The problem of information overloading is prevalent in recommendations websites and social networks. Users seek relevant recommendations from like-minded connections. User-item interactions (i.e., ratings) are prevalent in recommendation... more
The problem of information overloading is prevalent in recommendations websites and social networks. Users seek relevant recommendations from like-minded connections. User-item interactions (i.e., ratings) are prevalent in recommendation websites such as Netflix, whereas user-user connections are the interaction sought in social websites such as Twitter. Social recommender systems seek to generate recommendations for users based on similar preferences of their close friends. Because social networks do not normally contain user-item interactions, social recommender systems are typically hybridized with other recommenders (e.g., website recommenders such as Netflix) that provide such interaction. However, current systems are unaware of the user’s additional contextual information when coupled with social counterparts. In this paper, we propose a context-aware deep learning-based recommender system, US-NCF, in support for social recommender systems. Our experiments show US-NCF outperforms state-of-art counterparts.
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Recent research focuses on building Cloud-based solutions for big geospatial data analytics. Avalanches of georeferenced mobility data are being collected and processed daily. However, mobility data alone is not enough to unleash the... more
Recent research focuses on building Cloud-based solutions for big geospatial data analytics. Avalanches of georeferenced mobility data are being collected and processed daily. However, mobility data alone is not enough to unleash the opportunities for insightful analytics that may assist in mitigating the adverse effects of climate change. For example, answering complex queries such as follows: “what are the Top-3 neighborhoods in Buenos Aires in terms of vehicle mobility where the index of PM10 pollutant is greater than 40”. Similar queries are necessary for emergent health-aware smart city policies. For example, they can provide insights to municipality administrators so that they foster the design of future city infrastructure plans that feature citizen health as a priority. For example, building mobile maps for daily dwellers so that to inform them which routes to avoid passing-through during specific hours of a day to avoid being subjected to high-levels PM10. However, answering such a query would require joining real-time mobility and environment data. Stock versions of the current Cloud-based geospatial management systems do not include intrinsic solutions for such scenarios. In this paper, we report the design and implementation of a novel system MeteoMobil for the combined analytics of information representing mobility and environment. We have implemented our system atop Apache Spark for efficient operation over the Cloud. Our results show that MeteoMobil can be efficiently exploited for advanced climate change analytics.
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The easily reachable IoT edge devices have caused the accumulation of vast amounts of geo-referenced data traces that can help in performing deep insightful analytics. Geospatial data in real geometries are normally clumped into batches... more
The easily reachable IoT edge devices have caused the accumulation of vast amounts of geo-referenced data traces that can help in performing deep insightful analytics. Geospatial data in real geometries are normally clumped into batches and has strong autocorrelation properties which can be exploited in discovering interesting insights. Current plain Cloud computing frameworks are not attuned to the shape of data. Most importantly, data splitting is an important precursor in data parallelization mechanisms. Current systems mostly focus on general data workloads, thus are giving attention mostly to load balancing while splitting the data to Cloud computing resources. However, many benefits can be reaped by being attuned to the spatial characteristics while distributing the data, thus striking a plausible balance between load balancing and spatial data locality preservation normally leads to achieving better time-based QoS goals, which then leads to an optimized provisioning of Cloud computing resources. In this paper, we have designed a spatial batch processing engine that comprises a custom spatial data locality aware partitioning method for disseminating spatial data loads in Cloud computing clusters. We have also extended a state-of-art benchmark density-based clustering method that is known as DBSCAN-MR and implemented a standard compliant prototype on top of a best-in-breed de facto Cloud-based main memory processing framework, Apache Spark. Our results show that our partitioning method with the associated spatial query optimizers can achieve gains that significantly outperform baselines
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The widespread adoption of sensor-enabled and mobile ubiquitous devices has caused an avalanche of big data that is mostly geospatially tagged. Most cloud-based big data processing systems are designed for general-purpose workloads,... more
The widespread adoption of sensor-enabled and mobile ubiquitous devices has caused an avalanche of big data that is mostly geospatially tagged. Most cloud-based big data processing systems are designed for general-purpose workloads, neglecting spatial-characteristics. However, interesting analytics often seek answers for proximity-alike queries. We fill this gap by providing custom geospatial service layer atop of Apache Spark. To be more specific, we leverage Spark to design a custom spatial-aware partitioning method to boost geospatial query performances. Our results show that our patches outperform state-of-the-art implementations by significant fractions.
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Smart factory management is going through a remarkable change, in terms of quality and diversity of services provided to customers. The companies that produce manufacturing machines now can follow the products throughout the production... more
Smart factory management is going through a remarkable change, in terms of quality and diversity of services provided to customers. The companies that produce manufacturing machines now can follow the products throughout the production chain, from the project to the deployment in real scenarios. Industry 4.0 is pushing this trend forward, demanding for servitization of products and machines, mainly for the manufacturing sector where human and production machine are in strict collaboration. The data produced by the machines must be processed quickly to allow the implementation of reactive services such as predictive maintenance and remote control, always taking care of the safety of nearby people. This paper proposes a multilayer architecture to tackle the main issues in monitoring legacy manufacturing machines and to provide general guidelines to solve them. We derived some guidelines from a real Industry 4.0 transition experiment performed together with the company technical departments to accomplish an efficient system for monitoring and servitization of manufacturing machines, with a scalable platform that confirms its usefulness in many production facilities with different needs.
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The diversity of sensing options that IoT offers imposed requirements to evolve stream processing engines so to cope with highly heterogeneous and fast-pace data streams challenging their computing capacities. Location intelligence... more
The diversity of sensing options that IoT offers imposed requirements to evolve stream processing engines so to cope with highly heterogeneous and fast-pace data streams challenging their computing capacities. Location intelligence applications aim at exploiting those geo-referenced data in generating visualizations and dashboards that provide deep insights for assisting decision making in smart cities and urban planning. As data arriving are mostly geo-referenced and the rate is fluctuating in pace and skewness, computations upon streams should depend on approximation by applying methods such as sampling. Representativeness in sampling designs remains the pivotal concern in the literature. In spatial data streams contexts, it loosely means selecting proportional counts of spatial tuples from each group of tuples that belong to the same real geometry (i.e., geographically residing in the same proximity) within each streaming time window. This is challenging in streaming settings because spatial data is parametrized, loosing hence it is real geometries, which requires costly geometric operations to project them back to maps. To close this void, we have designed SpatialSPE in a previous work and incorporated an efficient fine-grained spatial online sampling method (SAOS) transparently within its layers. In this paper, we extend SAOS (the novel method is termed ex-SAOS) by new features that allow efficient online spatial sampling on a coarser level, which is a requirement in smart city scenarios. Our results show that ex-SAOS is efficient and effectively extends SAOS for more general smart city and urban computing scenarios.
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Current cloud-enabled NoSQL database frameworks support flexible and scalable storage of huge amounts of data arriving through various and often heterogeneous channels. However, they do not natively provide optimised processing of spatial... more
Current cloud-enabled NoSQL database frameworks support flexible and scalable storage of huge amounts of data arriving through various and often heterogeneous channels. However, they do not natively provide optimised processing of spatial data, thus making it more difficult to perform accurate data analytics needed in many smart city application scenarios. To improve the performance of spatial data computation in the NoSQL MongoDB storage framework, this article proposes a novel data partitioning method based on dimensionality reduction. The underlying key idea is to reduce a spatial data representation from multi to single dimensionality, by still maintaining its geometrical meaning and by employing a specific geo-encoding scheme, i.e., a geohash string. In particular, the geohash string is used as a sharding key in order to store geometrically-nearby objects into the same chunks (and consequently into the same shard). In addition, as a distinctive feature, we have extended the MongoDB framework with a custom spatial QoS-aware optimizer that exploits our novel partitioning scheme to support two, typically expensive, types of spatial queries with QoS guarantees. Those queries are containment (and consequently top-N) and proximity. The paper also contributes to the existing literature with extensive experimental results about the performance of both our partitioning method and query optimizer; the reported results show that our solutions outperform baselines by orders of magnitude.
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Crowdsensing systems are demonstrating to be effective in increasing the interest of citizens in actively participating to create a better environment around them. Getting information about users to create a representation of their... more
Crowdsensing systems are demonstrating to be effective in increasing the interest of citizens in actively participating to create a better environment around them. Getting information about users to create a representation of their interests and relationships allows creating richer profiles, of high relevance for crowdsensing systems. The increasing knowledge about users could provide administrators with all the information about who is the right person, e.g., to complete environmental monitoring tasks and, thus, help them in assigning tasks to the most suitable subset of users. In this way, it is possible to complete crowdsensing tasks more efficiently, with increased successful rate and minimized latency. Here, we propose to extend the ParticipAct platform by exploiting data obtained from the Facebook social network to extend users' profile for a better understanding of which tasks people feel more comfortable to execute. In particular, we propose a theoretical analysis, as well as a system implementation, of how to manage and benefit from users' relationships and interests to increase users' involvement in collaborative tasks over ParticipAct.
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The Internet of Things (IoT) paradigm is leading to the deployment of an extensive number of smart devices capable of assisting companies and people in their daily activities. For this paradigm to be effective, these devices must exchange... more
The Internet of Things (IoT) paradigm is leading to the deployment of an extensive number of smart devices capable of assisting companies and people in their daily activities. For this paradigm to be effective, these devices must exchange a huge amount of information and be coordinated in unpredictable, dynamic, and very complex scenarios. So far, cloud computing has centralized data storage and offered coordination of devices. However, as the number of deployed smart devices increases and the requirements of IoT solutions are more stringent, cloud computing hardly meets them. Fog computing has emerged as a middle layer between end\u2010devices and cloud environments to support the requirements of IoT applications that cannot be met by the current edge\u2010cloud model. A great effort has been devoted during the past few years to the development of this fog vision. Most of these solutions focused on improving specific characteristics, but not on supporting all the key requirements of an IoT solution. Thus, a deep investigation of these solutions to understand how they can be connected and coordinated to meet these necessities is essential. In this paper, we distinguish the most vital necessities that IoT solutions present to accomplish a right operation. Also, by analyzing the available solutions, we propose a novel global architectural model for fog computing meeting the recognized demands. We also provide a novel scientific taxonomy for breaking down the overviewed solutions. We conclude by analyzing the most essential recommendations in Fog computing for IoT, thereby distinguishing open issues and research frontiers that must be prioritized in order to have a totally developed fog computing environment, ready to meet the IoT solutions' prerequisites
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Cloud computing nowadays is the cornerstone for all the business applications, mainly because of its high fault tolerance characteristic. High resilience and availability typical of cloud-native applications are achieved using different... more
Cloud computing nowadays is the cornerstone for all the business applications, mainly because of its high fault tolerance characteristic. High resilience and availability typical of cloud-native applications are achieved using different technologies. Regarding the file system, the main fault tolerant application examples are distributed file systems, such as HDFS, Ceph, GlusterFS, and XtremeFS. These file systems have different architectures and deployment models than the Traditional Distributed File Systems (TDFSs), such as NFS. The primary goal of this work is to analyze and compare different Cloud Distributed File Systems (CDFSs) in terms of characteristics, architecture, reliability, and components. As a key feature, the paper benchmarks them considering as use case an IaaS platform.
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Mobile Crowd Sensing (MCS) is a new sensing paradigm exploiting the capabilities of smart devices (smartphones, wearables, etc.) to gather large volume of data. Gathering contextual information is a very expensive activity in terms of... more
Mobile Crowd Sensing (MCS) is a new sensing paradigm exploiting the capabilities of smart devices (smartphones, wearables, etc.) to gather large volume of data. Gathering contextual information is a very expensive activity in terms of mobile device resource consumption, so limiting this consumption is essential for user satisfaction. The architectural style applied to the MCS platform largely affects the consumption of these resources. A server-centric MCS is more efficient when there are many entities interested on the gathered information, whilst a mobile-centric architecture has lower consumption when real-time information is required. In this paper, we propose a platform combining both architectural styles. This allows us to reduce the resource consumption of mobile devices, since it is easier to take advantage of the benefits of each style, and to better facilitate user aggregation, being able to group users both at the server and at the client-side depending on the freshness of the required information and the sensing task to be assigned. Finally, we have evaluated this platform for two different case studies, obtaining very promising results.
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Elastic resource outsourcing is a growing trend that simplifies and makes more efficient the management of resources, by embracing all the features of the execution of services over public clouds, such as high availability and automated... more
Elastic resource outsourcing is a growing trend that simplifies and makes more efficient the management of resources, by embracing all the features of the execution of services over public clouds, such as high availability and automated scalability management of resources. Therefore, modern enterprise services are increasingly leveraging inter/intra-cloud deployments and the choice of the right cloud provider to support the execution of them becomes a fundamental operational choice. The paper presents our Audit4Cloud platform, an open-source tool for auditing the performance of virtual resources made available by various commercial cloud providers, with specific focus on cloud networking. In particular, we claim that Audit4Cloud is an enabling key in choosing the right cloud vendor as it not only offers the visibility of current values of some significant performance indicators about the offered cloud resources, but also provides users with a complete picture of those performance indicators over time. We have already performed a large experimental campaign by considering primary commercial cloud providers; the collected results show the feasibility of the approach and that Audit4Cloud can play the role of a solid third-party auditing tool to estimate real performance and costs of cloud resources.
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The exponential amount of geospatial data that has been accumulated in an accelerated pace has inevitably motivated the scientific community to examine novel parallel technologies for tuning the performance of spatial queries. Managing... more
The exponential amount of geospatial data that has been accumulated in an accelerated pace has inevitably motivated the scientific community to examine novel parallel technologies for tuning the performance of spatial queries. Managing spatial data for an optimized query performance is particularly a challenging task. This is due to the growing complexity of geometric computations involved in querying spatial data, where traditional systems failed to beneficially expand. However, the use of large-scale and parallel-based computing infrastructures based on cost-effective commodity clusters and cloud computing environments introduces new management challenges to avoid bottlenecks such as overloading scarce computing resources, which may be caused by an unbalanced loading of parallel tasks. In this paper, we aim to fill those gaps by introducing a generic framework for optimizing the performance of big spatial data queries on top of Apache Spark. Our framework also supports advanced management functions including a unique self-adaptable load-balancing service to self-tune framework execution. Our experimental evaluation shows that our framework is scalable and efficient for querying massive amounts of real spatial datasets.
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The advancement of networking and sensor-enabled devices have motivated the emergence of unprecedented initiatives, including Industry 4.0 and smart cities. Those are entwined in a way that makes their operation duly interconnected.... more
The advancement of networking and sensor-enabled devices have motivated the emergence of unprecedented initiatives, including Industry 4.0 and smart cities. Those are entwined in a way that makes their operation duly interconnected. Industry 4.0 will sooner become the biggest consumer of smart city big data. That data is geo-referenced, and its storage and processing need spatial-awareness, which is currently absent within the constellation of biggest big data management players of the market. We aim to fill this gap by providing spatial-aware big data management strategies in support for Industry 4.0 main principles. Our experimental results show that our strategies outperform those of state-of-the-art by orders of magnitude.
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The wide availability of accurate sensors currently hosted by smartphones are enabling new participative urban management opportunities. Mobile crowdsensing (MCS) allows people to actively participate in any aspect of urban planning, by... more
The wide availability of accurate sensors currently hosted by smartphones are enabling new participative urban management opportunities. Mobile crowdsensing (MCS) allows people to actively participate in any aspect of urban planning, by collecting and sharing data, reporting issues to public administrations, proposing solutions to urban planners, and delivering information of potential social interest to their community. Although collected data can be very helpful to enhance the quality of life of citizens, mobile users are still reluctant to use their devices to take advantages of the opportunities offered by the digitized society, mainly due to privacy issues. From August to December 2018, the city of Florianópolis, capital of Santa Catarina, in southern Brazil, was used as a living lab environment for an MCS application called ParticipACT Brazil, a socio/technical-aware crowdsensing platform. While the current literature focuses on MCS from a purely technical point of view, this ...
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Research Interests: Business, Information Systems, Engineering, Computer Science, Technology, and 14 moreIntelligent Manufacturing Systems, Internet of Things, Supply Chain, Standardization, Industry, Information and Communications Technology, Monitoring, Maintenance Engineering, Ice, Industries, Electrical And Electronic Engineering, Smart Manufacturing, Automatic Control and Systems Engineering, and production facilities
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The widespread availability of smartphones with on-board sensors has recently enabled the possibility of harvesting large quantities of monitoring data in urban areas, thus enabling so-called crowdsensing solutions, which make it possible... more
The widespread availability of smartphones with on-board sensors has recently enabled the possibility of harvesting large quantities of monitoring data in urban areas, thus enabling so-called crowdsensing solutions, which make it possible to achieve very large-scale and fine-grained sensing by exploiting all personal resources and mobile activities in Smart Cities. In fact, the information gathered from people, systems, and things, including both social and technical data, is one of the most valuable resources available to a city's stakeholders, but its huge volume makes its integration and processing, especially in a real-time and scalable manner, very difficult. This chapter presents and discusses currently available crowdsensing and participatory solutions. After presenting the current state-of-the-art crowdsensing management infrastructures, by carefully considering the related and primary design guidelines/choices and implementation issues/opportunities, it provides an in-d...
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Research Interests: Engineering, Computer Science, Biomedical Engineering, Agent Based Simulation, Historic Buildings, and 12 moreDecision Support Systems, Smart Grid, Mobile Communications, Citizen participation, Smart City, Intelligent Transportation System, Information and Communications, Multidisciplinary Approach, Electrical And Electronic Engineering, Smart Lighting, E Mobility, and Crowdsensing
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Many IT companies are embracing the new softwarization paradigm through the adoption of new architecture models, such as software-defined network and network function virtualization, primarily to limit the costs of maintaining and... more
Many IT companies are embracing the new softwarization paradigm through the adoption of new architecture models, such as software-defined network and network function virtualization, primarily to limit the costs of maintaining and deploying their network infrastructures, by giving the possibility to service/application providers to reconfigure and programmatically perform actions on the network. Accordingly, the dynamic management of the data center networks requires complex operations to ensure high availability and continuous reliability in order to guarantee full functionality of the virtualized resources. In this context, simulator-based approaches are helpful for planning and evaluating the deployment of the cloud data center networking, but existing cloud simulators have several limitations: they have too high overhead for wide-scale data center networks, complex configuration, and too abstract deployment models. For these motivations, we propose DCNs-2, a novel extension for ...
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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,... more
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...
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Efficient protocols for data packet delivery in Vehicular Ad-hoc NETworks (VANETs) are crucial to guarantee the correct forwarding of data. However, communication in VANETs is a challenging task not only for the high mobility of the... more
Efficient protocols for data packet delivery in Vehicular Ad-hoc NETworks (VANETs) are crucial to guarantee the correct forwarding of data. However, communication in VANETs is a challenging task not only for the high mobility of the vehicles, but as recent studies have shown, there is a negative impact on protocol performances caused by an obstacle such as another vehicle in the line-of-sight (LOS). Many different routing protocols were presented in the last years, but only few of them were considering the effect of non line-of-sight (NLOS) propagation. In this work, we present a new solution to improve the data packet delivery ratio considering the problems caused by a high mobility and NLOS condition. Our proposal can be integrated in every position-based routing protocol. In fact, our method creates a support context awareness of neighbors before letting the protocol choose the next hop, without interfering with the specific logic. Simulation results of our context-aware version have been compared with the original protocols and with a modified version in accordance with the principle that every violation of LOS involves a discard of the packet: our solution always perform better in terms of packet delivery ratio and delay.
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Technical and economic opportunities of cloud computing become the focus for Internet applications and at the same time also for telco support infrastructures and network services. In fact, many telco providers are consolidating their... more
Technical and economic opportunities of cloud computing become the focus for Internet applications and at the same time also for telco support infrastructures and network services. In fact, many telco providers are consolidating their service infrastructures towards converged and all-IP next generation networks providing typical telco services within LTE (and soon 5G) and also fixed network environments, e.g., often still adopting IP Multimedia Subsystem (IMS) architecture solutions. This telco service infrastructure evolution requires a significant upfront investment in the necessary hardware and software, thereby slowing down the adoption process significantly more than any other Internet application. Cloud computing applied to telco infrastructures can allow pay-per-use business models and significantly lower investment risks by providing telco infrastructure functionality as Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) platform. For this purpose we propose a quality audit and resource brokering framework that is fully NFV-compliant. Its current and reported implementation specifically targets IMS services because of the still relevant role played by IMS in converged provisioning and the wide availability of IMS deployment testbeds to validate the proposal. In particular, the proposed solution can monitor the quality offered by VNFs and scale in/out depending on dynamic requirements; it is fully based on industrial standards and open-source reference implementations, thus enabling rapid adoption in real industrial environments.
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Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT)... more
Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo-referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top-N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance be...
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The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and... more
The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interes...
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Today’s spread of chronic diseases and the need to control infectious diseases outbreaks have raised the demand for integrated information systems that can support patients while moving anywhere and anytime. This has been promoted by... more
Today’s spread of chronic diseases and the need to control infectious diseases outbreaks have raised the demand for integrated information systems that can support patients while moving anywhere and anytime. This has been promoted by recent evolution in telecommunication technologies, together with an exponential increase in using sensor-enabled mobile devices on a daily basis. The construction of Mobile Health Communities (MHC) supported by Mobile CrowdSensing (MCS) is essential for mobile healthcare emergency scenarios. In a previous work, we have introduced the COLLEGA middleware, which integrates modules for supporting mobile health scenarios and the formation of MHCs through MCS. In this paper, we extend the COLLEGA middleware to address the need in real time scenarios to handle data arriving continuously in streams from MHC’s members. In particular, this paper describes the novel COLLEGA support for managing the real-time formation of MHCs. Experimental results are also provid...
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Industry 4.0 environments pose unique challenges for the realization of the communication substrate at the shop floor, due to the strict Quality of Service (QoS) requirements, the high heterogeneity of the employed data exchange... more
Industry 4.0 environments pose unique challenges for the realization of the communication substrate at the shop floor, due to the strict Quality of Service (QoS) requirements, the high heterogeneity of the employed data exchange protocols, and the different network technologies and addressing schema toward the machines. To address those issues, the paper proposes a distributed support based on a Message Oriented Middleware (MOM) and a Software Defined Network (SDN) control plane that coordinate to enable semantic routing by also allowing traffic differentiation as well as in-network processing at intermediate network nodes. Seminal results, collected in realistic industrial settings, confirm the feasibility of our proposal.
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Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City... more
Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of the challenging technical issues related to the efficient assignment of Mobile Crowd Sensing (MCS) data collection tasks to volunteers in a crowdsensing campaign. In particular, the paper originally describes how to increase the effectiveness of the proposed sensing campaigns through the inclusion of several new facilities, including accurate participant selection algorithms able to profile and predict user mobility patterns, gaming techniques, and timely geo-notification. The reported results show the feasibility of exploiting profiling trends/prediction techniques from volunteers' behavior; moreover, they quantitatively compare different MCS task assignment strategies based on large-scal...