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.

Urban scenarios present various concerns to micromobility users such as cyclists, which are an important part of the mobility infrastructure. As smarter cities increasingly focus on enhancing citizen well-being and infrastructure efficiency, navigation systems have primarily been designed for car drivers, with a focus on traffic conditions, while disregarding metrics that are important for micromobility users. As a matter of fact, they tend to privilege other aspects of their journey that enhance its comfort, rather than the mere path length. To address this gap, this paper presents a holistic architectural framework for micromobility-oriented navigation systems, incorporating nominal, data-driven and crowdsensed metrics in the loop. The paper presents the definition of metrics and a real world case study for each category of metrics, providing a real implementation. We demonstrate the effectiveness of our approach in a controlled environment before implementing and deploying the complete system in a real-world city, where we provide a detailed analysis of the results.

Anterior cruciate ligament (ACL) rupture remains a leading cause of long-term absence in football and is frequently triggered by sub-optimal mechanics during rapid change-of-direction (COD) manoeuvres. To transform laboratory motion analyses into actionable field feedback, a three-stage pipeline is presented. First, 45 kinematic and kinetic variables recorded from 1,009 youth footballers during COD tests are embedded with t-SNE and clustered by agglomerative clustering (Euclidean distance, Ward linkage), revealing four force-and-control phenotypes aligned with established ACL-risk mechanisms. Second, a Random-Forest classifier recreates these phenotypes from a set of 12 features selected for both importance and ease of capture, achieving a macro-averaged F1 = 0.85 (compared with 0.92 when all 45 variables are used). Third, the classifier is wrapped in a General Data Protection Regulation (GDPR) compliant application that accepts the 12 inputs, instantly assigns the athlete’s phenotype, and displays tailored exercise cues. The pipeline demonstrates that laboratory-grade biomechanics can be condensed into a rapid, interpretable decision-support tool, enabling data-driven ACL injury mitigation in routine sports medicine practice.

Mobile Crowd Sensing (MCS) leverages the widespread availability of smart devices to collect and analyze environmental and social data. While MCS has been widely applied in smart cities to optimize vehicle traffic and safety, the needs of pedestrians remain largely unaddressed. To address this need, we propose CrossTime, a sensing application designed to estimate waiting times at pedestrian crossings. Using GPS data, accelerometer readings, and open-source intersection location data, CrossTime autonomously detects when a user is waiting at an intersection. To evaluate its feasibility and accuracy, we conducted a test case on three routes in an urban environment, comparing system-detected waiting times with manually recorded values. Our results show that CrossTime effectively captures pedestrian waiting behavior, although there are some discrepancies due to sensor limitations and environmental factors.

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.

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.

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.

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.

Road surface quality is a major concern for bicycle riders and plays an important role in the mobility infrastructure. In the era where smarter cities aim to increase the well-being of citizens and the efficiency of infrastructures, navigation systems relying on Mobile Crowdsensing (MCS) are mostly designed for car drivers, and account for road traffic conditions. To cover the gap, in this paper, we propose a full architectural pipeline of an MCS-based navigation system for bicycle riders that accounts for the road surface quality. The MCS paradigm leverages the sensor data produced by the personal devices of participating citizens to describe phenomena of common interest. Our system classifies road segments using inertial sensor data gathered by users, using a combination of supervised and unsupervised methods, as human labeling in this context is impractical and too subjective. We prove the efficacy of our method in a controlled environment, and then we implement and deploy the full system in a real city, finally reporting on its results.

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.

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

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

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

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

Public Internet of Things (IoT) platforms, such as Thingspeak, significantly increased the availability of open IoT data and enabled faster and cheaper development of novel IoT applications by reducing or even eliminating the need for deploying their own IoT sensors and platforms. However, open IoT data is often heterogeneous, sparse, fuzzy, and lacks accurate description (which we refer to as IoT metadata). These limitations make open IoT data challenging to integrate and use, and prevent the efficient development of IoT applications. In fact, while several sensor data description models have been proposed and standardized, open IoT data currently lack or include only partial metadata description. Therefore, novel techniques for automatically annotating open IoT data are needed to fully unleash the power of open IoT. This article proposes a novel metadata-assisted cascading ensemble classification framework (MACE) for the automatic annotation of IoT data. MACE is capable of sequentially combining standalone classifiers, enabling it to cope with heterogeneous IoT data and different domains of information (e.g., numerical and textual), which have not been considered previously. MACE incorporates a novel ensemble approach for automatically selecting, sorting, filtering, and assembling classifiers in a way that improves annotation performance. This article presents extensive experimental evaluations of MACE using public IoT data sets. Results demonstrate that the MACE framework significantly outperforms existing solutions for open IoT data by as much as 10% in classification accuracy.

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.

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.

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.

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.

Mobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Typically, a large number of participants is required to make a sensing campaign successful. For such a reason, it is often not practical for researchers to build and deploy large testbeds to assess the performance of frameworks and algorithms for data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we present CrowdSenSim 2.0, a significant extension of the popular CrowdSenSim simulation platform. CrowdSenSim 2.0 features a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms. All these improvements boost the performances of the simulator and make the runtime execution and memory utilization significantly lower, also enabling the support for larger simulation scenarios. We demonstrate retro-compatibility with the older platform and evaluate as a case study a stateful data collection algorithm.

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.

Nowadays, Location Based Services (LBS) are fore- seen as a fundamental building block of modern mobile applications and services. Important examples of LBS concerns indoor environments in which GPS technology cannot be used. On the other hand, the great diffusion of pervasive Mobile Devices (MDs) as smartphones and tablets has enabled many positioning techniques, such as WiFi FingerPrinting (FP), that exploits all the MD's embedded sensors. This paper proposes and investigates the performance of a method exploiting a WiFi FP algorithm for indoor localization fed with information from the barometer to estimate the floor in which the MD is located. Our results, carried out in indoor areas at the University of Genoa (UniGE) and at the University of Bologna (UniBO), show that when more than 5 Access Points (APs) are used the proposed 3D positioning system is able to accurately localize the user with an error below 2 and 1.2 and meters for the UniBO and UniGE case, respectively.

The collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as smart cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such Internet of Things (IoT) scenarios need in order to be pervasive. In this paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, mobile crowdsensing management for environmental data, information representation, and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.

This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art.

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.

The Internet of Things (IoT) environments are no more a vision as they are already surrounding the everyday life of citizens. Its pervasive nature brings IoT ecosystems closer and closer to the end users, facilitating their domestic lives through home automation appliances and platforms. Several M2M communication technologies and data representation techniques have been standardized and often established by the manufacturers. For such reasons, many Home Automation Systems (HAS) are irreconcilable due to the incompatibility of their communication technologies. In order to take a step towards HAS interoperability, in this paper we propose RouteX, an experimental cross-technology platform able to manage home devices belonging to different networks and using different technologies. It leverages the potential of Service Oriented Architectures (SOA), providing the user with an abstraction layer over a multitude of home sensors and actuators. We also present a practical user interface, through which the user is able to administrate efficiently all his or her home appliances no matter which technology they use.

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.

The integration of automotive communications in 5G systems must build on a clear understanding of the performance of services for connected vehicles in today's LTE deployments. In this paper, we carry out a simulation-based performance evaluation of automotive communications in LTE, with particular attention to realism: to that end, we investigate the impact of different road traffic models, employ a state-of-the-art commercial LTE tool, and study a practical service use case. Our results demonstrate that unrealistic road traffic datasets can bias network simulations in urban vehicular environments, and provide insights on the limitations of the current radio access architecture, when confronted to connected vehicles.

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.

In this article, we assess the viability of underlay sensor networks in frequencies used by an incumbent digital TV broadcasting system, that is, in the so-called TV grayspaces (TVGS). Grayspace operations are particularly interesting when other unlicensed bands are overcrowded, for example, due to high-volume WiFi operations. We simulate the operational characteristics of the recent IEEE 802.15.4m standard for low-rate wireless personal area networks to evaluate the performance degradation of an incumbent Digital Video Broadcasting - Terrestrial (DVB-T) system if a secondary network of low-power low-rate devices are co-deployed in the same frequency bands. Our results show that short sensor messages will not disrupt the DVB-T service due to the existing error-correction capabilities. Furthermore, if sufficient separation distances to primary transmitters are maintained, transmit powers are sufficient to achieve reasonable connectivity levels of the secondary network. In order to obtain realistic figures on the predicted feasibility of grayspace sensor networks, we study the deployment constraints of a hypothetical secondary network co-located with the TV broadcasting network of Germany. Our analysis shows that if we aim to support a minimum sensor-sensor distance, no universal coverage can be maintained in this country. While our quantitative results are specific to Germany, we deem them indicative for the expected results also in other potential deployments. We found that while a secondary wireless sensor network in TVGS is technically possible, the necessary constraints on operational parameters and service levels for TVGS co-existence will significantly limit its practical viability.

Modern smartphones are nowadays equipped with a multitude of sensors, which extend their capabilities paving the way for a multitude of services. Among these, the ability to locate the device is exploited by many. While outdoor the GPS provides good accuracy, indoor localization is challenging to be performed with it, as buildings shadow the satellite signal. In particular, the barometric pressure sensor is often used to determine the altitude of the device from the ground floor, particularly for safety applications and indoor navigation. However, pressure changes during the day, and thus it is challenging to bind a static value to a specific altitude. In this work, we propose a self-adapting algorithm able to determine the height at which the device is in a building, by exploiting the barometric pressure. We implemented and tested our algorithm on an Android application, and we compared it against other techniques. We tested our proposal for three specific use-cases, and our results show the benefit of our proposal.

This paper presents a publish-subscribe architecture designed to support information level interoperability in smart space applications in the Internet of Things (IoT). The architecture is built on top of a generic SPARQL endpoint where publishers and subscribers use standard SPARQL Updates and Queries. Notifications about events [i.e., changes in the resource description framework (RDF) knowledge base] are expressed in terms of added and removed SPARQL binding results since the previous notification, limiting the network overhead and facilitating notification processing at subscriber side. A novel event detection algorithm, tailored on the IoT specificities (i.e., heterogeneous events need to be detected and continuous updates of few RDF triples dominate with respect to more complex updates), is presented along with the envisioned application design pattern and performance evaluation model. Eventually, a reference implementation is evaluated against a benchmark inspired by a smart city lighting case. The performance evaluation results show the capability to process up to 68k subscriptions/s triggered by simple single-lamp updates and up to 3.8k subscriptions/s triggered by more complex updates (i.e., 10 to 100 lamps).

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.

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.

The complex interactions between electric mobility on a large scale with the electric distribution grid constitute a considerable challenge regarding the feasibility, the efficiency and the stability of smart electric distribution grids. On the one hand, the steadily increasing share of decentralized power generation from renewable sources entails a move away from electro-mechanical generators with huge inertia towards systems with distributed small-and medium-scale generators which are coupled to the grid via inverters. On the other hand, large-scale electric mobility which interacts with such a decentralized grid will have a huge impact on the power generation, storage potential and consumption patterns of a grid. Grid infrastructure simulations which take into account the details of these interactions and which are backed by comprehensive demonstrators may help to shed light on crucial aspects of both energy and information exchange between the traffic and the electric energy infrastructure regime. This will be highlighted by selected topics which intend to shed light on the scope and the challenges inherent in this area of simulation.

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.

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.

It is our pleasure to introduce the WoWMoM 2011's workshops program. The Workshops associated with WoWMoM feature a large range of topics beyond the main conference, to better cover and stimulate discussion on the emerging research areas in the exciting field of wireless, mobile and multimedia networks. As the integral part of the main conference, the purpose of the workshops is to provide a less formal and more focused forum where novel and stimulating ideas and experiences are shared among researchers both from industry and academia, on all the topics of interest for the wireless, mobile and multimedia networking community. The number and quality of the workshops contribute to the relevance of WoWMoM as one of the reference forums for the research community working on these topics.

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.

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.

Connection-then-credits (CTC) is a novel end-to-end flow control protocol to handle message-dependent deadlocks in best-effort networks-on-chip (NoC) for embedded multicore systems-on-chip (SoCs). CTC is based on the classic end-to-end credit-based flow control protocol but differs from it because it uses a network interface microarchitecture where a single credit counter and a single input data queue are shared among all possible communications. This architectural simplification reduces the area occupation of the network interfaces and increases their design reuse; for instance, the same network interface can be used to connect a core independently of the number of incoming and outgoing communications. CTC, however, requires a handshake preamble to initialize the credit counter in the sender network interface based on the buffering capacity of the receiver network interface. While this necessarily introduces a latency overhead in the transfer of a message, simulation-based experimental results show that the penalty in performance is limited when large messages need to be transferred, thus, making CTC a valid solution for particular classes of applications such as video stream processing.

Special issue on ''quality of service and security in wireless and mobile networks''Recently, wireless and mobile communication systems have become increasingly popular as an inexpensive and promising means for ubiquitous communications and mobile computing.This trend will continue to affect the way in which we live, work, and play with innovative services based on the pervasive and mobile communications support.A preliminary condition for those services to become widely used is the effective support of quality of service (QoS) and security properties.For this reason, QoS and security management of wireless and mobile communication systems are very important issues for the future generation of wireless and mobile networks and services, and the design and management of solutions for QoS and security are some of the most challenging research tasks, in scenarios like the wireless and mobile systems, characterized by new assumptions and limitations.The main limitations typically involve the resources like battery energy, computation, and bandwidth.The new assumptions include the unprecedented dynamics caused by mobility, heterogeneity, variable channel conditions, the traffic characterization of new emerging applications, the open communication nature, and the distributed management architecture of wireless communication systems, like the wireless ad hoc, mesh, vehicular, and sensor networks.This special issue brings together nine contributions, selected from a total of 25 submissions, that address the issues of architectures, technologies, and protocols for wireless and mobile communication systems.The selected papers propose and analyze some of the most interesting and innovative solutions dealing with the QoS and Security management in wireless mobile networks, from different viewpoints, including the QoS-based channel time allocation and scheduling in IEEE 802.15.3 wireless PANs, QoS analysis of IEEE 802.15.4Low rate WPAN technology in industrial application scenarios, QoS routing with obstacle avoidance properties in wireless sensor

We present the aEqualized routing algorithm: a novel algorithm for the Spidergon Network on Chip. AEqualized combines the well known aFirst and aLast algorithms proposed in literature obtaining an optimized use of the channels of the network. This optimization allows to reduce the number of channels actually implemented on the chip while maintaining similar performances achieved by the two basic algorithms. In the second part of this paper, we propose a variation on the Spidergon's router architecture that enhances the performance of the network especially when the aEqualized routing algorithm is adopted.

The usage of sensors is emerging in the nowadays scenario where safety related applications are more and more used. These applications have timing constraints that need to be met by the control system. Thus, guaranteeing timeliness properties is considered a key challenge for research on wireless sensor networks. Among the different components that need to address this problem, we focus on the communication subsystem and, in particular, on the MAC protocols that handles transmissions. To provide timeliness behavior, the employed MAC protocol has to provide a time-bounded service. The contribution of this paper is to investigate the performance of two wireless MAC protocols that can be employed in wireless sensor networks: IEEE 802.11e and WRT-Ring. The former is part of the well know IEEE 802.11 standard, whereas the latter has characteristics that well fit the requirements of wireless sensor networks. Results show that two protocols have similar performances for networks with limited devices and/or traffic, whereas when devices and/or traffic increases, WRT-Ring has to be preferred.

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.

We propose Connection then Credits (CTC) as a new end-to-end flow control protocol to handle message-dependent deadlocks in networks-on-chip (NoC) for multicore systems-on-chip. CTC is based on the classic end-to-end credit-based flow control protocol but differs from it because it uses a network interface micro-architecture where a single credit counter and a single input data queue are shared among all possible communications. This architectural simplification reduces the area occupation of the network interfaces and increases their design reuse: for instance, the same network interface can be used to connect a core independently of the number of incoming and outgoing communications. CTC, however, requires a handshake preamble to initialize the credit counter in the sender network interface based on the buffering capacity of the receiver network interface. While this necessarily introduces a latency overhead in the transfer of a message, simulation-based experimental results show that the penalty in performance is limited when large messages need to be transferred, thus making CTC a valid solution for particular classes of applications such as video stream processing.

The advent of pervasive wireless communication technologies and the deployment of heterogeneous communication systems and architectures, from cellular communication systems to sensors and ad hoc networks, have fostered the research community to deal with the limitations and new assumptions of the wireless and mobile communication systems. The main limitations typically involve the resources like battery energy, computation and bandwidth. The new assumptions include the unprecedented dynamics caused by mobility, heterogeneity, variable channel conditions, the traffic characterization of new emerging applications and the open, distributed management architecture of wireless communication systems like the ad hoc and wireless sensor networks. This special issue brings together eight contributions on proposals for the resources management and mobility management in wireless networks, from different viewpoints, including QoS support, dynamic channel allocation, fault-tolerance, power saving and power efficiency, clustering and security issues. The papers have been selected from a total of more than 40 submitted papers, due to their originality and contribution to the field of wireless communications and mobile computing, with a particular emphasis on resources and mobility management in wireless networks – the theme of this special issue. The paper ‘An efficient distributed fault-tolerant protocol for dynamic channel allocation’ by Azzedine Boukerche, Tingxue Huang and Kaouther Abrougui, provides a proposal for a dynamic and distributed channel allocation scheme which incorporates the advantages of fault-tolerance and mobility support, and its performance evaluation. The paper ‘Mitigating the impact of node mobility on ad hoc clustering’ by Rituparna Ghosh and Stefano Basagni, evaluates the impact of different node mobility assumptions and provides some guidelines for improving the effectiveness and overhead reduction of clustering solutions in wireless ad hoc network scenarios. The paper ‘Transmission power and duration-aware playout control for packetized media streaming over wireless links’ by Yan Li and Nickolas Bambos illustrates and analyzes resource-saving solutions for transmission power control and playout buffer control to support QoS for media streaming over wireless links. The paper ‘Energy and connectivity performance of routing groups in multi-radio multi-hop networks’ by Michele Rossi, Leonardo Badia, Paolo Giacon and Michele Zorzi, provides a modelling and analysis of the in-network aggregation of wireless devices into groups, exploiting the connectivity potential of multiple radios to simplify the routing management in multi-hop wireless networks. The paper ‘MUSAQ: a multimedia session-aware QoS provisioning scheme for cellular networks’ by Mona El-Kadi Rizvi and Stephan Olariu, provides a QoS management scheme that reduces the self-contention of multiple streams composing a multimedia communication session, by exploiting the prioritization made possible by the knowledge of the relationships between competing flows in a multimedia session. The paper ‘A modified hopfield network for mobility management’ by Javid Taheri and Albert Y. Zomaya, proposes a novel and efficient approach based on heuristics and neural network techniques for optimizing the location management solutions of mobile devices. The paper ‘An application-driven approach to designing secure wireless sensor networks’ by Eric Sabbah, Kyoung-Don Kang, Nael Abu-Ghazaleh, Adnan Majeed and Ke Liu, introduces some concepts and guidelines for realizing a dynamically reconfigurable application-driven and architecture-dependent security framework coping with dynamics and resource limitations in wireless sensor networks. The paper ‘Should I send now or send later? A decision-theoretic approach to transmission scheduling in sensor networks with mobile sinks’ by Ladislau Boloni and Damla Turgut, proposes a decision theoretic approach aiming to reduce the energy and bandwidth resources used in wireless sensor networks with mobile sinks, under different assumptions regarding the knowledge of mobility parameters. We wish to thank all the authors for their great work and for considering the Wiley's WCMC journal for submitting their papers. Special thanks go to the anonymous reviewers for their help and dedication in reviewing the papers and providing useful comments to authors for paper improvements. Thanks to the EiC, Prof. Mohsen Guizani for allowing us to host this special issue in the prestigious Wiley's WCMC journal. We hope that this special issue will represent a timely and significant reference for future researches.

NoC architectures are considered the next generation of communication infrastructure for future systems-on- chip. Selection of the network architecture and mapping of IP nodes onto the NoC topology are two important research topics. In this paper we compare well known NoC interconnect systems, specifically, Ring, 2d-Mesh, Spidergon and unbuffered Crossbar using theoretical uniform traffic based on the request/reply paradigm as well as a realistic traffic based on a Mpeg4 application. The IP mapping is computed by the SCOTCH partitioning tool opportunely modified to maximize selected embedding quality criteria under multiple topological constraints.

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.

This paper illustrates the effects of the Hyper- Threading processor technology on the runtime performance of a parallel and distributed simulation middleware. A preliminary analysis of the middleware design and execution parameters is given to identify the tuning parameters and to evaluate the scalability of parallel simulation. A real testbed scenario has been illustrated, based on the ART'S parallel and distributed simulation middleware. The experimental analysis has provided some interesting guidelines about the way to adapt the parallel and distributed simulation middleware to Hyper-Threading and to increase the execution speed of the simulation.

The distributed and the Grid Computing architectures for the simulation of massively populated wireless systems have recently been considered of interest, mainly for cost reasons. Solutions for generalized proximity detection for mobile objects is a relevant problem, with a big impact on the design and the implementation of parallel and distributed simulations of wireless mobile systems. In this paper, a set of solutions based on tailored data structures, new techniques and enhancements of the existing algorithms for generalized proximity detection are proposed and analyzed, to increase the efficiency of distributed simulations. The paper includes the analysis of computation complexity of the proposed solutions and the performance evaluation of a testbed distributed simulation of ad hoc network models. Recent works have shown that the performance of distributed simulation of dynamic complex systems could benefit from a runtime migration mechanism of model entities, which reduces the communication overheads. Such migration mechanisms may interfere with the generalized proximity detection implementations. The analysis performed in this paper illustrates the effects of many possible compositions of the proposed solutions, in a real testbed simulation framework.

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.

In this work we define and test a new framework obtained as the integration of two recently developed middlewares defined to support the parallel and distributed simulation of large scale, complex and dynamically interacting system models (like wireless and mobile network systems). In a distributed simulation of highly interacting system models, the main bottleneck may become the communication and synchronization required to maintain the causality constrains between distributed model components. We designed and implemented the ARTÌS middleware as a new framework incorporating a set of features that allow an adaptive optimization of the communication layer management in a distributed simulation scenario. ARTÌS has been integrated with GAIA, a dynamic mechanism for the runtime management and adaptive allocation of model entities in a distributed simulation. By adopting a runtime evaluation of causal bindings between model entities GAIA adapts the dynamic and time-persistent causal effects of model interactions to dynamic migration of model entities. Preliminary results demonstrate that the combined effect of ARTÌS management and GAIA heuristics leads to a significant reduction in the communication and synchronization overheads between the physical execution units. Simulation performance enhancements have been obtained also in worst-case modelling assumptions and simulation scenarios.

The simulation of ad hoc and sensor networks often requires a large amount of computation, memory and time to obtain significant results. The parallel and distributed simulation approach can be a valuable solution to reduce the computation time, and to support model components' modularity and reuse. In this work we perform a testbed evaluation of a new middleware for the simulation of large scale wireless systems. The proposed middleware has been designed to adapt and to scale over a heterogeneous distributed execution infrastructure. To realize a testbed evaluation of the considered framework we implemented and investigated a set of wireless systems' models. Specifically, we identified two classes of widely investigated wireless models: mobile ad hoc, and static sensor networks. In this work we present the performances of the simulation framework, with respect to the heterogeneous set of execution architectures, and the modeled systems' characteristics. Results demonstrate that the framework leads to increased model scalability and speed-up, by transparently adapting and managing at runtime the communication and synchronization overheads, and the load balancing.

IEEE 802.11 is the standard for wireless local area networks (WLANs) promoted by the Institute of Electrical and Electronics Engineers. Wireless technologies in the LAN environment are becoming increasingly important and the IEEE 802.11 is the most mature technology to date. Previous works have pointed out that the standard protocol can be very inefficient and that an appropriate tuning of its congestion control mechanism (i.e., the backoff algorithm) can drive the IEEE 802.11 protocol close to its optimal behavior. To perform this tuning, a station must have exact knowledge of the network contention level; unfortunately, in a real case, a station cannot have exact knowledge of the network contention level (i.e., number of active stations and length of the message transmitted on the channel), but it, at most, can estimate it. We present and evaluate a distributed mechanism for contention control in IEEE 802.11 wireless LANs. Our mechanism, named asymptotically optimal backoff (AOB), dynamically adapts the backoff window size to the current network contention level and guarantees that an IEEE 802.11 WLAN asymptotically achieves its optimal channel utilization. The AOB mechanism measures the network contention level by using two simple estimates: the slot utilization and the average size of transmitted frames. These estimates are simple and can be obtained by exploiting information that is already available in the standard protocol. AOB can be used to extend the standard 802.11 access mechanism without requiring any additional hardware. The performance of the IEEE 802.11 protocol, with and without the AOB mechanism, is investigated through simulation. Simulation results indicate that our mechanism is very effective, robust, and has traffic differentiation potentialities.

To achieve more efficient usage of channel bandwidth and provide better protection for the media payload transmitted over lossy packet-switched networks, we introduce a new scheme of generic uneven level protection (ULP) forward error correction. The scheme provides different protection levels for data of different significance within a packet. The ULP scheme is designed to be independent from the nature of the media that it protects, and it is very flexible for any protection configuration the user might need without using any out-of-band signaling. Simulation using a video stream transmitted over a lossy packet-switched network shows that the ULP algorithm achieves significant gain for the quality of the transmission over a wide range of network conditions.