Nowadays, many Internet of Things (IoT) systems rely on sensing units that offload data to remote cloud servers for analytics. While this approach provides the computational power required to execute complex Deep Learning (DL) tasks, it introduces privacy vulnerabilities and becomes unfeasible in scenarios with constrained network bandwidth. In this paper, we investigate the possibility of completely offloading DL inference tasks to the Extreme Edge (EE) of an IoT system, consisting of a multi-hop network of microcontrollers or low-power PCs. To this end, we explore the splitting of DL models across the physical topology, taking into account the heterogeneity of EE devices and the characteristics of wireless links. To balance the trade-off between model accuracy and resource limitations, we focus on mixed-precision quantization strategies that adjust the precision of each sub-model based on the hardware capabilities of the target devices. Beyond the optimization problem formulation, we propose a Genetic Algorithm (GA) that determines the best model allocation and in-network inference path within the multi-hop IoT network by jointly optimizing energy efficiency and latency. Experimental results on three widely adopted DNN architectures (MobileNetV2, ResNet50, and VGG16) demonstrate that the proposed GA achieves up to a 66% reduction in the fitness function compared to the baseline greedy algorithm.

The rapid proliferation and vast diversity of Internet of Things (IoT) devices present significant challenges to achieving seamless interoperability within modern networks. Many legacy IoT systems still rely on outdated protocols, rendering them incompatible with newer devices and creating communication bottlenecks. Additionally, the need for real-time traffic adaptation and modification further complicates the integration of these heterogeneous systems. This paper explores the potential of data plane programmability as a solution to address these challenges, enabling dynamic control over network behavior and improving compatibility between diverse IoT devices. By allowing real-time adjustments to traffic flows and adapting protocols on the fly, programmable data planes offer a flexible and scalable approach to ensuring interoperability across both modern and legacy IoT systems. Experimental results demonstrate the feasibility and effectiveness of this approach, highlighting its potential to enhance IoT network performance and longevity.

The dynamic nature of modern wireless networks, combined with the need for efficient management, has heightened interest in Software-Defined Networking (SDN). This paper presents a simulation framework designed to effectively model SDN in wireless network environments. The framework addresses key challenges in managing heterogeneous networks, offering a versatile platform for the development and evaluation of SDN applications. A key feature of the framework is the integration of a Digital Twin (DT) module within the SDN controller, leveraging the controller's comprehensive view of the network. This integration allows for real-time construction and updating of a virtual representation of the network, enhancing the controller's decision-making capabilities. By predicting future network states through a neural network model, the DT facilitates proactive management strategies, such as routing adjustments and resource reallocation, which are essential for maintaining optimal network performance. The paper details the architectural design and implementation of the framework, including the integration of Mininet, OMNeT++, and the Ryu controller. Our results demonstrate the framework's effectiveness in simulating complex SDN scenarios and providing detailed analyses of network behavior.

The increasing computational capabilities of IoT end devices push the deployment of application logic tasks directly on the extreme edge rather than the cloud or edge nodes. However, there are still unresolved issues on the Over-The-Air (OTA) software update operations for IoT end devices: (i) the hardware heterogeneity in IoT settings requires custom code for each different device type; (ii) the growing complexity of microcontroller code couples the development of high-level processing tasks with low-level operations; (iii) efficient methods for updating target IoT devices in a specific geographical area are absent. To address these issues, we propose an OTA firmware update framework that utilizes WebAssembly (WASM) and incorporates location-aware features. We split the application logic in WASM from the rest of the firmware written in native code, in order to create a greater separation of concerns. WASM's platform independence creates an abstraction layer for the underlying hardware, allowing the same application logic to be deployed virtually to any IoT device. We integrate a location-aware extension of the MQTT protocol in our framework to enable software updates targeting devices in specific geographical areas. Finally, our experiments demonstrate that location awareness does not add significant overhead to the system and that the performance of WASM in a microcontroller is comparable to native code and superior to Micropython.

Unmanned Aerial Vehicles (UAVs) integrated with Internet of Things (IoT) systems represent a powerful solution for reestablishing connectivity in emergency scenarios where terrestrial infrastructure is damaged or overloaded. However, ensuring service continuity under UAV mobility and resource constraints poses significant orchestration challenges. This paper presents a mobility-aware orchestration framework tailored for UAV-enabled IoT networks in such critical contexts. Leveraging Kubernetes for service orchestration and Prometheus for telemetry monitoring, we design and implement a modular architecture that dynamically reallocates containerized services across mobile UAV nodes in real time. Our framework is validated through physical testbed experiments on Raspberry Pi-equipped UAVs, where container migration is triggered by geographic constraints and monitored across varying pod workloads and link conditions. Results demonstrate robust adaptability, sub-second recovery under favorable link quality, and a scalable orchestration strategy for mission-critical operations. This work advances the practical deployment of orchestrated UAV swarms, bridging the gap between theoretical frameworks and real-world mobility-aware computing.

Unmanned Aerial Vehicle (UAV) swarms enable the rapid deployment of IoT services in dynamic and challenging environments. While these swarms offer flexibility and close proximity to sensing and actuation points, efficiently deploying interdependent services at scale remains a core challenge. Traditional centralized methods struggle to handle the complexity of large UAV networks, leading to increased latency and limited reliability. In this paper, we propose a decentralized approach to service deployment in UAV swarms. Our method relies on local information at each node, allowing UAVs to make their own assignment decisions. Over time, these decisions are iteratively refined as nodes exchange status updates and adapt to network changes. This process avoids the bottlenecks of centralized coordination and enables more responsive resource allocation. Simulation results show that our approach supports the successful deployment of a high number of tasks while maintaining low latency. These findings indicate that decentralized methods with local resource knowledge, improves both scalability and responsiveness in UAV-based IoT systems.

In IoT-based smart agriculture systems, the acquisition of high-quality sensing data plays a key role in enabling informed decisions by farmers. However, the deployment of wireless sensors in remote agricultural areas often entails facing frequent network disconnections. Furthermore, the necessity for uninterrupted monitoring is in contrast with the energy-saving requirements of battery-operated IoT devices. In this paper, we propose a solution that decouples the connection between IoT sensors and the cloud by introducing an intermediary software stratum acting as an edge data proxy. More specifically, our framework assigns a Digital Shadow to each IoT device, endowed with the capability to predict forthcoming sensor values during devices' low-power modes or network disconnections. Three main contributions are provided in this paper. First, we present the architecture and operations of our framework, enabling the orchestration of phases of sensor readings and data forecasting and the consequential adjustment of the device sampling frequency. Second, we introduce a novel time series forecasting approach that aims at identifying diverse patterns in the sensor time series and at instantiating distinct Machine Learning (ML) models for each individual pattern. Third, we validate our framework through real-world soil moisture datasets. The experimental results showcase the efficacy of our approach in delivering accurate forecasts, outperforming single-model and context-aware methodologies.

In edge-cloud IoT scenarios, proactive caching strategies constitute an effective solution to optimize the use of resources while ensuring adequate Age of Information (Aol). However, the implementation of these strategies introduces significant privacy constraints, primarily stemming from the transmission of sensitive data to the cloud. To address such issue, Federated Learning (FL) has emerged as a promising approach which processes data at the edge, transmitting only the model updates to the cloud. This paper introduces CACHUUM (Cache Architecture for Cloud and Heterogeneous edge in the ContinUUM), a proactive and privacy-aware architecture designed to facilitate the deployment of various edge caching strategies within distributed edge environments. Our architecture supports three families of strategies: local, global and federated, each tailored to meet specific privacy requirements. Furthermore, our architecture is continuum-aware, accommodating different data caching locations, whether it be at the edge node, in the cloud, or somewhere in between. We demonstrate the effectiveness of CACHUUM on simulated IoT environments, by collecting metrics on forecast accuracy, caching precision and data overhead, for different strategies. The latter anticipate the optimal cache update timings for each IoT device, ensuring that Aol aligns with application requirements upon data request.

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario.

Human Activity Recognition (HAR) using wearable Internet of Things (IoT) devices represents a well investigated researched field encompassing various application domains. Many current approaches rely on cloud-based methodologies for gathering data from diverse users, resulting in the creation of extensive training datasets. Although this strategy facilitates the application of powerful Machine Learning (ML) techniques, it raises significant privacy concerns, which can become particularly severe given the sensitivity of HAR data. Moreover, the labeling process can be extremely time-consuming and even more challenging for IoT wearable devices due to the absence of efficient input systems. In this paper, we address both aforementioned challenges by designing, implementing, and validating edge-based Human Activity Recognition (HAR) systems that operate on resource-constrained IoT devices, which relies on the utilization of Self-Organizing Maps (SOM) for activity detection. We incorporate a feature selection process before training to reduce data dimensionality and, consequently, the SOM size, aligning with the resource limitations of wearable IoT devices. Additionally, we explore the application of Federated Learning (FL) techniques for HAR tasks, enabling new users to leverage SOM models trained by others on their respective datasets. Our federated Extreme Edge (EE)-aware HAR system is implemented on a wearable IoT device and rigorously tested against state-of-the-art and experimental datasets. The results demonstrate that our C++-based SOM implementation achieves a consistent reduction in model size compared to state-of-the-art approaches. Furthermore, our findings highlight the effectiveness of the FL-based approach in overcoming personalized training challenges, particularly in onboarding scenarios.

Several use-cases of the Internet of Things (IoT) rely on the development of large-scale Wireless Sensor Networks (WSNs) in harsh environments characterized by limited Internet connectivity and battery-powered operations. In such scenarios, the failure of a single node due to energy depletion or hardware issues may cause network partitions and disrupt partially or completely the system operations until the intervention of a human operator. In this paper, we investigate the usage of Unmanned Aerial Networks (UAVs) to enable sensory data collection and support resilient communications in presence of faulty sensor nodes. More specifically, we study the possibility of replacing the ground devices with UAVs which are able to temporarily restore the multi-hop communication towards the WSN sink. To this aim, we extended the Uhura framework, a platform for robotic networking, with novel features for automatic network partition detection and UAV-sink coordination. Then, we created a small test-bed composed of a Bluetooth Mesh WSN and one drone, and characterized the performance of the UAV-assisted WSN system in terms of packet delivery ratio of the end-to-end data flows.

Wearable Internet of Things (IoT) devices with inertial sensors can enable personalized and fine-grained Human Activity Recognition (HAR). While activity classification on the Extreme Edge (EE) can reduce latency and maximize user privacy, it must tackle the unique challenges posed by the constrained environment. Indeed, Deep Learning (DL) techniques may not be applicable, and data processing can become burdensome due to the lack of input systems. In this paper, we address those issues by proposing, implementing, and validating an EE-aware HAR system. Our system incorporates a feature selection mechanism to reduce the data dimensionality in input, and an unsupervised feature separation and classification technique based on Self-Organizing Maps (SOMs). We developed the system on an M5Stack IoT prototype board and implemented a new SOM library for the Arduino SDK. Experimental results on two HAR datasets show that our proposed solution is able to overcome other unsupervised approaches and achieve performance close to state-of-art DL techniques while generating a model small enough to fit the limited memory capabilities of EE devices.

Bluetooth Mesh is a recent SIG standard enabling the deployment of multi-hop Wireless Sensor Networks (WSNs) over Bluetooth Low Energy (BLE) communication links. The standard introduces many novel and interesting features in the Internet of Things (IoT) domain, such as the seamless integration among sensors and mobile and wearable devices, and the support for a wide range of different IoT application profiles. At the same time, fine-grained assessments of the performance are still needed to understand the potential of the technology. In this paper, we investigate the usage of Bluetooth Mesh solutions for the joint monitoring of indoor spaces and humans. Through the deployment of a test-bed, we evaluate the performance of Bluetooth Mesh WSNs under varying traffic loads and network sizes. In addition, by exploiting the short-range, multi-hop communications, we propose a procedure for the indoor localization of mobile devices and evaluate its accuracy. The results demonstrate that the technology supports reasonable delivery ratio under high traffic loads, however the network and localization performance sharply decreases when increasing the number of hops between the source and destination nodes.

In a swarm of unmanned aerial (UAVs) or ground vehicles (UGVs), nodes can autonomously coordinate their activities and cooperate to accomplish a given task as for instance the data exchange with Internet of Things (IoT) devices. However, due to the unpredictable environmental conditions, wireless communication on the air-to-air, ground-to-air and ground-to-ground links can experience completely different channel conditions. For this reason, several Machine-to-Machine (M2M) communication technologies have been proposed with different Quality of Service (QoS) characteristics in terms of range, bandwidth and energy consumption profile: at the same time, new challenges have arisen from the integration or joint utilization of multiple M2M stacks in heterogeneous robotic environments. In this work, we address such challenges through the design and development of a new framework, called Uhura, that eases the interaction among heterogeneous devices e.g., aerial platforms, ground vehicles, robots, sensors, and more. The Uhura framework provides communication facilities for swarm of UAVs/UGVs by abstracting from the underlying M2M technologies; in addition, it supports automatic selection of the M2M stack on multi-adapter UAVs/UGVs based on QoS requirements of the application. In this paper, we describe the Uhura architecture and its ROS-based implementation. Also, we report some results of two real-world experiments involving (i) a small swarm of UAVs and (ii) a multi-adapter UAV communicating to a ground IoT gateway.

Digital Twins are crucial in Industry 4.0 IoT scenarios, as they replicate physical assets and enable important tasks such as predictive analytics, what-if scenarios and real time monitoring. The heterogeneity of IoT use cases usually makes the development of digital twins extremely application-specific as well as prone to interoperability issues. To overcome these two challenges, we propose WoTwins, a framework that, on one side, leverages the W3C Web of Things (WoT) standard to model data and entities, and, on the other side, generates automatically Digital Twins of existing Web Things by modeling their state space through a Markov Decision Process (MDP) graph and by predicting its behavior though Machine Learning techniques. We conduct experiments on a simulated use cases related to IoT robotics to evaluate our proposal.

Recent Structural Health Monitoring (SHM) systems might take advantage of Internet of Things (IoT) technologies for fine-grained and autonomic sensors data management and processing. Moreover, current SHM deployments often demand for the installation of multi-type and heterogeneous sensor devices capable to perform long-term measurements; from here, the need for dedicated software platforms allowing for scalability and interoperability requirements arises. In this paper, we jointly address the two issues above by proposing MODRON, which is a SHM-dedicated IoT platform with sensor-to-cloud support. The software architecture leverages the W3C Web of Things (WoT) standard for multi-source sensors data acquisition and fusion. The platform includes an edge component, implementing the communication with the monitoring layer and the data exposition through WoT Web Things (WTs), and a cloud component, embedding sensor/WT management capabilities, which is in charge of distributed data storage, aggregation, visualization and analytics. We illustrate the abstract MODRON architecture and its current implementation that supports two different SHM sensor types (MEMS accelerometers and piezoelectric devices). In addition, we describe the system operations on a real-world SHM system, i.e. the monitoring of a metallic structure instrumented with multiple sensor networks.

The chaotic growth of the Internet of Things (IoT) determined a fragmented landscape with a huge number of devices, technologies and platforms available on the market, and consequential issues of interoperability on many system deployments. The recent W3C Web of Things (WoT) standards aimed to ease the deployment of heterogeneous systems by introducing uniform and well-defined software interfaces among the systems’ components. Although the WoT reference architecture is generic and agnostic to the target devices, its widespread adoption depends on the availability of specific tools named Servients, which enable the run-time operations of WoT applications. In this paper we aim at contributing to the adoption of the W3C WoT standards by presenting WoT Micro-Servient (WMS), a framework for bringing the WoT paradigm to the extreme edge of an IoT environment. Through WMS, developers can design, compile and install WoT applications on micro-controllers and embedded systems with constrained hardware capabilities. We describe the architecture and functionalities of the tool, and demonstrate its effectiveness in terms of reduced latency and energy consumption compared to the state-of-art proxy-based solution enabled by Node-wot, i.e. the official implementation of W3C WoT. Finally, we discuss a real-world application related to smart home, where WMS is used to enable a WoT-based remote monitoring and control of indoor plants, by enabling seamless integration between micro-controllers and mobile devices.

Warehouse management is a crucial task for most of nowadays' business activities. The usage of small Unmanned Aerial Vehicles (UAVs) has been recently proposed to automatize the inventory process while increasing the safety for human workers. However, the practical deployment of UAV swarms in the target use-case must face many severe technical issues, such as the indoor navigation, the package identification and the limited flight autonomy of the drones. In this challenging context, the paper addresses three novel research contributions. First, we propose a generic architecture for UAV-based inventory management within large-scale warehouses, including the components of UAV path planning, package identification (via QR Codes), data validation (via the Blockchain) and wireless charging; a prototype implementation of the data acquisition and management framework has been conducted by using low-cost mini-drones and single-board computers. Second, we analyze the system performance and specifically the trade-off between the inventory accuracy, i.e. rate of successful package identification, and the inventory completion time. Third, we derive the optimal UAV mobility parameters in terms of speed and number of visits for each shelf unit, and test the system operations and the configuration parameters through a small-case testbed.

Unmanned aerial systems (UASs) allow easy deployment, three-dimensional maneuverability and high reconfigurability, as they sustain communication network in the absence of pre-installed infrastructure. The proposed FOg Computing in UAS Software-defined mesh network (FOCUS) paradigm aims to realize an implementable network design that considers practical issues of aerial connectivity and computation. It allocates UASs to the tasks of data forwarding and in-network fog computing while maximizing number of ground-users in UAS coverage. FOCUS improves efficient utilization of network resources by introducing on-board computation and innovates on top of software-defined networking stack by integrating the capabilities of network and ground controllers to enable simultaneous orchestration of both UASs and communication flows. There are three main contributions of the paper: First, a SDN-based architecture is designed enabling autonomous configuration of computation and communication as well as managing multi-hop aerial links. Second, a global optimization problem to achieve optimal forwarding and computational allocation is formulated using Open Jackson Network model and solved via a heuristic approach with well defined complexity. Third, FOCUS framework is implemented on a small-scale testbed of Intel®Aero UASs performing image analysis with a full software stack. Experiments reveal at least 32% latency improvement in computation service time compared to traditional centralized computation at the end-server or greedy task allocation schemes within the network.

Accurate indoor localization constitutes a challenging yet fundamental research problem towards the large-scale deployment of next-generation mobile indoor location-based services. This paper addresses two key issues of indoor localization: (i) how to take benefit of the presence of inertial sensors, short-range and long-range radio interfaces on modern smartphones in order to achieve fine-grained localization and trajectory tracking, and-at the same time-(ii) how to perform it while limiting the impact on energy-constrained devices. To address the first issue, we propose a novel hybrid strategy which implements a dual-step fusion process, i.e., it merges the estimations produced by pattern matching algorithms applied to short-range and long-range wireless sources available on smartphones- and then it merges the estimations produced by Pedestrian Dead Reckoning (PDR) and Radio Fingerprinting (RF) techniques, in order to overcome the limitations of each approach. For the second issue, we describe the design and implementation of a novel client-server architecture, which offloads the computational expensive tasks to the infrastructure, while still guaranteeing acceptable localization lag. Finally, a modular, extensive evaluation is proposed on real-world scenarios, quantifying the impact of each sensor/source on the localization accuracy, and the gain induced by the dual-step fusion process over basic PDR localization techniques.

During the occurrence of an emergency, being it the consequence of a natural disaster or of human activities, the pervasiveness of user-owned mobile devices (e.g. smartphones, tablets) turns into a precious help to convey data and services to all the people involved. As a result, several emergency-related mobile applications have been proposed on the market; however, they are often limited by the networking capabilities of the devices, since they are often based on short-range Device-to-Device (D2D) communication technologies (e.g. the Wi-Fi Direct), or on the cellular infrastructure, which might be not available on the emergency scenario. In this paper, we attempt to overcome both such issues, by proposing a novel Emergency Communication System (ECS) which operates over infrastructure-less phone-based networks, and guarantees long-range D2D communication thanks to the LoRa technology. The system, named LOCATE, includes a mobile application, through which users can convey minimal yet vital emergency-related data, and a dissemination protocol, spreading the emergency requests over multi-hop LoRA links. The performance of LOCATE have been evaluated through: (i) an experimental study, assessing the capability of LoRa technology to convey short, emergency messages over long distances, and a (ii) simulation study, demonstrating the effectiveness of the dissemination protocol on large-scale scenarios when compared to state-of-the-art flooding schemes.

Internet of Things (IoTs)-based monitoring applications usually involve large-scale deployments of battery-enabled sensor nodes providing measurements at regular intervals. In order to guarantee the service continuity over time, the energy-efficiency of the networked system should be maximized. In this paper, we address such issue via a combination of novel hardware/software solutions including new classes of Wake-up radio IoT Nodes (WuNs) and novel data- and hardware-driven network management algorithms. Three main contributions are provided. First, we present the design and prototype implementation of WuN nodes able to support two different energy-saving modes; such modes can be configured via software, and hence dynamically tuned. Second, we show by experimental measurements that the optimal policy strictly depends on the application requirements. Third, we move from the node design to the network design, and we devise proper orchestration algorithms which select both the optimal set of WuN to wake-up and the proper energy-saving mode for each WuN, so that the application lifetime is maximized, while the redundancy of correlated measurements is minimized. The proposed solutions are extensively evaluated via OMNeT++ simulations under different IoT scenarios and requirements of the monitoring applications.

This paper proposes a network architecture and supporting optimization framework that allows Unmanned Aerial Vehicles (UAVs) to perform city-scale video monitoring of a set of Points of Interest (PoI). Our approach is systems-driven, relying on experimental studies to identify the permissible number of hops for multi-UAV video relaying in a noisy 3-D environment. Our architecture itself is innovative in the sense that it defines a mathematical framework for selecting the UAVs for periodic re-charging by landing on public transportation buses, and then `riding' the bus to the successive chosen Pol. Specifically, we show that our UAV scheduler can be modeled as an instance of multicommodity flow problems, and mathematically solved through Mixed Integer Linear Programming (MILP) techniques. Thus, our centralized formulation identifies the UAV, the next bus, and the next PoI, given the information about energy thresholds, the bus routes in the city and their next arrival times, to ensure persistent and reliable video coverage of all PoIs in the city. Finally, our work is validated via emulation of a city environment with live traffic updates from a real bus transportation network.

This paper proposes deployment strategies for consumer unmanned aerial vehicles (UAVs) to maximize the stationary coverage of a target area and to guarantee the continuity of the service through energy replenishment operations at ground charging stations. The three main contributions of our work are as follows. 1) A centralized optimal solution is proposed for the joint problem of UAV positioning for a target coverage ratio and scheduling the charging operations of the UAVs that involves travel to the ground station. 2) A distributed game-theory-based scheduling strategy is proposed using normal-form games with rigorous analysis on performance bounds. Furthermore, a bio-inspired scheme using attractive/repulsive spring actions are used for distributed positioning of the UAVs. 3) The cost-benefit tradeoffs of different levels of cooperation among the UAVs for the distributed charging operations is analyzed. This paper demonstrates that the distributed deployment using only 1-hop messaging achieves approximation of the centrally computed optimum, in terms of coverage and lifetime.

The merging of networking and control fields has always brought interesting innovations but the tools and structures for proper and easy management of experiments still lag behind. Different solutions have been proposed to handle general control problems and, more in detail, for fine control of UAVs (Unmanned Aerial Vehicles) dynamics. They lack, however, an efficient and detailed network-side simulation, usually available only on dedicated software. On the other hand, current advancements in network simulations suites often do not include an accurate simulation of controlled systems. In the middle 2010s, integrated solutions are still lacking. For these reasons, in this paper we propose a simulation architecture for networked control systems. The architecture is based on well-known solutions in both the fields of networking simulation and UAV control simulation. We integrate them into a compact and efficient solution that shows scalability features and negligible architectural delays, as experimental results demonstrate.

A software suite that is capable to simulate Unmanned Aerial Vehicle (UAV) dynamics and, at the same time, network-side behaviors is still lacking in literature. Despite the recent merge between the fields of networking and control, the existing solutions are either dedicated to only one between the aforementioned topics. In this demonstration paper, we describe a novel simulation architecture to implement distributed networked control systems. Our proposal consists in the integration of already-present and validated solutions into a compact package that features scalability and negligible architectural delays.

Several applications involving the utilization of Small Unmanned Aerial Vehicles (SUAVs) require stationary and long-term coverage of a target area. Unfortunately, this goal is hard to achieve due the need for coordination and the limited flight autonomy of the SUAVs. In this paper, we investigate how to guarantee persistent coverage of a target area through SUAVs by exploiting characteristics of fixed terrestrial infrastructure and inherent energy limitations. This paper makes three main contributions. First, the problem of SUAV activity scheduling is formulated for pre-existing fixed placements, and centrally solved to maximize the network lifetime given a target coverage ratio. Second, a distributed, bio-inspired algorithm is devised using local (1-hop) communication only, i.e., the scheme takes into account both positioning and charging issues allowing the SUAVs to self-organize into a maximum-coverage connected swarm, and coordinate the charging operations. Third, the performance of the distributed scheme is compared to the optimal solution, and the impact of the system parameters like the placement height and the discharging rate on the coverage metrics is discussed.

Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion's share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15% of average quality index) in scenarios in which the Wi-Fi is received at a low strength.

Spectrum scarcity demands for additional bandwidth where new services can be deployed on. However, today's spectrum allocation leaves almost no bands unallocated. Thus, Cognitive Radio has been studied to bring relief to the lack of spectrum, moving towards a more efficient and dynamic spectrum access. In this domain TV White Space have been proposed as a possible solution to bring new, valuable spectrum for opportunistic services. However, their availability is quite low in highly populated areas, and thus their viability is limited. This is mainly because the availability of TV White Space is typically considered at the rooftop, through two-dimensional propagation models which do not account for possible spectrum re-utilization policies within a building, or in a small-scale area. In this paper, we show that much more communication opportunities can be found when we consider also the third-dimension, i.e. the height from the terrain, and novel per-floor allocation policies. We propose three main contributions in this paper. First, we describe an analytical model through which we derive the number of available spectrum resources for indoor secondary networks, considering PU protection policies in the same building, and in surrounding buildings. Second, we estimate the number of TV Gray Space (TVGS) over realistic scenarios in candidate cities, considering realistic street topology and buildings locations, and we show that this value can be much higher than what reported in the spectrum database. Finally, we investigate co-existence of secondary networks on TVWS, when novel per-floor spectrum sharing models are used.

Guaranteeing network connectivity in post-disaster scenarios is challenging yet crucial to save human lives and to coordinate the operations of first responders. In this paper, we investigate the utilization of low-altitude aerial mesh networks composed by Small Unmanned Aerial Vehicles (SUAVs) in order to re-enstablish connectivity among isolated end-user (EU) devices located on the ground. Aerial ad-hoc networks provide the advantage to be deployable also on critical scenarios where terrestrial mobile devices might not operate, however their implementation is challenging from the point of view of mobility management and of coverage lifetime. In this paper, we address both these issues with three novel research contributions. First, we propose a distributed mobility algorithm, based on the virtual spring model, through which the SUAV-based mesh node-called also Repairing Units (RUs) in this study- can self-organize into a mesh structure by guaranteeing Quality of Service (QoS) over the aerial link, and connecting the maximum number of EU devices. Second, we evaluate our scheme on a realistic 3D environment with buildings, and we demonstrate the effectiveness of the aerial deployment compared to a terrestrial one, in terms of coverage and wireless link reliability. Third, we address the problem of energy lifetime, and we propose a distributed charging scheduling scheme, through which a persistent coverage of RUs can be guaranteed over the emergency scenario.

Nowadays several mobile applications connect to the internet through 2G/3G/LTE, which are becoming more crowded. Cognitive wireless networks have been proposed as a possible solution to supply additional bandwidth, and more recently TV White Spaces (TVWS) have been investigated as one candidate. TVWS devices should contact a remote spectrum database, which will reply with the channels available to use. It is not specified how devices should contact the remote spectrum database, so in this work we focus on the usage of a cellular connection, where however the number of the queries could rapidly grow and occupy considerable bandwidth. In this paper we present the idea of Femto-Databases, i.e. devices which act as distributed mobile databases able to satisfy the spectrum requests by opportunistic devices. Extensive simulations through the Omnet++ platform show that our approach can effectively reduce the load on the cellular infrastructure, and improve the latency of the query communication to the remote spectrum database.

Machine-to-Machine communications is envisioned to become one of the fundamental pillars of the future Internet of Things paradigm, enabling platoons of devices to be seamlessly connected and to cooperate over smart spaces. Among the possible application scenarios, smart metering represents an already existing technology that might take benefit from the capability of autonomous configuration and setup of M2M networks. At present, smart meters communicate over the 2G/3G network, however the utilization of the cellular technology poses several problems, such as low coverage and spectrum shortage over dense areas. To overcome these issues, in this paper we investigate the application of cognitive radio principles over TV White Spaces to M2M communication for the smart metering scenario. Following the recent regulations of FCC and Ofcom, that foresees the presence of a spectrum database for TV white spaces detection, we study the trade-off between protection of licensees and energy consumption in a cluster of smart meters. We provide three novel research contributions: (i) an analytical model to estimate the lifetime of a cluster of smart meters; (ii) centralized and distributed algorithms to determine the schedule operations of Master/Slave devices foreseen by the spectrum regulations; (iii) performance evaluation of the proposed framework through extensive Omnet++ simulations.

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.