Digital Twins (DT) have evolved from static digital mirrors into executable cyber-physical counterparts that predict, optimize, and control complex systems. However, the practical deployment of DT in Internet of Things (IoT) environments suffers from limited data fidelity, model brittleness, and resource constraints across the edge–cloud continuum. Generative DT (GDT) is DT augmented with Generative AI (GenAI). They enable the synthesis of high-fidelity data, bridge model-driven and data-driven paradigms, and provide adaptive decision support under uncertainty. This paper systematically reviews the research progress on GDT in the Manufacturing Internet of Things (MIoT), covering system architectures, key enabling technologies, and representative application scenarios. It also summarizes the main limitations of existing studies and outlines future research directions.

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.

In the Internet of Things (IoT) domain, resource-constrained devices are highly susceptible to malicious interference due to limited duty cycles, finite energy reserves, and an inability to perform clear channel assessment. These vulnerabilities are further exacerbated in low-power wide-area networks (LPWANs), where the absence of centralized coordination renders the network an attractive target for jamming attacks. In this work, we introduce a novel split and classification mechanism designed to enhance LoRa packet delivery under both constant and reactive jamming conditions. Our approach partitions each LoRa frame into sub-packets and applies a realtime classification algorithm to identify and mitigate corrupted segments, thereby improving the effective data extraction ratio (DER). We validate the mechanism through extensive simulated scenarios across diverse network densities. Comparative analysis against the standard LoRa protocol demonstrates a significant increase in packet success rate and commensurate gain in DER, without incurring additional energy overhead.

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.

Many-objective computation offloading (MOCO) has emerged as a critical research issue in vehicular edge computing. A key challenge in the MOCO problem is how to optimize task offloading under limited edge computing resources to effectively balance multiple objectives, such as latency, energy consumption, and load balancing. To address this challenge, we formulate the MOCO problem by modeling the task computation and offloading procedure of vehicle terminals based on queuing theory, which aims to minimize the average delay time, average energy consumption, and average offloading cost for each vehicle terminal task, as well as the average load variance of edge resources. To tackle the MOCO problem, we propose a novel evolutionary algorithm based on Bayesian Maximum Entropy and incremental learning (BMEILEA) for efficient optimization of all objectives. A novel many-objective fitness evaluation mechanism based on Bayesian maximum entropy is proposed to evaluate and select solutions in the evolving population. An adaptive dynamic reference point strategy based on incremental learning is developed to effectively guide the evolutionary process. Extensive experimental results show that BMEILEA outperforms other well-known many-objective algorithms in solving the MOCO problem and achieves better convergence and diversity in the obtained nondominated solutions.

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 emerging modality, increasingly used by edge devices, to train machine learning models in a distributed and cooperative way is Federated Learning (FL). It combines an increase in the quality of the learning process with data privacy needs. Alongside the advantages of this emerging paradigm, however, there is a critical factor that risks seriously affecting its effectiveness in future 5G and 6G application scenarios: the possible delays deriving from the scarcity of communication resources to connect the clients to the server, which risks slowing down the process excessively and making it less effective in the presence of new types of real-time applications typical of 5G/6G scenarios. To face this issue, the paper proposes a new approach to client selection that, unlike the various approaches to streamlining FL communications proposed so far, starts from a typically networking research point of view and makes use of the potential of the Software-Defined Networking (SDN) paradigm for the choice and continuous dynamic update of the clients participating in the FL process. This allows to keep the distributed learning process at high levels of effectiveness and efficiency, i.e., guaranteeing an overall time reduction of the FL process under different network traffic load conditions, as demonstrated by the performance evaluation campaign conducted through the implementation of a testbed platform.

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.

Localization services play an important role in Internet of Connected Vehicles (IoCV) and vehicle predictive localization information can greatly improve traffic efficiency and reduce accidents. However, a huge amount of computing and communication overhead is required to obtain such information by traditional methods. In this work, we propose a Behavior-based Clustering Method (BCM) to analyze the behavioral correlation between vehicles and classify them into different clusters. Based on BCM results coupled with a deep learning model, we further propose a Clustering-learning-based Long-term Predictive Localization (CLPL) algorithm to predict vehicles' future location distribution. In the proposed CLPL algorithm, all the traffic roads are divided into consecutive small segments in order to pinpoint vehicles' precise current locations and to obtain long-term predictions. Extensive simulations, notably involving real dataset, have been carried out to evaluate BCM and CLPL in terms of several performance criteria including matching rates. The analysis of the results validated how the designed methods can predict vehicle location much more accurately than existing algorithms.

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.

Wireless sensor networks (WSNs) have well known limitations such as battery energy, computing power and bandwidth resources that sometimes limit their widespread use. Current researches are mainly concentrated to propose solutions for nodes energy optimization, network load balancing and the improvement of WSN robustness; however, the software defined network (SDN) paradigm uses the theory of forwarding phase separating from control, simplifying management and configuration of the network to improve network extension and flexibility. It could further optimize WSNs deployment and improve their transmission performance. In this paper, we firstly describe the general architecture and the main features of software defined networks; then, we analyze the current integrated SD-WSN scheme and summarize these results in detail.

Achieving global interoperability among IoT systems has become a very real possibility due to the heterogeneity at all levels of IoT. Besides achieving interoperability, it will become very important to establish social relationships and communications among IoT devices (or things), humans and applications. Social relationships in IoT have been realized through the Social IoT (SIoT) paradigm which is one of the trending feature in the IoT. The SIoT is currently consisted of two types of communications: things-to-things and things-to-human communications; in addition, we propose social cross-domain IoT (SCDIoT), a third type of SIoT communication at a global level which enables application-to-application communication in the IoT. Although interoperability allows the exchange and reuse of data among various applications, it does not focus on the social relationships among IoT applications through which those applications can closely collaborate with each other. SCDIoT fills this gap by operating one level above interoperability. It allows collaboration among IoT applications by enabling them to talk to each other, building social relations and benefitting from each other via various useful services, truly exploiting the advantages of interoperability. We present the concept of SCDIoT, its logical framework and some potential use case scenarios, together with the challenges and possible future research directions.

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.

In a smart factory environment, a much larger amount of data are transmitted in the workshop networks bringing big challenges to data transfer capability and energy usage efficiency. In the workshop, two main networks, i.e., wired/wireless fieldbus networks and wireless sensor networks, are usually used to collect and transmit data separately; thus, this paper proposes a mobile and hierarchical data transmission architecture to integrate these two networks also taking advantages from the existing mobile intelligence in smart factories, such as automatic guided vehicles (AGVs), to implement a novel data and materials delivery scheme well suited for modern industrial wireless sensor networks (IWSNs). Simulation experiments demonstrated how the proposed approach, running within the IWSN, significantly increases data delivery efficiency along with achieving better energy usage, by 4 times, with respect to the separated networks without any mobile intelligence support.

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.

This paper aims to expand the concept of resource sharing among members of a wireless community network also considering the participation of mobile nodes presenting high intermittent behaviours and low reliability. The conducted simulative analysis demonstrates how to minimize the issues due to the presence of mobile users by designing a novel scheduling mechanism able to take into account the reliability of such nodes throughout specific utility functions and satisfaction indexes. The reliability-based resource sharing scheduler outperforms classical approaches by allowing the participation of mobile users to the community network cloud services.

In this paper, we address the problem of re-establishing the network connectivity in post-disaster scenarios, where the original wireless infrastructure has been partitioned into multiple network fragments (called islands), operating on different frequencies. To this purpose, we propose the utilization of swarms of dedicated repairing units, called Stem-Nodes (SNs). SNs are provided with Cognitive Radio (CR) and self-positioning capabilities, in order to offer maximum reconfigurability in terms of mobility and wireless technologies supported. Moreover, swarms of SNs can self-organize into STEM-Mesh structure, that works as a dynamic backbone to connect heterogeneous islands using different technologies (e.g. Wi-Fi, Wi-MAX, etc). In this paper, we present three contributions pertaining to STEM-Mesh: (i) we describe a distributed motion control scheme (based on virtual springs approach) that enables SNs to self-organize into dynamic STEM-Mesh structures, (ii) we introduce a discovery scheme, through which SNs can explore the scenario in both spatial and frequency domains, and possibly connect the islands to the STEM-Mesh backbone and (iii) we validate the correctness of the proposed scheme, by verifying the optimal placements of the SNs composing the STEM-Mesh on a simplified scenario (e.g. chain topology). Finally, we evaluate through Omnet++ simulations the ability of STEM-Mesh to maximally re-establish connectivity on partitioned network scenarios.

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).

This paper proposes a distributed Neural/Genetic algorithm able to compute both the more suitable positioning and transmission modulation schemes for fixed/mobile wireless nodes equipped with software defined radio abilities. Devices considered in this work are able to move towards new positions by applying the concept of controlled mobility. The selection of the more suitable modulation scheme is realized through the SDR (Software Defined Radio) paradigm. The synergistic combination of controlled mobility and SDR in a totally distributed way, allows to obtain a high degree of self-configurability; moreover, the extreme adaptability to the network conditions and application level constraints in terms of coverage and guaranteed connectivity, make the proposed approach well suited for quite different communication scenarios such as classical monitoring or disaster recovery. The obtained results, validated throughout an intensive simulation campaign, show how the controlled mobility paradigm applied to the wireless devices and the intrinsic re-configuring SDR capabilities, increase the performance of the network both in terms of coverage and connectivity respect to other algorithms.

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.

The paper proposes the integration of new cognitive capabilities within the well known OpenBTS architecture in order to make the system able to react in a smart way to the changes of the radio channel. In particular, the proposed spectrum sensing strategy allows the OpenBTS system to be aware of other active transmissions by forcing to choose a new radio channel, within the GSM frequency band, when a licensed primary user has to transmit on a busy channel. The implemented scheme, representing a solid step forward in the cognitive direction, has been validated throughout a detailed testbed pointing out strengths and limitations in realistic communication environments.

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.

Power consumption is an essential issue in wireless multimedia sensor networks (WMSNs) due to the elevated processing capabilities requested by the video acquisition hardware installed on the generic sensor node. Hence, node placement scheme in WMSNs greatly impacts the overall network lifetime. In this context, the paper first proposes a suitable hardware architecture to implement a feasible WMS node based on off-the-shelf technology, then it shows how the energy spaced placement of the wireless nodes reduces the energy consumption in comparison with the classical evenly spaced placement, without affecting the video quality of multimedia traffic.

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.

The deployment of WLAN-based solutions for efficient distribution of multimedia content over wireless links is rapidly increasing in the last few years. Nevertheless many performance issues concerning the delivery of high bit rate stream- oriented content using current IEEE 802.11 standards and the measurements of the customers satisfactions in terms of perceived quality, are still open and of great interest. This paper proposes a simple curve fitting technique for estimating, in a fast and accurate way, the perceived quality of streaming media contents delivered within a wireless network. The model accounts for the effects of various network parameters such as congestion, radio link power and video transmission bit rate. The correctness of the designed model has been verified through many different measurements in realistic wireless environments through an ad-hoc test bed.

Wireless meshing has been envisioned as the economically viable networking paradigm to build up broadband and large-scale wireless commodity networks. Several different mesh network architectures have been conceived by both industry and academia; however many issues on the deployment of efficient and fair transport protocols are still open. In this paper, an adaptive and responsive transport protocol (AR- TP) is proposed for WMNs in order to fairly allocate the network resources among multiple flows, while minimizing the performance overhead. Compared to the classical end-to-end rate control mechanisms, an hop-by-hop congestion control approach is designed to keep track of dynamic multi-hop network characteristics in a responsive manner. In addition, a coarse-grained end-to-end reliability algorithm is integrated with the proposed hop-by-hop congestion control mechanism to provide packet level reliability at the transport layer. Performance evaluation via extensive simulation experiments show that the AR-TP protocol achieves high performance in terms of network throughput and fairness.

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.

This paper proposes a novel demand sensitive model (DSM) for tuning price service over SDMB (satellite digital multimedia broadcast) system offering a price discount to large amount of users while maintaining profit for the service provider. The discounted price is determined based on a marginal decision rule using a simple pseudo-linear function of the weight factor and the number of users. For testing the proposed model, an integrated satellite-terrestrial network architecture offering SDMB services to mobile users through the satellite or terrestrial UMTS segment, has been utilized. Simulations have been carried out to show the goodness of the proposed model within the system architecture; moreover the model, based on simple parameters, is very flexible and can be easily integrated in a "price adapter" module making the offered service attractive for end users and in the same time profitable for the satellite operator.

The need of new wireless communication infrastructure which can provide high-speed multimedia mobile communication service to users who are not satisfied with low-speed data and voice service provided by existing wireless network are rapidly increasing. Cellular communications using high altitude platform (HAPs) are studied for next generation broadband wireless network providing also third generation (3G) mobile services. The connection admission control issue in a HAP-UMTS communication system represents a key problem in order to increase the capacity in term of admitted users preserving the agreement quality of service. In this paper we propose a novel strategy for the admission procedure based on dynamic power sharing in order to guarantee an high fairness level to all users of the projected cellular system. Simulation results show that the proposed CAC scheme can achieve good performances managing different mixed traffic scenarios.

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.

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 aims at contributing to the dimensioning of the signalling bandwidth of a broadband geo-satellite system for multimedia communications. Signalling channels are used by the satellite terminals to set up connections and ask for on-demand resource assignment. A smart dimensioning of the satellite bandwidth plays a key role in the fulfillment of the service quality required by multimedia traffic and the effective exploitation of the satellite spectrum. We accomplish this task by designing a statistical centralised CAC accorded with an intelligent scheduling of the resource requests on board the satellite. The proposed traffic and resource management techniques have been tested over a simulation platform for the EuroSkyWay satellite system.

In this paper we propose and compare different techniques used by the satellite user terminals to randomly access signaling channels used for connection set-up. The reference scenario is represented by EuroSkyWay [G. Losquadro, 1996], a multimedia geostationary satellite platform conceived for providing personal communications.

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.