combat the effects of high-latency in geostationary satellite systems. In-band request signalling is introduced to manage burst-based bandwidth demand from satellite terminals transmitting real-time variable bit rate traffic. Performance evaluation has shown the effectiveness of this technique in guaranteeing the service quality negotiated at connection set-up between the user and the network.