arXiv subjects

Marco Conti

Publications and source records attributed to Marco Conti.

At least 55 records · Page 3Linked to original sources

arXiv2023

Service Differentiation and Fair Sharing in Distributed Quantum Computing

In the future, quantum computers will become widespread and a network of quantum repeaters will provide them with end-to-end entanglement of remote quantum bits. As a result, a pervasive quantum computation infrastructure will emerge, which will unlock several novel applications, including distributed quantum computing, that is the pooling of resources on multiple computation nodes to address problem instances that are unattainable by any individual quantum computer. In this paper, we first investigate the issue of service differentiation in this new environment. Then, we define the problem of how to select which computation nodes should participate in each pool, so as to achieve a fair share of the quantum network resources available. The analysis is performed via an open source simulator and the results are fully and readily available.

quant-ph↗

arXiv2022

Signed ego network model and its application to Twitter

The Ego Network Model (ENM) describes how individuals organise their social relations in concentric circles (typically five) of decreasing intimacy, and it has been found almost ubiquitously in social networks, both offline and online. The ENM gauges the tie strength between peers in terms of interaction frequency, which is easy to measure and provides a good proxy for the time spent nurturing the relationship. However, advances in signed network analysis have shown that positive and negative relations play very different roles in network dynamics. For this reason, this work sets out to investigate the ENM when including signed relations. The main contributions of this paper are twofold: firstly, a novel method of signing relationships between individuals using sentiment analysis and, secondly, an investigation of the properties of Signed Ego Networks (Ego Networks with signed connections). Signed Ego Networks are then extracted for the users of eight different Twitter datasets composed of both specialised users (e.g. journalists) and generic users. We find that negative links are over-represented in the active part of the Ego Networks of all types of users, suggesting that Twitter users tend to engage regularly with negative connections. Further, we observe that negative relationships are overwhelmingly predominant in the Ego Network circles of specialised users, hinting at very polarised online interactions for this category of users. In addition, negative relationships are found disproportionately more at the more intimate levels of the ENM for journalists, while their percentages are stable across the circles of the other Twitter users

cs.SI↗

arXiv2022

Models for digitally contact-traced epidemics

Contacts between people are the absolute drivers of contagious respiratory infections. For this reason, limiting and tracking contacts is a key strategy for the control of the COVID-19 epidemic. Digital contact tracing has been proposed as an automated solution to scale up traditional contact tracing. However, the required penetration of contact tracing apps within a population to achieve a desired target in the control of the epidemic is currently under discussion within the research community. In order to understand the effects of digital contact tracing, several mathematical models have been proposed. In this article, we survey the main ones and we propose a compartmental SEIR model with which it is possible, differently from the models in the related literature, to derive closed-form conditions regarding the control of the epidemic as a function of the contact tracing apps penetration and the testing efficiency. Closed-form conditions are crucial for the understandability of models, and thus for decision makers (including digital contact tracing designers) to correctly assess the dependencies within the epidemic. With our model, we find that digital contact tracing alone can rarely tame an epidemic: for unrestrained COVID-19, this would require a testing turnaround of around 1 day and app uptake above 80% of the population, which are very difficult to achieve in practice. However, digital contact tracing can still be effective if complemented with other mitigation strategies, such as social distancing and mask-wearing.

cs.CY↗

arXiv2022

In-Network Computing With Function as a Service at the Edge

Offloading computation from user devices to nodes with processing capabilities at the edge of the network is a major trend in today's network/service architectures. At the same time, serverless computing has gained a huge traction among the cloud computing technologies and has, thus, promoted the adoption of Function-as-a-Service (FaaS). The latter has some characteristics that make it generally suitable to edge applications, except for its cumbersome support of stateful applications. This work is set to provide a broad view on the options available for supporting stateful FaaS, which are distilled into four reference execution models that differ on where the state resides. While further investigation is needed to advance our understanding of the opportunities offered by in-network computing through stateful FaaS, initial insights are provided by means of a qualitative analysis of the four alternatives and their quantitative comparison in a simulator.

cs.NI↗

arXiv2022

FaaS Execution Models for Edge Applications

In this paper, we address the problem of supporting stateful workflows following a Function-as-a-Service (FaaS) model in edge networks. In particular we focus on the problem of data transfer, which can be a performance bottleneck due to the limited speed of communication links in some edge scenarios and we propose three different schemes: a pure FaaS implementation, StateProp, i.e., propagation of the application state throughout the entire chain of functions, and StateLocal, i.e., a solution where the state is kept local to the workers that run functions and retrieved only as needed. We then extend the proposed schemes to the more general case of applications modeled as Directed Acyclic Graphs (DAGs), which cover a broad range of practical applications, e.g., in the Internet of Things (IoT) area. Our contribution is validated via a prototype implementation. Experiments in emulated conditions show that applying the data locality principle reduces significantly the volume of network traffic required and improves the end-to-end delay performance, especially with local caching on edge nodes and low link speeds.

cs.NI↗

arXiv2022

Quality of Service in Quantum Networks

In the coming years, quantum networks will allow quantum applications to thrive thanks to the new opportunities offered by end-to-end entanglement of qubits on remote hosts via quantum repeaters. On a geographical scale, this will lead to the dawn of the Quantum Internet. While a full-blown deployment is yet to come, the research community is already working on a variety of individual enabling technologies and solutions. In this paper, with the guidance of extensive simulations, we take a broader view and investigate the problems of Quality of Service (QoS) and provisioning in the context of quantum networks, which are very different from their counterparts in classical data networks due to some of their fundamental properties. Our work leads the way towards a new class of studies that will allow the research community to better understand the challenges of quantum networks and their potential commercial exploitation.

quant-ph↗

arXiv2022

Journalists' ego networks in Twitter: invariant and distinctive structural features

Ego networks have proved to be a valuable tool for understanding the relationships that individuals establish with their peers, both in offline and online social networks. Particularly interesting are the cognitive constraints associated with the interactions between the ego and the members of their ego network, which limit individuals to maintain meaningful interactions with no more than 150 people, on average, and to arrange such relationships along concentric circles of decreasing engagement. In this work, we focus on the ego networks of journalists on Twitter, considering 17 different countries, and we investigate whether they feature the same characteristics observed for other relevant classes of Twitter users, like politicians and generic users. Our findings are that journalists are generally more active and interact with more people than generic users, regardless of their country. Their ego network structure is very aligned with reference models derived in anthropology and observed in general human ego networks. Remarkably, the similarity is even higher than the one of politicians and generic users ego networks. This may imply a greater cognitive involvement with Twitter for journalists than for other user categories. From a dynamic perspective, journalists have stable short-term relationships that do not change much over time. In the longer term, though, ego networks can be pretty dynamic, especially in the innermost circles. Moreover, the ego-alter ties of journalists are often information-driven, as they are mediated by hashtags both at their inception and during their lifetime. Finally, we found that relationships between journalists are assortative in popularity: journalists tend to engage with other journalists of similar popularity, in all layers but especially in their innermost ones.

cs.SI↗

arXiv2022

Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks

Being able to recommend links between users in online social networks is important for users to connect with like-minded individuals as well as for the platforms themselves and third parties leveraging social media information to grow their business. Predictions are typically based on unsupervised or supervised learning, often leveraging simple yet effective graph topological information, such as the number of common neighbors. However, we argue that richer information about personal social structure of individuals might lead to better predictions. In this paper, we propose to leverage well-established social cognitive theories to improve link prediction performance. According to these theories, individuals arrange their social relationships along, on average, five concentric circles of decreasing intimacy. We postulate that relationships in different circles have different importance in predicting new links. In order to validate this claim, we focus on popular feature-extraction prediction algorithms (both unsupervised and supervised) and we extend them to include social-circles awareness. We validate the prediction performance of these circle-aware algorithms against several benchmarks (including their baseline versions as well as node-embedding- and GNN-based link prediction), leveraging two Twitter datasets comprising a community of video gamers and generic users. We show that social-awareness generally provides significant improvements in the prediction performance, beating also state-of-the-art solutions like node2vec and SEAL, and without increasing the computational complexity. Finally, we show that social-awareness can be used in place of using a classifier (which may be costly or impractical) for targeting a specific category of users.

cs.SI↗

arXiv2022

Ego Network Structure in Online Social Networks and its Impact on Information Diffusion

In the last few years, Online Social Networks (OSNs) attracted the interest of a large number of researchers, thanks to their central role in the society. Through the analysis of OSNs, many social phenomena have been studied, such as the viral diffusion of information amongst people. What is still unclear is the relation between micro-level structural properties of OSNs (i.e. the properties of the personal networks of the users, also known as ego networks) and the emergence of such phenomena. A better knowledge of this relation could be essential for the creation of services for the Future Internet, such as highly personalised advertisements fitted on users' needs and characteristics. In this paper, we contribute to bridge this gap by analysing the ego networks of a large sample of Facebook and Twitter users. Our results indicate that micro-level structural properties of OSNs are interestingly similar to those found in social networks formed offline. In particular, online ego networks show the same structure found offline, with social contacts arranged in layers with compatible size and composition. From the analysis of Twitter ego networks, we have been able to find a direct impact of tie strength and ego network circles on the diffusion of information in the network. Specifically, there is a high correlation between the frequency of direct contact between users and her friends in Twitter (a proxy for tie strength), and the frequency of retweets made by the users from tweets generated by their friends. We analysed the correlation for each ego network layer identified in Twitter, discovering their role in the diffusion of information.

cs.SI↗

arXiv2022

Design and evaluation of a cognitive approach for disseminating semantic knowledge and content in opportunistic networks

In cyber-physical convergence scenarios information flows seamlessly between the physical and the cyber worlds. Here, users' mobile devices represent a natural bridge through which users process acquired information and perform actions. The sheer amount of data available in this context calls for novel, autonomous and lightweight data-filtering solutions, where only relevant information is finally presented to users. Moreover, in many real-world scenarios data is not categorised in predefined topics, but it is generally accompanied by semantic descriptions possibly describing users' interests. In these complex conditions, user devices should autonomously become aware not only of the existence of data in the network, but also of their semantic descriptions and correlations between them. To tackle these issues, we present a set of algorithms for knowledge and data dissemination in opportunistic networks, based on simple and very effective models (called cognitive heuristics) coming from cognitive sciences. We show how to exploit them to disseminate both semantic data and the corresponding data items. We provide a thorough performance analysis, under various different conditions comparing our results against non-cognitive solutions. Simulation results demonstrate the superior performance of our solution towards a more effective semantic knowledge acquisition and representation, and a more tailored content acquisition.

cs.NI↗

arXiv2022

Structure of Ego-alter relationships of Politicians in Twitter

We analyze the ego-alter Twitter networks of 300 Italian MPs and 18 European leaders, and of about 14,000 generic users. We find structural properties typical of social environments, meaning that Twitter activity is controlled by constraints that are similar to those shaping conventional social relationships. However, the evolution of ego-alter ties is very dynamic, which suggests that they are not entirely used for social interaction, but for public signaling and self-promotion. From this standpoint, the behavior of EU leaders is much more evident, while Italian MPs are in between them and generic users. We find that politicians, more than generic users, create relationships as a side effect of tweeting on discussion topics, rather than by contacting specific alters.

cs.SI↗

arXiv2022

On the impact of the physical layer model on the performance of D2D-offloading in vehicular environments

Offloading data traffic from Infrastructure-to-Device (I2D) to Device-to-Device (D2D) communications is a powerful tool for reducing congestion, energy consumption, and spectrum usage of mobile cellular networks. Prior network-level studies on D2D data offloading focus on high level performance metrics as the offloading efficiency, and take into account the radio propagation aspects by using simplistic wireless channel models. We consider a D2D data offloading protocol tailored to highly dynamic scenarios as vehicular environments, and evaluate its performance focusing on physical layer aspects, like energy consumption and spectral efficiency. We do this by taking into account more realistic models of the wireless channel, with respect to the simplistic ones generally used in the previous studies. Our objective is twofold: first, to quantify the performance gain of the considered D2D offloading protocol with respect to a classic cellular network, based on I2D communications, in terms of energy consumption and spectral efficiency. Second, to show that using simplistic channel models may prevent to accurately evaluate the performance gain. Additionally, the use of more elaborated models allows to obtain insightful information on relevant system-level parameters settings, which would not be possible to obtain by using simple models. The considered channel models have been proposed and validated, in the recent years, through large-scale measurements campaigns. Our results show that the considered protocol is able to achieve a reduction in the energy consumption of up to 35%, and an increase in the system spectral efficiency of 50%, with respect to the benchmark cellular system. The use of different channel models in evaluating these metrics may result, in the worst case, in a sixfold underestimation of the achieved improvement.

cs.NI↗

arXiv2022

Accurate and Efficient Modeling of 802.15.4 Unslotted CSMA/CA through Event Chains Computation

Many analytical models have been proposed for evaluating the performance of event-driven 802.15.4 Wireless Sensor Networks (WSNs), in Non-Beacon Enabled (NBE) mode. However, existing models do not provide accurate analysis of large-scale WSNs, due to tractability issues and/or simplifying assumptions. In this paper, we propose a new approach called Event Chains Computation (ECC) to model the unslotted CSMA/CA algorithm used for channel access in NBE mode. ECC relies on the idea that outcomes of the CSMA/CA algorithm can be represented as chains of events that subsequently occur in the network. Although ECC can generate all the possible outcomes, it only considers chains with a probability to occur greater than a pre-defined threshold to reduce complexity. Furthermore, ECC parallelizes the computation by managing different chains through different threads. Our results show that, by an appropriate threshold selection, the time to derive performance metrics can be drastically reduced, with negligible impact on accuracy. We also show that the computation time decreases almost linearly with the number of employed threads. We validate our model through simulations and testbed experiments, and use it to investigate the impact of different parameters on the WSN performance, in terms of delivery ratio, latency, and energy consumption.

cs.NI↗

arXiv2022

The Internet of People: A human and data-centric paradigm for the Next Generation Internet

The cyber-physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards a data-centric Internet where the human user becomes more central than ever. We argue that this will profoundly impact primarily on the way data should be handled in the Next Generation Internet. It will require a radical change of the Internet data-management paradigm, from the current platform-centric to a human-centric model. In this paper we present a new paradigm for Internet data management that we name Internet of People (IoP) because it embeds human behavior models in its algorithms. To this end, IoP algorithms exploit quantitative models of the humans' individual and social behavior, from sociology, anthropology, psychology, economics, physics. IoP is not a replacement of the current Internet networking infrastructure, but it exploits legacy Internet services as (reliable) primitives to achieve end-to-end connectivity on a global-scale. In this opinion paper, we first discuss the key features of the IoP paradigm along with the underlying research issues and challenges. Then, we present emerging data-management paradigms that are anticipating IoP.

cs.NI↗

arXiv2022

The Internet of People (IoP): A New Wave in Pervasive Mobile Computing

Cyber-Physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards an Internet where the human user becomes more central than ever, and where their personal devices become their proxies in the cyber world, in addition to acting as a fundamental tool to sense the physical world. The current Internet paradigm, which is infrastructure-centric, is not the right one to cope with such emerging scenario with a wider range of applications. This calls for a radically new Internet paradigm, that we name the Internet of People (IoP), where the humans and their personal devices are not seen merely as end users of applications, but become active elements of the Internet. Note that IoP is not a replacement of the current Internet infrastructure, but it exploits legacy Internet services as (reliable) primitives to achieve end-to-end connectivity on a global-scale. In this visionary paper, we first discuss the key features of the IoP paradigm along with the underlying research issues and challenges. Then we present emerging networking and computing paradigms that are anticipating IoP

cs.NI↗

arXiv2022

Service Composition in Opportunistic Networks: A Load and Mobility Aware Solution

Pervasive networks formed by users' mobile devices have the potential to exploit a rich set of distributed service components that can be composed to provide each user with a multitude of application level services. However, in many challenging scenarios, opportunistic networking techniques are required to enable communication as devices suffer from intermittent connectivity, disconnections and partitions. This poses novel challenges to service composition techniques. While several works have discussed middleware and architectures for service composition in well-connected wired networks and in stable MANET environments, the underlying mechanism for selecting and forwarding service requests in the significantly challenging networking environment of opportunistic networks has not been entirely addressed. The problem comprises three stages: i) selecting an appropriate service sequence set out of available services to obtain the required application level service; ii) routing results of a previous stage in the composition to the next one through a multi-hop opportunistic path; and iii) routing final service outcomes back to the requester. The proposed algorithm derives efficiency and effectiveness by taking into account the estimated load at service providers and expected time to opportunistically route information between devices. Based on this information the algorithm estimates the best composition to obtain a required service. It is shown that using only local knowledge collected in a distributed manner, performance close to a real-time centralized system can be achieved. Applicability and performance guarantee of the service composition algorithm in a range of mobility characteristics are established through extensive simulations on real/synthetic traces.

cs.NI↗

arXiv2022

Service Provisioning in Mobile Environments through Opportunistic Computing

Opportunistic computing is a paradigm for completely self-organised pervasive networks. Instead of relying only on fixed infrastructures as the cloud, users' devices act as service providers for each other. They use pairwise contacts to collect information about services provided and amount of time to provide them by the encountered nodes. At each node, upon generation of a service request, this information is used to choose the most efficient service, or composition of services, that satisfy that request, based on local knowledge. Opportunistic computing can be exploited in several scenarios, including mobile social networks, IoT and Internet 4.0. In this paper we propose an opportunistic computing algorithm based on an analytical model, which ranks the available (composition of) services, based on their expected completion time. Through the model, a service requesters picks the one that is expected to be the best. Experiments show that the algorithm is accurate in ranking services, thus providing an effective service-selection policy. Such a policy achieves significantly lower service provisioning times compared to other reference policies. Its performance is tested in a wide range of scenarios varying the nodes mobility, the size of input/output parameters, the level of resource congestion, the computational complexity of service executions.

cs.NI↗

arXiv2022

Heterogeneity-aware P2P Wireless Energy Transfer for Balanced Energy Distribution

The recent advances in wireless energy transfer (WET) provide an alternate and reliable option for replenishing the battery of pervasive and portable devices, such as smartphones. The peer-to-peer (P2P) mode of WET brings improved flexibility to the charging process among the devices as they can maintain their mobility while replenishing their battery. Few existing works in P2P-WET unrealistically assume the nodes to be exchanging energy at every opportunity with any other node. Also, energy exchange between the nodes is not bounded by the energy transfer limit in that inter-node meeting duration. In this regard, the parametric heterogeneity (in terms of device's battery capacity and WET hardware) among the nodes also affects the energy transfer bound in each P2P interaction, and thus, may lead to unbalanced network energy distributions. This inherent heterogeneity aspect has not been adequately covered in the P2P-WET literature so far, especially from the point of view of maintaining a balanced energy distribution in the networked population. In this work, we present a Heterogeneity-aware Wireless Energy Transfer (HetWET) method. In contrast to the existing literature, we devise a fine-grained model of wireless energy transfer while considering the parametric heterogeneity of the participating devices. Thereafter, we enable the nodes to explore and dynamically decide the peers for energy exchange. The performance of HetWET is evaluated using extensive simulations with varying heterogeneity settings. The evaluation results demonstrate that HetWET can maintain lower energy losses and achieve more balanced energy variation distance compared to three different state-of-the-art methods.

cs.NI↗