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 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.

Effective motor rehabilitation relies on correct exercise execution and timely feedback. Automatically assessing exercise quality remains a key challenge for remote rehabilitation systems. Although numerous approaches have been explored, each comes with its own set of limitations: camera-based systems can be costly and raise privacy concerns, while purely Inertial Measurement Unit (IMU)-based approaches often struggle with sensor drift and precise quality assessment. This paper investigates the feasibility of real-time exercise quality assessment using only simple wearable IMU sensors coupled with on-device edge intelligence. We propose a distributed, data-driven framework where Transformer-based Deep Learning (DL) models are deployed directly onto wearable IoT device placed on the patient joints when performing exercises. Each device processes local IMU data for initial exercise recognition and angle estimation. The system then consolidates these distributed inferences achieving a consensus on the performed exercise and to evaluate movement quality against physiotherapist-defined thresholds. Experimental results demonstrate high accuracy in exercise identification (97% F1-Score) and the capability for precise joint angle estimation (the MAE varied between 0.16 to 0.31 depending of the exercised performed).

Digital Twins (DTs) have emerged as a powerful tool for simulating, predicting, and optimizing the behavior of complex real-world systems. In many cases, DTs rely on real-world data generated by Internet of Things (IoT) systems and leverage Deep Learning (DL) techniques for predictive future system states. However, DTs often struggle to adapt to dynamic scenarios where additional loT devices and new configurations are introduced, requiring costly data collection and retraining. In this paper, we address this challenge by exploring the use of Generative Deep Learning (GDL) to build robust DTs capable of handling sparse, noisy, and biased sensor data streams. We propose a transformer-based GDL model that encodes variable-length collections of loT devices while supporting predictive capabilities under novel device configurations and the addition of new loT devices. We evaluate our proposed GDL model in two loT scenarios: smart homes and smart hydraulics systems, where physical models are used to generate the ground truth data. Experimental results demonstrate our approach's adaptability without retraining, achieving an$R^{2} =99.5\%$when adding a temperature sensor in the smart home scenario (only 0.3% degradation), and$R^{2}=83.8 \%$with 5 valves in the smart hydraulics scenario, comparing favorably to VARX despite VARX being retrained on each dataset.

The rapid expansion of the Internet of Things (IoT) has led to significant data reliability and system transparency challenges, aggravated by the centralized nature of existing IoT architectures. This centralization often results in siloed data ecosystems, where interoperability issues and opaque data handling practices compromise both the utility and trustworthiness of IoT applications. To address these issues, we introduce ZONIA (Zero-trust Oracle Network for IoT Applications), a novel blockchain oracle system designed to enhance data integrity and decentralization in IoT environments. Unlike traditional approaches that rely on Trusted Execution Environments and centralized data sources, ZONIA utilizes a decentralized, zero-trust model that allows for anonymous participation and integrates multiple data sources to ensure fairness and reliability. This paper outlines ZONIA’s architecture, which supports semantic and geospatial queries, details its data reliability mechanisms, and presents a comprehensive evaluation demonstrating its scalability and resilience against data falsification and collusion attacks. Both analytical and experimental results demonstrate ZONIA’s scalability, showcasing its feasibility to handle an increasing number of nodes in the system under different system conditions and workloads. Furthermore, the implemented reputation mechanism significantly enhances data accuracy, maintaining high reliability even when 40% of nodes exhibit malicious behavior.

Promoting sustainable water usage is a critical imperative across all sectors of society. Households are no exception since a significant portion of water is wasted daily due to inefficient appliances or improper habits. Thus, there is a need for innovative solutions that not only improve water utilization but also raise residents' awareness about this issue. This paper presents a promising solution leveraging the Internet of Things (IoT) and Machine Learning (ML) techniques to detect water wastage stemming from sink usage automatically. We have designed and developed a low-cost prototype equipped with an array of sensors, including a microphone, an ultrasonic sensor, and a PIR, to monitor sink usage. A deep learning model based on Gated Recurrent Units (GRU) has been trained to classify the wastage events. To validate our concept, we have gathered a small dataset relative to nine common daily water usage activities through the IoT prototype. Our preliminary findings demonstrate the feasibility of our solution, with an average accuracy exceeding 90% in detecting wastage events.

Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.

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.

Electric micromobility is increasingly being adopted as a urban transportation means. This emerging class of vehicles, thanks to its onboard hardware and embedded sensors, can be used for gathering IoT data across cities or for producing valuable insights on users’ driving behavior. However, the limited availability of public datasets on e-mobility poses a significant challenge for research, restricting the potential for improving the modeling and optimization of city-scale mobility systems. In this paper, we present an in-depth analysis of electric micromobility using GPS traces from three Italian cities. Furthermore, we identify significant spatio-temporal patterns and develop algorithms to reconstruct rental trips and model user demands, enabling the generation of synthetic vehicular traces for various urban scenarios and arbitrary number of vehicles. Our findings reveal distinct temporal patterns influenced by seasons and time of day, and spatial patterns showing varying distances covered by different vehicle types. Notably, our simulations for the city of Bologna demonstrate the potential of using electric micromobility vehicles as mobile sensors for city-sensing applications, providing extensive urban coverage with minimal units.

Train model identification can enhance the structural monitoring of railway infrastructures by providing contextual information about train passages. While approaches relying on timetables are impractical due to delays, camera-based solutions present challenges related to deployment costs and privacy concerns. In this paper, we propose RATTLE, a self-contained framework for train tracking and identification based on audio signal fingerprinting. We have developed a prototype IoT system tailored for train tracking and ground truth assessment, enabling the acquisition of a real-world dataset spanning four months of measurements. Then, we conducted a comparative analysis of several traditional Machine Learning (ML) and Deep Learning (DL) algorithms for audio features classification, mel spectrogram classification, and image classification (serving as baselines). Our findings highlight that mel-trained CNN algorithms achieve high accuracy (97%) comparable to the best video-based DL solution, while substantially reducing model size. Furthermore, we explored the potential for migrating the classification task to the edge through quantisation techniques.

In recent years, there has been a growth in the development of numerous software algorithms dedicated to pedometers (or step counters). This surge has subsequently spurred the creation of various context-aware smartphone applications for sports, healthcare, and other fields. Most works that compare commercial offerings do not adopt a sound and rigorous method, as human testers are asked to stick to a defined set of constraints, and experiments are carried out within controlled environments. However, each application is still tested separately, with no guarantee that the conditions are really the same, plus these conditions cannot resemble the real environment where pedometers are going to be used. Our proposal features a software solution that records the sensor readings of human testers and inject the exact same sensor values into different pedometer applications to produce a sound result by using the same testing conditions. We implement our solution and perform with it a comparison study.

The Cloud-to-Things (C2T) continuum combines the proximity of edge infrastructure to the devices with cloud resources to optimize data processing and response time in the Internet of Things (IoT). Proactive edge caching is a potential solution for meeting latency constraints in C2T environments, enabling efficient data processing and storage while reducing redundant computation and cost. However, while 5G/6G infrastructural aspects and caching strategies are extensively studied, no frameworks facilitate the design and deployment of caching strategies or address IoT’s unique requirements. This paper proposes CACHE-IT, a proactive edge caching framework that decouples the caching strategy algorithm from the underlying architecture, enabling customization based on application-specific requirements. Through extensive simulations, we analyzed the impact of different configurations on system metrics and verified that the CACHE-IT positively impacts the system latency and hit rate. By implementing a scenario-specific caching strategy, we illustrate the CACHE-IT deployment in a real-world Structure Health Monitoring (SHM) system. The evaluation demonstrates that CACHE-IT impacts positively in terms of latency, hit rate, and the number of requests sent to data providers.

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.

Assessing the integrity of industrial and civil appliances has become a priority worldwide. Noteworthy, this goal requires a strong synergy between multiple tools, disciplines, and approaches to be attained via a joint hardware-software co-design of the different Structural Health Monitoring (SHM) system components. This work proposes the$\sf{MAC4PRO}$architecture, a sensor-to-cloud monitoring platform that seamlessly integrates sensing and software technologies for accurate data measurement, transmission, and analysis. The developed solution stands out for its interoperability and versatility, making it a promising candidate for integration in the next generation of smart structures. Our platform was validated during extensive experimental campaigns targeted at various industrial scenarios. The results show that the$\sf{MAC4PRO}$architecture can identify subtle changes, such as 1mm size leakage events in pipeline circuits, or less than 1% frequency drifts in civil buildings after seismic excitation, while ensuring more than 90% reduction in the edge-to-cloud data transfer process.

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.

Mobile crowdsensing (MCS) is an emerging paradigm that leverages the pervasive presence of mobile devices to collect and analyze data from the environment. However, the choice of a push- or pull-based architecture for MCS can result in a loss of flexibility and limitations for the creators of the campaigns (crowdsourcers). To address this issue, we propose a hybrid push-pull architecture for MCS campaigns that leverages the W3C Web of Things (WoT) to standardize the interfaces and interactions of devices through well-consolidated Web technologies. Furthermore, we present the design and implementation of a WoT-enabled Android application for MCS. We evaluate our proposal through simulations in a vehicular scenario based on a real dataset, showing that the hybrid architecture provides greater flexibility to crowdsourcers, supporting simultaneously the push and pull paradigms.

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.

Recently, Deep Learning (DL) techniques have shown their effectiveness for Human Activity Recognition (HAR) tasks. However, due to the storage and computational requirements, most of the existing HAR solutions assume that the training and inference phases are offloaded to the cloud or an external server, with a harmful impact on the network load of the mobile/wearable device as well as on the user’s privacy. A promising solution is represented by the emerging Edge Artificial Intelligence (AI) techniques that aim at moving the data analytics closer to the sensing units or directly on them. In this paper, we present our preliminary results about the offloading of HAR inference tasks on low-power microcontroller units. We consider the problem of detecting critical movements (e.g. falling, running) of workers within an industrial environment for safety purposes. The full pipeline of the HAR system is presented by using an Arduino BLE 33 Sense as a wearable unit: for the detection task, a DL model based on a Convolutional Neural Networks (CNN) is trained on the inertial sensor data. A dynamic range quantization technique is used to reduce the size of the model which is then loaded on the firmware. Preliminary results show that the accuracy of the CNN model is 97% and overcomes baseline, non-DL techniques, while the quantization technique ensures a reduction of 53% of the model size.

Interoperable and secure data management techniques are fundamental for most of large-scale Structural Health Monitoring (SHM) systems. Indeed, given the relevance of SHM critical measurements, data integrity must be protected against tampering or falsifications. In this paper, we propose a four-layer SHM architecture that allows to build an effective data pipeline from sensors to consumer applications, passing through the cloud. The architecture is built on top of the MODRON platform and exploits the recent advances of the W3C Web of Things (WoT) standard for interoperability. We then discuss how third-party services can take benefit of the W3C WoT architecture to retrieve the SHM critical data and to publish them on the Ethereum Blockchain through an SHM-specific Smart Contract, for data protection and traceability purposes. We test the effectiveness of the Smart Contract implementation in terms of latency and costs under simulated workloads.

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.

Nowadays, several Internet of Things (IoT) deployments use publish-subscribe paradigms to disseminate IoT data to a pool of interested consumers. At the moment, the most widespread standard for such scenarios is MQTT. We also register an increasing interest in IoT-enabled Location-Based Services, where data must be disseminated over a target area and its spatial relevance and the current positions of the consumers must be taken into account. Unfortunately, the MQTT protocol does not support location awareness, and hence it may result in notifying consumers that are geographically far from the data source, causing increased network overhead and poor Quality of Service (QoS). We address the issue by proposing LA-MQTT , an extension to standard MQTT supporting spatial-aware publish-subscribe communications on IoT scenarios. LA-MQTT is broker-agnostic and fully backward compatible with standard MQTT. As monitoring the position of subscribers over time may cause privacy concerns, LA-MQTT carefully supports location privacy preservation, for which the optimal tradeoff with the QoS of the spatial notifications is addressed via a learning-based algorithm. We demonstrate the effectiveness of LA-MQTT by experimentally evaluating its features via large-scale hybrid simulations, including real and virtual components. Finally, we provide a Proof of Concept real implementation of an LA-MQTT scenario.

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.

Condition Monitoring is one of the most critical applications of the Internet of Things (IoT) within the context of Industry 4.0. Current deployments typically present interoperability and management issues, requiring human intervention along the engineering process of the systems; in addition, the fragmentation of the IoT landscape, and the adoption of poor architectural solutions often make it difficult to integrate third-party devices in a seamless way. In this paper, we tackle these issues by proposing a tool-driven architecture that supports heterogeneous sensor management through well-established interoperability solutions for the IoT domain, i.e. the Eclipse Arrowhead framework and the recent Web of Things (WoT) standard released by the W3C working group. We deploy the architecture in a real Structural Health Monitoring (SHM) scenario, which validates each developed tool and demonstrates the increased automation derived from their combined usage.

The Internet of Things (IoT) is nowadays affected by significant interoperability issues. One of the most popular countermeasures is the Web of Things (WoT), proposed recently in a consistent standardization effort. On the other hand, several IoT-oriented frameworks are already established in industrial scenarios and provide SOA-like features such as discovery and orchestration. In this paper, we study how to bridge these two worlds by proposing a tool that enables a two-way translation between a WoT ecosystem and a System-of-Systems composed of well-described Web services. We evaluate the efficiency and scalability of our solution over the Eclipse Arrowhead framework through a series of experiments that assess the scalability of our solution under realistic workloads.

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.

The rapid and exponential growth of the Internet of Things (IoT) has been generating a new breed of technologies that introduce several different protocols and interfaces. The Web of Things (WoT) architecture stands out as an emerging and potential solution to improve interoperability across IoT platforms by describing well-defined software interfaces. However, few studies analyze and compare WoT to other interoperability solutions proposed in the IoT literature. In this paper, we attempt to bridge the gap by three main contributions. First, we qualitative compare the WoT approach with the well-known FIWARE-based interoperability solution.Second, based on the previous analysis, we design and implement a connector to bridge the WoT architecture to the FIWARE ecosystem. Third, we conduct a performance analysis emulating a real IoT-based environment to understand scalability, response time, and computer resource usage of the two interoperability solutions. The results reveal that conceptual design choices impact the applications’ performance: the WoT architecture effectively enables interoperability across IoT Platforms, though it incorporates several characteristics that hinder the implementation of applications. On the other hand, the FIWARE IoT Agent solution is platform-specific. Hence new implementations are needed for each different IoT data model.

We propose a framework called AirID that identifies friendly/authorized UAVs using RF signals emitted by radios mounted on them through a technique called as RF fingerprinting. Our main contribution is a method of intentionally inserting `signatures' in the transmitted I/Q samples from each UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process. Specifically, AirID addresses the challenge of how to overcome the channel-induced perturbations in the transmitted signal that lowers identification accuracy. AirID is implemented using Ettus B200mini Software Defined Radios (SDRs) that serve as both static ground UAV identifiers, as well as mounted on DJI Matrice M100 UAVs to perform the identification collaboratively as an aerial swarm. AirID tackles the well-known problem of low RF fingerprinting accuracy in `train on one day test on another day' conditions as the aerial environment is constantly changing. Results reveal 98% identification accuracy for authorized UAVs, while maintaining a stable communication BER of 10-4for the evaluated cases.

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.

The Web of Things (WoT) architecture recently proposed by the W3C working group constitutes a promising approach to handle interoperability issues among heterogeneous devices and platforms, by semantically describing interfaces and interaction patterns among the Things. One of the main advantage of the W3C architecture is the possibility to decouple the description of the Things` behavior from their implementation and communication strategies, hence greatly simplifying the deployment of novel applications and services on top of it. Starting from such state-of-art, and envisioning a Web of seamlessly interacting W3C Things, this paper focuses on the next steps, i.e.: how to effectively support the discovery of Things? and: how to ease the distribution of applications running on Things? We answer to both the questions above through the proposal of the WOT STORE, a novel software platform supporting the distribution, discovery and installation of applications for the W3C WoT. The WOT STORE allows users to perform semantic discovery of the available Things, to search for compatible applications available on the market, and to install them over the target devices, all within the same framework. We describe the platform architecture and its proof-of-concept implementation, providing two alternative interfaces to interact with our tool: a Web portal, and new modules developed for the popular Node,-RED platform. Finally, we discuss two realistic use-cases of the WOT STORE for industrial IoT and home automation systems, remarking theadvantages of our solution in terms of deployment costs and interoperability support.

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.

The growing spectrum crunch has motivated exploratory efforts in the use of LTE in the 5 GHz bands for downlink traffic. However, this paradigm raises concerns of fair sharing of the spectrum and the adverse impact of scheduled LTE frames on Wi-Fi Packet Success Rates (PSR). To address this issue, we propose E-Fi, an interference-evasion mechanism that allows Wi-Fi devices to survive LTE transmissions without any cooperation between these two different standards. Different from existing approaches, we argue that the simple use of Almost Blank Subframes (ABS) within the LTE standard offering short channel access windows overestimates opportunities for Wi-Fi. The pilots embedded in the ABS not only interfere with Wi-Fi but also adversely impact the carrier sensing function. E-Fi mitigates this problem through a two-fold approach. It uses a combination of (i) Wi-Fi Direct with packet relaying and (ii) classical distributed coordination function to reach distant nodes. Second, it ensures load balancing for both Wi-Fi uplink and downlink traffic with high PSR by creating node-groups based with dedicated contention-based medium access intervals. Our approach is validated by comprehensive simulation and experimental results that indicate significantly higher throughput in E-Fi compared to classical Wi-Fi.

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.

Cognitive radio (CR) is an enabling technology that allows opportunistic use of under-utilized licensed spectrum allocated to primary users (PUs). However, the frequent channel sensing and switching interferes with the transport layer functions, leading to slow flow convergence during active transmissions by the CR. In this paper, we propose TCP C2, a method that greatly improves the flow responsiveness to abrupt variation of underlying layer spectrum availability in cellular CR architectures. The key idea of C2is to allow the sender to estimate the current bottleneck link bandwidth and network load by observing variance in the throughput and round trip time. Following this, fast congestion window scaling allows the flow to converge quickly to the optimal sending rate. Analytic derivations and packet-based simulation results show the increased resiliency of our approach over classical end-to-end TCP protocols in the presence of intermittent spectrum sensing and disruptions caused by PU arrival. Additionally, we show that C2enforces fairness among flows, and also coexists well with classical TCP flavors.

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.

It is our great pleasure to welcome you to the Fourth IEEE International Workshop on Emerging COgnitive Radio Applications and aLgorithms (CORAL 2016), held in Coimbra, Portugal on June 21, 2016, in conjunction with the IEEE WoWMoM 2016 Conference.

Abstract In this paper, we study the problem of how to detect the current transportation mode of the user from the smartphone sensors data, because this issue is considered crucial for the deployment of a multitude of mobility‐aware systems, ranging from trace collectors to health monitoring and urban sensing systems. Although some feasibility studies have been performed in the literature, most of the proposed systems rely on the utilization of the GPS and on computational expensive algorithms that do not take into account the limited resources of mobile phones. On the opposite, this paper focuses on the design and implementation of a feasible and efficient detection system that takes into account both the issues of accuracy of classification and of energy consumption. To this purpose, we propose the utilization of embedded sensor data (accelerometer/gyroscope) with a novel meta‐classifier based on a cascading technique, and we show that our combined approach can provide similar performance than a GPS‐based classifier, but introducing also the possibility to control the computational load based on requested confidence. We describe the implementation of the proposed system into an Android framework that can be leveraged by third‐part mobile applications to access context‐aware information in a transparent way. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high bandwidth inter-vehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases introduce additional challenges in this highly mobile environment, where the CR enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations. As the rules allow local spectrum sensing only under the assurance of high accuracy, there is an associated tradeoff in obtaining assuredly correct spectrum updates from the database at a finite cost, compared to locally obtained sensing results that may have a finite error probability. This paper aims to answer the question of when to undertake local spectrum sensing and when to rely on database updates through a novel method of exploiting the correlation between 2G spectrum bands and TV whitespace. We describe experimental studies that validate our approach and quantify the cost savings made possible by intermittent database queries.

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.

The spectrum scarcity is a known problem for a multitude of services. Several bands have been licensed, and nowadays it is difficult to find unused spectrum. Cognitive radio networks have been proposed as a possible solution to contrast the experienced spectrum scarcity. One case of particular interest come from the scarce utilization of TV frequencies, which form the so-called TV White Spaces. In this paper we investigate the utilization of occupied frequencies by secondary devices for indoor communication. We conduct spectrum measurements to quantify the availability of spectrum, and study how indoor communications could impact the DTV receiver. We show that this portions of spectrum, called gray spaces, can be utilized under certain circumstances, for example in highly populated areas, which is the scenario in which it is harder to find TV White Spaces. Simulation studies show the impact gray spaces can have on the available spectrum for opportunistic use.

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.

Recent rules from FCC and OFCOM foresee the utilization of spectrum database as the main solution to provide accurate spectrum information about TV white spaces for secondary users in cognitive vehicular networks. Spectrum database provides maximum protection to licensed users, however, its implementation is not straight-forward in vehicular environments, due to the significant query overhead generated by mobile vehicles in congested urban scenarios. For this reason, in this paper we investigate possible solutions to integrate cooperative sensing and spectrum database querying, with the goal to minimize the primary users detection overhead while guaranteeing maximum protection for these primary users. We propose both theoretical and practical contributions in this field. First, we introduce a topological study to determine the optimal ratio between cooperation and spectrum database querying (referred to as ModeI, ModeII, cooperative Sensing-only devices according to FCC terminology) in order to minimize the network utilization for white space detection. We investigate various system parameters such as spectrum database query frequency, broadcast frequency (for cooperative sensing) and vehicles' velocity, and study their impact on system performance. Then, we propose a distributed (bio-inspired) protocol for network deployment that enables multi-interfaces vehicles to dynamically decide the detection mode to use, in order to minimize the database load, while providing high enough protection for licensed users.

The Internet of Energy (IoE) for Electric Mobility is an European research project that aims at deploying a communication infrastructure to facilitate and support the operations of Electric Vehicles (EVs). In this paper, we present three research contributions of IoE. First, we describe a software architecture to support the deployment of mobile and smart services over an Electric Mobility (EM) scenario. The proposed architecture relies on an ontology-based data representation, on a shared repository of information (Service Information Broker), and on software modules (called Knowledge Processors -KPs) for standardized data access/management. As a result, information sharing among the different stakeholders of the EM scenario (i.e. EVs, EVSEs, City Services, etc) is enabled, and the interoperability of smart services offered by heterogeneous providers is guaranteed by the common ontology. Second, we rely on the proposed architecture to develop a remote charging reservation system, that runs on top of mobile smarthphones, and allows drivers to monitor the current state-of-charge of their EV, and to reserve a charging slot at a specific EVSE. Finally, we validate our architecture through a benchmark framework, that supports the embedding of mobile EV applications and of real KPs into a simulated vehicular scenario, including realistic traffic, wireless communication and battery models. Evaluation results confirm the scalability of our architecture, and the ability to support EVs charging operations on a large-scale scenario (i.e. the downtown of Bologna).

It is our great pleasure to welcome you to the Second IEEE International Workshop on Emerging COgnitive Radio Applications and aLgorithms (CORAL 2013), held in Madrid on June 4, 2013, in conjunction with the IEEE WoWMoM 2013 Conference.

Cognitive Radio (CR) networks allow users to opportunistically transmit in the licensed spectrum bands, as long as the performance of the Primary Users (PUs) of the band is not degraded. Consequently, variation in spectrum availability with time and periodic spectrum sensing undertaken by the CR users have a pronounced effect on the higher layer protocol performance, such as at the transport layer. This paper investigates the limitations of classical TCP newReno in a CR ad hoc network environment, and proposes TCP CRAHN, a window-based TCP-friendly protocol. Our approach incorporates spectrum awareness by a combination of explicit feedback from the intermediate nodes and the destination. This is achieved by adapting the classical TCP rate control algorithm running at the source to closely interact with the physical layer channel information, the link layer functions of spectrum sensing and buffer management, and a predictive mobility framework that is developed at the network layer. An analysis of the expected throughput in TCP CRAHN is provided, and simulation results reveal significant improvements by using our approach. To the best of our knowledge, our approach takes the first steps toward the design of a transport layer for CR ad hoc networks.

The IEEE 1609.4-Multi-Channel Operation protocol has been proposed to support the co-existence of safety and non-safety (infotainments) applications over the Dedicated Short Range Communication (DSRC) channels at the 5.9 GHz band. However, the multi- channel approach over a single-radio transceiver might result in several performance degradations that have not been thoroughly investigated yet. In this paper, we analyze the performance of safety- related applications over multi-channel vehicular networks. We demonstrate through a simulation study that the synchronous channel switching enforced by the IEEE 1609.4 protocol might easily compromise the performance of safety applications that rely on the periodic exchange of short lived broadcasts. Thus, we propose in this work the WAVE-enhanced Adaptive Broadcast (WAB) scheme. WAB provides a novel MAC contention control mechanism intended to reduce the impact of packet collisions caused by the synchronous channel switching, and to increase the packet delivery rate of broadcast messages in congested vehicular scenarios. The WAB scheme dynamically adapts to the channel conditions through a distributed load estimator metric, and implements priority mechanisms to ensure fairness among vehicles. Simulation results reveal that the proposed WAB scheme can significantly increase the packet delivery rate of broadcast messages when compared to the existing IEEE 802.11p/1609.4 scheme.

It is our great pleasure in welcoming you to the First IEEE International Workshop on Emerging COgnitive Radio Applications and aLgorithms (CORAL), held in conduction with the IEEE WoWMoM conference. We have a great technical program lined up on this fast emerging and potentially disruptive field of Cognitive Radio, bringing together researchers, practitioners, and students from both industry and academia. We are excited to have a forum facilitating the cross-pollination of ideas from both theoretical and practical perspective, and look forward to your participation.

Recently, the IEEE 1609.4 WAVE protocol has been proposed to enable multi-channel communication in Vehicular Ad Hoc Networks (VANETs). While the usage of multi-channel technology can favor the co-existence of safety and non-safety vehicular applications, its implementation on single-radio transceivers poses some major concerns about the effective utilization of the channel resources. In this paper, we investigate the performance of safety-related applications on multi-channel VANETs. We demonstrate that the synchronous channel switching operations enforced by the IEEE 1609.4 protocol introduce additional delays to the delivery of safety messages, that might compromise the viability of such applications. To cope with this problem, we propose a WAVE-enhanced Safety message Delivery scheme (WSD) that minimizes the delivery delay of safety messages in multi-channel VANETs, while preserving compatibility with the IEEE 1609.4/802.11p standards. WSD attempts to transmit high priority safety message during SCH intervals while guaranteeing reception by all neighbors. We formulate the broadcast problem in multi-channel VANETs as a scheduling problem over the different DSRC channels. We extend the problem formulation to cooperative scenarios, in which multiple vehicles contribute to disseminate the safety message over the different channels. We evaluate our proposed WSD scheme through a simulation study. We show that our proposed solution can provide effective reduction of the delivery delay in multi-channel VANETs, when compared to the legacy IEEE 1609.4/802.11p protocols.

Distributed spectrum allocation in Cognitive Radio (CR) systems requires each Secondary User (SU) to learn the optimal spectrum policy which maximizes the network performance while minimizing the impact to the Primary Users (PUs). To this aim, each SU must rely on local sensing information which however can be biased by interference and fading effects on the received signal. Thus, if each SU works in isolation, the convergence to the system-wide optimal policy can not be guaranteed. In this paper, we formulate the spectrum allocation problem as a cooperative learning task in which each SU can learn the spectrum availability of each channel and share such knowledge with the other SUs. We propose a correlation model through which different SUs can leverage the experience of other nodes, and we integrate it into a distributed channel allocation scheme. At the same time, we investigate mechanisms to bound the cooperation overhead based on the performance of the distributed learning process. Simulation results confirm the ability of the cooperative learning scheme in providing higher sensing accuracy and convergence time when compared with noncooperative spectrum allocation schemes for CR networks.

Cognitive Radio (CR) technology constitutes a promising approach to increase the capacity of Wireless Mesh Networks (WMNs). Using this technology, Mesh Routers (MRs) and the attached Mesh Clients (MCs) are allowed to opportunis- tically transmit on the licensed band, but under the constraint not to interfere with the Primary Users (PUs) of the spectrum. Thus, the effective deployment of CR- WMNs require that each MR must be able to: sense the current spectrum, select an available PU-free channel and perform the spectrum handoff to a new channel in case of PU arrival on the current one. How to coordinate these actions in the optimal way which maximizes the performance of the CR-WMNs while minimizing the interference to the PUs constitutes an open research issue in CR systems. In this paper, we propose an adaptive spectrum scheduling and allocation scheme which allows a MR to identify the best schedule of (i) when to sense the current channel, (ii) when to transmit, (iii) when to perform a spectrum handoff. Due the large number of parameters involved, we propose Reinforcement Learning (RL) techniques to allow a MR to learn by itself the optimal balance between spectrum sensing-exploitation- exploration actions based on network feedbacks coming from the MCs. We perform extensive simulations which confirm the adaptivity and efficiency of our approach in terms of increased throughput when compared with non-learning based schemes for CR-WMNs.

Cognitive radio (CR) ad hoc networks are composed of wireless nodes that may opportunistically transmit in licensed frequency bands without affecting the primary users of that band. In such distributed networks, gathering the spectrum information is challenging as the nodes have a partial view of the spectrum environment based on the local sensing range. Moreover, individual measurements are also affected by channel uncertainties and location-specific fluctuations in signal strength. To facilitate the distributed operation, this paper makes the following contributions: (i) First, an experimental study is undertaken to measure the signal characteristics for indoor and outdoor locations for the TV channels 21 – 51, and these results are used to identify the conditions under which nodes may share information. (ii) Second, a Cooperative reinforcement LearnIng scheme for Cognitive radio networKs (CLICK) is designed for combining the spectrum usage information observed by a node and its neighbors. (iii) Finally, CLICK is integrated within a MAC protocol for testing the benefits and overhead of our approach on a higher layer protocol performance. The proposed learning framework and the protocol design are extensively evaluated through a thorough simulation study in ns-2 using experimental traces of channel measurements.

Urban-X is a new architecture for Multi-Radio Cognitive Mesh Networks based on principles from Dynamic Spectrum Access Networks. In the Urban-X, spectrum sensing and mobility are challenging a transport control protocol (TCP) whose performance is sensitive to varying delay and available bandwidth. Also high packet loss due to primary users lets standard TCP to operate inefficiently. In this study, we propose a new transport protocol TP-UrbanX for the Urban-X which exploits reinforcement based learning techniques to learn an optimal sending rate and adapt this rate given the dynamics of the environment such as spectrum sensing and channel mobility.

Spectrum sensing allows the cognitive radio (CR) devices to determine the presence of licensed users in the chosen spectrum band. Though several sensing methods based on energy detection and cyclostationary feature extraction have been proposed, they fail to account for the location-specific results, and provide no incentive for the node to perform sensing at the cost of its own data throughput. In this work, a node admission policy called CORAL is proposed for CR wireless mesh networks that ranks candidate joining mesh clients (MCs) based on their distinct contributions towards the spectrum sensing coverage area. Moreover, CORAL incorporates different traffic classes, and attempts to keep the higher ranked MCs affiliated to the mesh cluster for longer durations of time. Simulation results reveal improved throughput and enhanced PU protection in the area, in which the licensed and CR users co-exist.

Wireless sensor networks are equipped with batteries with limited charge, and are often deployed in conditions that make their retrieval and replacement infeasible. Thus, energy conservation has been a primary consideration for protocol design for such networks. Recent advancements in the transfer of energy wirelessly over large distances, such as through radio frequency electromagnetic (EM) waves and magnetic coupling, may give rise to a new class of networks that allow the sensors to be charged on the field, thereby prolonging the network lifetime. Moreover, wireless charging though EM waves may be undertaken in the same unlicensed band as that used for communication, leading to several unique protocol design challenges for such a network. The contribution of this paper is threefold: First, a set of experiments is undertaken to investigate the effect of distance and location on the energy transfer through EM waves. Second, a new routing metric based on the charging ability of the sensor nodes is proposed. Finally, an optimization framework is developed to determine the optimal charging and transmission cycle for the sensor network, resulting in enhanced lifetime of the network under user-specified end-to-end constraints of throughput and latency.

A wireless mesh network (WMN) has been popularly researched as a wireless backbone for Internet access with off-the-shelf and inexpensive equipments. Nowadays, several applications like contents sharing, multicast video streaming, vehicular networks encourage to build mesh networks in urban areas. However, the deployment of WMNs in unlicensed bands of dense urban areas imposes many challenges. While previous research has mostly focused on optimal channel allocation under inter or intra-flow interferences within mesh nodes, the practical deployment of WMNs also requires to consider the interference caused by external entities such as residential access points that do not belong to the WMN. To address this issue, we propose Urban-X which is a first attempt towards a new architecture for Multi-Radio Cognitive Mesh Networks. Based on a cross-layer scheme, we develop novel routing and forwarding schemes reflecting the residential traffic state of external users. Through an extensive simulation analysis using the ns-2 simulator, we demonstrate the feasibility of our routing scheme and we show its robustness to the variations of channel environment and external traffics.

Existing research in transport protocols for wireless ad-hoc networks has focused on reliable end-to-end packet delivery under uncertain channel conditions, route failures due to node mobility and link congestion. In a cognitive radio (CR) environment, there are several key challenges that must be addressed apart from the above concerns. The intermittent spectrum sensing undertaken by the CR users, the activity of the licensed users of the spectrum, large-scale bandwidth variation based on spectrum availability, and the channel switching process need to be considered in the transport protocol design. In this paper, a window-based transport protocol for CR ad-hoc networks, TP-CRAHN, is proposed that distinguishes each of these events by a combination of explicit feedback from the intermediate nodes and the destination. This is achieved by adapting the classical TCP rate control algorithm running at the source to closely interact with the physical layer channel information, the link layer functions of spectrum sensing and buffer management, and a predictive mobility framework that is developed at the network layer. To the best of our knowledge, this is the first work on the transport layer to specifically address the concerns of the CR ad-hoc networks and our approach is thoroughly validated by simulation experiments.

Cognitive Radio (CR) technology constitutes a new paradigm to provide additional spectrum utilization opportunities in wireless ad hoc networks. Recent research in this field has mainly focused on devising spectrum sensing and sharing algorithms, to allow an opportunistic usage of licensed portions of the spectrum by Cognitive Radio Users (CRUs). However, it is also important to consider the impact of such schemes on the higher layers of the protocol stack, in order to provide efficient end-to-end data delivery. Since TCP is the de facto transport protocol standard on Internet, it is crucial to estimate its ability in providing stable end-to-end communication over Cognitive Radio Ad Hoc Networks (CRAHNs). The contributions of this paper are twofold. First, we propose an extension of the NS-2 simulator to support realistic simulation of CRAHNs. Our extension allows to model the activities of Primary Users (PUs), and the opportunistic spectrum management by CRUs in the licensed band. Second, we provide an accurate simulation analysis of the TCP performance over CRAHNs, by considering the impact of three factors: (i) spectrum sensing cycle, (ii) interference from PUs and (iii) channel heterogeneity. The simulation results show that the sensing interval and the PU activity play a critical role in deciding the optimal end-to-end performance, and reveals the inadequacy of classical TCP to adapt to variable spectrum conditions.

In this paper, we illustrate the design of a cross-layered MAC and clustering solution for supporting the fast propagation of broadcast messages in a Vehicular Ad Hoc Network (VANET). A distributed dynamic clustering algorithm is proposed to create a dynamic virtual backbone in the vehicular network. The vehicle-members of the backbone are responsible for implementing an efficient messages propagation. The backbone creation and maintenance are proactively performed aiming to balance the stability of backbone connections as well as the cost/efficiency trade-off and the hops-reduction when forwarding broadcast messages. A fast multi-hop MAC forwarding mechanism is defined to exploit the role of backbone vehicles, under a cross-layered approach. Simulation results show the effectiveness of the mutual support of proactive clustering and MAC protocols for efficient dissemination of broadcast messages in VANETs.

In this paper, we present a novel solution for the fine-grained parallel and distributed simulation of Vehicular ad hoc networks' (VANETs) services and applications, based on vehicular traffic mobility and wireless IEEE 802.11-standard models. A Mobile Wireless Vehicular Environment Simulation (MoVES) framework supports dynamic partition of geographic areas and dynamic entity mapping of the modeled scenarios over parallel and distributed simulation platforms. This could be a viable solution to enhance simulation performances and to exploit low-cost commercial-off-the-shelf (COTS) simulation architectures. Testbed performance evaluation for realistic modeled scenarios has shown the effectiveness of our approach in terms of simulation efficiency (speedup), and simulation accuracy, also providing guidelines for future enhancements of the framework performances, and model characterization of inter-vehicular communication.