2015
Software
Metadata Only Access
SIDEMAN: Service Discovery Algorithm for Mobile Social Networks
Girolami M
SIDEMAN is a service discovery algorithm that exploits human mobility patterns for the advertisement and discovery of services. SIDEMAN takes advantage of two aspects of daily human behaviour, namely that users visit periodically a restricted number of communities, and that users in the same community share interests for similar services.
Girolami M
SIDEMAN is a service discovery algorithm that exploits human mobility patterns for the advertisement and discovery of services. SIDEMAN takes advantage of two aspects of daily human behaviour, namely that users visit periodically a restricted number of communities, and that users in the same community share interests for similar services.
See at:
CNR IRIS 
2007
Other
Restricted
Design and implementation of context-information virtualization and integration layers for wireless sensor networks
Girolami M
Context-aware systems [32] provide to application developers and to end-users new kinds of applications, that can adapt their behaviour according to context- information. These systems adapt their operations to the current context without explicit user intervention, thus increasing the flexibility and the efficiency of the system. The context-aware systems modify their behaviour according to several environmental attribute changes. There are different context-aware definitions, in 1992 Want et al. introduced the Active Badge Location System designed to determine the user location and to forward user calls to the nearest user phone. In 1994 Schiltit and Theimer defined the context as location, identities of nearby people, objects and changes to those objects. Dey and Abowd [33] refer to context as "any information that can be used to characterize the situation of entities (i.e., whether a person, place or object) that are considered relevant to the interaction between a user and an application, including the user and the application themselves".
Girolami M
Context-aware systems [32] provide to application developers and to end-users new kinds of applications, that can adapt their behaviour according to context- information. These systems adapt their operations to the current context without explicit user intervention, thus increasing the flexibility and the efficiency of the system. The context-aware systems modify their behaviour according to several environmental attribute changes. There are different context-aware definitions, in 1992 Want et al. introduced the Active Badge Location System designed to determine the user location and to forward user calls to the nearest user phone. In 1994 Schiltit and Theimer defined the context as location, identities of nearby people, objects and changes to those objects. Dey and Abowd [33] refer to context as "any information that can be used to characterize the situation of entities (i.e., whether a person, place or object) that are considered relevant to the interaction between a user and an application, including the user and the application themselves".
2011
Other
Restricted
universAAL - Part III: The universAAL Reference Architecture for AAL (D1.3-C)
Girolami M, Burla A, Pileggi S F, Asim M, Fides V Á, Arnaudov V
This deliverable documents the current status of AAL Reference Architecture (RA) as being developed in the project universAAL. The RA needs to guarantee some level of compliance among concrete architectures, and at the same time make sure to allow innovation and competition in the real world. This is the main challenge facing us when defining the RA. We follow a Service Oriented Architecture development methodology and notation called SOAML (UML-based SOA). We summarize the business context for the RA as investigated and documented in universAAL (in deliverable D1.1 and D1.2). The business context contains mainly the stakeholders and the value propositions they offer to each other. These value propositions are then mapped to specific services that can be traded among the stakeholders. By mapping these services onto a technological ICT architecture we show how the services can be implemented and deployed in the real world. We provide examples of typical deployments demonstrating simple and more complicated scenarios. In our work we have tried to use empirical data as much as possible. The RA is developed through a combination of bottom-up empirical data (collected during the universAAL consolidation process) and top-down definitions. An example of how the RA can be used in real world is provided.Project(s): UNIVERSAAL
Girolami M, Burla A, Pileggi S F, Asim M, Fides V Á, Arnaudov V
This deliverable documents the current status of AAL Reference Architecture (RA) as being developed in the project universAAL. The RA needs to guarantee some level of compliance among concrete architectures, and at the same time make sure to allow innovation and competition in the real world. This is the main challenge facing us when defining the RA. We follow a Service Oriented Architecture development methodology and notation called SOAML (UML-based SOA). We summarize the business context for the RA as investigated and documented in universAAL (in deliverable D1.1 and D1.2). The business context contains mainly the stakeholders and the value propositions they offer to each other. These value propositions are then mapped to specific services that can be traded among the stakeholders. By mapping these services onto a technological ICT architecture we show how the services can be implemented and deployed in the real world. We provide examples of typical deployments demonstrating simple and more complicated scenarios. In our work we have tried to use empirical data as much as possible. The RA is developed through a combination of bottom-up empirical data (collected during the universAAL consolidation process) and top-down definitions. An example of how the RA can be used in real world is provided.Project(s): UNIVERSAAL
2013
Other
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Service discovery strategies for smart infrastructures
Girolami M
he goal of the Service Discovery is to find the services (or more generally, the resources) available on the network. The entrance of a new node in the network, may cause the provisioning of some services that an end-user or another node can discover and access according to a query expressed by the user. The description of the service is matched against the query and, eventually, a response is sent back to the user. In the case where more than one provider offers the same service, the user selects the best one according to some criteria. Many service discovery protocols have been already proposed for different application domains, most of them are suitable for wired and static network. This report describes some research challenges for the SD protocols in a mobile and dynamic scenario such as the Smart Infrastructures (SiS). The document introduces the SD problem, describes the SiS scenario and it concludes with some key aspects to be considered for a valuable SD protocol.
Girolami M
he goal of the Service Discovery is to find the services (or more generally, the resources) available on the network. The entrance of a new node in the network, may cause the provisioning of some services that an end-user or another node can discover and access according to a query expressed by the user. The description of the service is matched against the query and, eventually, a response is sent back to the user. In the case where more than one provider offers the same service, the user selects the best one according to some criteria. Many service discovery protocols have been already proposed for different application domains, most of them are suitable for wired and static network. This report describes some research challenges for the SD protocols in a mobile and dynamic scenario such as the Smart Infrastructures (SiS). The document introduces the SD problem, describes the SiS scenario and it concludes with some key aspects to be considered for a valuable SD protocol.
2013
Conference article
Restricted
A common platform for AAL services and a common future - The universAAL project
Broberg L M, Girolami M
universAAL is a European project set in motion to push and aid the market for AAL services and products. Its main objective is to make it technically possible and eco- nomically affordable to develop AAL services by providing a common, open, European platform, which sets up a mar- ket for buyers, sellers and users to meet and by supplying tools, a reference architecture and software components which aid the developing process. Simultaneously, a com- munity is being established to foster interest and to gain widespread adoption of the universAAL platform.Project(s): OASIS
Broberg L M, Girolami M
universAAL is a European project set in motion to push and aid the market for AAL services and products. Its main objective is to make it technically possible and eco- nomically affordable to develop AAL services by providing a common, open, European platform, which sets up a mar- ket for buyers, sellers and users to meet and by supplying tools, a reference architecture and software components which aid the developing process. Simultaneously, a com- munity is being established to foster interest and to gain widespread adoption of the universAAL platform.Project(s): OASIS
2011
Other
Restricted
universAAL - Generic platform services, AAL platform services and ontology artefacts (D2.2-B)
Girolami M, Tazari S, Frufari F
This document corresponds to deliverable D2.2 of type "Prototype". It provides a report about software development within Task 2.2 "Implement universAAL Generic Platform Services, AAL Platform services and ontologies support". The requirements and the scope of D2.2 are equivalent to those defined among the following four expert groups: Service Infrastructure (SIEG), Context Management (CM), User Interaction Management (UIM), and Remote Interoperability (RIEG). Each one of these Expert Groups provides the specification of the software under development in comparison with the design decisions from input projects in terms of a set of wiki pages. In addition to the specifications, the Wiki pages also provide detailed information about the status of the developments and the plans for the next steps with time and resource allocation. To reflect the latest status of these Wiki pages at the time of closing the version B of D2.2, a snapshot of them has been added to this report as appendices. The main part of the report provides a very compact overview of the detailed info available in those appendices.Project(s): UNIVERSAAL
Girolami M, Tazari S, Frufari F
This document corresponds to deliverable D2.2 of type "Prototype". It provides a report about software development within Task 2.2 "Implement universAAL Generic Platform Services, AAL Platform services and ontologies support". The requirements and the scope of D2.2 are equivalent to those defined among the following four expert groups: Service Infrastructure (SIEG), Context Management (CM), User Interaction Management (UIM), and Remote Interoperability (RIEG). Each one of these Expert Groups provides the specification of the software under development in comparison with the design decisions from input projects in terms of a set of wiki pages. In addition to the specifications, the Wiki pages also provide detailed information about the status of the developments and the plans for the next steps with time and resource allocation. To reflect the latest status of these Wiki pages at the time of closing the version B of D2.2, a snapshot of them has been added to this report as appendices. The main part of the report provides a very compact overview of the detailed info available in those appendices.Project(s): UNIVERSAAL
2015
Other
Open Access
ReAAL - D3.3: UniversAAL compliance guidelines
Sala P, Fides A, Girolami M, Tazari S, Ben Hmida H
This document is reporting on the process and progress of task T3.3 Coaching of application providers by platform experts. Within this task, the platform technical experts have accompanied the porting process from T3.2 by providing training and technical support to the application providers and pilot investors with the aim of ensuring that the porting is done with good quality. The ultimate goal is that both the related knowledge is spread more widely and failure risks during deployment and operation are minimized. The document provides first, an introduction about the terminology used in relation to the universAAL platform, describing the basics of the platform and the principles to adapt an application to be used on top of universAAL platform, which in the context of the project is known as universAALization. Then, an overview is given of the experience gained in ReAAL, that has enabled to develop compliance guidelines in the form of development patterns and best practices. Finally, the information about how the coaching process in ReAAL has been established is provided, reporting on the methods and tools used by the different pilots and platform experts as well as the lessons learned from the process.Project(s): make it ReAAL
Sala P, Fides A, Girolami M, Tazari S, Ben Hmida H
This document is reporting on the process and progress of task T3.3 Coaching of application providers by platform experts. Within this task, the platform technical experts have accompanied the porting process from T3.2 by providing training and technical support to the application providers and pilot investors with the aim of ensuring that the porting is done with good quality. The ultimate goal is that both the related knowledge is spread more widely and failure risks during deployment and operation are minimized. The document provides first, an introduction about the terminology used in relation to the universAAL platform, describing the basics of the platform and the principles to adapt an application to be used on top of universAAL platform, which in the context of the project is known as universAALization. Then, an overview is given of the experience gained in ReAAL, that has enabled to develop compliance guidelines in the form of development patterns and best practices. Finally, the information about how the coaching process in ReAAL has been established is provided, reporting on the methods and tools used by the different pilots and platform experts as well as the lessons learned from the process.Project(s): make it ReAAL
See at:
CNR IRIS 
| ISTI Repository | CNR IRIS
2016
Other
Open Access
Device interoperability and service discovery in smart environments
Girolami M
Smart Environments (SE), and in particular Smart Homes, have attracted the attention of many researchers and industrial vendors. In such environments, according to the Ambient Intelligence paradigm, devices operate collectively using any information describing the environment (also known as the context-information) in order to support users in accomplishing their tasks. SE devices are characterized by several properties: they are designed to react autonomously to specific events, they are aware of the context, they manage sensitive information concerning the users, they adopt a service-oriented model in order to interact with other devices, and they interact by means of various applications and communication protocols. Cooperation with devices in SE is thus complex. This thesis deals with two problems that still represent a barrier to the development of many SE applications. The thesis examines how to interact with low-power devices, which is refereed to as device interoperability, and how to discover the functionalities that mobile devices offer, namely the service discovery problem. The rest part of the thesis describes the design of ZB4O an integration gateway for low-power devices based on the ZigBee specification. The growing market for ZigBee-ready appliances makes the ZigBee specification an important technology-enabler for SE. However, accessing such devices entails an easy interaction model with IP-based networks that are already present in most SE. Therefore, this work presents an open source platform that seamlessly integrates ZigBee devices with applications running on SE. The thesis describes the evaluation process of ZB4O with various trials organized over the last year of two EU projects, as well as the integration of ZB4O with UPnP and a RESTful approach. SE devices can also export their functionalities with a service-oriented approach. In fact, every resource offered by a device can be seen as a service available for other devices. The second problem studied in this thesis is the service discovery and it deals with how to advertise and query services in SE. The scenario considered for the service discovery problem is characterized by mobile devices carried by people roaming in SE. Hence, mobility and sociality are two key-factors that make the service discovery problem more complex and challenging. The thesis presents two algorithms, termed SIDEMAN and CORDIAL, for the service discovery in Mobile Social Networks (MSN) which are evaluated with real and synthetic simulation scenarios.
Girolami M
Smart Environments (SE), and in particular Smart Homes, have attracted the attention of many researchers and industrial vendors. In such environments, according to the Ambient Intelligence paradigm, devices operate collectively using any information describing the environment (also known as the context-information) in order to support users in accomplishing their tasks. SE devices are characterized by several properties: they are designed to react autonomously to specific events, they are aware of the context, they manage sensitive information concerning the users, they adopt a service-oriented model in order to interact with other devices, and they interact by means of various applications and communication protocols. Cooperation with devices in SE is thus complex. This thesis deals with two problems that still represent a barrier to the development of many SE applications. The thesis examines how to interact with low-power devices, which is refereed to as device interoperability, and how to discover the functionalities that mobile devices offer, namely the service discovery problem. The rest part of the thesis describes the design of ZB4O an integration gateway for low-power devices based on the ZigBee specification. The growing market for ZigBee-ready appliances makes the ZigBee specification an important technology-enabler for SE. However, accessing such devices entails an easy interaction model with IP-based networks that are already present in most SE. Therefore, this work presents an open source platform that seamlessly integrates ZigBee devices with applications running on SE. The thesis describes the evaluation process of ZB4O with various trials organized over the last year of two EU projects, as well as the integration of ZB4O with UPnP and a RESTful approach. SE devices can also export their functionalities with a service-oriented approach. In fact, every resource offered by a device can be seen as a service available for other devices. The second problem studied in this thesis is the service discovery and it deals with how to advertise and query services in SE. The scenario considered for the service discovery problem is characterized by mobile devices carried by people roaming in SE. Hence, mobility and sociality are two key-factors that make the service discovery problem more complex and challenging. The thesis presents two algorithms, termed SIDEMAN and CORDIAL, for the service discovery in Mobile Social Networks (MSN) which are evaluated with real and synthetic simulation scenarios.
See at:
etd.adm.unipi.it | CNR IRIS | CNR IRIS
2017
Other
Open Access
Human-enabled edge computing: when mobile crowd-sensing meets mobile edge computing
Foschini L, Girolami M
EC is an architectural model and specification proposal (i.e., by European Telecommunications Standards Institute - ETSI) that aims at evolving the traditional two-layers cloud-device integration model, where mobile nodes directly communicate with a central cloud through the Internet, with the introduction of a third intermediate middleware layer that executes at so-called network edges. This promotes a new three-layer device-edge-cloud hierarchical architecture, which is recognized as very promising for several application domains [1]. In fact, the new MEC model allows moving and hosting computing/storage resources at network edges close to the targeted mobile devices, thus overcoming the typical limitations of direct cloud-device interactions, such as high uncertainty of available resources, limited bandwidth, unreliability of the wireless network trunk, and rapid deployment needs. Although various MEC solutions based on fixed edges enable an increase of the quality and performance of several cloud-assisted device services, currently there are still several non-negligible weaknesses that affect this emerging new model. First, the number of edges is generally limited because edges are deployed statically (usually by telco providers) and their configuration and operation introduce additional costs for the supported services, such as deployment, maintenance, and configuration costs. Second, once deployed, edges are rarely re-deployed (due to the high re-configuration cost) in other positions and this might result in high inefficiency, e.g., as service load conditions might significantly change dynamically. Finally, some geographical areas might become interesting hotspots for a service only during specific time slots, such as a square becoming crowded due to an open market taking place only at a specific timeslot and day of the week. At the same time, the possibility to leverage people roaming though the city with their sensor-rich devices has recently enabled Mobile Crowd-Sensing (MCS). In fact, by installing an MCS application, any smartphone can become part of a (large-scale) mobile sensor network, partially operated by the owners of the phones themselves. However, for some high-demanding MCS applications (e.g., a surveillance service that, for security purposes, monitors an environment with smartphone cameras that capture photos/videos of the surroundings and exploits face recognition to trace suspicious users' movements), regular smartphones often have not enough capabilities to timely perform the requested local tasks, in particular if considering their possible immersion in hostile environments with possible frequent intermittent disconnections from the global cloud. In other words, we claim that there are several practical cases of large and growing relevance where the joint exploitation of MEC and MCS would bring highly significant benefits in terms of efficient resource usage and perceived service quality. However, notwithstanding recent advances in both MEC and MCS, to the best of our knowledge, only a very limited number of seminal works has explored the mutual advantages in the joint use of these two classes of solutions, and they are mostly focused on pure technical communication aspects without considering the crucial importance of having humans as central contributors in the loop [2, 3, 4]. The paper reports some research ideas and findings in a brand new area that we call Human-driven Edge Computing (HEC) defined as a new model to ease the provisioning and deployment of MEC platforms as well as to enable more powerful MEC-enabled MCS applications. First and foremost, HEC eases the planning and deployment of the basic MEC model: it mitigates the potential weaknesses of having only Fixed MEC entities (FMEC) by exploiting MCS to continuously monitor humans and their mobility patterns, as well as to dynamically re-identify hot locations of potential interest for the deployment of new edges. Second, to overcome FMEC limitations, HEC enables the implementation and dynamic activation of impromptu and temporary Mobile MEC entities (M2EC) that leverage resources of locally available mobile devices. Hence, a M2EC is a local middleware proxy dynamically activated in a logical bounded location where people tend to stay for a while with repetitive and predictive mobility patterns [5], thus realizing a mobile, opportunistic, and participatory edge node. Third, given that M2EC, differently from FMEC, does not implement powerful backhaul links toward the core cloud, HEC exploits local one-hop communications and the store-and-forward principle by using humans (moving with their devices) as VM/container couriers to enable migrations between well-connected FMEC and local M2EC.
Foschini L, Girolami M
EC is an architectural model and specification proposal (i.e., by European Telecommunications Standards Institute - ETSI) that aims at evolving the traditional two-layers cloud-device integration model, where mobile nodes directly communicate with a central cloud through the Internet, with the introduction of a third intermediate middleware layer that executes at so-called network edges. This promotes a new three-layer device-edge-cloud hierarchical architecture, which is recognized as very promising for several application domains [1]. In fact, the new MEC model allows moving and hosting computing/storage resources at network edges close to the targeted mobile devices, thus overcoming the typical limitations of direct cloud-device interactions, such as high uncertainty of available resources, limited bandwidth, unreliability of the wireless network trunk, and rapid deployment needs. Although various MEC solutions based on fixed edges enable an increase of the quality and performance of several cloud-assisted device services, currently there are still several non-negligible weaknesses that affect this emerging new model. First, the number of edges is generally limited because edges are deployed statically (usually by telco providers) and their configuration and operation introduce additional costs for the supported services, such as deployment, maintenance, and configuration costs. Second, once deployed, edges are rarely re-deployed (due to the high re-configuration cost) in other positions and this might result in high inefficiency, e.g., as service load conditions might significantly change dynamically. Finally, some geographical areas might become interesting hotspots for a service only during specific time slots, such as a square becoming crowded due to an open market taking place only at a specific timeslot and day of the week. At the same time, the possibility to leverage people roaming though the city with their sensor-rich devices has recently enabled Mobile Crowd-Sensing (MCS). In fact, by installing an MCS application, any smartphone can become part of a (large-scale) mobile sensor network, partially operated by the owners of the phones themselves. However, for some high-demanding MCS applications (e.g., a surveillance service that, for security purposes, monitors an environment with smartphone cameras that capture photos/videos of the surroundings and exploits face recognition to trace suspicious users' movements), regular smartphones often have not enough capabilities to timely perform the requested local tasks, in particular if considering their possible immersion in hostile environments with possible frequent intermittent disconnections from the global cloud. In other words, we claim that there are several practical cases of large and growing relevance where the joint exploitation of MEC and MCS would bring highly significant benefits in terms of efficient resource usage and perceived service quality. However, notwithstanding recent advances in both MEC and MCS, to the best of our knowledge, only a very limited number of seminal works has explored the mutual advantages in the joint use of these two classes of solutions, and they are mostly focused on pure technical communication aspects without considering the crucial importance of having humans as central contributors in the loop [2, 3, 4]. The paper reports some research ideas and findings in a brand new area that we call Human-driven Edge Computing (HEC) defined as a new model to ease the provisioning and deployment of MEC platforms as well as to enable more powerful MEC-enabled MCS applications. First and foremost, HEC eases the planning and deployment of the basic MEC model: it mitigates the potential weaknesses of having only Fixed MEC entities (FMEC) by exploiting MCS to continuously monitor humans and their mobility patterns, as well as to dynamically re-identify hot locations of potential interest for the deployment of new edges. Second, to overcome FMEC limitations, HEC enables the implementation and dynamic activation of impromptu and temporary Mobile MEC entities (M2EC) that leverage resources of locally available mobile devices. Hence, a M2EC is a local middleware proxy dynamically activated in a logical bounded location where people tend to stay for a while with repetitive and predictive mobility patterns [5], thus realizing a mobile, opportunistic, and participatory edge node. Third, given that M2EC, differently from FMEC, does not implement powerful backhaul links toward the core cloud, HEC exploits local one-hop communications and the store-and-forward principle by using humans (moving with their devices) as VM/container couriers to enable migrations between well-connected FMEC and local M2EC.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2018
Journal article
Open Access
A Note on the Convergence of IoT, Edge, and Cloud Computing in Smart Cities
Fazio M Ranjan R, Girolami M, Taheri J, Dustdar S, Villari M
Source: IEEE CLOUD COMPUTING, vol. 5 (issue 5), pp. 22-24
DOI: 10.1109/mcc.2018.053711663
Metrics:
Fazio M Ranjan R, Girolami M, Taheri J, Dustdar S, Villari M
Source: IEEE CLOUD COMPUTING, vol. 5 (issue 5), pp. 22-24
DOI: 10.1109/mcc.2018.053711663
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ieeexplore.ieee.org | ISTI Repository | doi.org | CNR IRIS
2018
Journal article
Open Access
Human-Enabled Edge Computing: Exploiting the Crowd as a Dynamic Extension of Mobile Edge Computing
Bellavista P, Chessa S, Foschini L, Gioia L, Girolami M
e interaction with mobile nodes via local control decisions and actuation. MEC has already been proposed as an enabler for several Internet of Things and cyber-physical systems application sce- narios, and also mutual benefits due to the inte- gration of MEC and mobile crowdsensing (MCS). The article originally proposes human-driven edge computing (HEC) as a new model to ease the provisioning and to extend the coverage of tra- ditional MEC solutions. From a methodological perspective, we show how it is possible to exploit MCS i) to support the effective deployment of fixed MEC (FMEC) proxies and ii) to further extend their coverage through the introduction of impromptu and human-enabled mobile MEC (M2EC) proxies. In addition, we describe how we have implemented these novel concepts in the MCS ParticipAct platform through the integration of the MEC Elijah platform in the ParticipAct liv- ing lab, an ongoing MCS real-world experiment that involved about 170 students at the University of Bologna for more than two years. Reported experimental results quantitatively show the effec- tiveness of the proposed techniques in elastically scaling the load at edge nodes according to run- time provisioning needs.Source: IEEE COMMUNICATIONS MAGAZINE (PRINT), vol. 56 (issue 1), pp. 145-155
DOI: 10.1109/mcom.2017.1700385
Metrics:
Bellavista P, Chessa S, Foschini L, Gioia L, Girolami M
e interaction with mobile nodes via local control decisions and actuation. MEC has already been proposed as an enabler for several Internet of Things and cyber-physical systems application sce- narios, and also mutual benefits due to the inte- gration of MEC and mobile crowdsensing (MCS). The article originally proposes human-driven edge computing (HEC) as a new model to ease the provisioning and to extend the coverage of tra- ditional MEC solutions. From a methodological perspective, we show how it is possible to exploit MCS i) to support the effective deployment of fixed MEC (FMEC) proxies and ii) to further extend their coverage through the introduction of impromptu and human-enabled mobile MEC (M2EC) proxies. In addition, we describe how we have implemented these novel concepts in the MCS ParticipAct platform through the integration of the MEC Elijah platform in the ParticipAct liv- ing lab, an ongoing MCS real-world experiment that involved about 170 students at the University of Bologna for more than two years. Reported experimental results quantitatively show the effec- tiveness of the proposed techniques in elastically scaling the load at edge nodes according to run- time provisioning needs.Source: IEEE COMMUNICATIONS MAGAZINE (PRINT), vol. 56 (issue 1), pp. 145-155
DOI: 10.1109/mcom.2017.1700385
Metrics:
See at:
IEEE Communications Magazine | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ISTI Repository | IEEE Communications Magazine | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2020
Other
Open Access
Game theory in mobile crowdsensing: A comprehensive survey
Dasari Vs, Kantarci B, Pouryazdan M, Foschini L, Girolami M
Source: SENSORS (BASEL), vol. 20 (issue 7)
DOI: 10.3390/s20072055
Metrics:
Dasari Vs, Kantarci B, Pouryazdan M, Foschini L, Girolami M
Source: SENSORS (BASEL), vol. 20 (issue 7)
DOI: 10.3390/s20072055
Metrics:
See at:
Sensors | Sensors | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | CNR IRIS | ISTI Repository | Sensors | Sensors | CNR IRIS
2021
Contribution to book
Open Access
Social behaviour and cognitive monitoring in healthy ageing
Rocke C, Guye S, Girolami M, Kniestedt I
Social integration is a key predictor of health in later life, as is cognitive functioning. This chapter describes the evidence related to levels of and interventions on social integration and cognitive functioning for older adults and outlines how this evidence was translated into the personalized coaching approach in NESTORE in both of these domains. From the technological side, social beacons are used to obtain objective contact measures for users' local social networks and thus complement self-report information on social interactions beyond face-to-face contacts. In the cognitive domain, a serious game involving a multidomain cognitive training was developed on the basis of evidence-based game and training principles.Source: RESEARCH FOR DEVELOPMENT, pp. 103-114
DOI: 10.1007/978-3-030-72663-8_6
Project(s): NESTORE
Metrics:
Rocke C, Guye S, Girolami M, Kniestedt I
Social integration is a key predictor of health in later life, as is cognitive functioning. This chapter describes the evidence related to levels of and interventions on social integration and cognitive functioning for older adults and outlines how this evidence was translated into the personalized coaching approach in NESTORE in both of these domains. From the technological side, social beacons are used to obtain objective contact measures for users' local social networks and thus complement self-report information on social interactions beyond face-to-face contacts. In the cognitive domain, a serious game involving a multidomain cognitive training was developed on the basis of evidence-based game and training principles.Source: RESEARCH FOR DEVELOPMENT, pp. 103-114
DOI: 10.1007/978-3-030-72663-8_6
Project(s): NESTORE
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2022
Journal article
Open Access
A mobility-based deployment strategy for edge data centers
Girolami M, Vitello P, Capponi A, Fiandrino C, Foschini L, Bellavista P
The main objective of Multi-access Edge Computing (MEC) is to bring computational capabilities at the edge of the network to better support low-latency applications. Such capabilities are typically offered by Edge Data Centers (EDC). The MEC paradigm is not tied to a single radio technology, rather it embraces both cellular and other radio access technologies such as WiFi. Distributed intelligence at the edge for AI purposes requires careful spatial planning of computing and storage resources. The problem of EDC deployment in urban environments is challenging and, to the best of our knowledge, it has been explored only for cellular connectivity so far. In this paper, we study the possibility of deploying EDC without analyzing the expected data traffic load of the cellular network, a kind of information rarely shared by network operators. To this purpose, we propose in this work CLUB, CLUstering-Based strategy tailored on the analysis of urban mobility. We analyze two experimental mobility data sets, and we analyze some mobility features in order to characterize their properties. Finally, we compare the performance of CLUB against state-of-the-art techniques in terms of the outage probability, namely the probability an EDC is not able to serve a request. Our results show that the CLUB strategy is always comparable with respect to our benchmarks, but without using any information related to network traffic.Source: JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, vol. 164, pp. 133-141
DOI: 10.1016/j.jpdc.2022.03.007
Metrics:
Girolami M, Vitello P, Capponi A, Fiandrino C, Foschini L, Bellavista P
The main objective of Multi-access Edge Computing (MEC) is to bring computational capabilities at the edge of the network to better support low-latency applications. Such capabilities are typically offered by Edge Data Centers (EDC). The MEC paradigm is not tied to a single radio technology, rather it embraces both cellular and other radio access technologies such as WiFi. Distributed intelligence at the edge for AI purposes requires careful spatial planning of computing and storage resources. The problem of EDC deployment in urban environments is challenging and, to the best of our knowledge, it has been explored only for cellular connectivity so far. In this paper, we study the possibility of deploying EDC without analyzing the expected data traffic load of the cellular network, a kind of information rarely shared by network operators. To this purpose, we propose in this work CLUB, CLUstering-Based strategy tailored on the analysis of urban mobility. We analyze two experimental mobility data sets, and we analyze some mobility features in order to characterize their properties. Finally, we compare the performance of CLUB against state-of-the-art techniques in terms of the outage probability, namely the probability an EDC is not able to serve a request. Our results show that the CLUB strategy is always comparable with respect to our benchmarks, but without using any information related to network traffic.Source: JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, vol. 164, pp. 133-141
DOI: 10.1016/j.jpdc.2022.03.007
Metrics:
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | CNR IRIS | CNR IRIS | CNR IRIS
2022
Contribution to book
Open Access
Welcome from the Demo Chairs
Girolami M., Peltonen E.
Welcome Message Demo Session IEEE PerCom 2022DOI: 10.1109/percomworkshops53856.2022.9767348
Metrics:
Girolami M., Peltonen E.
Welcome Message Demo Session IEEE PerCom 2022DOI: 10.1109/percomworkshops53856.2022.9767348
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS
2023
Contribution to book
Open Access
Welcome from the demo chairs
Girolami M, Yasumoto K
This year we had 33 demo proposals submitted and 23 of them have been accepted by the committee. These papers address important problems in several application domains ranging from IoT, wearable and mobile devices, security/privacy, and real-life applications in pervasive computing. During PerCom, one of the selected demos receives the "Best Demo Award" based on its research value, originality, and presentation. We thank all the authors who submitted their innovative demo papers to PerCom this year, and the committee members for volunteering their time and hard to benefit the PerCom community by providing high-quality reviews.DOI: 10.1109/percomworkshops56833.2023.10150290
Metrics:
Girolami M, Yasumoto K
This year we had 33 demo proposals submitted and 23 of them have been accepted by the committee. These papers address important problems in several application domains ranging from IoT, wearable and mobile devices, security/privacy, and real-life applications in pervasive computing. During PerCom, one of the selected demos receives the "Best Demo Award" based on its research value, originality, and presentation. We thank all the authors who submitted their innovative demo papers to PerCom this year, and the committee members for volunteering their time and hard to benefit the PerCom community by providing high-quality reviews.DOI: 10.1109/percomworkshops56833.2023.10150290
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS
2024
Journal article
Open Access
PRORL: Proactive Resource Orchestrator for Open RANs Using Deep Reinforcement Learning
Staffolani A., Darvariu V., Foschini L., Girolami M., Bellavista P., Musolesi M.
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.Source: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
DOI: 10.1109/tnsm.2024.3373606
Project(s): The Alan Turing Institute
Metrics:
Staffolani A., Darvariu V., Foschini L., Girolami M., Bellavista P., Musolesi M.
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.Source: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
DOI: 10.1109/tnsm.2024.3373606
Project(s): The Alan Turing Institute
Metrics:
See at:
UCL Discovery | doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | IRIS Cnr | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | GitHub | CNR IRIS
2024
Journal article
Open Access
A UAV deployment strategy based on a probabilistic data coverage model for mobile CrowdSensing applications
Girolami M., Cipullo E., Colella T., Chessa S.
Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user’s trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%.Source: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol. 16 (issue 2), pp. 241-268
DOI: 10.3233/ais-220601
Metrics:
Girolami M., Cipullo E., Colella T., Chessa S.
Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user’s trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%.Source: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol. 16 (issue 2), pp. 241-268
DOI: 10.3233/ais-220601
Metrics:
See at:
IRIS Cnr | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Journal of Ambient Intelligence and Smart Environments | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS
2024
Contribution to book
Open Access
HCCS 2024: 4th Workshop on Human-Centered Computational Sensing - Welcome and Committees
Delmastro F., Girolami M., Theoleyre F.
We are pleased to present the proceedings of the Fourth edition of the International IEEE Workshop on Human-Centered Computational Sensing (HCCS'24) held in conjunction with IEEE PerCom 2024. The HCCS workshop aims to advance and promote research about how unobtrusive observation of human cognitive, behavioral, physiological, and contextual data increasingly enables new computing experiences and effective intervention opportunities. The workshop also seeks to stimulate dialogue about the implications of human-centered computational sensing for societyDOI: 10.1109/percomworkshops59983.2024.10503049
Metrics:
Delmastro F., Girolami M., Theoleyre F.
We are pleased to present the proceedings of the Fourth edition of the International IEEE Workshop on Human-Centered Computational Sensing (HCCS'24) held in conjunction with IEEE PerCom 2024. The HCCS workshop aims to advance and promote research about how unobtrusive observation of human cognitive, behavioral, physiological, and contextual data increasingly enables new computing experiences and effective intervention opportunities. The workshop also seeks to stimulate dialogue about the implications of human-centered computational sensing for societyDOI: 10.1109/percomworkshops59983.2024.10503049
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2011
Other
Restricted
universAAL - Execution Environment installation packages and hardware abstraction layer (D2.1-B)
Furfari F, Girolami M
This document corresponds to deliverable D2.1 of type "Prototype". It provides a report about software development within Task 2.1 "Create the universAAL Execution Environment and Hardware Abstraction Layer". The requirements and the scope of D2.1 are equivalent to those defined among the following three expert groups: Middleware, Local Device Discovery and Integration (LDDI), and Security. Each one of these Expert Groups provides the specification of the software under development in comparison with the design decisions from input projects in terms of a set of wiki pages. In addition to the specifications, the Wiki pages also provide detailed information about the status of the developments and the plans for the next steps with time and resource allocation. To reflect the latest status of these Wiki pages at the time of closing the version B of D1.2, a snapshot of them has been added to this report as appendices. The main part of the report provides a very compact overview of the detailed info available in those appendices.Project(s): UNIVERSAAL
Furfari F, Girolami M
This document corresponds to deliverable D2.1 of type "Prototype". It provides a report about software development within Task 2.1 "Create the universAAL Execution Environment and Hardware Abstraction Layer". The requirements and the scope of D2.1 are equivalent to those defined among the following three expert groups: Middleware, Local Device Discovery and Integration (LDDI), and Security. Each one of these Expert Groups provides the specification of the software under development in comparison with the design decisions from input projects in terms of a set of wiki pages. In addition to the specifications, the Wiki pages also provide detailed information about the status of the developments and the plans for the next steps with time and resource allocation. To reflect the latest status of these Wiki pages at the time of closing the version B of D1.2, a snapshot of them has been added to this report as appendices. The main part of the report provides a very compact overview of the detailed info available in those appendices.Project(s): UNIVERSAAL