The convergence of satellite networks and Internet of Things (IoT) technologies offers transformative solutions for enhancing connectivity in remote and underserved regions where traditional network and power infrastructure are lacking. Despite the potential of LoRaWAN-a widely adopted communication protocol for IoT due to its low energy consumption, extended communication range, and cost-efficiency-its integration with Low Earth Orbit (LEO) satellite systems presents critical technical challenges. Limited satellite visibility leads to intermittent connectivity, while high device density during satellite visibility periods results in data collisions and communication failures, affecting network performance and scalability. Addressing these issues is imperative to unlock the full potential of satellitesupported IoT networks for applications ranging from environmental monitoring to disaster response in hard-to-reach areas. This Ph.D. Thesis introduces two novel frameworks for satellite LoRaWAN systems: REACT (LoRaWAN differential time synchronization) and SALSA (Scheduling Algorithm for LoRa to LEO Satellite). REACT tackles the challenge of temporal misalignment by enabling precise synchronization between IoT devices and satellite visibility windows, optimizing data transmission opportunities. SALSA addresses packet collisions and congestion by implementing an intelligent scheduling algorithm that enhances network throughput and reliability. The proposed solutions underwent extensive validation through simulations and real-world testbed deployments across diverse geographical regions and under multiple LEO satellite constellation configurations. This multi-scenario evaluation demonstrates the generalizability of the proposed frameworks, confirming their robustness, adaptability, and effectiveness in improving transmission efficiency, scalability, and overall network performance under varying satellite orbits.

Providing high-speed and reliable wireless connectivity can be challenging in rural areas. 5 G private networks can be utilized to provide wireless connectivity in these areas to enhance business-critical applications for different types of industries, small-medium enterprises, agriculture, government entities, etc. This paper presents results of the Horizon Europe COMMECT project regarding 5G private network deployments in rural areas. Specifically, use cases that can be enabled in rural and remote areas by deploying 5 G private networks are presented, and typical challenges of 5G private network deployments are addressed. Moreover, two selected use cases, namely, a forestry industry use case and a livestock transportation use case, are detailed with a focus on connectivity requirements, possible network deployment strategies, and initial insights from their respective field trials. This paper provides one of the first contributions related to the deployment of 5 G private networks in rural areas.

Through the transition from 5G to 6G, a significant rise in the number of Internet of Things (IoT) devices is anticipated, enabling pervasive and uninterrupted connectivity for several applications, in different verticals. Coping with the substantial influx of IoT devices and fulfilling the high capacity demands of different IoT technologies, such as NB-IoT, will necessitate the involvement of Non-Terrestrial Networks (NTNs), which will serve as crucial complements to terrestrial systems, enhancing the availability, resilience, and coverage of the network and will guarantee cost/benefit for some services and will fully satisfy some key requirements. Nevertheless, a primary obstacle to be faced when integrating IoT terrestrial communication systems in NTN, in particular with Non-Geostationary (NGSO) satellites, lies in the short visibility time of the flying platform due to its high speed. The latter introduces criticalities in various communication phases, including the Random Access (RA) procedure. In a highly congested scenario, the large Round Trip Delay and a limited visibility window, which varies for each user within the satellite’s coverage area, contribute to reducing the number of users successfully concluding the RA procedure. In this paper, to enhance the percentage of users who successfully conclude the RA, we introduce the concept of Coverage Enhancement Levels in time and a novel backoff mechanism, namely Smart Backoff, that leverages the beam coverage visibility period of individual users to adjust the random backoff interval. The numerical results obtained from our proposed scheme substantiate significant improvements compared to the standard backoff scheme. Specifically, our approach yields an increase of up to 16% per channel in the percentage of users who successfully complete the RA process.

One of the main application scenario that the upcoming 5G systems are expected to support is Internet of Things (IoT) traffic backhauling. With the exponential increase of devices, it follows the critical challenge of handling the huge data volume that they produce. Terrestrial networks may not be sufficient for that, or not always available. Satellites then come into play, and they can provide the ubiquitous coverage targeted by 5G. On the other side, aggregation can be an efficient strategy to reduce the amount of traffic, and use network resources in an efficient manner. Building on that, in this work, we consider a hybrid IoT-satellite network architecture for collecting message queuing telemetry transport (MQTT) traffic. We design an advanced filter, namely, MQTT message filter aggregator (MQTT-MFA), that performs MQTT topics aggregation in close proximity of MQTT Publishers before data are sent over the satellite link. The developed MQTT-MFA implementation leverages on MQTT bridge, and it is compliant with Mosquitto, an open-source MQTT broker. Extensive simulation results have demonstrated that MQTT-MFA requires only 10% of the TCP/IP overhead generated in a baseline architecture (where the bridge is not set up) and 1/4 number of bytes to transmit the same amount of information, when compression of the data payload is also enabled.

Offering seamless connectivity for Next-Generation Internet (NGI) services has become a widespread concern to achieve global sustainability goals. In this position paper, we focus on the design of a smart connectivity platform that can extend its capabilities beyond the boundaries of what today’s 5G architecture can support. The proposed platform encompasses and integrates a wide range of technologies, spanning from Non-Terrestrial Networks, NTNs (GEO/LEO satellites, and airborne) to terrestrial cellular 5G and long-range low-power Internet of Things (IoT) networks. We enhance 5G technology with novel network architectures and network intelligence to meet coverage and capacity requirements; and by strengthening infrastructure management and control components with autonomous capabilities to decouple operational costs from growth in network traffic, cloud computing, and IoT. Finally, we discuss the technical, commercial and market challenges that need to be overcome before the commercial deployment of the designed platform becomes reality.

Given that several services can benefit from the adoption of a group communication model, the IETF has specifically standardized the usage of CoAP group communication. However, CoAP responses are still sent in unicast from each single CoAP server to the CoAP client, which results in a substantial traffic load. Such problem becomes more severe in integrated IoT-Satellite networks given the limited bandwidth of the satellite return channel and the large number of IoT devices in a massive MTC (mMTC) scenario. To reduce network traffic overhead in group communication and improve the network responsiveness, this paper proposes an aggregation scheme for the CoAP group communication in combination with Observer pattern and proxying. Results obtained by using the openSAND emulator and CoAPthon library corroborate the merit of our optimization in terms of overhead reduction and delay.

Although many target applications in VANETs are information-centric, the performance of Named Data Networking (NDN) in vehicular ad-hoc networks is severely hampered by persistent network partitioning, typical of many vehicular scenarios. Existing approaches try to address this issue by relying on opportunistic communications. However, they leave open the crucial issue of how to guarantee content persistence and tight QoS levels while optimizing the resource utilization in the vehicular environment. In this work we propose DeepNDN, a communication scheme based on the joint application of NDN and of probabilistic spatial content caching, which enables content retrieval in fragmented and dynamic network topologies with tight delay constraints. We present a data-based approach to DeepNDN management, based on locally modulating content replication and delivery in order to achieve a target hit ratio in a resource-efficient manner. Our management algorithm employs a Convolutional Neural Network (CNN) architecture for effectively capturing the complex relations between spatio-temporal patterns of mobility and content requests and DeepNDN performance. Its numerical assessment in realistic, measurement-based scenarios suggest that our management approach achieves its target set goals while outperforming a set of reference schemes.

5G-enabled vehicular networks will soon allow their users to exchange safety and non-safety related information over heterogeneous communication interfaces. Routing vehicular data flows over multi-hop Vehicle-to-Vehicle (V2V) communications is one of the hardest challenges in vehicular networking, and it has been tackled in literature by using distributed algorithms. The distributed approach has shown significant inefficiencies in such dynamic vehicular scenarios, mainly due to poor network congestion control. To overcome the complexity of the envisioned architecture, and the inefficiency of distributed routing algorithms, we hereby propose to leverage the coordination capabilities of Software-Defined Networking (SDN) to determine optimal V2V multi-hop paths and to offload traffic from the Vehicle-to-Infrastructure-to-Vehicle (V2I2V) to the V2V communications, using both cellular and Wi-Fi technologies. In order to achieve this goal, we propose Multi-Flow Congestion-Aware Routing (MFCAR), a centralized routing algorithm that relies on graph theory to choose short and uncongested V2V paths. Realistic simulations prove that MFCAR outperforms well- established centralized routing algorithms (e.g. Dijkstra's) in terms of Packet Delivery Ratio (PDR), goodput and average packet delay, up to a five-fold performance gain.

Floating Content (FC) has recently been proposed as an attractive application for mobile networks, such as VANETs, to operate opportunistic and distributed content sharing over a given geographic area, namely Anchor Zone (AZ). FC performances are tightly dependent on the AZ size, which in literature is classically chosen by the node that generates the floating message. In the present work, we propose a method to improve FC performances by optimizing the AZ size with the support of a Software Defined Network (SDN) controller, which collects mobility information, such as speed and position, of the vehicles in its coverage range.

The surging interest in autonomous coordinateddriving and in proactive safety services, exploiting the wealth ofsensing and computing resources which are gradually permeatingthe urban and vehicular environments, is making provisioning ofhigh levels of QoS in vehicular networks an urgent issue. At thesame time, the spreading model of a smart car, with a wealthof infotainment applications, calls for architectures for vehicularcommunications capable of supporting traffic with a diverse setof performance requirements. So far efforts have been revolvedtowards enabling a single specific QoS level. But the issues of howto support traffic with tight QoS requirements (no packet loss, and delays inferior to 1ms), and of designing a system capable atthe same time of efficiently sustaining such traffic together withtraffic from infotainment applications, are still open. In this paper we present the approach taken in the SNFCONTACT project in order to tackle these issues. The goal ofthe project is to investigate how an architecture for vehicularcommunications which integrates content-centric networking, software-defined networking as well as context aware floatingcontent schemes can properly support the very diverse set ofapplication and services currently envisioned for the vehicularenvironment.

The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology, which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.

Despite increasing deployment of software-defined networks (SDN), little is yet known about how to actually manage such kind of networks. When service degradation (generically termed here “trouble”) happens, the first step is to localise under-performing network paths (“troubleshooting”). SDN-RADAR gives a novel approach to ease large network troubleshooting by leveraging SDN features and incorporating distributed monitoring of network traffic. The demo shows how the SDN-RADAR tool can help network administrators understand which is the most likely faulty network link.

Software-defined deployments are growing into data center and enterprise network infrastructures. The typical promises of software-defined networks (SDN) are improved time for market, decreased risk and operational costs for services, flexibility and unified management. However, little is known and shared about how to actually manage an SDN network, especially in localising underperforming network paths (what we call “troubleshooting”). We describe a novel approach to ease large network troubleshooting by leveraging SDN features and incorporating distributed monitoring of network traffic. We suggest SDN-RADAR, a tool that can help network administrators understand which is the most likely faulty network link. To the best of our knowledge this is the first troubleshooting solution that combines user-side performance measurements with network data extracted from the SDN controller.

This paper capitalizes on two emerging trends, i.e., the growing use of wireless at the edge of industrial control networks and the growing interest to integrate IP into said networks. This is facilitated by recent design contributions from the IEEE and the IETF, where the former developed a highly efficient deterministic time-frequency scheduled medium access control protocol in the form of IEEE 802.15.4e timeslotted channel hopping (TSCH) and the latter IPv6 networking paradigms in the form of 6LoWPAN/ROLL, and scheduling approaches in the form of 6TiSCH. The focus of the present work is on advancing the state-of-the-art of deterministic 6TiSCH schedules toward more flexible but equally reliable distributed approaches. In addition, this paper aims to introduce the first implementation of 6TiSCH networks for factory automation environments: it outlines the challenges faced to overcome the scalability issues inherent to multihop dense low-power networks; the experimental results confirm that the naturally unreliable radio medium can support time-critical and reliable applications. These developments pave the way for wireless industry-grade monitoring approaches.

The emerging IEEE802.15.4e standard and IETF RPL routing protocol are core to the organization of multi-hop Low-power and Lossy Networks. They provide key functionalities useful for a really viable Internet of Things. However, several open issues still remain and require research efforts to be solved. Among others, the design of effective scheduling schemes in such systems is one of the major problems; in fact, there are no specifications about how schedules should be realized. Trying to fill this gap, this paper presents a new Decentralized Traffic-Aware Scheduling algorithm, which is able to construct optimum multi-hop schedules in a distributed fashion. Its effectiveness has been proved by using simulation results and comparing it with a centralized scheme. The reported performance results encourage the use of the developed scheduling technique, since it allows a very efficient queue management, and thus it minimizes packet discards due to buffer overflows, while at the same time minimizing the network duty cycle.

The Time Synchronized Channel Hopping (TSCH) protocol is part of the newly defined IEEE 802.15.4e standard and represents the latest generation of highly reliable low-power MAC protocols. With implementation details left open, we conceive here a novel Traffic Aware Scheduling Algorithm (TASA) by extending the theoretically well-established graph theory methods of matching and coloring by means of an innovative approach based on network topology and traffic load. TASA is able to support emerging industrial applications requiring low latency at low duty cycle and power consumption. Preliminary simulation results have also been reported to highlight the effectiveness of the proposed algorithm.