Indoor positioning systems based on Bluetooth 5.1 Direction Finding technology have recently attracted significant attention due to their capability to estimate the Angle of Arrival (AoA) of radio signals using commercial off-the-shelf devices. Despite this progress, the availability of large-scale, well-annotated experimental datasets collected under realistic conditions remains limited. This paper presents a comprehensive experimental dataset for indoor localization based on BLE 5.1 devices, providing synchronized azimuth and elevation AoA measurements together with Received Signal Strength (RSS) values and precise ground-truth annotations. Data were acquired in a 110 m 2 indoor environment with an adjacent corridor, deploying nine anchors in two configurations (wall-mounted and ceiling-mounted) and two wearable BLE tags. The campaign covers three scenarios: calibration (187 static reference points), static measurements with body orientation variations, and multiple mobility use-cases reproducing realistic walking patterns and posture changes. Overall, more than 4.5 million samples were collected. A preliminary analysis highlights the impact of anchor geometry, field of view, and body shadowing on angular accuracy. The dataset is publicly available and provides a comprehensive benchmark for evaluating angle-based localization, fingerprinting, filtering, and machine-learning approaches in realistic indoor environments.

Several radio-frequency technologies have been investigated to develop accurate indoor localization systems, each offering distinct techniques for estimating a target’s position in indoor environments. Among them, the Ultra-WideBand (UWB) technology is a promising approach because it can estimate the distance and angle between a tag and an anchor. In this work, we evaluate the performance of a commercial UWB kit with a systematic data collection campaign. We gather data in a realistic setting, comparing estimated and actual Angles of Arrival (AoA) and distances. Results highlight that, while the system performs reliably in most scenarios, a few instances reveal noticeable deviations from the Ground Truth (GT) data.

This contribution outlines an experimental setup and methodology employed to extensively characterize LoRa propagation in the demanding scenario of avalanche search and rescue (SaR), where the transmitter is buried under snow. The considered scenario presents challenges, including the absence of line-of-sight between the transmitter and receiver, as well as signal attenuation due to environmental factors such as temperature, humidity, and snow conditions. We analyze the variations in Received Signal Strength (RSS) and Signal-to-Noise Ratio (SNR) with increasing distance between the transmitter and receiver. Our data collection campaign completely characterizes, for the first time, the snow type during wireless communication tests. Finally, we test the maximum distance at which the LoRa signal can be received from the buried transmitter, demonstrating the technology's potential in challenging environments.

Indoor localization is crucial for developing intelligent environments capable of understanding user contexts and adapting to environmental changes. Bluetooth 5.1 Direction Finding is a recent specification that leverages the angle of departure (AoD) and angle of arrival (AoA) of radio signals to locate objects or people indoors. This paper presents a set of algorithms that estimate user positions using AoA values and the concept of the Confidence Region (CR), which defines the expected position uncertainty and helps to remove outlier measurements, thereby improving performance compared to traditional triangulation algorithms. We validate the algorithms with a publicly available dataset, and analyze the impact of body orientation relative to receiving units. The experimental results highlight the limitations and potential of the proposed solutions. From our experiments, we observe that the Conditional All-in algorithm presented in this work, achieves the best performance across all configuration settings in both line-of-sight and non-line-of-sight conditions.

A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker’s profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision.

In this work, we investigate the performance of a proximity detection system for visitors in an indoor museum exploiting data collected from the crowd. More specifically, we propose a CrowdSensing-based technique for proximity detection. Users’ smartphones can collect and upload RSS (Received Signal Strength) values of nearby Bluetooth tags to a backend server, together with some context-information. In turn, the collected data are elaborated with the goal of calibrating two proximity detection algorithms: a range-based and a learning-based algorithm. We embed the algorithms with R-app, a visiting museum application tested in the Monumental Cemetery’s museum located in Piazza dei Miracoli, Pisa (IT). We detail in this work an experimental campaign to measure the performance improvements of the CrowdSensing approach with respect to state-of-the-art algorithms widely adopted in the field of proximity detection. Experimental results show a clear improvement of the performance when data from the crowd are exploited with the proposed architecture.

Open Radio Access Network (O-RAN) is an emerging paradigm proposed for enhancing the 5G network infrastructure. O-RAN promotes open vendor-neutral interfaces and virtualized network functions that enable the decoupling of network components and their optimization through intelligent controllers. The decomposition of base station functions enables better resource usage, but also opens new technical challenges concerning their efficient orchestration and allocation. In this paper, we propose Proactive Resource Orchestrator based on Reinforcement Learning (PRORL), a novel solution for the efficient and dynamic allocation of resources in O-RAN infrastructures. We frame the problem as a Markov Decision Process and solve it using Deep Reinforcement Learning; one relevant feature of PRORL is that it learns demand patterns from experience for proactive resource allocation. We extensively evaluate our proposal by using both synthetic and real-world data, showing that we can significantly outperform the existing algorithms, which are typically based on the analysis of static demands. More specifically, we achieve an improvement of 90% over greedy baselines and deal with complex trade-offs in terms of competing objectives such as demand satisfaction, resource utilization, and the inherent cost associated with allocating resources.

The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.

Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.

Proximity detection is the process of estimating the closeness between a target and a point of interest, and it can be estimated with different technologies and techniques. In this paper we focus on how detecting proximity between people with a TinyML-based approach. We analyze RSS values (Received Signal Strength) estimated by a micro-controller and propagated by Bluetooth’s tags. To this purpose, we collect a dataset of Bluetooth RSS signals by considering different postures of the involved people. The dataset is adopted to train and test two neural networks: a fully-connected and an LSTM model that we compress to be executed directly on-board of the micro-controller. Experimental results conducted over the dataset show an average precision and recall metrics of 0.8 with both of the models, and with an inference time less than 1 ms.

The widespread use of Location Based Services (LBS) drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs), require managing the transition from one ILS to another in order to continue localizing the user’s device even when moving indoors or outdoor-to-indoor environments. In this paper, we focus on the handoff procedure, whose goal is enabling a device to trigger the transition between ILSs when specific conditions are verified. We describe the activation of handoff procedures by considering three types of ILS design and deployment, each with increasing complexity. Moreover, this work defines three handoff algorithms based on the proximity detection, and we test them in a realistic environment characterized by two contiguous ILSs.

In the context of a 5G and beyond network operating in a smart city, in which the fixed network infrastructure is supported by a flock of unmanned aerial vehicles (UAV) operating as carriers of Virtual Network Functions (VNF), we propose a Mixed Integer Linear Programming (MILP) model to place chains of VNFs on a hybrid UAV-terrestrial infrastructure so to maximize the UAV lifetime while considering resource constraints and by taking into account the network traffic originated by crowds of people assembling in the city at given hotpoints. We formalize the UAV deployment problem and we test our solution with a practical scenario based on DoS detection system. The experimental results assess the deployment in a practical scenario of a DoS detection system and show that the proposed solution can effectively enhance the capability of the system to process the input flows under a DoS attack.

The Mobile Edge Computing paradigm shifts the computation back to places where it is required. A traditional MEC architecture comprises a number of Edge Data Centers (EDC) in charge of seamlessly providing services to users with wireless network technologies. In this scenario, it becomes crucial to deploy the EDCs in strategic locations, such as highly visited places. In this paper we focus on the deployment phase of an EDC. In particular, we propose a probabilistic model designed to measure the location converge, namely the probability that a candidate location for an EDC is visited by users. Our model is based on the analysis of user’s trajectories and on the probability of detouring towards the target locations for the EDS. The information returned by our model offers the possibility of implementing mobility-aware deployment strategies in urban environments. We test the model with two real-world mobility data sets, evaluating its applicability of realistic settings.

User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.

The Bluetooth 5.1. Direction Finding (DF) specification opens to the possibility of estimating the angle between an emitting and a receiving device. Such angle is generally measured estimating the Angle of Arrival (AoA) or the Angle of Departure (AoD). In particular, knowledge about AoA between a set of anchor nodes and a moving target could be used to localize the target, with greater accuracy with respect to traditional approaches based on the Received Signal Strength of the received messages. In this work, we rigorously evaluate the performance of a commercial kit implementing the DF specification, with the purpose of understanding how the AoA measure varies with respect to the angles' ground truth. We describe two real-world experimental scenarios and we compute the errors between the estimated and actual angles. We also discuss three key aspects for the purpose of adopting BT 5.1 in indoor localization applications.

Indoor localisation is a thriving field, whose progresses are mainly led by innovations in sensor technology, both hardware and software. With a focus on smartphone-based personal navigation, we examine the evolution of sensing technologies in eleven leading applications. In order to select applications we choose among independently-tested prototypes, as opposed to simulation or laboratory-only experiments. To this end, we look at the best performers in the smartphone-based Tracks of IPIN competitions. This selection is particularly severe and significant, as this competition Track is performed live, without an opportunity for competitors to instrument or prepare the site or to know the path in advance and with only two attempts allowed, of which the best result is taken. An independent actor holds in hand the smartphone running the competing system, and results are downloaded from the phone immediately after the competition path is completed, without any post-processing. We show how sensing technologies have evolved from 2014 to 2019 and show a trend towards improving accuracy performance. Last, we provide insight in the role that sensors and algorithms play in the evolution of smartphone-based indoor localisation solutions.

The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key-metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

The Mobile CrowdSensing (MCS) paradigm has been increasingly adopted in the last years. Its adoption has been proved as beneficial for different scenarios, such as environmental monitoring and mobility analysis. However, one of the major barriers of the MCS initiatives, is the difficulty in recruiting users for the purpose of collecting data. We focus in this work to such limitation, and we analyze the mobility traces collected with a real-world MCS experiment, namely ParticipAct. Our goal is to discuss how to exploit the mobility features of the recruited users, as grounding information to plan and optimize a MCS data collection campaign. In detail, we analyze the quality of the data set, its accuracy and several features of human mobility such as radius of gyration and the real entropy of the locations visited. We discuss the impact of such metrics on the task scheduling, allocation and how to obtain a certain Tcoverage of data from visited locations.

Human-enabled edge computing (HEC) is a recent smart city technology designed to combine the advantages of massive mobile crowdsensing (MCS) techniques with the potential of multiaccess edge computing (MEC). In this context, the architectural hierarchy of the network shifts the management of sensing information close to terminal nodes through the use of intermediate entities (edges) bridging the direct Cloud-Device communication channel. Recent proposals suggest the implementation of those edges, not only employing fixed MEC nodes, but also opportunistically using as edge nodes mobile devices selected among the terminal ones. However, inappropriate selection techniques may lead to an overestimation or an underestimation of the number of nodes to be used in such a layer. In this article, we propose a probabilistic model for the estimation of the number of mobile nodes to be selected as substitutes of fixed ones. The effectiveness of our model is verified with tests performed on real-world mobility traces.

The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.

Mobile crowdsensing aims at the collection of sensor data on the environment by leveraging personal devices, usually smartphones. Its popularity is due to the ability of reaching capillary even the most remote areas (provided humans live there), with no infrastructure costs. This is possible because it leverages on existing 4G/5G communication infrastructures that are now rapidly evolving towards edge computing models. In this work we address the synergy between mobile crowdsensing and multi-access edge computing by analysing and assessing strategies for the selection of fixed and mobile edges to support the collection of mobile crowdsensing data.

In an increasingly interconnected world, mobile and wearable devices, through short range communication interfaces and sensors, become needful tools for collecting and disseminating information in high population density environments. In this context Mobile Crowdsensing (MCS), leveraging people's roaming and their devices' resources, raised the citizen from mere walk-on parts to active participant in the knowledge building and data dissemination process. At the same time, Mobile Edge Computing (MEC) architecture has recently enhanced the two-layer cloud-device architectural model easing the exchange of information and shifting most computational cost from devices towards middle-layer proxies, namely, network edges. We introduce Human-driven Edge Computing, a new model which melts together the power of MEC platform and the large-scale sensing of MCS to realize a better data spreading and environmental coverage in smart cities. In addition, it will be briefly discussed the main sociological aspects related to human behavior and how they can influence the exchange of data in large-scale sensor networks.

Mobile Edge Computing (MEC) opens to the opportunity of moving high-volumes of data from the cloud to locations where the information is actually accessed. In turn, the combination of MEC with the Mobile Crowdsensing approach, using a restricted number of devices with respect the number of base stations, matches the performance of the conventional MEC middleware layer ensuring the same spatial coverage. In this work, we envision a MEC architecture composed by mobile and fixed edges. Their goal is to optimize the share of contents among users by exploiting their mobility and sociality. We first present an algorithm to identify a suitable set of mobile edges and we show how such selection increases the performance of a content-sharing scenario. Our experiments are based on the ParticipAct dataset, which captures the mobility of about 170 users for 10 months. The experiments show that the number of requests that can be served mobile edges is similar to that of requests served by fixed edges, and then that mobile edges can be considered a viable (and lowcost) alternative to fixed edges.

Some recent research projects, inspired by the widespread availability of sensor-provided smartphones, have built harvesting experiments to collect large quantities of data in urban areas. These efforts produced new real-world datasets, typically focusing on different technological aspects (GPS and Bluetooth mobility traces or WiFi indicators) and, more recently, also on user-related data, from low-level accelerometer samples to higher-level social networking data. At the same time, Mobile Crowd Sensing (MCS) blossomed with a few very recent project, with the goal to efficiently coordinate user participation, both to collect sensor data and to allow active collaboration in participatory tasks. This paper aims to shed some light and to propose new research directions on the MCS by employing the notable results already obtained in the Mobile Social Network area to the study of human dynamics. The reported results, comparing three MCS datasets available in the literature, lead to an in-depth discussion of some lessons we learned about sociotechnical management aspects of MCS. The results we present are valuable for the MCS community to design new MCS campaigns and to refine the whole MCS process to the purpose of better efficiency and scalability.

Indoor environments are becoming more and more sensorized. Technologies such as Bluetooth, Wi-Fi and RFID are commonly used to provide connectivity to people living in such spaces. However, these technologies can also be exploited to automatically detect empty/occupied indoor areas and who is occupying that area. Our work goes toward such direction proposing an occupancy detection strategy based on the Bluetooth Low Energy (BLE) stack. We designed our solution by considering two fundamental challenges: costs and power-efficiency. We tested our system in several offices of our research institute by deploying few Bluetooth receivers and assigning to people a Bluetooth tag integrated with the institute's badge. We analyzed the performance of our solution with Bluetooth tags emitting at one single power first, and then we further refined our solution by considering the possibility of using beacons emitting simultaneously at different powers. The obtained results show a high accuracy without significantly affecting the energy consumption of the tags.

The increasing diffusion of smart devices opens to a new era for collecting large quantities of data from urban areas. Sensing information can be collected by using existing network infrastructures, but also by adopting small, cheap and configurable aerial vehicles, namely drones. Our work focusses on studying how to optimize their adoption for smart city applications designed to gather sensing data from user's devices roaming on the ground. To this purpose, we used HUMsim, a tool which generates realistic human traces, to mimic pedestrian mobility. From this dataset, we extract some sociality features that we exploit to plan a social-aware drone trajectory with the goal of maximizing the opportunities of interaction between drone and devices. Our experiments compare social-aware and social-oblivious trajectories showing that knowing the way people move and interact boosts the amount of retrievable data.

While indoor localization techniques based on Wi-Fi RSS measurements have been extensively studied, their application to eavesdropping Wi-Fi probe requests sent from mobile devices in large indoor environments, such as shopping malls, is scarce or absent in the literature. The idea behind this work is to observe Wi-Fi enabled smartphones, especially when they are not associated to a network. They periodically perform active network scanning by issuing probe requests, which are detected by networked sniffing devices produced by Cloud4Wi®. We experimentally investigate the opportunities offered by passive gathering of Wi-Fi probes for purposes of crowd positioning in areas of interest. Our preliminary experimental setting convincingly shows that a small number of sniffing devices may be enough for analysing crowd movements in indoor areas.

IoT and cloud represent the breakthrough for making concrete the envisioned scenarios for Smart Environments and specifically, those scenarios devoted to the human well-being. Under this respect, we propose a system focused on the quality of the environments where employees work with particular attention to the energy consumption. We describe a long-term monitoring system together with the Stigma algorithm designed to detect the presence or the absence of a worker by exploiting sensing information.

Mobile Crowd Sensing (MCS) is an emerging paradigm that exploits the ubiquity of smartphones and cheap sensor devices to collect data and thus contribute to the provision of useful services, especially in the domains of urban life. While many MCS implementations have been proposed for different applications, the lack of common performance metrics means that their efficiency cannot be easily compared. In this paper, we formalize a generic coverage model for the class of MCS systems sampling spatial phenomena before introducing a way to produce one such a metric by exploiting a spatio-temporal estimator. We avail of a large-scale dataset of users' mobility traces to demonstrate the use of the newly introduced metric in informing the resolution of a typical problem in MCS system design.

Underwater communications through acoustic modems rise several networking challenges for the Underwater Acoustic Sensor Networks (UASN). In particular, opportunistic routing is a novel but promising technique that can remarkably increase the reliability of the UASN, but its use in this context requires studies on the nature of mobility in UASN. Our goal is to study a real-world mobility dataset obtained from the Argo project. In particular, we observe the mobility of 51 free-drifting floats deployed on the Mediterranean Sea for approximately one year and we analyze some important properties of the underwater network we built. Specifically, we analyze the contact-time, inter-contact time as well density and network degree while varying the connectivity degree of the whole dataset. We then consider three known routing algorithms, namely Epidemic, PROPHET and Direct Delivery, with the goal of measuring their performance in real conditions for USAN. We finally discuss the opportunities arising from the adoption of opportunistic routing in UASN showing that, even in a very sparse and strongly disconnected network, it is still possible to build a limited but working networking framework.

Device-free localization algorithms attract, among others, the attention of researchers working in the Ambient Assisted Living (AAL) scenarios, where the target user might not be able or willing to wear any devices. We concentrate on systems that exploit the Received Signal Strength indicator coming from wireless devices whose position is known, called anchors. In this paper we select and test the main device-free localization solutions and experimentally compare their performance using a smaller number of anchors than commonly found in the literature. We illustrate the procedure used to validate our comparing procedure and we give suggestions on usability in the application scenarios typical of AAL. To the best of our knowledge, this is the first direct comparison between different device-free algorithms using the same input data for all of them, and the first one that compares their performance with a varying number of anchors. Thanks to the characteristics of our comparison procedure, we can make suggestions about the more appropriate algorithms to use for different kinds of applications.

Indoor localization applications that involve Wireless Sensor Networks (WSNs) identify the target position by measuring the Received Signal Strength (RSS), the Time of Arrival (ToA), the Time Difference of Arrival (TDoA) or the Angle of Arrival (AoA). Of these, the most promising for low-cost applications are those based on measures of the RSS, which exploit the relationship between RSS and the distance, or more reliably the relation between the multi-path interference (shadowing) and the position of the target. These methods work with WSNs based on Wi-Fi, Bluetooth and ZigBee sensor technologies. In this paper we concentrate on device-free RSS-based indoor localization methods. These methods, which have generated much research interest in the last few years, are now starting to hit the market. Specifically, the purpose of this paper is to assess the performance improvements of a Variance-based Radio Tomographic Imaging technique, when scanning various radio channels with respect to using only one, the latter being the “minimum introduced interference” option. Moreover, in this paper we will discuss in which application scenario the multi-channel scanning technique is usable and appropriate. The experimental data used for target localization are captured by wireless sensors deployed in the localization area and the localization error metrics include the mean square error and percentiles of the error distribution. Specifically, we aim to study the localization error reduction obtained by using multiple ZigBee channels, with respect to using a single channel.

The increasing demand for services and higher comfort levels inside buildings, together with the rise in time spent indoor, ensure an upward trend in indoor localization demand for the future. Evaluation of indoor localization systems is particularly challenging due to the complexity of such systems and to the variety of solutions adopted and services offered. EvAAL is an international competition aimed at evaluating and assessing indoor localization systems. The fifth edition of EvAAL promotes competitions on indoor localization in large environments. This paper describes its technical aspects, the competing systems and the results.

Mobile Crowd Sensing (MCS) aims to coordinate and activate the participation of volunteers willing to use their smartphones to harvest large quantities of data as they move in urban areas. One of the most important requirements in MCS is maximizing the effectiveness of the data gathering campaign. In fact, also due to the initial low penetration rate of MCS apps and to avoid making the MCS process cumbersome to users, only a small portion of the whole citizenship can be involved in such campaign, while most citizens are not part of the process. This paper proposes a novel approach that combines participatory and opportunistic techniques to amplify the amount of data harvested from the crowd. The core idea is that people with similar interests, such as employees of the same company, students or friends tend to meet more frequently with respect to people with different interests. Accordingly, it is possible to opportunistically involve in the crowd sensing loop people in the volunteers' neighborhood. Following that main design guideline, our work assesses the SOcial amplification FActor (SOFA) that allows to increase the number of samples retrievable during a crowd sensing campaign. We show the benefits of SOFA by using the ParticipAct MCS platform and we analyze three different application scenarios. Highly realistic simulation results, based on ParticipAct mobility traces, show the advantages of SOFA, with an amplification factor increasing up to 4.45.

CORDIAL is a collaborative service discovery strategy designed for mobile and opportunistic networks, which takes advantage of some features of human behavior, namely the periodicity of movements, the membership to a restricted number of communities and the sharing of interests among members of a community. CORDIAL exploits these features by adapting the strategies of query and service advertisements by exploiting the cooperation of nodes with higher social centrality in order to improve the chance of finding the desired service. The evaluation of CORDIAL by simulation shows that, as compared with similar approaches, it improves proactivity and accuracy levels, and it reaches performance comparable to the flooding technique but at a much lower cost.

We present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. SIDEMAN takes advantage of mobile social networking characteristics, such as user membership to a restricted number of communities and interest for similar services among users in the same community. We evaluated the performance of SIDEMAN via simulations in a scenario based on traces collected at the IEEE conference Infocom in 2006. Our algorithm has been compared to the social version of two popular data dissemination techniques, namely, flooding and gossiping. We have measured how proactive an algorithm is in distributing services of interest (Recall), how many services are already with a user when they are needed (Gain), the energy cost for service discovery, and the time needed to reply a service query. We show that SIDEMAN obtains perfect Recall and a Gain that is always comparable to that of the other algorithms. Furthermore, most services are retrieved in reasonable time and at a lower energy cost than that of the flooding and gossiping-based solutions.