2015
Software
Unknown
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 ExploRA
2011
Report
Unknown
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.Source: Project report, UniversAAL, Deliverable D1.3-C, 2011
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.Source: Project report, UniversAAL, Deliverable D1.3-C, 2011
Project(s): UNIVERSAAL
See at: CNR ExploRA
2013
Report
Unknown
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.Source: ISTI Technical reports, 2013
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.Source: ISTI Technical reports, 2013
See at: CNR ExploRA
2013
Conference article
Unknown
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.Source: eTELEMED 2013 - The Fifth International Conference on eHealth, Telemedicine, and Social Medicine, pp. 153–155, Nice, France, 24 February - 1 March 2013
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.Source: eTELEMED 2013 - The Fifth International Conference on eHealth, Telemedicine, and Social Medicine, pp. 153–155, Nice, France, 24 February - 1 March 2013
Project(s): OASIS
See at: CNR ExploRA
2015
Report
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.Source: Project report, ReAAL, D3.3, 2015
Project(s): 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.Source: Project report, ReAAL, D3.3, 2015
Project(s): ReAAL
See at:
ISTI Repository 
| CNR ExploRA
2015
Doctoral thesis
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 | ISTI Repository | CNR ExploRA
2017
Report
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.Source: ISTI Technical reports, 2017
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.Source: ISTI Technical reports, 2017
See at:
ISTI Repository | CNR ExploRA
2018
Contribution to journal
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 Online 5 (2018): 22–24. doi:10.1109/MCC.2018.053711663
DOI: 10.1109/mcc.2018.053711663
Metrics:
Fazio M. Ranjan R., Girolami M., Taheri J., Dustdar S., Villari M.
Source: IEEE cloud computing Online 5 (2018): 22–24. doi:10.1109/MCC.2018.053711663
DOI: 10.1109/mcc.2018.053711663
Metrics:
See at:
ieeexplore.ieee.org | ieeexplore.ieee.org | ISTI Repository | doi.org 
| CNR ExploRA
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) 56 (2018): 145–155. doi:10.1109/MCOM.2017.1700385
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) 56 (2018): 145–155. doi:10.1109/MCOM.2017.1700385
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 | ieeexplore.ieee.org | CNR ExploRA
2020
Contribution to journal
Open Access
Game theory in mobile crowdsensing: A comprehensive survey
Dasari V. S., Kantarci B., Pouryazdan M., Foschini L., Girolami M.
Source: Sensors (Basel) 20 (2020). doi:10.3390/s20072055
DOI: 10.3390/s20072055
Metrics:
Dasari V. S., Kantarci B., Pouryazdan M., Foschini L., Girolami M.
Source: Sensors (Basel) 20 (2020). doi:10.3390/s20072055
DOI: 10.3390/s20072055
Metrics:
See at:
Sensors | Sensors | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | ISTI Repository | Sensors | Sensors | CNR ExploRA
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: Digital Health Technology for Better Aging. A multidisciplinary approach, edited by G. Andreoni, C. Mambretti, pp. 103–114, 2021
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: Digital Health Technology for Better Aging. A multidisciplinary approach, edited by G. Andreoni, C. Mambretti, pp. 103–114, 2021
DOI: 10.1007/978-3-030-72663-8_6
Project(s): NESTORE
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
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 (Print) 164 (2022): 133–141. doi:10.1016/j.jpdc.2022.03.007
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 (Print) 164 (2022): 133–141. doi:10.1016/j.jpdc.2022.03.007
DOI: 10.1016/j.jpdc.2022.03.007
Metrics:
See at:
ISTI Repository | www.sciencedirect.com | CNR ExploRA
2022
Contribution to book
Open Access
Welcome from the Demo Chairs
Girolami M., Peltonen E.
Welcome Message Demo Session IEEE PerCom 2022Source: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2022
DOI: 10.1109/percomworkshops53856.2022.9767348
Metrics:
Girolami M., Peltonen E.
Welcome Message Demo Session IEEE PerCom 2022Source: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2022
DOI: 10.1109/percomworkshops53856.2022.9767348
Metrics:
See at:
ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
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.Source: 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023
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.Source: 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023
DOI: 10.1109/percomworkshops56833.2023.10150290
Metrics:
See at:
ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
2011
Report
Unknown
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.Source: Project report, UniversAAL, Deliverable D2.1-B, 2011
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.Source: Project report, UniversAAL, Deliverable D2.1-B, 2011
Project(s): UNIVERSAAL
See at: CNR ExploRA
2011
Report
Unknown
universAAL - AAL reference architecture requirements (D1.2-B)
Furfari F., Girolami M., Wolf P.
D1.2 reports about the requirements analysis process in universAAL and summarises the related results. The current document D1.2-B, due at month twelve, is the second report from four planned versions. The main focus of this document is to present the approach to and the results of the second iteration of the requirements analysis process within universAAL. For more general information about the role of this report in universAAL, please refer to Section 2 that provides more details on the purpose of D1.2, its relationships to the other universAAL deliverables as well as the positioning of D1.2-B in relation to the other versions of D1.2. The second iteration, in contrast to the first iteration that focused on a list of consolidated requirements, is continuing the analysis process in an incremental and iterative fashion. As shortly discussed in Section 3 this iteration is mainly dealing with four significant drawbacks inherent in the consolidated list of requirements, namely: . The high number of around 480 consolidated requirements that makes it difficult to use this list efficiently within the project; . The non-technical nature of some of the requirements which makes it difficult to derive architectural design decisions; . The inconsistency in the description when it comes to level of abstraction which sometimes prevents the architecture designers to derive clear conclusions and decisions from requirements; . The scope and content of individual requirements that were not in line with other decisions regarding scope and content of the project as captured by other artefacts produced in the project, especially the concerns. To deal with these drawbacks the second iteration is therefore narrowing the focus to reference architecture requirements. This is achieved by analysing the purpose of reference architecture requirements within the project in Section 4 and by defining a set of features that helps in identification and formulation of those requirements. Additionally, the relation towards the project's main concerns, as captured in D1.1 is newly assessed as part of Section 5 that leads to re-categorisation of requirements that is also presented in Section 5. Finally, these considerations lead to a reformulation of the requirements resulting in a considerably more focused list of approximately 100 reference architecture requirements. This list of requirements is also the final result of the second iteration and can be viewed at the very end of Section 5. As outlined in the conclusion section at the very end, D1.2-C and D1.2-D will continue with this iterative refinement process and, thereby, enhance the quality, the completeness and the clarity of requirements in each of the next iterations until a final set of universAAL reference architecture requirements is achievedSource: Project report, UniversAAL, Deliverable D1.2-B, 2011
Project(s): UNIVERSAAL
Furfari F., Girolami M., Wolf P.
D1.2 reports about the requirements analysis process in universAAL and summarises the related results. The current document D1.2-B, due at month twelve, is the second report from four planned versions. The main focus of this document is to present the approach to and the results of the second iteration of the requirements analysis process within universAAL. For more general information about the role of this report in universAAL, please refer to Section 2 that provides more details on the purpose of D1.2, its relationships to the other universAAL deliverables as well as the positioning of D1.2-B in relation to the other versions of D1.2. The second iteration, in contrast to the first iteration that focused on a list of consolidated requirements, is continuing the analysis process in an incremental and iterative fashion. As shortly discussed in Section 3 this iteration is mainly dealing with four significant drawbacks inherent in the consolidated list of requirements, namely: . The high number of around 480 consolidated requirements that makes it difficult to use this list efficiently within the project; . The non-technical nature of some of the requirements which makes it difficult to derive architectural design decisions; . The inconsistency in the description when it comes to level of abstraction which sometimes prevents the architecture designers to derive clear conclusions and decisions from requirements; . The scope and content of individual requirements that were not in line with other decisions regarding scope and content of the project as captured by other artefacts produced in the project, especially the concerns. To deal with these drawbacks the second iteration is therefore narrowing the focus to reference architecture requirements. This is achieved by analysing the purpose of reference architecture requirements within the project in Section 4 and by defining a set of features that helps in identification and formulation of those requirements. Additionally, the relation towards the project's main concerns, as captured in D1.1 is newly assessed as part of Section 5 that leads to re-categorisation of requirements that is also presented in Section 5. Finally, these considerations lead to a reformulation of the requirements resulting in a considerably more focused list of approximately 100 reference architecture requirements. This list of requirements is also the final result of the second iteration and can be viewed at the very end of Section 5. As outlined in the conclusion section at the very end, D1.2-C and D1.2-D will continue with this iterative refinement process and, thereby, enhance the quality, the completeness and the clarity of requirements in each of the next iterations until a final set of universAAL reference architecture requirements is achievedSource: Project report, UniversAAL, Deliverable D1.2-B, 2011
Project(s): UNIVERSAAL
See at: CNR ExploRA
2012
Report
Unknown
universAAL - universAAL execution environment, installation packages, hardware abstraction layer, generic platform services / AAL platform services and ontology artefacts
Girolami M., Furfari F., Stocklöw C., Owda Z.
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" and Task 2.2 "Implement universAAL Generic Platform Services, AAL Platform services and ontologies support". 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 C 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. Please note that this deliverable (D2.1-C) combines the results of the two tasks T2.1 and T2.2. Until version B the results were reported in two separate deliverables (D2.1-B and D2.2-B).Source: Project report, UniversAAL, Deliverable D2.1-C, 2012
Project(s): UNIVERSAAL
Girolami M., Furfari F., Stocklöw C., Owda Z.
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" and Task 2.2 "Implement universAAL Generic Platform Services, AAL Platform services and ontologies support". 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 C 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. Please note that this deliverable (D2.1-C) combines the results of the two tasks T2.1 and T2.2. Until version B the results were reported in two separate deliverables (D2.1-B and D2.2-B).Source: Project report, UniversAAL, Deliverable D2.1-C, 2012
Project(s): UNIVERSAAL
See at: CNR ExploRA
2013
Report
Unknown
universAAL - AAL Reference Architecture Requirements
Stocklöw C., Girolami M., Furfari F.
Specification of consolidated requirements coming from original projects and identification of new requirements not taken into account before. The deliverable will include: A consolidated version of Technical Requirements for AAL systems, a list of functional and non-functional requirements to be taken into account in the reference architecture and a glossary of Technical concepts.Source: Project report, UniversAAL, Deliverable D1.2-D, 2013
Project(s): UNIVERSAAL
Stocklöw C., Girolami M., Furfari F.
Specification of consolidated requirements coming from original projects and identification of new requirements not taken into account before. The deliverable will include: A consolidated version of Technical Requirements for AAL systems, a list of functional and non-functional requirements to be taken into account in the reference architecture and a glossary of Technical concepts.Source: Project report, UniversAAL, Deliverable D1.2-D, 2013
Project(s): UNIVERSAAL
See at: CNR ExploRA
2013
Conference article
Restricted
universAAL: provisioning platform for AAL services
Ram R., Furfari F., Girolami M., Ibanez-Sanchez G., Lazaro-Ramos J., Mayer C., Prazak-Aram B., Zentek T.
Sommario in IngleseuniversAAL is a European research project that aims at creating an open platform and standards which will make it technically feasible and economically viable to develop Ambient Assisted Living (AAL) solutions. It defined hardware and software infrastructure for smart environments called AAL Spaces, which enable context sharing and reasoning about activities carried out by the assisted person. AAL Services developed with the universAAL platform may be a combination of hardware, software and human resources. Tools for the development, publishing and provisioning of such services have been defined to support the whole chain of stakeholders involved in the AAL domain. The paper focuses on the provisioning of AAL Services by describing the main components involved in the service life cycle.Source: ISAmI 2013 - 4th International Symposium on Ambient Intelligence, pp. 105–112, Salamanca, Spain, 22-25 May 2013
DOI: 10.1007/978-3-319-00566-9_14
Project(s): UNIVERSAAL
Metrics:
Ram R., Furfari F., Girolami M., Ibanez-Sanchez G., Lazaro-Ramos J., Mayer C., Prazak-Aram B., Zentek T.
Sommario in IngleseuniversAAL is a European research project that aims at creating an open platform and standards which will make it technically feasible and economically viable to develop Ambient Assisted Living (AAL) solutions. It defined hardware and software infrastructure for smart environments called AAL Spaces, which enable context sharing and reasoning about activities carried out by the assisted person. AAL Services developed with the universAAL platform may be a combination of hardware, software and human resources. Tools for the development, publishing and provisioning of such services have been defined to support the whole chain of stakeholders involved in the AAL domain. The paper focuses on the provisioning of AAL Services by describing the main components involved in the service life cycle.Source: ISAmI 2013 - 4th International Symposium on Ambient Intelligence, pp. 105–112, Salamanca, Spain, 22-25 May 2013
DOI: 10.1007/978-3-319-00566-9_14
Project(s): UNIVERSAAL
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2014
Report
Unknown
Service discovery in mobile social networks
Girolami M., Chessa S., Basagni S., Furfari F.
We present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. In particular, 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 necessary for service discovery, and the time needed to reply to a service query. Our results 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 remarkably lower energy cost than that of the flooding and gossiping-based solutions.Source: ISTI Technical reports, 2014
Project(s): UNIVERSAAL
Girolami M., Chessa S., Basagni S., Furfari F.
We present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. In particular, 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 necessary for service discovery, and the time needed to reply to a service query. Our results 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 remarkably lower energy cost than that of the flooding and gossiping-based solutions.Source: ISTI Technical reports, 2014
Project(s): UNIVERSAAL
See at: CNR ExploRA