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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 ability of unmanned aerial vehicles (UAVs) to communicate and cooperate among themselves in an aerial swarm network is of paramount importance for collaborative missions. It enables the swarm to function as a collective unit to plan trajectories, share tasks, coordinate flight route for collision avoidance or to optimize a given mission objective. In this work, a cellular radio interface known as "sidelink"is investigated for establishing efficient UAV-to-UAV (U2U) multi-hop communication within the aerial swarm network. First, we look into the salient features of cellular sidelink (PC5 radio interface) as a key enabler of U2U communication, and then present a multi-hop network model design to extend cellular connectivity services to out-of-coverage UAVs during missions. Using extensive simulations, we evaluate and study the performance of sidelink-assisted multi-hop U2U communication model in terms of the packet delivery ratio (PDR) for different key scheduling parameters of mode-4 sensing-based autonomous resource selection.

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.

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 -4 for 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.

The IoT is spreading heavily around us and many new standard at different architectural layers proliferate. This creates a heavy interoperability gap that many architectural proposals and new standards have tried to cope with over the years. In particular, we take into account the Arrowhead interoperability Framework, a powerful and promising paradigm for interconnecting local clouds and we demonstrate the possibility to integrate it with the emerging W3C Web of Things standard. We study the usage of the Arrowhead Framework as a potential candidate for discovery operation and inclusion of WoT ecosystems within Service Oriented Architectures (SOA), in order to support the integration of legacy and proprietary systems. To this aim, a three-layered architecture is proposed and validated experimentally on a proof-of-concept use case, by highlighting the advantages of the proposed integration.

Smartphone devices can play a key role on emergency scenarios thanks to their pervasiveness, and the possibility to convey emergency requests from the involved people to the rescue teams. At the same time, the effective utilization of such devices on critical scenarios with limited mobile Internet access is challenging. As an alternative, several recent research studies have proposed Emergency Communication System (ECS) based on short-range Device-to-Device (D2D) solutions available on Commercial Off The Shelf (COTS) devices (e.g. Wi-Fi Direct); however, the target of these solutions is constituted by small indoor areas, since the scalability on large-scale environments is often a problem. In this paper, we overcome such issue by proposing LOCATE , a novel phone-based ECS enabling long-range communication among survivors and rescue teams over critical environments where 3/4G cellular connectivity is not available and the traditional geo-localization technologies (e.g. the GPS) provide only partial coverage of the environment. The proposed system consists of a mobile application connected to a LoRa transceiver via Bluetooth Low Energy (BLE); through the app, users can send emergency requests that are re-broadcasted by other peers until reaching a rescue personnel who is able to handle the emergency. Three novel contributions are provided in this paper. First, we provide extensive measurements of the LoRa technology, and investigate its suitability for ECS-related applications. Second, we describe the LOCATE prototype and the enabling algorithms; specifically, we propose a novel multi-hop dissemination algorithm which maximizes the probability to deliver an emergency request to the destination (e.g. rescue personnel) within a user-defined temporal threshold, while minimizing the number of message re-transmissions. Third, we extensively evaluate the LOCATE performance through OMNeT++ simulations, assessing the capability of the dissemination protocol to spread out the emergency requests over large-scale scenarios, and through experiments, assessing the capability of LoRa-based trilateration technique to provide accurate GPS-free localization. The results demonstrate that the LOCATE protocol is able to minimize the time required to handle the emergency when compared to other dissemination strategies (e.g. flooding, continuous, once-per-contact), and they highlight the considerable improvement provided by the LoRa technology over other D2D solutions available on COTS smartphones (e.g. Wi-Fi Direct).

UAV-aided Wireless Sensor Networks (WSNs) constitute an energy- and cost-effective solution for the deployment of large-scale IoT monitoring systems. At the same time, the integration of UAVs into IoT scenarios poses some practical challenges which are far to be addressed, like the synchronization issue (how can the Wireless Ground Sensors (WGSs) be aware of the presence of the UAV?) and the channel contention in high-dense scenarios (how can the WGSs cope with the limited transmission opportunity?). In this paper, we address both the issues above by proposing a novel Energy Load Aware Sensor To aIr Communication (ELASTIC) platform, supporting the deployment of scalable and energy-efficient UAV-aided WSNs. The proposal includes two main components, working in synergy at the PHY and MAC layers. First, the paper describes the design and implementation of Active Wake-up Radio mechanisms through which the WGSs can be notified of the UAV’s presence and hence of the transmission opportunity; the proposed architecture supports two operative modes (Switch and Low-Power) that can be properly orchestrated so that the energy consumption of the WGSs in standby phase is minimized. Second, since multiple WGSs can be activated from a wake-up signal and access the channel at the same time, a novel priority-based MAC scheme has been designed: the proposed solution performs distributed contention control among the WGSs, by taking into account the length of the transmission opportunity for each WGS and the actual channel load. The simulation results demonstrate that the proposed ELASTIC MAC protocol guarantees a +50% throughput improvement when compared to the legacy CSMA/CA MAC in high-dense WSNs, and show its ability to adapt to different traffic loads and UAV speeds.

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.

Aerial surveillance systems are a cost-effective and on-demand solution for smart city monitoring. However, the deployment of a swarm of unmanned aerial vehicles (UAVs) for continuous video capture of mobile ground target (MGT) activities poses new research issues in terms of energy management, scenario coverage, and multidevice task coordination. In this article, we address these challenges by proposing Persistent Crowd Tracking Using UAVs (PERCEIVE), a novel framework for continuous video surveillance via UAVs that are periodically replenished through mobile charging stations (MCSs).

This paper explores how the national tourism organizations (NTOs) of the top 10 most-visited countries by international tourists strategically employ Facebook to promote and market their destinations. Based on big data retrieved from the NTOs’ Facebook pages, and leveraging advanced metrics for capturing user engagement, the study sheds light on the factors contributing to superior level of social activity. The findings indicate that the way Facebook is tactically employed varies significantly across the NTOs. The panel data regression analyses suggest that engagement is positively affected by posting visual content (namely photos), and posting during the weekends, and negatively affected by evening posting. Post frequency displays no statistically significant effect on social engagement. The study also shows that most of the NTOs (except for Italy, Spain, Turkey and the UK) deploy Facebook with a top-down approach, and spontaneous user-generated content (UGC) is allowed to a very little extent.

Several recent studies demonstrated that the utilization of Unmanned Aerial Vehicles (UAVs) in combination with Wireless Sensor Networks (WSNs) can enhance the system performance in terms of lifetime and data delivery ratio; at the same time, the communication among Wireless Ground Sensors (WGSs) and UAVs poses several technical challenges which are far from being solved. Specifically, in this paper, two main issues are addressed: (i) how to ensure seamless synchronization among ground and aerial devices at each transit, and (ii) how to compute energy-efficient schedules for the WGSs and feasible trajectories for the UAVs. To this purpose, BEE-DRONES, a novel framework for data collection in wake-up radio based UAV-aided WSNs is proposed; in order to solve the synchronization issue, a solution where UAVs transfer energy toward selected WGSs is presented. The solution allows WGSs to power down the main radio when not requested. The joint WGSs wake-up scheduling and UAV path planning optimization problem is formulated, by taking into account the limited autonomy of the UAVs, the energy consumption of both UAVs and WGSs, and the data requirements of the applications, and it is solved via a two-step heuristic. The OMNeT++ simulation results demonstrate that the BEE-DRONES framework is able to enhance the WSN lifetime, and to optimize the quality of gathered data in terms of minimal temporal/spatial correlation.

Nowadays, Unmanned Aerial Vehicles (UAVs) represent a significant aid on scenarios where fixed, ground infrastructures are temporarily or permanently not available; this is the case of large-scale applications of the Internet of Things (IoTs), e.g. smart city and agriculture 3.0, where the UAVs can be employed as mobile data mules and gather the data from Wireless Ground Sensors (WGSs). UAV-aided wireless sensor networks (WSNs) introduce considerable advantages both in terms of performance and costs since they avoid the need of error-prone multi-hop communications, and also the installation of static gateways; at the same time, they pose formidable research challenges for their implementation, like the synchronization issue between the UAV and the WGS and the path planning, which should take into account the extremely limited flight autonomy of the UAVs. In this paper, we address both the issues above by proposing BEE-DRONES , a novel framework for large-scale, ultra low-power UAV-aided WSNs. In order to mitigate the synchronization problem, we investigate the utilization of passive Wake-up Radio (WR) technology on the WGSs, and of wireless power transfer from the UAVs: by harvesting the energy from the UAV hovering over it, the WGS is activated only for the short time required to transfer the data toward the mobile sink, while it experiences zero-consumption in sleep mode. We investigate the performance of passive WR-based WGS through real measurements, under different WGS-UAV distances and antenna orientations. Then, based on such results, we formulate the joint WGS scheduling and UAV path planning problem, where the goal is to determine the optimal trajectory of the UAVs activating the WR-based WGSs while taking into account the Value of the Sensing (VoS) as well as the total lifetime of the WSN. The original problem is transformed into a multi-commodity flow problem, and both centralized and distributed heuristics over the multi-graph are proposed. Finally, we evaluate the proposed algorithms through extensive OMNeT++ simulations; the results demonstrate the gain of BEE-DRONES in terms of extended lifetime compared to traditional, non WR-based solutions (e.g. duty-cycle), and in terms of reduced data-correlation compared to non VoS-aware path planning solutions.

In a VANET communication, link stability can neither be guaranteed nor make\ud the established route link permanent due to the dynamic nature of the network. In V2V communication\ud without the involvement of any infrastructural units like RSU access points or gateway,\ud the probability of successful link establishment decreases when vehicle’s speed varies, red traffic\ud light increases, cross-road increases and finally when the density of the running vehicles is sparse.\ud To ensure route establishment and control route request broadcast in a sparse VANET with crossroad\ud layout, RSUs are used in this paper for route discovery within one gateway zone when a\ud next hop vehicle to relay the route request packet is unavailable. RSUs are static but the vehicles\ud are dynamic in nature, so relying completely on RSU for forwarding data is not recommended\ud because chances of link failure, link re-establishment, and handoff overhead will be high. So, in\ud this paper, RSUs and Gateways are evoked for route discovery and data forwarding only when\ud necessary. Moreover, a local route repair is attempted in this paper when the path length is high\ud to reduce or avoid loss of buffered packets along the route and to maintain a more stable link\ud with the help of RSUs.

Modern smartphones integrate multiple functionalities into a single device: they can establish peer-to-peer wireless links, they can sense the environment through several embedded sensors, they are provided with a multi-core CPU. Hence, they can play a crucial role on emergency scenarios, where there is need of acquiring data from the environment, processing, and quickly conveying them to the people involved. In this paper, we describe how to turn a group of dense and heterogeneous smartphones into a phone-sensing platform, through the SENSE-ME software. This latter integrates three layers: (i) a network layer, that allows the creation of a spontaneous device-to-device communication structure; (ii) a sensing layer, that allows retrieving local and remote sensing data; (iii) a decision layer, that allows mining the sensed data, in order to extract context-aware information and -based on them-infer decisions about proper actions to take. In the following, we describe the SENSE-ME architecture and some enabling technologies at each layer. Moreover, we discuss the open challenges that must be addressed for the complete system deployment.

The multi-channel multi-radio technology represents a straightforward approach to expand the capacity of wireless mesh networks (WMNs) in broadband wireless access scenarios. However, the effective leveraging of this technology in WMNs requires (i) enhanced MAC protocols, to coordinate the access to multiple channels with a limited number of radio interfaces, and (ii) efficient channel allocation schemes, to mitigate the impact of co-channel interference. The design of channel assignment schemes and MAC protocols is strictly interrelated, so that joint design should be considered to optimize the mesh network performance. In this paper, a channel assignment and fast MAC architecture (CAFMA) is proposed, which exploits the benefits provided by the multi-channel multi-radio technology to (i) enhance the performance of multi-hop communications, (ii) maximize the resource utilization, and (iii) support differentiation of traffic classes with different quality of service (QoS) requirements. CAFMA is designed with a cross-layer approach and includes (1) a novel MAC scheme, which provides multi-channel coordination and fast data relaying over multi-hop topologies, and (2) a distributed channel allocation scheme, which works in cooperation with the routing protocol. Simulation results confirm the effectiveness of CAFMA when compared with other single-layer and cross-layer solutions for multi-radio multi-channel WMNs.

Active safety systems based on dissemination of alert messages are one of the most important applications of Vehicular ad Hoc Networks (VANETs). These systems typically require to provide efficient and reliable delivery of safety information to all the vehicles travelling over a geographical area. Reducing the delivery delay is also a crucial issue due to the real-time nature of the communication. To meet these requirements, in this paper we design a cross-layered MAC and clustering solution for the fast propagation of broadcast messages in a VANET. A distributed dynamic clustering algorithm is proposed in order to create a dynamic virtual backbone inside the vehicular network. The vehicle-members of the backbone are responsible for implementing an efficient message propagation. The backbone creation and maintenance are proactively performed aiming to balance the stability of backbone connections as well as cost/efficiency trade-off and hops-reduction when forwarding the broadcast messages. A fast multi-hop MAC forwarding scheme is defined to exploit the role of backbone vehicles, under a cross-layered approach. Simulation results show that our cross-layer scheme guarantees high message delivery ratio, reduces the delivery delay and provides efficient channel utilization when compared with other dissemination schemes for VANETs.

In this paper, we illustrate a Mobile Wireless Vehicular Environment Simulation (MoVES) framework for the parallel and distributed simulation of vehicular wireless ad hoc networks (VANETs). The proposed framework supports extensible, module-based and layered modeling, and scalable, accurate and efficient simulation of vehicular scenarios integrated with wireless communication and mobile services/applications. The vehicular layer includes models for vehicles, synthetic and trace-driven mobility, driver behavior, GPS-based street maps, intersection policies and traffic lights. The wireless communication layer currently includes models for physical propagation, and a network protocol stack including IEEE 802.11 Medium Access Control, up to the Application layer. MoVES provides a platform for microscopic modeling and simulation-based analysis of wireless vehicular scenarios and communication-based services and applications, like Intelligent Transportation Systems, communication-based monitoring/control and info-mobility services. The framework includes design solutions for scalable, accurate and efficient parallel and distributed simulation of complex, vehicular communication scenarios executed over cost-effective, commercial-off-the-shelf (COTS) simulation architectures. Dynamic model partition and adaptation-based load balancing solutions have been designed by exploiting common assumptions and model characteristics, in a user-transparent way. Test-bed performance evaluation for realistic scenarios has shown the effectiveness of MoVES in terms of simulation efficiency, scalability, adaptation and simulation accuracy.

Nowadays, most of the applications proposed for Vehicular Ad Hoc Networks (VANETs) rely on geocasting, i.e. on the possibility to identify the end-points of the communication through their geographic coordinates instead of their network addresses. At the same time, each vehicular application has its own set of Quality-of-Service (QoS) requirements which must be guaranteed by the underlying dissemination protocol. This has lead to a proliferation of geocast protocols proposed in the literature of VANETs, usually based on reactive or proactive approaches. However, most of these solutions are tailored for a specific application (e.g. active-safety applications), and thus might not be suitable for generic deployments of VANETs. In this paper, we propose a Dynamic Backbone Assisted (DBA) MAC protocol as a general solution to support geocast communication on highway scenarios for different classes of vehicular applications. DBA-MAC combines the benefits of proactive and reactive schemes through a cross-layer architecture which includes: (i) a novel distributed clustering algorithm and (ii) a fast information dissemination mechanism. First, DBA-MAC creates and maintains a virtual backbone of vehicles inside the highway scenario. Then, it provides fast dissemination of geocast messages through a combination of contention-free and contention-based forwarding mechanisms at the link layer. We provide a complete and exhaustive evaluation of the DBA-MAC scheme under different application requirements and network load conditions. We propose three metrics for the selection of backbone members which provide different characteristics in terms of delivery delay and ratio, and we analyze their performance with both analytical models and simulation studies. Then, we evaluate the performance of DBA-MAC compared to traditional geocast schemes for three classes of vehicular applications: active-safety applications, traffic-information applications, multimedia streaming (audio/video) applications. The simulation results confirm that DBA-MAC is able to greatly reduce the delivery delay for active-safety applications, and to adequately meet the requirements of multimedia applications for VANETs.

Recently, the IEEE 1609.4 protocol has been defined to enable multi-channel operations in a vehicular environment, and to guar- antee interference-free co-existence of safety-related and non-safety related applications in the same network. To meet these goals, the protocol assumes strict time synchronization among vehicles, and time/frequency separation in the Dedicated Short Range Commu- nication (DSRC) band. However, recent studies have demonstrated that this approach might not be suitable for safety-related broadcast applications with strict Quality-of-Service (QoS) requirements. In this paper, we investigate the potentials of Cognitive Radio (CR) technology to enhance the delivery ratio of safety-related broadcast applications in a multi-channel vehicular scenario. In this research field, we propose three novel contributions: (i) we introduce an analytical model to study the delivery ratio of broadcast applica- tions in IEEE 802.11p/1609.4 multi-channel vehicular networks, and the impact of MAC and PHY parameters on the system per- formance; (ii) we propose a framework to jointly decide the opti- mal values of the Contention Window size (CW) at the MAC layer and of the Control CHannel (CCH) bandwidth at PHY layer, so that each vehicle is able to transmit all its safety data during the CCH interval with a minimum MAC collision probability, and (iii) we discuss the framework implementation in a realistic vehicular scenario. Our on-demand bandwidth allocation algorithm utilizes vacant frequencies in the DSRC band to increase the bandwidth of the CCH, leveraging the spectrum agile capabilities offered by the CR technology. Simulation results confirm the effectiveness of our proposal in enhancing the delivery rate of broadcast applications under varying network scenarios and load conditions.

Nowadays, the increasing popularity of smartphone devices equipped with multiple sensors (e.g. accelerometer, gyroscope, etc.) have opened several possibilities to the deployment of novel and exciting context-aware mobile applications. In this paper, we exploit one of this possibility, by investigating how to detect the user motion type through sensors data collected by a smartphone. Our study combines experimental and analytical contributions, and it is structured in three parts. First, we collected experimental data that demonstrate the existence of specific sensors data patterns associated to each motion type, and we propose methods for data analysis and features extraction. Second, we compare the performance of different supervised algorithms for motion type classification, and we demonstrate that jointly utilizing the multiple sensor inputs of a smartphone (i.e. the accelerometer and the gyroscope) can significantly improve the accuracy of the classifiers. At the same time, we analyze the impact of sampling parameters (e.g. the sampling rate) on the system performance, and the corresponding trade- off between classification accuracy and energy consumption of the device. Third, we integrate the motion type recognition algorithm into an Android application, that allows to associate a specific smartphone configuration to each detected motion type, and to provide this information at system-level to other context- aware Android applications. Experimental results demonstrate the ability of our application in detecting the user’s motion type with high accuracy, and in mitigating the classification errors caused by random data fluctuations.

In recent years, Cognitive Radio (CR) technology has received significant attention from the research community as it enables on-demand spectrum utilization, based on the requests of the end-users. An interesting application area of CR technology is Vehicular Ad Hoc Networks (VANETs). In such networks, several innovative services and applications based on inter-vehicular communication have strict requirements in terms of bandwidth and delay, which might not be guaranteed by a fixed spectrum allocation paradigm. In this paper, we propose two key contributions pertaining to CR-VANETs: (i) an experimental study of the spectrum availability and sensing accuracy in a moving vehicle and (ii) a collaborative spectrum management framework (called Cog-V2V), which allows the vehicles to share spectrum information, and to detect spectrum opportunities in the licensed band. As part of this framework, we design a collaborative sensing and decision algorithm, enabling the vehicles to share spectrum information and to know in advance the spectrum availability at future locations along their motion paths. The simulation results, produced through a novel integrated simulation platform for CR networks, reveal significant improvements of Cog-V2V in sensing accuracy and pair-wise communication performance compared to classical fixed spectrum approaches.

Cognitive radio ad hoc networks (CRAHNs) constitute a viable solution to solve the current problems of inefficiency in the spectrum allocation, and to deploy highly reconfigurable and self-organizing wireless networks. Cognitive radio (CR) devices are envisaged to utilize the spectrum in an opportunistic way by dynamically accessing different licensed portions of the spectrum. To this aim, most of the recent research has mainly focused on devising spectrum sensing and sharing algorithms at the link layer, so that CR devices can operate without interfering with the transmissions of other licensed users, also called primary users (PUs). 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. At present, routing and transport layer protocols constitute an important yet not deeply investigated area of research over CRAHNs. This paper provides three main contributions on the modeling and performance evaluation of end-to-end protocols (e.g. routing and transport layer protocols) for CRAHNs. First, we describe NS2-CRAHN, an extension of the NS-2 simulator, which is designed to support realistic simulation of CRAHNs. NS2-CRAHN contains an accurate yet flexible modeling of the activities of PUs and of the cognitive cycle implemented by each CR user. Second, we analyze the impact of CRAHNs characteristics over the route formation process, by considering different routing metrics and route discovery algorithms. Finally, we study TCP performance over CRAHNs, by considering the impact of three factors on different TCP variants: (i) spectrum sensing cycle, (ii) interference from PUs and (iii) channel heterogeneity. Simulation results highlight the differences of CRAHNs with traditional ad hoc networks and provide useful directions for the design of novel end-to-end protocols for CRAHNs.

Fast delivery and high reliability are two of the main requirements which must be guaranteed by multi-hop broadcast protocols of alert messages in VANETs. Most of the existing schemes work by selecting the optimal forwarder vehicle at each hop through a distributed contention phase, thus introducing an additional delay in the forwarding process. An alternative approach is to identify the optimal set of relay vehicles before the actual dissemination of the alert messages, by creating and maintaining a virtual backbone of vehicles inside the VANET. For this purpose, we propose here an extended version of the Dynamic Backbone-Assisted MAC (DBA-MAC) scheme which supports fast and efficient multi-hop broadcast communication in VANETs. The DBA-MAC scheme comprises two main components: (i) a distributed clustering scheme, which builds a virtual backbone of vehicles, and a (ii) fast multihop forwarding scheme, which provides contention-free forwarding of the alert messages among the backbone vehicles. Compared to our previous works on DBA-MAC, we proposes here additional metrics for backbone creation which account for the channel receiver characteristics of each vehicle, through the estimation of the Link Budget (LB) between communicating vehicles. Moreover, we show through analytical and simulation results that the performance of the DBA-MAC in terms of delivery delay can be bounded between those of a static backbone with nodes placed at the optimal distance and those of a traditional contention-based scheme, which attempts to select the farthest vehicle at each hop. Finally, through the OMNET++ and SUMO tools, we propose an extended evaluation of the DBA-MAC scheme over realistic urban scenarios by modeling the impact of vehicular mobility and shadowing effects on the protocol performance.

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.

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 Ad Hoc Networks (CRAHNs) must identify the best operational characteristics based on the local spectrum availability, reachability with other nodes, choice of spectrum, while maintaining an acceptable end-to-end performance. The distributed nature of the operation forces each node to act autonomously, and yet has a goal of optimizing the overall network performance. These unique characteristics of CRAHNs make reinforcement learning (RL) techniques an attractive choice as a tool for protocol design. In this paper, we survey the state-of-the-art in the existing RL schemes that can be applied to CRAHNs, and propose modifications from the viewpoint of routing, and link layer spectrum-aware operations. We provide a framework of applying RL techniques for joint power and spectrum allocation as an example of Q-learning. Finally, through simulation study, we demonstrate the benefits of using RL schemes in dynamic spectrum conditions.

Wireless mesh networks, composed of interconnected clusters of mesh router (MR) and multiple associated mesh clients (MCs), may use cognitive radio equipped transceivers, allowing them to choose licensed frequencies for high bandwidth communication. However, the protection of the licensed users in these bands is a key constraint. In this paper, we propose a reinforcement learning based approach that allows each mesh cluster to independently decide the operative channel, the durations for spectrum sensing, the time of switching, and the duration for which the data transmission happens. The contributions made in this paper are threefold. First, based on accumulated rewards for a channel mapped to the link transmission delays, and the estimated licensed user activity, the MRs assign a weight to each of the channels, thereby selecting the channel with highest performance for MCs operations. Second, our algorithm allows dynamic selection of the sensing time interval that optimizes the link throughput. Third, by cooperative sharing, we allow the MRs to share their channel table information, thus allowing a more accurate learning model. Simulations results reveal significant improvement over classical schemes which have pre-set sensing and transmission durations in the absence of learning.

Video streaming over Wireless Mesh Networks (WMNs) can take advantage of the presence of multiple paths between each source and destination pair, as well as of the availability of multiple radio interfaces on the mesh nodes. In this paper, we propose a novel Channel Assignment and Multipath Routing Architecture (CAMRA) for supporting robust video streaming over Multi-Radio Multi-Channel WMNs. During route setup, multiple node disjoint paths are discovered between a source and a destination node. Channel allocation is performed so that the mutual among different paths is greatly mitigated, for the same source-destination pair. In order to enhance the reliability of video transmission, a load balancing scheme is introduced so that video frames are redundantly forwarded on multiple paths, based on the performance on each path and on packet-level loss estimations. Simulation results show that the proposed scheme is able to mitigate the impact of packet loss on streaming quality, while maintaining low overhead cost and low implementation complexity.

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.

Due to their self-configuration and auto-configuration properties, Wireless Mesh Networks (WMNs) are expected to support a plethora of applications, including traditional Internet services and novel multimedia applications. Service differentiation is needed to support applications with different Quality of Service (QoS) requirements. In this paper, we propose a novel cross-layer framework that provides efficient communication and service differentiation in multi-radio multichannel WMNs. The proposed solution includes a distributed channel allocation scheme, integrated in the routing protocol, and a multi-channel MAC protocol, inspired to the IEEE 802.11e EDCA (Enhanced Distributed Channel Access). At the MAC layer, we propose “fast-forwarding” mechanisms to reduce the contention delay of delay-sensitive applications in multi-hop topologies. At the network layer, the channel allocation scheme aims at loading channels with an equal number of per-class traffic flows. The simulation results confirm the effectiveness of the proposed scheme in providing differentiated treatment to traffic classes with different QoS requirements.

Multi-channel Multi-radio technology constitutes a viable approach to expand the capacity of Wireless Mesh Networks(WMNs). While many routing and channel assignment schemes have been proposed for WMNs, it is also important to integrate physical layer considerations into the routing process. In particular, multi-path fading is a challenging physical phenomenon, and it has been shown to be an important reason for packet loss in WMNs. In a multi-channel environment, the problem becomes more involved because different channels may undergo fading to varying extents. In addition there exists significant interference owing to the power leakage between the channels used by adjacent Mesh Routers (MRs). In this paper, we propose a Cross-layer CHannel Adaptive Routing protocol (XCHARM) for Multi-Radio Multi-Channel WMNs. XCHARM integrates routing and channel assignment during route setup, so that the interference caused by nodes transmitting on non-orthogonal channels in the same carrier sensing domain is greatly mitigated. Moreover, it jointly addresses the problems of channel and transmission rate selection based on fading and interference concerns. The simulation results confirm the benefits of the proposed architecture when compared with other routing protocols for WMNs, under realistic channel conditions and different networks topologies.

Wireless mesh networks (WMNs) are a promising technology for providing Internet connectivity to large residential areas. In a typical WMN deployment, end-users access the Internet through a backbone of static routers, which are responsible for traffic forwarding towards the closest Internet gateway (IG). In such a scenario, multiple paths may likely exist between the end-users and the IGs. For this reason, multi-path routing schemes appear as a viable approach to provide higher aggregate throughput, fault-tolerance and load-balancing properties. However, route-coupling problems may occur when the paths are concurrently used and certain nodes on separate paths are within each other's interference range. To overcome this problem, we propose a cross-layer architecture that comprises a novel joint multi-path routing and channel allocation scheme for multi-radio WMNs. The routing scheme discovers multiple node-disjoint paths between a source node (i.e. the end-user) and the gateway node. During the route setup, channel allocation is performed so that nodes on different paths, but within the same interference domain, transmit on different channels. As a result, the effect of route coupling is strongly mitigated. Moreover, we propose a load-balancing scheme, which allows to distribute traffic among the available node- and channel-disjoint paths. The simulation results show that the proposed scheme reduces the end-to-end delay and significantly enhances the system goodput over several network topologies and under different traffic workloads.

Wireless Mesh Networks (WMNs) have emerged as a new trend in wireless technology due to their low costs, ease of deployment, and wide coverage capabilities. The capacity of multi-hop WMNs can be expanded by using multiple radios each with multiple channels on each mesh router. However, using multi-radio multi-channel routers creates the so-called deafness and multi-channel hidden terminal problems. To address these problems a novel MAC protocol called Future Channel Reservation MAC (FCR-MAC) protocol is introduced in this paper. In our protocol, each node uses one common control channel and multiple data channels. Moreover, an on-demand channel allocation scheme is utilized which enables the nodes to reserve certain data channels in advance when all of them are occupied. Extensive simulations confirm that compared to other existing MAC solutions, our proposed FCR-MAC protocol achieves a very high data channel utilization, reduces the end-to-end delay and increases the system goodput significantly over several topologies and under different network workloads. Moreover, the FCR-MAC has the ability (i) to provide effective channel access differentiation among different traffic classes with different quality-of-service (QoS) requirements and (ii) to support multi-hop communication over multi-hop topologies by enabling channel reservation beyond the single-hop.

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.

Multipath routing schemes could exploit multiple disjoint routes between any source and destination node, in order to provide aggregate bandwith, fault-tolerance and load-balancing properties. In this paper, we propose a multipath routing scheme for mobile ad hoc networks which extends the Ad Hoc On-demand Multipath Distance Vector (AOMDV) routing protocol, by introducing a novel load-balancing approach to concurrently distribute the traffic among the multiple paths. The traffic-path allocation scheme is based on cross-layer measurements of path statistics reflecting the size and congestion level of each path. In order to provide an efficient support to the proposed routing scheme at the MAC layer, we also study the composition effect of a MAC forwarding mechanism called Fast Forward (FF), which attempts to reduce the effects of self-contention among frames at the MAC layer. The combined solutions have been modeled and simulated for a large set of scenarios. Results show advantages and drawbacks resulting in guidelines for multi-hop communication under both static and mobile scenarios and different traffic loads.

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.

The Web of Things (WoT) paradigm was proposed first in the late 2000s, with the idea of leveraging Web standards to interconnect all types of embedded devices. More than ten years later, the fragmentation of the IoT landscape has dramatically increased as a consequence of the exponential growth of connected devices, making interoperability one of the key issues for most IoT deployments. Contextually, many studies have demonstrated the applicability of Web technologies on IoT scenarios, while the joint efforts from the academia and the industry have led to the proposals of standard specifications for developing WoT systems. Through a systematic review of the literature, we provide a detailed illustration of the WoT paradigm for both researchers and newcomers, by reconstructing the temporal evolution of key concepts and the historical trends, providing an in-depth taxonomy of software architectures and enabling technologies of WoT deployments and, finally, discussing the maturity of WoT vertical markets. Moreover, we identify some future research directions that may open the way to further innovation on WoT systems.

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.

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.