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A new generation of Wireless Local Area Networks (WLANs) will make its appearance in the market in the forthcoming years based on the amendments to the IEEE 802.11 standards that have recently been approved or are under development. Examples of the most expected ones are IEEE 802.11aa (Robust Audio Video Transport Streaming), IEEE 802.11ac (Very-high throughput at < 6 GHz), IEEE 802.11af (TV White Spaces) and IEEE 802.11ah (Machine-to-Machine communications) specifications. The aim of this survey is to provide a comprehensive overview of these novel technical features and the related open technical challenges that will drive the future WLAN evolution. In contrast to other IEEE 802.11 surveys, this is a use case oriented study. Specifically, we first describe the three key scenarios in which next-generation WLANs will have to operate. We then review the most relevant amendments for each of these use cases focusing on the additional functionalities and the new technologies they include, such as multi-user MIMO techniques, groupcast communications, dynamic channel bonding, spectrum databases and channel sensing, enhanced power saving mechanisms and efficient small data transmissions. We also discuss the related work to highlight the key issues that must still be addressed. Finally, we review emerging trends that can influence the design of future WLANs, with special focus on software-defined MACs and the internet-working with cellular systems.

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.

Cognitive radio can be applied to a multitude of domains, one of which is M2M communication. Specifically, M2M communication refers to communication between devices without human intervention. Hence, devices should be able to organize themselves and run the communication protocol autonomously. If cognitive radio is used, tasks such as dynamic spectrum access (DSA), spectrum sensing, and alike present additional challenges compared to traditional network, as all the decision framework should be implemented and automatized in the devices. In this chapter, we focus on DSA techniques for M2M. The main difference from other kinds of communication is relative both to the energy efficiency and to the low protocol overhead, as devices should run for long periods of time and run without human intervention. At first we present related work from literature, categorizing the different tasks devices which want to leverage DSA on M2M have to perform. At the end of the chapter, we present a proof of concept of a general framework, which can be applied to different scenario concerning M2M, encompassing all the spectrum management and measurement tasks M2M devices should generally perform. Finally, we derive open challenges and future research directions concerning this scenario.

At present, battery-charging operations constitute one of the most critical obstacles toward a large-scale uptake of electric mobility (EM), due to performance issues and implementation complexities. Although several solutions based on the utilization of information and communication technologies and on mobile applications have been investigated to assist electric vehicle (EV) drivers and to coordinate charging operations, there is still the problem of how to evaluate and validate such solutions on realistic scenarios, due to the lack of accurate simulators integrating vehicular mobility, wireless communication, and battery charging/discharging models. In this paper, we attempt to fill this gap by proposing a novel EV simulation platform that can assist in the predeployment of charging infrastructures and services on realistic large-scale EM scenarios. The simulation platform, which is realized within the ARTEMIS EU project “Internet of Energy for Electric Mobility,” supports two utilization modes, i.e., evaluation of EM scenarios and immersive emulation of EM-related mobile applications, due to a semantic architecture through which virtual and real components can be integrated in a seamless way. We provide three major contributions with respect to the state of the art. First, we extend the existing cosimulation platform composed of SUMO (a vehicular traffic simulator) and OMNET++ (a network simulator) with realistic models of EVs, electric vehicle supply equipment, and ontology-based communication protocols that enable the deployment of city-wide mobile services (e.g., charging reservation). Second, we validate the battery model against the consumptions data of target EVs, and we evaluate the operations of EVs on a large-scale scenario (the city of Bologna, Italy), by analyzing the effectiveness of the charging reservation process and the resulting impact to the smart grid. Finally, we introduce the Mobile Application Zoo, which is a sandbox through which EM-related mobile applications can be seamlessly integrated within the simulation platform to be validated on virtual environments before their deployment on real scenarios, and we describe the implementation of an Android application for battery monitoring and charging reservation.

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.

Machine-to-Machine (M2M) communication technologies enable autonomous networking among devices without human intervention. Such autonomous control is of paramount importance for several deployments of the Internet of Things (IoT), including smart manufacturing applications, healthcare systems and home automation just to name a few. As a result, several M2M technologies are nowadays available on the market as either proprietary solutions or the effort of standardization initiatives, each targeted for a specific class of IoT applications and characterized by unique features in terms of achievable performance, frequency in use and supported network topologies. In this paper, we aim to organize the existing M2M approaches and technologies into a consistent framework that provides an in-depth vision of the main trends, future directions and open issues. We provide three main contributions in this survey. First, we identify the main use cases and requirements of M2M scenarios and we introduce a multi-layer taxonomy for M2M solutions, taking into account both deployment types and PHY/MAC characteristics. Second, in light of such characteristics, we provide an in-depth review of the existing M2M wireless technologies, considering both proprietary and open/standardized solutions for proximity-based, short-range and large-scale networks. Finally, we perform a critical comparison of the surveyed solutions over different M2M use cases and requirements, and we identify the research directions and open issues that still have to be addressed.

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.

The Internet of Things (IoTs) is growing fast both in terms of number of devices connected and of complexity of deployments and applications. Several research studies an- alyzing the economical impact of the IoT worldwide identify the interoperability as one of the main boosting factor for its growth, thanks to the possibility to unlock novel commercial opportunities derived from the integration of heterogeneous systems which are currently not interconnected. However, at present, interoperability constitutes a relevant practical issue on any IoT deployments that is composed of sensor platforms mapped on different wireless technologies, network protocols or data formats. The paper addresses such issue, and investigates how to achieve effective data interoperability and data reuse on complex IoT deployments, where multiple users/applications need to consume sensor data produced by heterogeneous sensor networks. We propose a generic three-tier IoT architecture, which decouples the sensor data producers from the sensor data consumers, thanks to the intermediation of a semantic broker which is in charge of translating the sensor data into a shared ontology, and of providing publish-subscribe facilities to the producers/consumers. Then, we describe the real-world implementation of such architecture devised at the Advanced Research Center on Electronic System (ARCES) of the University of Bologna. The actual system collects the data produced by three different sensor networks, integrates them through a SPARQL Event Processing Architecture (SEPA), and supports two front- end applications for the data access, i.e. a web dashboard and an Amazon Alexa voice service.

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.

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smart-phones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition., Comment: Pre-print of the accepted version for the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 2018

The current merging of networking and control research fields within the scope of robotic applications is creating fascinating research and development opportunities. However, the tools for a proper and easy management of experiments still lag behind. Although different solutions have been proposed to simulate and emulate control systems and, more specifically, fleets of Unmanned Aerial Vehicles (UAVs), still they do not include an efficient and detailed network-side simulation, which is usually available only on dedicated software. On the other hand, current advancements in network simulations suites often do not include the possibility to include an accurate description of controlled systems. In the middle 2010s, integrated solutions of networking and control for fleets of UAVs are still lacking. In this paper, we fill such gap by presenting a simulation architecture for networked control systems which is based on two well-known solutions in both the fields of networking simulation (the NS-3 tool) and UAV control simulation (the FL-AIR tool). Three main research contributions are provided: (i) first, we show how the existing tools can be integrated on a closed-loop architecture, so that the network propagation model (NS-3 side) is influenced by the drone mobility and by the 3D scenario map (FL-AIR side); (ii) second, we implement a novel module, which allows modeling realistic 3D environments by importing city-wide characteristics by the popular OpenStreetMap service; (iii) third, we demonstrate the modeling capabilities of the CUSCUS framework on two realistic use-cases, corresponding to well-known application scenarios of UAVs, i.e. dynamic formation control and static coverage of a target area.

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 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.

Television (TV) white space (WS) constitutes a key technology to support the increasing worldwide growth of spectrum demand with several regulation standards that are already available for long- and medium-range communications. At the same time, estimations based on WS spectrum databases (SDBs) indicate that the availability of TVWS is often very limited in dense urban areas where spectrum resources are more needed. Therefore, the benefits provided by the utilization of TVWS have yet to be fully assessed. In this article, we rethink the utilization of TVWS in indoor communication environments through novel three-dimensional (3-D) spectrum-sharing mechanisms. Based on measurements that demonstrate the differences in terms of spectrum opportunities at different floors of the same building, we propose an underlay spectrum-sharing architecture to enable a per-building finegrained reuse of TV frequencies while protecting the operations of TV receivers in a neighborhood. We evaluate the effectiveness of the proposed spectrum-sharing architecture over several urban environments in Italy by taking into account many real characteristics of the scenarios. Our results demonstrate that through our architecture, more spectrum resources than what are reported in the SDB can be available for indoor scenarios, even in highly congested urban areas, paving the way to novel TVWS applications.

The Internet of things is foreseen as one of the next imminent Internet revolutions, as many devices will seamlessly communicate together to provide new and exciting services to the end users. One of the challenges that the IoT has to face is about both the heterogeneity of the data available and the heterogeneity of the communication. In this paper we focus on the former, by presenting an architecture able to integrate data coming from different sources, including custom made deployments and government data. New services can be deployed directly by the end users, using reliable or unreliable data sources, and new processed data can be gathered by these services and used by others.

In most countries the share of circulating electric vehicles (EV) is less than 1%. Beside cost issues, the detrimental factors that most discourage people from purchasing EVs are their limited range and the rather poor coverage area of the recharging infrastructure. This clearly indicates the need for higher investments in vehicular technologies and infrastructures; however, software services and applications also play a major role in the transition to electric mobility, e.g. to mitigate EV driver anxiety. In this paper we illustrate the recipe proposed by the European Internet of Energy (IoE) project toward interoperable services for large-scale EV mobility scenarios: it consists of a Service-Oriented Architecture (SoA) to collect and integrate data coming from the heterogeneous actors of the EV scenario thanks to a semantic data tier. On top of it, we describe the implementation of an advanced Route Planning service to compute the optimal path towards a destination according to user-defined parameters and goals; differently from other approaches, our tool integrates reservation mechanisms of charging slots, so that a complete planning of the itinerary can be produced. Effectiveness of the route planning service is evaluated on large-scale EV scenarios (e.g. the Italian Emilia-Romagna region) thanks to an innovative emulation-simulation platform.

In the recent years the Smart City paradigm has gained interest worldwide. Services are built on top of data sensed in the city and then analyzed in order to enhance people's quality of life. Nowadays users are also able to participate in such a data gathering, mostly thanks to a reduction in the cost of the sensing devices. Moreover, smartphones encompass many useful sensors and can be leveraged to obtain data by end users on the move, within the scope of mobile crowdsensing. In this paper, we propose SenSquare, a mobile crowdsensing architecture for Smart Cities, built to embrace both data availability and devices heterogeneity. SenSquare also offers the possibility for stakeholders to reward users sharing their data. Finally, we compare our proposal against a non-smart ideal architecture, showing the benefits and the advantages of a smart architecture such as SenSquare.

After the digital TV switch-over, national spectrum regulators are considering opportunistic spectrum access techniques in the TV White Spaces (TVWS) frequency band. At present, the reference solution envisages the utilization of geolocation spectrum databases (GLDBs), in which spectrum availability is computed through complex propagation models. However, recent studies indicate that the used path loss model in GLDBs could be either inaccurate or too much conservative, possibly reducing the use of TVWS for opportunistic use by secondary networks. In this paper, we investigate the possibility to enhance the estimation accuracy of GLDBs with sensing reports produced by a swarm of Unmanned Aerial Scanning Vehicles (UASVs). These latter are able to explore the scenario in both space and frequencies, and to build a fine-grained shadowing map which can be used to tune the accuracy of propagation model used by GLDB. A novel distributed mobility algorithm is described for the sensing coverage of the scenario, and an aggregation mechanism for the map creation is illustrated. Simulation results confirm the effectiveness of our scheme in terms of TVWS detection accuracy and scenario coverage issues.

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).

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.

Nowadays, the Wi-Fi Direct technology is supported by most of smartphones on the market, and provides a viable solution to guarantee opportunistic communication among group of devices in a 1-hop range. However, the current specifications of the standard do not support the inter-group communication, which constitutes a key requirement for content-delivery applications like the public safety ones. In this paper, we provide an in-depth analysis of the utilization of the Wi-Fi Direct technology for safety message dissemination over emergency and post-disaster scenarios. Three main contributions are provided. First, we show the experimental results of the Wi-Fi Direct technology on a test-bed composed of multiple heterogeneous smartphones, and we analyze the main factors affecting the system performance, like the network setup overhead, the communication range and the network throughput. Second, we investigate how to create multi-group Peer-to-Peer (P2P) networks by leveraging on the presence of P2P relay devices, which are in charge of offloading the data among different P2P groups, although being connected to only one P2P group at a time. An analytical model is proposed in order to derive the optimal group switching time which provides the best trade-off between the multihop delay and the delivery rate, by taking into account the buffer size of the P2P Group Owners (GO) devices. Finally, we describe the implementation of the network formation algorithm within the Emergency Direct mobile application, which allows the multi-hop dissemination of instantaneous and geo-localized alert messages among the smartphones located in the scenario of the emergency.

According to several recent studies, the overhead caused by charging operations to the users' daily activities constitutes one of the main issues discouraging the purchasing of an Electric Vehicle (EV). At present, multiple factors contribute to such overhead, including the limited EV range, the duration of the charging phase, and the non-uniform coverage of the Equipment Vehicle Service Stations (EVSSs) in most areas of the world. Although the situation is going to improve in the long term thanks to the technological advances of the EVs and of the charging infrastructures, ICT-based solutions are needed to minimize the overhead in the short term. To this aim, in this paper we propose the WhatIF application, a software that allows the planning and simulation of EV-related scenarios. Through a mobile client, the users of our system can register their daily journeys, including driving activities and planned stops. A back-end module allows verifying the feasibility of a journey with an EV, scheduling the (eventual) recharging operations during the planned stops. A feasibility energy-optimal algorithm minimizing the overall charged energy is proposed. The performance of the WhatIF application has been tested over a large-scale simulated EV scenario (i.e. the Italian Emilia-Romagna region), considering realistic road topology, EVSSs locations, EV battery models and mobility patterns. Simulation results indicate that 76\% of the EVs are able to complete their journeys when scheduling charging operations during the stops, hence confirming the effectiveness of the WhatIF application in mitigating the overhead of EV mobility.

The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches.

In the aftermath of a natural calamity, relief operations can be hindered by damages to the terrestrial infrastructures (e.g. cellular base stations) that might lead to the disruption of wireless communication services. As a result, network partitions made up of isolated End-User (EU) devices, heterogeneous in terms of wireless access technologies and transmitting frequency bands, can occur within the scenario. In this paper, we address the problem of how to deploy a temporary and dynamic wireless network in order to quickly re-establish the end-to-end connectivity among isolated devices in a post-disaster environment. To this purpose, we propose the utilization of Repairing Units (RUs), consisting of Unmanned Ground Vehicles (UGVs) equipped with multiple Cognitive Radio (CR) devices; swarms of RUs are able to self-organize into a Repairing Mesh Network (RMN) that connects the isolated EU devices. Three main contributions are provided in this paper. First, we address the theoretical problem of determining the optimal deployment of the RMN (in terms of position and channel allocation on each RU), so that the number of connected EU devices is maximized, given a constrained number of available RUs. We further divide the deployment problem into a multi-channel spatial coverage and mesh connectivity problems, and we provide an approximated (optimal) solution. Second, we propose a distributed algorithm—based on the virtual spring force model—through which the RUs are able to explore the scenario in terms of space/frequency, and to create the RMN. Third, we evaluate connectivity and adaptiveness of the distributed solution through extensive Omnet++ simulations and a small scale test-bed. Simulation results show that the distributed RMN deployment algorithm provides performance close to the approximated solution in terms of covered EU devices. Experimental results demonstrate the ability of the distributed virtual spring model to adapt to dynamic propagation conditions, in order to maximize the quality of the wireless links of the RMN.

Nowadays, the convergence of Internet of Things (IoT) networking and mobile applications is favoring the deployment of novel and advanced smart parking systems through which users can be informed in real-time about the presence of vacant parking spots close to their destinations. In this paper, we provide an example of such opportunity, by describing Park Here!, a novel mobile application that aims at mitigating the overhead caused by parking spot seeking operations in urban areas. Our solution targets common city environments, where no per-spot sensors are available, and there is no remote service allowing the reservation in-advance of a parking spot. For this scenario, we propose a novel algorithm for the automatic detection of parking actions performed by the user, through the analysis of smartphone embedded sensors' (accelerometer/gyroscope), and of the Bluetooth connectivity. Once a parking event has been detected, an adaptive strategy allows disseminating the information over the target scenario, using a combination of Internet connection to a remote server, and Device-to-Device (D2D) connections over WiFi Direct links. Preliminary experiments demonstrate the accuracy of the proposed algorithm in correctly identifying parking events in an automatic way, and hence in notifying information to other potentially interested users.

In modern smart cities, mobility based on Electric Vehicles (EVs) is considered a key factor to reduce carbon emissions and pollution. However, despite the global interest and the investments worldwide, the user acceptance level is still low, mainly due to the lack of charging services support. This is one of the main causes for the so called “EV driver's anxiety”, and has led people to consider EV mobility suitable only for short routes. To contrast this issue, we propose here a route planner application supporting EV mobility also on medium and long routes, through prediction of range and charging stops. Our application estimates the minimal energy consumption path, by also considering the overhead to reach the charging stations along the way towards the destination. We demonstrate the optimality of the algorithm and we describe its implementation within a Web-application which connects to charging providers' services (to retrieve the locations of charging spots) and to Google services (for routing directions and real-time traffic data). Finally, we evaluate the scalability of our application, and we study its effectiveness in supporting EV routes on large-scale scenarios (e.g. the Emila-Romagna region in Italy) through immersive simulation techniques.

Electric Vehicles (EVs) represent one of the most promising solutions toward sustainable transportation systems. However, some aspects of EV-based mobility pose challenges for a larger market uptake. Among the others, the overhead of charging operations (e.g. Long recharge time), and the lack of accurate information about availability of EV supply stations (EVSSs) while being on board of an EV are perceived by customers as important limitations, and determine a low user acceptance. To tackle these issues, additional assistance must be provided to EV drivers, through the utilization of ICT-based solutions. In this paper, we describe the implementation of a mobile Android application, which has been deployed within the EU Internet of Energy (IoE) project, with the goal of supporting a larger uptake of EV-based mobility. The application provides full assistance to EV drivers, through functionalities of battery monitoring, dynamic range prediction, and EVSS discovery along the way. Moreover, it supports the IoE semantic architecture, and allows EV drivers reserving a charging slot based on their preferences, and on current availability of EVSSs. The user acceptance of the application has been tested through a questionnaire. Test results confirm the importance of charging reservation mechanisms to mitigate EV driver anxiety problems.

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.

In the recent years, wireless communication experienced a huge growth, and nowadays many services are built on top of it. Much of the user demands comes from indoor environments, in which obstructions like walls and floors decrease the received signal to levels not suitable for reliable and high speed communication. Recently, national regulators worldwide started to investigate the usage of TV bands, thanks to the switch from analog to digital TV. These regulations gave birth to different wireless standards to make use of this new opportunistic spectrum. In this paper, we show how different 802.11 standard behave in indoor environment. Namely, we analyze IEEE 802.11n networks in the 2.4 GHz band, and IEEE 802.11af in the TV band, by means of theoretical analisys and simulations. Then, we design an algorithm that leverages the use of the different IEEE 802.11 amendments, by monitoring the achievable data rates with respect to the packet-error-rate, and provide simulation results on its performance on different modeled scenarios.

The use of wireless technology to communicate has spread to a variety of devices. Traditional solutions based on the well known IEEE 802.11 struggle to cope with a large number of contending nodes. The need to improve throughput and meet QoS requirements has driven the quest for more elaborated channel access mechanisms. The main contribution of this work is to propose a fair and fast channel access resolution protocol, named CRP (Collision Resolution Protocol). CRP explores the use of pulse and tone signaling to select the appropriate transmitting node among a number of contending stations, allowing for frame transmissions without collision. We perform extensive simulations, and the results show that CRP is capable to grant channel access in less than 1/600 of the time of other similar mechanisms while being able to deliver over 32% more transmissions per second. Furthermore, the CRP provides lower channel access latency and a fair resource allocation, which makes it suitable to support applications with demanding QoS.

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.