Sharing entanglement among multiple users remains a central challenge for scalable quantum networks. Recent work proposed an on-demand entanglement packet architecture in which a controller uses a Time Division Multiple Access (TDMA) approach to allocate network resources. Quantum nodes are assigned a periodic schedule that probabilistically fulfills application requests for end-to-end entanglements. The schedule is recomputed periodically using well-known algorithms, such as Earliest Deadline First (EDF). However, a static schedule offers limited flexibility when outcomes are stochastic and arrivals are asynchronous. To overcome this limitation, we propose an online scheduler that dynamically schedules, defers, retries, or drops entanglement distribution reservations. In our simulations, the dynamic scheduler achieves lower completion time, higher completion ratio, and higher throughput than the static baseline. Furthermore, when the network is overloaded, the dynamic scheduler continues to construct deadline-feasible schedules and degrades gracefully.

Federated Learning (FL) enables decentralized machine learning while preserving data privacy, making it ideal for sensitive applications where data cannot be shared. While FL has been widely studied in supervised contexts, its application to unsupervised learning remains underdeveloped. This work introduces FedCRef, a novel unsupervised federated learning method designed to uncover all underlying data distributions across decentralized clients without requiring labels. This task, known as Federated Clustering, presents challenges due to heterogeneous, non-uniform data distributions and the lack of centralized coordination. Unlike previous methods that assume a one-cluster-per-client setup or require prior knowledge of the number of clusters, FedCRef generalizes to multi-cluster-per-client scenarios. Clients iteratively refine their data partitions while discovering all distinct distributions in the system. The process combines local clustering, model exchange and evaluation via reconstruction error analysis, and collaborative refinement within federated groups of similar distributions to enhance clustering accuracy. Extensive evaluations on four public datasets (EMNIST, KMNIST, Fashion-MNIST and KMNIST49) show that FedCRef successfully identifies true global data distributions, achieving an average local accuracy of up to 95 %. The method is also robust to noisy conditions, scalable, and lightweight, making it suitable for resource-constrained edge devices.

Abstract Lockdown measures, implemented by governments during the initial phases of the COVID-19 pandemic to reduce physical contact and limit viral spread, imposed significant restrictions on in-person social interactions. Consequently, individuals turned to online social platforms to maintain connections. Ego networks, which model the organization of personal relationships according to human cognitive constraints on managing meaningful interactions, provide a framework for analyzing such dynamics. The disruption of physical contact and the predominant shift of social life online potentially altered the allocation of cognitive resources dedicated to managing these digital relationships. This research aims to investigate the impact of lockdown measures on the characteristics of online ego networks, presumably resulting from this reallocation of cognitive resources. To this end, a large dataset of Twitter users was examined, covering a seven-year period of activity. Analyzing a seven-year Twitter dataset (including five years pre-pandemic and two years post), we observe clear, though temporary, changes. During lockdown, ego networks expanded, social circles became more structured, and relationships intensified. Simultaneously, we observed an asymmetric emotional response: the proportion of negative interactions showed a significant acceleration, while the proportion of positive interactions remained statistically stable. Thematic diversity, however, did not show a significant increase during the lockdown. Once restrictions were lifted, these structural and emotional shifts largely reverted to pre-pandemic norms, suggesting a temporary adaptation to an extraordinary social context.

Fully decentralised federated learning enables collaborative model training among edge devices without relying on a central coordinator, thereby avoiding single points of failure and supporting spontaneous collaboration in pervasive environments. However, the absence of coordination introduces challenges that go beyond data heterogeneity alone. In realistic decentralised settings, devices often start from different model initializations, possess limited and non-IID local data, and interact over unstructured communication graphs, making naive parameter averaging ineffective and potentially destructive. In this paper, we address decentralised learning under combined data and initial model heterogeneity by proposing DecDiff+VT, a coordination-free decentralised learning algorithm specifically designed for such environments. DecDiff+VT integrates two complementary mechanisms: DecDiff, a disruption-aware aggregation strategy that updates local models toward their neighborhood average with a magnitude inversely proportional to model disagreement, and a lightweight virtual teacher (VT) mechanism based on soft-label regularization to improve local generalization in the absence of strong or centralized teacher models. Extensive experiments on image classification and activity recognition benchmarks (MNIST, Fashion-MNIST, EMNIST, CIFAR-10, and UCI-HAR) show that DecDiff+VT consistently outperforms or matches state-of-the-art decentralised baselines, achieving faster convergence, improved generalization, and greater robustness to overfitting, without incurring additional communication or memory overhead compared to standard decentralised averaging.

Online social networks (OSNs) have transformed the way individuals fulfill their social needs and consume information. As OSNs become increasingly prominent sources for news dissemination, individuals often encounter content that influences their opinions through both direct interactions and broader network dynamics. In this article, we propose the Friedkin–Johnsen on cascade (FJC) model, which, to the best of our knowledge, is the first attempt to integrate information cascades and opinion dynamics, specifically using the very popular Friedkin–Johnsen model. Our model, validated over real social cascades, highlights how the convergence of socialization and sharing news on these platforms can disrupt opinion evolution dynamics typically observed in offline settings. Our findings demonstrate that these cascades can amplify the influence of central opinion leaders, making them more resistant to divergent viewpoints, even when challenged by a critical mass of dissenting opinions. This research underscores the importance of understanding the interplay between social dynamics and information flow in shaping public discourse in the digital age.

Online Community Question Answering (CQA) platforms have become indispensable tools for users seeking expert solutions to their technical queries. The effectiveness of these platforms relies on their ability to identify and direct questions to the most knowledgeable users within the community, a process known as Expert Finding (EF). EF accuracy is crucial for increasing user engagement and the reliability of the provided answers. We present TUEF, a Topic-Oriented User-Interaction Model for EF , which aims to fully and transparently leverage the heterogeneous information available within online CQA platforms. TUEF integrates content and social data by constructing a multi-layer graph that maps user relationships based on their answering patterns on specific topics. By combining these sources of information, TUEF identifies the most relevant users for any given question and ranks them using learning-to-rank techniques. Our findings indicate that TUEF’s topic-oriented model significantly enhances performance, particularly in large communities discussing well-defined topics. Additionally, we show that the interpretable learning-to-rank algorithm integrated into TUEF offers transparency and explainability with minimal performance tradeoffs. The exhaustive experiments conducted across six CQA communities show that TUEF outperforms all competitors, achieving a minimum performance boost of 42.42% in P@1, 32.73% in NDCG@3, 21.76% in R@5, and 29.81% in MRR.

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

Decentralized federated learning (DFL) enables devices to collaboratively train models over complex network topologies without relying on a central controller. In this setting, local data remains private, but its quality and quantity can vary significantly across nodes. The extent to which a fully decentralized system is vulnerable to poor-quality or corrupted data remains unclear, but several factors could contribute to potential risks. Without a central authority, there can be no unified mechanism to detect or correct errors, and each node operates with a localized view of the data distribution, making it difficult for the node to assess whether its perspective aligns with the true distribution. Moreover, models trained on low-quality data can propagate through the network, amplifying errors. To explore the impact of low-quality data on DFL, we simulate two scenarios with degraded data quality—one where the corrupted data is evenly distributed in a subset of nodes and one where it is concentrated on a single node—using a decentralized implementation of FedAvg. Our results reveal that averaging-based decentralized learning is remarkably robust to localized bad data, even when the corrupted data resides in the most influential nodes of the network. Counterintuitively, this robustness is further enhanced when the corrupted data is concentrated on a single node, regardless of its centrality in the communication network topology. This phenomenon is explained by the averaging process, which ensures that no single node—however central—can disproportionately influence the overall learning process.

We consider a hybrid LiFi/WiFi network consisting of commercially available equipment, for mobile scenarios, where WiFi backs up communications, through vertical handovers, in case of insufficient LiFi QoS. When QoS requirements in terms of goodput are defined, tools are needed to anticipate the vertical handover relative to what is possible with standard basic mechanisms, which are only based on a complete loss of connectivity. We introduce two such mechanisms, based on signal power level readings and CRC-based packet failure ratio, and evaluate their performance in terms of QoS-outage duration, considering as a benchmark an existing baseline solution based on the detection of a connectivity loss. In doing this, we provide insights into the interplay between such mechanisms and the LiFi protocol channel adaptation capabilities. Our experimental results are obtained using a lab-scale testbed equipped with a conveyor belt, which allows us to accurately replicate experiments with devices in motion. With the proposed methods, we achieve QoS outages below one second for a QoS level of 20 Mbps, compared to outage durations of a few seconds obtained with the baseline solution.

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

In the active landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated and to share knowledge extracted from local data among themselves through an interactive process of collaborative refinement. This paradigm supports scenarios where data cannot leave the data owner node due to privacy or sovereignty reasons or real-time constraints imposing proximity of models to locations where inference has to be carried out. The distributed nature of decentralised learning implies significant new research challenges with respect to centralised learning. Among them, in this paper, we focus on robustness issues. Specifically, we study the effect of nodes’ disruption on the collective learning process. Assuming a given percentage of “central” nodes disappear from the network, we focus on different cases, characterised by (i) different distributions of data across nodes and (ii) different times when disruption occurs with respect to the start of the collaborative learning task. Through these configurations, we are able to show the non-trivial interplay between the properties of the network connecting nodes, the persistence of knowledge acquired collectively before disruption or lack thereof, and the effect of data availability pre- and post-disruption. Our results show that decentralised learning processes are remarkably robust to network disruption. As long as even minimum amounts of data remain available somewhere in the network, the learning process is able to recover from disruptions and achieve significant classification accuracy. This clearly varies depending on the remaining connectivity after disruption, but we show that even nodes that remain completely isolated can retain significant knowledge acquired before the disruption.

The tremendous success of major Online Social Networks (OSNs) platforms has raised increasing concerns about negative phenomena, such as mass control, fake news, and echo chambers. In addition, the increasingly strict control over users’ data by platform owners questions their trustworthiness as open interaction tools. These trends and, notably, the recent drastic change in X (formerly Twitter) policies and data accessibility through public APIs, have fuelled significant migration of users towards Fediverse platforms (primarily Mastodon). In this work, we provide an initial analysis of the microscopic properties of Mastodon users’ social structures. Specifically, according to the Ego network model, we analyse interaction patterns between a large set of users (egos) and the other users they interact with (alters) to characterise the properties of those users’ ego networks. As was observed previously in other OSNs, we found a quite regular structure compatible with the reference Dunbar’s Ego Network model. Quite interestingly, our results show clear signs of ego network formation during the initial diffusion of a social networking tool, coherent with the recent surge of Mastodon activity. Therefore, our analysis motivates the use of Mastodon as an open "big data microscope" to characterise human social behaviour, making it a prime candidate to replace those OSN platforms that, unfortunately, cannot be used anymore for this purpose.

5G and beyond support the deployment of vertical applications, which is particularly appealing in combination with network slicing and edge computing to create a logically isolated environment for executing customer services. Even if serverless computing has gained significant interest as a cloud-native technology its adoption at the edge is lagging, especially because of the need to support stateful tasks, which are commonplace in, e.g., cognitive services, but not fully amenable to being deployed on limited and decentralized computing infrastructures. In this work, we study the emerging paradigm of stateful Function as a Service (FaaS) with lightweight task abstractions in WebAssembly. Specifically, we assess the implications of deploying inter-dependent tasks with an internal state on edge computing resources using a stateless vs. stateful approach and then derive a mathematical model to estimate the energy consumption of a workload with given characteristics, considering the power used for both processing and communication. The model is used in extensive simulations to determine the impact of key factors and assess the energy trade-offs of stateless vs. stateful.

We present a computational modelling approach which targets capturing the specifics on how to virtually augment a Metaverse user’s available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We motivate why this is a fundamental building block to model large-scale social networks in the Metaverse, and emerging properties herein. We envision a Metaverse-focused extension of the traditional avatar concept: An avatar can be as well programmed to operate independently when its user is not controlling it directly, thus turning it into an agent-based digital human representation. This way, we highlight how such an independent avatar could help its user to better navigate their social relationships and optimize their socializing time in the Metaverse by (partly) offloading some interactions to the avatar. We model the setting and identify the characteristic variables by using selected concepts from social sciences: ego networks, social presence, and social cues. Then, we formulate the problem of maximizing the user’s non-avatar-mediated spare time as a linear optimization. Finally, we analyze the feasible region of the problem and we present some initial insights on the spare time that can be achieved for different parameter values of the avatar-mediated interactions.

We anticipate increased instances of humans and AI systems working together in what we refer to as a hybrid team. The increase in collaboration is expected as AI systems gain proficiency and their adoption becomes more widespread. However, their behavior is not error-free, making hybrid teams a very suitable solution. As such, we consider methods for improving performance for these teams of humans and AI systems. For hybrid teams, we will refer to both the humans and AI systems as agents. To improve team performance over that seen for agents operating individually, we propose a manager which learns, through a standard Reinforcement Learning scheme, how to best delegate, over time, the responsibility of taking a decision to any of the agents. We further guide the manager's learning so they also minimize how many changes in delegation are made resulting from undesirable team behavior. We demonstrate the optimality of our manager's performance in several grid environments which include failure states which terminate an episode and should be avoided. We perform our experiments with teams of agents with varying degrees of acceptable risk, in the form of proximity to a failure state, and measure the manager's ability to make effective delegation decisions with respect to its own risk-based constraints, then compare these to the optimal decisions. Our results show our manager can successfully learn desirable delegations which result in team paths near/exactly optimal with respect to path length and number of delegations.

Fully decentralized learning is gaining momentum for training AI models at the Internet’s edge, addressing infrastructure challenges and privacy concerns. In a decentralized machine learning system, data is distributed across multiple nodes, with each node training a local model based on its respective dataset. The local models are then shared and combined to form a global model capable of making accurate predictions on new data. Our exploration focuses on how different types of network structures influence the spreading of knowledge - the process by which nodes incorporate insights gained from learning patterns in data available on other nodes across the network. Specifically, this study investigates the intricate interplay between network structure and learning performance using three network topologies and six data distribution methods. These methods consider different vertex properties, including degree centrality, betweenness centrality, and clustering coefficient, along with whether nodes exhibit high or low values of these metrics. Our findings underscore the significance of global centrality metrics (degree, betweenness) in correlating with learning performance, while local clustering proves less predictive. We highlight the challenges in transferring knowledge from peripheral to central nodes, attributed to a dilution effect during model aggregation. Additionally, we observe that central nodes exert a pull effect, facilitating the spread of knowledge. In examining degree distribution, hubs in Barabási–Albert networks positively impact learning for central nodes but exacerbate dilution when knowledge originates from peripheral nodes. Finally, we demonstrate the formidable challenge of knowledge circulation outside of segregated communities, and discuss the impact of class cross-correlations.

Apache Kafka is a widely-used event streaming platform for reliable high-volume real-time data exchange following a producer–consumer pattern. Despite its popularity, Apache Kafka requires expertise and attention to detail, and there are no default guidelines that can be applied to all use cases without careful consideration. In this paper, we propose a novel approach to optimise the number of partitions and brokers in Apache Kafka, which are two key configuration parameters, under the given characteristics and constraints of the target applications. In particular, we consider the distribution of data-intensive real-time flows exchanged between a set of producers and consumers, which is representative of fog computing environments for ML/AI analytics. We introduce a methodology for modelling the topic partitioning process in Apache Kafka and formulate an optimisation problem to determine the optimal number of partitions to satisfy the application requirements and constraints. We propose two efficient heuristics to solve the optimisation problem, considering the trade-off between resource utilisation and application performance. We evaluate the performance of our approach through numerical simulations, and we demonstrate its practicality by implementing a prototype on an Apache Kafka cluster and conducting experiments in three different scenarios focused on mass consumption vs. production and real-time data streaming. To carry out repeatable experiments in controlled conditions, we developed a reusable framework that fully automatises cluster setup and performance assessment, and we make it available to the community as open-source software.

Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions.

When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control for the team. To determine the best selection of a control agent, we propose the addition of an AI manager (via Reinforcement Learning) which learns as an outside observer of the team. The manager learns a model of behavior linking observations of agent performance and the environment/world the team is operating in, and from these observations makes the most desirable selection of a control agent. From our review of current state of the art, we present a novel manager model for oversight of hybrid teams by our support for diverse agents and decision-maker operations across multiple time steps and decisions. In our model, we restrict the manager’s task by introducing a set of constraints. The manager constraints indicate acceptable team operation, so a violation occurs if the team enters a condition which is unacceptable and requires manager intervention. To ensure minimal added complexity or potential inefficiency for the team, the manager should attempt to minimize the number of times the team reaches a constraint violation and requires subsequent manager intervention. Therefore, our manager is optimizing its selection of authorized agents to boost overall team performance while minimizing the frequency of manager intervention. We demonstrate our manager’s performance in a simulated driving scenario representing the case of a hybrid team of agents composed of a human driver and autonomous driving system. We perform experiments for our driving scenario with interfering vehicles, indicating the need for collision avoidance and proper speed control. Our results indicate a positive impact on our manager, with some cases resulting in increased team performance up to \(\approx 187\%\) that of the best solo agent performance.

The “ego network of words” model captures structural properties in language production associated with cognitive constraints. While previous research focused on the layer-based structure and its semantic properties, this article argues that an essential element, the concept of anactive network, is missing. Theactivepart of the ego network of words only includes words that are regularly used by individuals, akin to the ego networks in the social domain, where the active part includes relationships regularly nurtured by individuals, and hence demanding cognitive effort. In this work, we define a methodology for extracting the active part of the ego network of words and validate it using interview transcripts and tweets. The robustness of our method to varying input data sizes and temporal stability is demonstrated. We also demonstrate that without the active network concept (and a tool for properly extracting the active network from data), the “ego network of words” model is not able to properly estimate the cognitive effort involved and it becomes vulnerable to the amount of data considered (leading to the disappearance of the layered structure in large datasets). Our results are well-aligned with prior analyses of the ego network of words, where the limitation of the data collected led automatically (and implicitly) to approximately consider the active part of the network only. Moreover, the validation on the transcripts dataset (MediaSum) highlights the generalizability of the model across diverse domains and the ingrained cognitive constraints in language usage.

In this demonstration, we showcase the realization of a prototype of an edge computing network, where the client and edge domains both host simulated Quantum Key Distribution devices, for a hospital use case. In particular, digital health applications using the Function-as-a-Service (FaaS) paradigm will invoke remote functions provided by an Apache OpenWhisk cluster deployed in the edge infrastructure, where the arguments and return value are encrypted using keys generated through an underlying simulated QKD point-to-point network. All the interactions in the control/management plane are handled through standard interfaces defined by the ETSI MEC and QKD industry study groups.

Quantum Key Distribution (QKD) enables secure communications via the exchange of cryptographic keys exploiting the properties of quantum mechanics. Nowadays the related technology is mature enough for production systems, thus field deployments of QKD networks are expected to appear in the near future, starting from local/metropolitan settings, where edge computing is already a thriving reality. In this paper, we investigate the interplay of resource allocation in the QKD network vs. edge nodes, which creates unique research challenges. After modeling mathematically the problem, we propose practical online policies for admitting edge application requests, which also select the edge node for processing and the path in the QKD network. Our simulation results provide initial insights into this emerging topic and lead the way to upcoming studies on the subject.

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.

Wireless power transfer (WPT) techniques are emerging as a fundamental component of next-generation energy management in mobile networks. In this context, the use of UAVs opens many possibilities, either using them as mobile energy storage devices to recharge IoT nodes, or to prolong their operation time via smart charging themselves at ground stations. This paper surveys the recent literature on WPT as it applies to UAVs and identifies several open research challenges for the future. As a first step, we tessellate the related research corpus in four fundamental categories (architectures, power and communications enabling technologies, optimization with respect to spatial concepts, optimization of operational aspects). Second, for each category, we provide a critical review of the recent WPT UAV approaches with respect to the way they specialize the general concept of WPT and the extent of their applicability. The survey presents the latest advances in WPT UAV methodologies and related energy-centric services, spanning all the way from the communications aspects deep in the small- and large-scale deployments, up to the operational and applications aspects. Finally, motivated by the rich conclusions of this critical analysis, we identify open challenges for future research. Our approach is horizontal, as the selected publications were drawn from across all vertical areas of research on UAVs. This paper can help the readers to deeply understand how WPT is currently applied to UAVs, and select interesting open research opportunities to pursue.

In the world of cloud technologies, serverless computing has now settled as a stable and promising resident. This gives a cloud provider the flexibility to provide its users with both Platform-as-a-Service (PaaS), i.e., the back-end application runs in a dedicated container, or Function-as-a-Service (FaaS), i.e., the back-end logic is offered as elementary functions that are invoked by the client applications. In parallel, edge computing has attracted a significant interest, due its enticing promises of reducing the outbound traffic of telco operators, while at the same time cutting down the user latency. As a result, in the near future, PaaS and FaaS containers are going to cohabit in a versatile computation infrastructure spanning from the far edge up to the cloud. In this paper we propose a mathematical formulation of a resource allocation problem that optimizes the assignment of both types of containers and can be solved efficiently by an edge orchestrator. We evaluate the proposed solution via extensive simulation experiments, which show that our approach, which takes into account the characteristics of PaaS vs. FaaS, provides significant performance benefits compared to less sophisticated strategies, despite its relatively low run-time complexity.

Given the popular presupposition of human reasoning as the standard for learning and decision making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. As such, topics including Game Theory, Theory of Mind, and Machine Learning, among others, integrate concepts that are assumed components of human reasoning. These serve as techniques to replicate and understand the behaviors of humans. In addition, next-generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, allowing them not only to replicate human models as a technique to “learn” but also to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this article is to provide a succinct yet systematic review of important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques that learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without necessarily learning via trial and error.

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 article, 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. 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 graph neural network (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 prediction performance, beating also state-of-the-art solutions such as node2vec and learning from Subgraphs, Embeddings and Attributes for Link prediction (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.

For decades, researchers have been trying to understand how people form their opinions. This quest has become even more pressing with the widespread usage of online social networks and social media, which seem to amplify the already existing phenomenon of polarization. In this work, we study the problem of polarization assuming that opinions evolve according to the popular Friedkin–Johnsen (FJ) model. The FJ model is one of the few existing opinion dynamics models that has been validated on small/medium-sized social groups. First, we carry out a comprehensive survey of the FJ model in the literature (distinguishing its main variants) and of the many polarization metrics available, deriving an invariant relation among them. Second, we derive the conditions under which the FJ variants are able to induce opinion polarization in a social network, as a function of the social ties between the nodes and their individual susceptibility to the opinion of others. Third, we discuss a methodology for finding concrete opinion vectors that are able to bring the network to a polarized state. Finally, our analytical results are applied to two real social network graphs, showing how our theoretical findings can be used to identify polarizing conditions under various configurations.

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.

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 smart-phones. 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.

Federated learning (FL) is proving to be one of the most promising paradigms for leveraging distributed resources, enabling a set of clients to collaboratively train a machine learning model while keeping the data decentralized. The explosive growth of interest in the topic has led to rapid advancements in several core aspects like communication efficiency, handling non-IID data, privacy, and security capabilities. However, the majority of FL works only deal with supervised tasks, assuming that clients' training sets are labeled. To leverage the enormous unlabeled data on distributed edge devices, in this paper, we aim to extend the FL paradigm to unsupervised tasks by addressing the problem of anomaly detection (AD) in decentralized settings. In particular, we propose a novel method in which, through a preprocessing phase, clients are grouped into communities, each having similar majority (i.e., inlier) patterns. Subsequently, each community of clients trains the same anomaly detection model (i.e., autoencoders) in a federated fashion. The resulting model is then shared and used to detect anomalies within the clients of the same community that joined the corresponding federated process. Experiments show that our method is robust, and it can detect communities consistent with the ideal partitioning in which groups of clients having the same inlier patterns are known. Furthermore, the performance is significantly better than those in which clients train models exclusively on local data and comparable with federated models of ideal communities' partition.

The evolution of quantum computing technologies has been advancing at a steady pace in the recent years, and the current trend suggests that it will become available at scale for commercial purposes in the near future. The acceleration can be boosted by pooling compute infrastructures to either parallelize algorithm execution or solve bigger instances that are not feasible on a single quantum computer, which requires an underlying Quantum Internet: the interconnection of quantum computers by quantum links and repeaters to exchange entangled quantum bits. However, Quantum Internet research so far has been focused on provisioning point-to-point flows only, which is suitable for (e.g.) quantum sensing and metrology, but not for distributed quantum computing. In this paper, after a primer on quantum computing and networking, we investigate the requirements and objectives of smart computing on distributed nodes from the perspective of quantum network provisioning. We then design a resource allocation strategy that is evaluated through a comprehensive simulation campaign, whose results highlight the key features and performance issues, and lead the way to further investigation in this direction.

With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human. In the case of humans and artificial AI agents operating in the same environment, we note the significance of comprehension and response to the actions or capabilities of a human from an agent's perspective, as well as the possibility to delegate decisions either to humans or to agents, depending on who is deemed more suitable for a given context. Such capabilities will ensure an improved responsiveness and utility of the entire human-AI system. To that end, we investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior, and associated performance with respect to a certain goal, is used to delegate control between humans and AI agents through the use of an intermediary entity. As we demonstrate, this allows overcoming potential shortcomings of either humans or agents in the pursuit of a goal.

With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human. In the case of humans and artificial AI agents operating in the same environment, we note the significance of comprehension and response to the actions or capabilities of a human from an agent's perspective, as well as the possibility to delegate decisions either to humans or to agents, depending on who is deemed more suitable at a certain point in time. Such capabilities will ensure an improved responsiveness and utility of the entire human-AI system. To that end, we investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior, and associated performance with respect to a certain goal, is used to delegate control between humans and AI agents through the use of an intermediary entity. As we demonstrate, this allows overcoming potential shortcomings of either humans or agents in the pursuit of a goal.

Battery aging is one of the major concerns for the pervasive devices such as smartphones, wearables and laptops. Current battery aging mitigation approaches only partially leverage the available options to prolong battery lifetime. In this regard, we claim that wireless crowd charging via network-wide smart charging protocols can provide a useful setting for applying battery aging mitigation. In this paper, for the first time in the state-of-the-art, we couple the two concepts and we design a fine-grained battery aging model in the context of wireless crowd charging, and two network-wide protocols to mitigate battery aging. Our approach directly challenges the related contemporary research paradigms by (i) taking into account important characteristic phenomena in the algorithmic modeling process related to fine-grained battery aging properties, (ii) deploying ubiquitous computing and network-wide protocols for battery aging mitigation, and (iii) fulfilling the user QoE expectations with respect to the enjoyment of a longer battery lifetime. Simulation-based results indicate that the proposed protocols are able to mitigate battery aging quickly in terms of nearly 46.74-60.87 % less reduction of battery capacity among the crowd, and partially outperform state-of-the-art protocols in terms of energy balance quality.

We consider a hybrid wireless local area network composed of both WiFi and LiFi Access Points (AP) and wireless devices. Each device is identified in the network by a unique IP address, using a virtual network interface obtained by bonding the WiFi and LiFi physical interfaces, implemented through commercially available products. We measure the time it takes to switch between the two physical interfaces and its impact on the traffic flow, under different settings of the mechanisms used by the interface bonding driver. Different specific triggering events are considered for the switch, namely: an (simulated) interface malfunctioning or unintended shutdown, a signal loss, and a manual (intended) switch. Our experimental results show that the different types of triggering events have an impact on the time it takes to reconfigure the currently active physical interface (which is used by the virtual interface to send/receive data), with connection recovery times ranging from few tens milliseconds to few seconds. This entails a packet loss on active flows which, in the worst case, we quantify in a maximum loss of up to 1% of the traffic flowing during 1 second.

Serverless computing has emerged as a very popular cloud technology, together with its companion Function-as-a-Service (FaaS) programming model enabling invocations of stateless functions from clients. An evolution of serverless is now taking place, shifting it towards the edge of the network and broadening its scope to stateful functions, as well. In this paper we argue that stateless vs. stateful is not a dichotomy of the application per se, but rather a time-varying property of most (if not all) applications, as confirmed by the analysis of real traces collected in a production environment. Based on this observation, we propose a mathematical formulation of a resource allocation problem that jointly encompasses both operation modes, dubbed lambda vs. mu, which can be solved efficiently at run-time by an edge orchestrator. We evaluate the proposed solution via simulation experiments in realistic network and workload conditions, which leads the way to the practical realization of a system where applications can freely adapt their current operation mode and optimize their performance at a minimum cost of operation from the network's perspective.

The advancements in peer-to-peer wireless power transfer (P2P-WPT) have empowered the portable and mobile devices to wirelessly replenish their battery by directly interacting with other nearby devices. The existing works unrealistically assume the users to exchange energy with any of the users and at every such opportunity. However, due to the users' mobility, the inter-node meetings in such opportunistic mobile networks vary, and P2P energy exchange in such scenarios remains uncertain. Additionally, the social interests and interactions of the users influence their mobility as well as the energy exchange between them. The existing P2P-WPT methods did not consider the joint problem for energy exchange due to user's inevitable mobility, and the influence of sociality on the latter. As a result of computing with imprecise information, the energy balance achieved by these works at a slower rate as well as impaired by energy loss for the crowd. Motivated by this problem scenario, in this work, we present a wireless crowd charging method, namely MoSaBa, which leverages mobility prediction and social information for improved energy balancing. MoSaBa incorporates two dimensions of social information, namely social context and social relationships, as additional features for predicting contact opportunities. In this method, we explore the different pairs of peers such that the energy balancing is achieved at a faster rate as well as the energy balance quality improves in terms of maintaining low energy loss for the crowd. We justify the peer selection method in MoSaBa by detailed performance evaluation. Compared to the existing state-of-the-art, the proposed method achieves better performance trade-offs between energy-efficiency, energy balance quality and convergence time.

A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific domain maturing over years and adopting the best practices inherited from more fundamental disciplines. The design of past, present and future networking components and architectures have been assisted, among other methods, by experimentally-driven research and in particular by the deployment of test platforms, usually named as testbeds . However, often experimentally-driven networking research used scattered methodologies, based on ad-hoc, small-sized testbeds , producing hardly repeatable results. We believe that computer networks needs to adopt a more structured methodology, supported by appropriate instruments, to produce credible experimental results supporting radical and incremental innovations. This paper reports lessons learned from the design and operation of test platforms for the scientific community dealing with digital infrastructures. We introduce the SLICES initiative as the outcome of several years of evolution of the concept of a networking test platform transformed into a scientific instrument. We address the challenges, requirements and opportunities that our community is facing to manage the full research-life cycle necessary to support a scientific methodology.

The COVID-19 crisis resulted in a sudden and dramatic change in how manufacturing environments operate. Safe distancing among workers plays a pivotal role in preventing the spread of viral diseases such as COVID-19. Although general purpose commercial products already help prevention, enforcing ad hoc distancing without manufacturing production optimization can significantly decrease the production performance throughput. In this paper, we highlight the intrinsic trade-off of two concepts: worker health preservation versus factory productivity. We first motivate the importance of safe distancing in manufacturing shop-floors by analyzing a worker mobility dataset in a manual assembly scenario, given the safe distancing public health recommendations. Then, we suggest the quantification of the relation of the two concepts through exploiting pervasive computing technologies. Furthermore, we provide an insightful recommendation on the need of a holistic methodological framework, specifically tailored for addressing the Industry 4.0 requirements, as well as the worker necessities.

Peer-to-peer wireless power transfer (P2P-WPT) enables portable devices to mutually exchange energy. In opportunistic mobile networks, P2P-WPT can be uncertain due to the varying user inter-meeting duration. Existing P2P-WPT methods (unrealistically) assume the users to be exchanging energy at each opportunity, to be able to interact with all users, or the inter-node meeting duration to be unaffected by users' mobility. In this paper, in contrast to the state-of-the-art, not only we constitute more fine-grained, realistic assumptions for P2P-WPT, but also we design MobiWeb, a mobility-aware energy balancing method, which employs (for the first time) a predictor for estimating the mobility information of users. MobiWEB selects the different pairs of peers for energy exchange, such that the network energy distribution is balanced while minimizing the loss and energy difference between the peers. MobiWEB, when compared to the state-of-the-art, achieves different performance trade-offs between energy balance quality, convergence time, and energy-efficiency.

In this paper, we address the problem of supporting chains of stateful function invocations 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, such as wide-area Internet of Things (IoT) networks, and we propose three different solutions: 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 show via simulation that StateLocal, by applying the data locality principle, can significantly enhance the performance by reducing the application delay due to data transfer and keeping a lower traffic volume in the network. This study sheds light on some aspects within the unexplored area of stateful FaaS, which is very promising among the edge computing technologies and has several open research directions associated.

Deployment of computing infrastructures at the edge of the network will drive a revolution in integrated solutions for smart mobility in the cities of the future, thanks to the promises of reduced latency and outbound traffic. The adoption of serverless computing will help realising this vision since it simplifies management while at the same time providing the application developers with a neat and clean Function-as-a-Service (FaaS) programming model. Today FaaS relies on HTTP over TCP, but QUIC is emerging fast as a replacement because it is more robust to packet losses and it allows connection roaming: both these advantages are especially important for mobile scenarios. In this paper we report the results of a preliminary evaluation of QUIC+HTTP/3 when used instead of TCP+HTTP within a framework for decentralized dispatching of FaaS function invocations, which shows that this direction is promising and deserves to be delved further in the future.

Serverless computing is becoming widely adopted among cloud providers, thus making increasingly popular the Function-as-a-Service (FaaS) programming model, where the developers realize services by packaging sequences of stateless function calls. The current technologies are very well suited to data centers, but cannot provide equally good performance in decentralized environments, such as edge computing systems, which are expected to be typical for Internet of Things (IoT) applications. In this article, we fill this gap by proposing a framework for efficient dispatching of stateless tasks to in-network executors so as to minimize the response times while exhibiting short- and long-term fairness, also leveraging information from a virtualized network infrastructure when available. Our solution is shown to be simple enough to be installed on devices with limited computational capabilities, such as IoT gateways, especially when using a hierarchical forwarding extension. We evaluate the proposed platform by means of extensive emulation experiments with a prototype implementation in realistic conditions. The results show that it is able to smoothly adapt to the mobility of clients and to the variations of their service request patterns, while coping promptly with network congestion.

Considering device-to-device (D2D) wireless links as a virtual extension of 5G (and beyond) cellular networks to deliver popular contents has been proposed as an interesting approach to reduce energy consumption, congestion, and bandwidth usage at the network edge. In the scenario of multiple users in a region independently requesting some popular content, there is a major potential for energy consumption reduction exploiting D2D communications. In this scenario, we consider the problem of selecting the maximum allowed transmission range (or equivalently the maximum transmit power) for the D2D links that support the content delivery process. We show that, for a given maximum allowed D2D energy consumption, a considerable reduction of the cellular infrastructure energy consumption can be achieved by selecting the maximum D2D transmission range as a function of content class parameters such as popularity and delay-tolerance, compared to a uniform selection across different content classes. Specifically, we provide an analytical model that can be used to estimate the energy consumption (for small delay tolerance) and thus to set the optimal transmission range. We validate the model via simulations and study the energy gain that our approach allows to obtain. Our results show that the proposed approach to the maximum D2D transmission range selection allows a reduction of the overall energy consumption in the range of 30% to 55%, compared to a selection of the maximum D2D transmission range oblivious to popularity and delay tolerance.

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed "Internet of People" paradigm, accessing relevant data dynamically generated in the environment nearby is one of the key services. Moreover, personal mobile devices become proxies of their human users while exchanging data in the cyber world and, thus, largely use ONs and D2D communications for exchanging data directly. Recently, researchers have successfully demonstrated the viability of embedding human cognitive schemes in data dissemination algorithms for ONs. In this paper, we consider one such scheme based on the recognition heuristic, a human decision-making scheme used to efficiently assess the relevance of data. While initial evidence about its effectiveness is available, the evaluation of its behaviour in large-scale settings is still unsatisfactory. To overcome these limitations, we have developed a novel hybrid modelling methodology, which combines an analytical model of data dissemination within small-scale communities of mobile users, with detailed simulations of interactions between different communities. This methodology allows us to evaluate the algorithm in large-scale city- and country-wide scenarios. Results confirm the effectiveness of cognitive data dissemination schemes, even when content popularity is very heterogenous.

Future pervasive applications, like mobile augmented reality, have huge bandwidth and computation demands and very stringent delay constraints. Edge computing has been proposed to cope with such challenging requirements, since it shortens significantly the distance between the end users and the servers. On the other hand, serverless computing is emerging among cloud technologies to respond to the need of highly scalable event-driven execution of stateless tasks. In this paper, we investigate the convergence of the two to enable very low-latency execution of short-lived stateless tasks whose computation is offloaded from the user terminal to servers hosted by or close to edge devices in mobile pervasive environments. We realized a proof-of-concept implementation to delve into the specific issue of efficient dispatching of tasks in a distributed manner to achieve high scalability. We evaluated our proposed algorithm with experiments in a large-scale emulated network environment, showing that our solution achieves similar or better delay performance than a centralized solution, with far less network utilization.

The vast amounts of data generated in wireless industrial networked deployments introduce significant challenges on the data distribution process to consumer nodes within the timeframes imposed by the requirements of the Industry 4.0 paradigm. Using technological and methodological enablers, we can compose centralized or decentralized data distribution methods, which are able to help meeting the data requirements of the industrial applications. In this paper, using the technological enablers of WirelessHART, RPL and the methodological enabler of proxy selection as building blocks, we compose the protocol stacks of four different methods (both centralized and decentralized) for data distribution in wireless industrial networks over the IEEE 802.15.4 physical layer. Although there have been several comparisons of relevant methods in the recent literature, we identify that most of those comparisons are either theoretical, or based on abstract simulation tools, unable to uncover the specific, detailed impacts of the methods to the underlying networking infrastructure. We implement the presented methods in OMNeT++ and we evaluate their performance via a detailed simulation analysis. Interestingly enough, we demonstrate that the careful selection of a limited set of proxies for data caching in the network can lead to increased data delivery success rate and low data access latency.

We consider a Software Defined Networking (SDN)-enabled edge computing domain, where networking devices also have processing capabilities. In particular, we investigate the problem of dynamic allocation of stateless computations, that we call lambda functions, and propose an architectural framework through which requests for execution of lambda functions originated by mobile nodes can be appropriately routed to specific edge devices following a serverless model. In addition, we propose a detailed emulation environment to test the architecture. Our framework supports many possible distributed algorithms to dynamically adapt the choice where requests should be executed, in order to optimize a given performance target. In the paper we consider a few such policies, to test the flexibility of the architecture. We thus present extensive performance results of the considered policies.

We consider a scheme for offloading the delivery of contents to mobile devices in a vehicular environment. Each content can be delivered to the requesting device either by a neighboring device or, at the expiration of a maximum delay timeout, by the network infrastructure nodes. We propose an analytical model to compute the expression of the probability that the content delivery is offloaded through a Device-to-Device (D2D) communication as a function of the maximum transmission range allowed for D2D communications, the content popularity, and the vehicles speed. We show that, using the proposed analytical model, it is possible to identify the optimal maximum transmission range, which minimizes the total energy consumption (of the infrastructure plus mobile devices).

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 requester 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, and the computational complexity of service executions.

Industrial IoT networks are typically used for monitoring systems and supporting control loops, as well as for movement detection systems, process control and factory automation. To this end, data generated by monitoring IoT devices are collected, elaborated and sent to controllers and actuators. The routing of data from IoT sensors to actuators is an integral part of any large-scale industrial network for maintaining critical delay requirements. Centralised schemes are typically used, whereby data are transferred to a central network controller, from where they are accessed by any other node requiring them. This may result in significant overheads and suboptimal resource consumption. In this paper, we propose a distributed, cooperative Data Management Layer (DML), whereby nodes cooperate to store data within the network. The DML is decoupled yet interacts with the underlying Network Plane. Specifically, given a set of data, the sets of nodes generating and requesting them, and a maximum access delay that requesting nodes can tolerate, the DML efficiently identifies a limited set of proxies in the network where data are stored. Given the mentioned constraints, we investigate the (computationally difficult) problem of finding which network nodes to select as proxies and we propose a simple method to address it. We demonstrate that the proposed method (i) guarantees that access delay stays below the given threshold, and (ii) significantly outperforms centralised and even distributed approaches, both in terms of access latency and in terms of maximum latency guarantees.

The most widely adopted approach for knowledge extraction from raw data generated at the edges of the Internet (e.g., by IoT or personal mobile devices) is through global cloud platforms, where data is collected from devices, and analysed. However, with the increasing number of devices spread in the physical environment, this approach rises several concerns. The data gravity concept, one of the basis of Fog and Mobile Edge Computing, points towards a decentralisation of computation for data analysis, whereby the latter is performed closer to where data is generated, for both scalability and privacy reasons. Hence, data produced by devices might be processed according to one of the following approaches: (i) directly on devices that collected it (ii) in the cloud, or (iii) through fog/mobile edge computing techniques, i.e., at intermediate nodes in the network, running distributed analytics after collecting subsets of the data. Clearly, (i) and (ii) are the two extreme cases of (iii). It is worth noting that the same analytics task executed at different collection points in the network, comes at different costs in terms of traffic generated over the network. Precisely, these costs refer to the traffic generated to move data towards the collection point selected (e.g. the Edge or the Cloud) and the one induced by the distributed analytics process. Until now, deciding if to use intermediate collection points, and which one they should be in order to both obtain a target accuracy and minimise the network traffic, is an open question. In this paper, we propose an analytical framework able to cope with this problem. Precisely, we consider learning tasks, and define a model linking the accuracy of the learning task performed with a certain set of collection points, with the corresponding network traffic. The model can be used to identify, given the specification of the learning problem (e.g. binary classification, regression, etc.), and its target accuracy, what is the optimal level for collecting data in order to minimise the total network cost. We validate our model through simulations in order to show that setting, in simulation, the level of intermediate collection indicated by our model, leads to the minimum cost for the target accuracy.

The number and capabilities of IoT devices will exponentially grow over the next years. Together with the pervasive diffusion of smart personal mobile devices this opens up unprecedented opportunities for contextualised services provided to mobile users, based on their current interests and behaviours. In addition, most of these services will be content-centric rather than host-centric. Cloud computing and Information-Centric Networking (ICN) are therefore two key technologies in this perspective. In both cases, solutions are typically designed for global Internet platforms, while mobile nodes are seen as edge devices from which data are fetched and sent back through pervasive wireless networks (typically, LTE). However, it is questionable whether such an approach will work as expected, e.g., due to data privacy concerns and expected bandwidth shortage of even last-generation cellular networks. In this paper we present a general framework where global cloud and ICN platforms are complemented in a totally synergic way by local clouds formed at the edge of the network by mobile devices, where service provisioning and data management functionalities are offloaded whenever possible (and appropriate). This results in a multi-layer, content- and service-centric approach to IoT data management and service provisioning. We then present performance evaluation results from applying this framework to a specific case where data-centric services are jointly provided by edge devices and by a global cloud platform. Results show that this approach is very promising, as it is able to drastically cut the related cellular-network traffic, and, at the same time, improve the effectiveness of service provisioning to users.

It is commonly assumed that in a smart city there will be thousands of mostly mobile/wireless smart devices (e.g. sensors, smart-phones, etc.) that will continuously generate big amounts of data. Data will have to be collected and processed in order to extract knowledge out of it, to feed users' and smart city applications. A typical approach to process such big amounts of data is to i) gather all the collected data on the cloud through wireless pervasive networks, and ii) perform data analysis operations exploiting machine learning techniques. However, according to many studies, this centralised cloud-based approach may not be sustainable from a networking point of view. The joint effect of data-intensive users' multimedia applications and smart cities monitoring and control applications may result in severe network congestions making applications hardly usable. To cope with this problem, in this paper we propose a distributed machine learning approach that does not require to move data in a centralised cloud platform, but processes it directly where it is collected. Specifically, we exploit Hypothesis Transfer Learning (HTL) to build a distributed machine learning framework. In our framework we train a series of partial models, each ''residing'' in a location where a subset of the dataset is generated. We then refine the partial models by exchanging them between locations, thus obtaining a unique complete model. Using an activity classification task on a reference dataset as a concrete example, we show that the classification accuracy of the HTL model is comparable with that of a model built out of the complete dataset, but the cost in term of network overhead is dramatically reduced. We then perform a sensitiveness analysis to characterise how the overhead depends on key parameters. It is also worth noticing that the HTL approach is suitable for applications dealing with privacy sensitive data, as data can stay where they are generated, and do not need to be transferred to third parties, i.e., to a cloud provider, to extract knowledge out of it.

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. We show 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.

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.

Distributed Online Social Networks (DOSN) are a valid alternative to OSN based on peer-to-peer communications. Without centralised data management, DOSN must provide the users with higher level of control over their personal information and privacy. Thus, users may wish to restrict their personal network to a limited set of peers, depending on the level of trust with them. This means that the effective social network (used for information exchange) may be a subset of the complete social network, and may present different structural patterns, which could limit information diffusion. In this paper, we estimate the capability of DOSN to diffuse content based on trust between social peers. To have a realistic representation of a OSN friendship graph, we consider a large-scale Facebook network, from which we estimate the trust level between friends. Then, we consider only social links above a certain threshold of trust, and we analyse the potential capability of the resulting graph to spread information through several structural indices. We test four possible thresholds, coinciding with the definition of personal social circles derived from sociology and anthropology. The results show that limiting the network to “active social contacts” leads to a graph with high network connectivity , where the nodes are still well-connected to each other, thus information can potentially cover a large number of nodes with respect to the original graph. On the other hand, the coverage drops for more restrictive assumptions. Nevertheless the re-insertion of a single excluded friend for each user is sufficient to obtain good coverage (i.e., always higher than 40 %) even in the most restricted graphs. We also analyse the potential capability of the network to spread information (i.e., network spreadability ), studying the properties of the social paths between any pairs of users in the graph, which represent the effective channels traversed by information. The value of contact frequency between pairs of users determines a decay of trust along the path (the higher the contact frequency the lower the decay), and a consequent decay in the level of trustworthiness of information traversing the path. We show that selecting the link to re-insert in the network with probability proportional to its level of trust is the best re-insertion strategy, as it leads to the best connectivity/spreadability combination.

LTE is rapidly gaining momentum for building future 4G cellular systems, and real operational networks are under deployment worldwide. To achieve high throughput performance, in addition to an advanced physical layer design LTE exploits a combination of sophisticated mechanisms at the radio resource management layer. Clearly, this makes difficult to develop analytical tools to accurately assess and optimise the user perceived throughput under realistic channel assumptions. Thus, most existing studies focus only on link-layer throughput or consider individual mechanisms in isolation. The main contribution of this paper is a unified modelling framework of the MAC-level downlink throughput of a sigle LTE cell, which caters for wideband CQI feedback schemes, AMC and HARQ protocols as defined in the LTE standard. We have validated the accuracy of the proposed model through detailed LTE simulations carried out with the ns-3 simulator extended with the LENA module for LTE.

In typical Opportunistic Networking (OppNets) scenarios, mobile devices collaborate to cooperatively disseminate data toward interested nodes. However, the limited resources and knowledge available at each node, compared to possibly vast amounts of data to be delivered, makes it difficult to devise efficient dissemination schemes. Recent solutions propose to use data dissemination algorithms built on human information processing schemes, modelled in cognitive sciences as Cognitive Heuristics. In general, they are methods used by the human brain to quickly assess relevance of information so to drop what is irrelevant. Recent solutions for data dissemination in OppNets based on these heuristics proved to be effective and efficient in terms of network overhead. However, to the best of our knowledge, none takes into consideration the structure of users' social relationships, which is known to determine movement patterns and thus contact opportunities between nodes. In this paper we propose a social-based data dissemination scheme, built on the Social Circle Heuristic (SCH). SCH exploits the structure of the social environment of users to infer the relevance of discovered information for the individual and their social communities. We compare the proposed scheme against state-of-the-art solutions based on non-social cognitive heuristics, both in terms of effectiveness (i.e., bringing messages to users that request it) and efficiency (i.e., doing so minimising the network traffic). We show that the scheme based on SCH significantly outperforms non-social cognitive schemes along both dimensions. In particular, the difference becomes more and more evident as scenarios becomes more and more dynamic. We finally show that in scenarios where new content is generated over time, the scheme based on SCH is the only one able to bring content to the interested users, while non-social schemes fail to do so while at the same time generating significant higher network traffic.

Until recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However, the emergence of Opportunistic Networking (ON) has opened up crowdsourcing to the spatial domain. In this article, we bring the ON model for potential crowdsourcing in the smart city environment. We introduce cognitive features of the ON that allow users’ mobile devices to become aware of the surrounding physical environment. Specifically, we exploit cognitive psychology studies on dynamic memory structures and cognitive heuristics—mental models that describe how the human brain handles decision making among complex and real-time stimuli. Combined with ON, these cognitive features allow devices to act as proxies in their users’ cyberworlds and exchange knowledge to deliver awareness of places in an urban environment. This is done through tags associated with locations. They represent features that are perceived by humans about a place. We consider the extent to which this knowledge becomes available to participants using interactions with locations and other nodes. This is assessed taking into account a wide range of cognitive parameters. Outcomes are important because this functionality could support a new type of recommendation system that is independent of the traditional forms of networking.

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.

The intermeeting time, i.e., the time between two consecutive contacts between a pair of nodes, plays a fundamental role in the delay of messages in opportunistic networks. A desirable property of message delay is that its expectation is finite, so that the performance of the system can be predicted. Unfortunately, when intermeeting times feature a Pareto distribution, this property does not always hold. In this paper, assuming heterogeneous mobility and Pareto intermeeting times, we provide a detailed analysis of the conditions for the expectation of message delay to be finite (i.e., to converge) when social-oblivious or social-aware forwarding schemes are used. More specifically, we consider different classes of social-oblivious and social-aware schemes, based on the number of hops allowed and the number of copies generated. Our main finding is that, in terms of convergence, allowing more than two hops may provide advantages only in the social-aware case. At the same time, we show that using a multi-copy scheme can in general improve the convergence of the expected delay. We also compare social-oblivious and social-aware strategies from the convergence standpoint and we prove that, depending on the mobility scenario considered, social-aware schemes may achieve convergence while social-oblivious cannot, and vice versa. Finally, we apply the derived convergence conditions to three popular contact data sets available in the literature (Cambridge, Infocom, and RollerNet), assessing the convergence of each class of forwarding protocols in these three cases.

Offloading is gaining momentum as a technique to overcome the cellular capacity crunch due to the surge of mobile data traffic demand. Multiple offloading techniques are currently under investigation, from modifications inside the cellular network architecture, to integration of multiple wireless broadband infrastructures, to exploiting direct communications between mobile devices. In this paper we focus on the latter type of offloading, and specifically on offloading through opportunistic networks. As opposed to most of the literature looking at this type of offloading, in this paper we consider the case where requests for content are non-synchronised, i.e. users request content at random points in time. We support this scenario through a very simple offloading scheme, whereby no epidemic dissemination occurs in the opportunistic network. Thus our scheme is minimally invasive for users' mobile devices, as it uses only minimally their resources. Then, we provide an analysis on the efficiency of our offloading mechanism (in terms of percentage of offloaded traffic) in representative vehicular settings, where content needs to be delivered to (subsets of the) users in specific geographical areas. Depending on various parameters, we show that a simple and resource-savvy offloading scheme can nevertheless offload a very large fraction of the traffic (up to more than 90%, and always more than 20%). We also highlight configurations where such a technique is less effective, and therefore a more aggressive use of mobile nodes resources would be needed.

In opportunistic networks, putting devices in energy saving mode is crucial to preserve their battery, and hence to increase the lifetime of the network and to foster user cooperation. However, a side effect of duty cycling is to reduce the number of usable contacts for delivering messages, thus increasing intercontact times and delays. In order to understand the effect of duty cycling in opportunistic networks, in this paper we propose a general model for deriving the pairwise intercontact times when a duty cycling policy is superimposed on the original encounter process determined only by node mobility. Then, we specialise this model when the original intercontact times are exponential (an assumption popular in the literature), and we show that, in this case, the intercontact times measured after duty cycling are, approximately, again exponential, but with a rate proportional to the inverse of the duty cycle.

Adaptive Modulation and Coding (AMC) in LTE networks is commonly employed to improve system throughput by ensuring more reliable transmissions. Most of existing AMC methods select the modulation and coding scheme (MCS) using pre-computed mappings between MCS indexes and channel quality indicator (CQI) feedbacks that are periodically sent by the receivers. However, the effectiveness of this approach heavily depends on the assumed channel model. In addition CQI feedback delays may cause throughput losses. In this paper we design a new AMC scheme that exploits a reinforcement learning algorithm to adjust at run-time the MCS selection rules based on the knowledge of the effect of previous AMC decisions. The salient features of our proposed solution are: i) the low-dimensional space that the learner has to explore, and ii) the use of direct link throughput measurements to guide the decision process. Simulation results obtained using ns3 demonstrate the robustness of our AMC scheme that is capable of discovering the best MCS even if the CQI feedback provides a poor prediction of the channel performance.

The delay tolerant networking paradigm aims to enable communications in disconnected environments where traditional protocols would fail. Opportunistic networks are delay tolerant networks whose nodes are typically the users’ personal mobile devices. Communications in an opportunistic network rely on the mobility of users: each message is forwarded from node to node, according to a hop-by-hop decision process that selects the node that is better suited for bringing the message closer to its destination. Despite the variety of forwarding protocols that have been proposed in the recent years, there is no reference framework for the performance modelling of opportunistic forwarding. In this paper we start to fill this gap by proposing an analytical model for the first two moments of the delay and the number of hops experienced by messages when delivered in an opportunistic fashion. This model seamlessly integrates both social-aware and social-oblivious single-copy forwarding protocols, as well as different hypotheses for user contact dynamics. More specifically, the parameters of model can be solved in a closed form in the case of exponential and Pareto inter-meeting times, two popular cases emerged from the literature on human mobility analysis. In order to exemplify how the proposed framework can be used, we discuss its application to two case studies with different mobility settings. Then, we discuss how the framework can be also extended to accommodate inter-meeting times following a hyper-exponential distribution. This case is particularly relevant as hyper-exponential distributions are able to approximate the large class of high-variance distributions (distributions with coefficient of variation greater than one), which are those more challenging, e.g., from the delay standpoint. Finally, we provide a validation for the framework with both ideal contacts (i.e., exactly following a given distribution) and contacts extracted from a real mobility trace. This evaluation highlights the strength of the framework in terms of its ability both to provide very accurate predictions under ideal mobility and to effectively approximate the behaviour of the delay moments under real mobility.

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-to-peer message forwarding. The current approach to human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they arose from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a flexible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility. Our framework, named SPoT, is able to incorporate the three dimensions–spatial, social, and temporal–of human mobility. The way SPoT does this is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases, and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the flexibility of the framework, and we show that SPoT is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a thorough mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge.

Online Social Networks are amongst the most important platforms for maintaining social relationships online, supporting content generation and exchange between users. They are therefore natural candidate to be the basis of future humancentric networks and data exchange systems, in addition to novel forms of Internet services exploiting the properties of human social relationships. Understanding the structural properties of OSN and how they are influenced by human behaviour is thus fundamental to design such human-centred systems. In this paper we analyse a real Twitter data set to investigate whether well known structures of human social networks identified in “offline” environments can also be identified in the social networks maintained by users on Twitter. According to the well known model proposed by Dunbar, offline social networks are formed of circles of relationships having different social characteristics (e.g., intimacy, contact frequency and size). These circles can be directly ascribed to cognitive constraints of human brain, that impose limits on the number of social relationships maintainable at different levels of emotional closeness. Our results indicate that a similar structure can also be found in the Twitter users' social networks. This suggests that the structure of social networks also in online environments are controlled by the same cognitive properties of human brain that operate offline.

Opportunistic computing is a new computational paradigm enabling mobile users to access the heterogeneous services present in a pervasive mobile environment. With respect to conventional service-oriented approaches, in opportunistic computing services are provided by the users' mobile devices themselves, and are accessed exploiting opportunistically direct contacts between devices, i.e. without relying exclusively on fixed infrastructures such as the cloud. Pair-wise contacts are exploited to collect information on services and providers available in the network. A proper support may exploit this information to choose the most efficient composition of services satisfying a service request issued either by a user or an application. This paper defines a support for service selection and composition in opportunistic environments based on a mathematical model able to describe the different phases of the execution of a service composition. The model enables an estimation of the execution time of a composition and is exploited by the support for choosing the best composition among a set of available alternatives. The paper presents a set of simulations proving the effectiveness of our approach. The experiments show that our approach achieves better query resolution time and better load balancing of the service requests on the providers with respect to reference alternative approaches.

Opportunistic networking is one of the key paradigms to support direct communication between devices in a mobile scenario. In this context, the high volatility and dynamicity of information and the fact that mobile nodes have to make decisions in condition of partial or incomplete knowledge, makes the development of effective and efficient data dissemination schemes very challenging. In this paper we present algorithms based on well-established models in cognitive sciences, in order to disseminate both data items, and semantic information associated with them. In our approach, semantic information represents both meta-data associated to data items (e.g., tags associated to them), and meta-data describing the interests of the users (e.g., topics for which they would like to receive data items). Our solution exploits dissemination of semantic data about the users' interests to guide the dissemination of the corresponding data items. Both dissemination processes are based on models coming from the cognitive sciences field, named cognitive heuristics, which describe how humans organise information in their memory and exchange it during interactions based on partial and incomplete information. We exploit a model describing how semantic data can be organised in each node in a semantic network, based on how humans organise information in their memory. Then, we define algorithms based on cognitive heuristics to disseminate both semantic data and data items between nodes upon encounters. Finally, we provide initial performance results about the diffusion of interests among users, and the corresponding diffusion of data items.

Opportunistic Networks (OppNets) offer a very volatile and dynamic networking environment. Several applications proposed for OppNets - such as social networking, emergency management, pervasive and urban sensing - involve the problem of sharing content amongst interested users. Despite the fact that nodes have limited resources, existing solutions for content sharing require that the nodes maintain and exchange large amount of status information, but this limits the system scalability. In order to cope with this problem, in this paper we present and evaluate a solution based on cognitive heuristics. Cognitive heuristics are functional models of the mental processes, studied in the cognitive psychology field. They describe the behavior of the brain when decisions have to be taken quickly, in spite of incomplete information. In our solution, nodes maintain an aggregated information built up from observations of the encountered nodes. The aggregate status and a probabilistic decision process is the basis on which nodes apply cognitive heuristics to decide how to disseminate content items upon meeting with each other. These two features allow the proposed solution to drastically limit the state kept by each node, and to dynamically adapt to both the dynamics of item diffusion and the dynamically changing node interests. The performance of our solution is evaluated through simulation and compared with other solutions in the literature.

The widespread use of online social networks, such as Facebook and Twitter, is generating a growing amount of accessible data concerning social relationships. The aim of this work is twofold. First, we present a detailed analysis of a real Facebook data set aimed at characterising the properties of human social relationships in online environments. We find that certain properties of online social networks appear to be similar to those found “offline” (i.e., on human social networks maintained without the use of social networking sites). Our experimental results indicate that on Facebook there is a limited number of social relationships an individual can actively maintain and this number is close to the well-known Dunbar’s number (150) found in offline social networks. Second, we also present a number of linear models that predict tie strength (the key figure to quantitatively represent the importance of social relationships) from a reduced set of observable Facebook variables. Specifically, we are able to predict with good accuracy (i.e., higher than 80%) the strength of social ties by exploiting only four variables describing different aspects of users interaction on Facebook. We find that the recency of contact between individuals – used in other studies as the unique estimator of tie strength – has the highest relevance in the prediction of tie strength. Nevertheless, using it in combination with other observable quantities, such as indices about the social similarity between people, can lead to more accurate predictions

In the convergence of the Cyber-Physical World , user devices will act as proxies of the humans in the cyber world. They will be required to act in a vast information landscape, asserting the relevance of data spread in the cyber world, in order to let their human users become aware of the content they really need. This is a remarkably similar situation to what the human brain has to do all the time when deciding what information coming from the surrounding environment is interesting and what can simply be ignored. The brain performs this task using so called cognitive heuristics, i.e. simple, rapid, yet very effective schemes. In this article, we propose a new approach that exploits one of these heuristics, the recognition heuristic , for developing a self-adaptive system that deals with effective data dissemination in opportunistic networks. We show how to implement it and provide an extensive analysis via simulation. Specifically, results show that the proposed solution is as effective as state-of-the-art solutions for data dissemination in opportunistic networks, while requiring far less resources. Finally, our sensitiveness analysis shows how various parameters depend on the context where nodes are situated, and suggest corresponding optimal configurations for the algorithm.

Foundational work in the area of opportunistic networks has shown that the distribution of intercontact times between pairs of nodes has a key impact on the network properties, for example, in terms of convergence of forwarding protocols. Specifically, forwarding protocols may yield infinite expected delay if the intercontact time distributions present a particularly heavy tail. While these results hold for the distributions of intercontact times between individual pairs, most of the literature uses the aggregate distribution, i.e., the distribution obtained by considering the samples from all pairs together, to characterize the properties of opportunistic networks. In this paper, we provide an analytical framework that can be used to check when this approach is correct and when it is not, and we apply it to a number of relevant cases. We show that the aggregate distribution can be way different from the distributions of individual pair intercontact times. Therefore, using the former to characterize properties that depend on the latter is not correct in general, although this is correct in some cases. We substantiate this finding by analyzing the most representative distributions characterizing real opportunistic networks that can be obtained from reference traces. We review key cases for opportunistic networking, where the aggregate intercontact time distribution presents a heavy tail with or without exponential cutoff. We show that, when individual pairs follow Pareto distributions, the aggregate distribution consistently presents a heavy tail. However, heavy tail aggregate distributions can also emerge in networks where individual pair intercontact times are not heavy tailed, for example, exponential or Pareto with exponential cutoff distributions. We show that an exponential cutoff in the aggregate appears when the average intercontact times of individual pairs are finite. Finally, we discuss how to use our analytical model to know whether collecting aggregate information about intercontact times is sufficient or not, to decideâin practiceâwhich type of routing protocols to use.

When faced with large amounts of data, human brains are able to swiftly react to stimuli and assert relevance of discovered information, even under uncertainty and partial knowledge. These efficient decision-making abilities rely on so-called cognitive heuristics, which are rapid, adaptive, light-weight yet very effective schemes used by the brain to solve complex problems. In a content-centric future Internet where users generate and disseminate large amounts of content through opportunistic networking techniques, individual nodes should exhibit those properties to support a scalable content dissemination system. We therefore study whether such cognitive heuristics can also be used in such a networking environment. To this end, in this paper we develop an analytical model that describes a content dissemination mechanism for opportunistic networks based on one such heuristics, known as the recognition heuristic. Our model takes into account the different popularities of content types, and highlights the impact of the shared memory contributed by individual nodes to make the dissemination process more efficient. Furthermore, our model allows us to investigate the performance of the dissemination process for very large number of nodes, which might be very difficult to carry out through a simulation-based study.

Modeling human mobility is crucial in the performance analysis and simulation of mobile ad hoc networks, where contacts are exploited as opportunities for peer-to-peer message forwarding. The current approach to human mobility modeling has been based on continuously modifying models, trying to embed in them the newest features of mobility properties (e.g., visiting patterns to locations or inter-contact times) as they came up from trace analysis. As a consequence, typically these models are neither flexible (i.e., features of mobility cannot be changed without changing the model) nor controllable (i.e., the exact shape of mobility properties cannot be controlled directly). In order to take into account the above requirements, in this paper we propose a mobility framework whose goal is, starting from the stochastic process describing the arrival patterns of users to locations, to generate pairwise inter-contact times and aggregate inter-contact times featuring a predictable probability distribution. We validate the proposed framework by means of simulations. In addition, assuming that the arrival process of users to locations can be described by a Bernoulli process, we mathematically derive a closed form for the pairwise and aggregate inter-contact times, proving the controllability of the proposed approach in this case.

Thanks to the diffusion of mobile user devices (e.g. smartphones) with rich computing and networking capabilities, we are witnessing an increasing integration between the cyber world of devices and the physical world of users. In this perspective, a possible evolution of pervasive networking (hereafter referred to as social pervasive networks, SPNs) consists in closely mapping human social structures in the network of the devices. Links between devices would correspond to social relationships between users, and communication events between devices would correspond to communications between users. It can be shown that fundamental convergence properties of SPN forwarding protocols are determined by the distributions of inter-contact times between the individual nodes (i.e. the time elapsed between two successive communication events between the nodes). Individual pairs inter-contact times are hard to completely charaterise, while the distribution of the aggregate inter-contact times is often a much more convenient figure. However, the aggregate distribution is not always representative of the individual pairs distributions. Therefore using it to characterise the properties of SPN forwarding protocols might not be correct. In this paper we provide an analytical model based on fundamental models of human social networks from the anthropology literature, which shows the exact dependence between the two in heterogeneous SPNs. Moreover, we use the model to i) study cases in which analysing the aggregate distribution is not enough, and ii) find sufficient conditions that guarantee that studying the aggregate distribution is enough to characterise the properties of SPN forwarding protocols.

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, mobile and pervasive networks suffer from intermittent connectivity, disconnections and partitions, such that opportunistic networking techniques are required to enable communication. This poses novel challenges to service composition techniques. While several works have discussed middleware and architecture 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 addressed. The problem comprises three stages: i) selecting an appropriate service sequence set out of available services; ii) forwarding service inputs to the device hosting the next service in the composition; and iii) routing final service outcomes back to the requester. The proposed algorithm derives efficiency and effectiveness by taking into account the service load and location of devices providing the services, as well as intermittent connectivity, to select a particular service set. Through extensive simulations on real and synthetic traces, we show that by using only local knowledge collected in a distributed manner, performance close to a real-time centralized system can be achieved.

A pioneering body of work in the area of mobile opportunistic networks has shown that characterising inter-contact times between pairs of nodes is crucial. In particular, when inter-contact times follow a power-law distribution, the expected delay of a large family of forwarding protocols may be infinite. The most common approach adopted in the literature to study inter-contact times consists in looking at the distribution of the inter-contact times aggregated over all nodes pairs, assuming it correctly represents the distributions of individual pairs. In this paper we challenge this assumption. We present an analytical model that describes the dependence between the individual pairs and the aggregate distributions. By using the model we show that in heterogeneous networks - when not all pairs contact patterns are the same - most of the time the aggregate distribution is not representative of the individual pairs distributions, and that looking at the aggregate can lead to completely wrong conclusions on the key properties of the network. For example, we show that aggregate power-law inter-contact times (suggesting infinite expected delays) can frequently emerge in networks where individual pairs inter-contact times are exponentially distributed (meaning that the expected delay is finite). From a complementary standpoint, our results show that heterogeneity of individual pairs contact patterns plays a crucial role in determining the aggregate inter-contact times statistics, and that focusing on the latter only can be misleading.

It is our great pleasure to welcome you to the Twelfth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2011. Over the years, WoWMoM has emerged to be a flagship forum that brings together researchers from academia, industry, and government laboratories who are involved in various aspects of mobile multimedia networking technologies, ranging from communications platforms to services and applications.

It is often argued that the Future Internet will be a very large scale content-centric network. Scalability issues will stem even more from the amount of content nodes will generate, share and consume. In order to let users become aware and retrieve the content they really need, these nodes will be required to swiftly react to stimuli and assert the relevance of discovered data under uncertainty and only partial information. The human brain performs the task of information filtering and selection using the so-called cognitive heuristics, i.e. simple, rapid, low-resource demanding, yet very effective schemes that can be modeled using a functional approach. In this paper we propose a solution based on one such heuristics, namely the recognition heuristic, for dealing with data dissemination in opportunistic networks. We show how to model an algorithm that exploits the environmental information in order to implement an effective dissemination of data based on the recognition heuristic, and provide a performance evaluation of such a solution via simulation.

In this paper we present and evaluate a social network model which exploits fundamental results coming from the social anthropology literature. Specifically, our model focuses on ego networks, i.e., the set of active social relationships for a given individual. The model is based on a function that correlates the level of emotional closeness of a social relationship to the time invested in it. The size of the social network is limited by the time budget a person invests in socializing. We exploit the model to define a constructive algorithm to generate synthetic social networks. Experimental results show that our model satisfies, on average, known properties of ego networks such as the size, the composition and the hierarchical structure.

Opportunistic networks are created dynamically by exploiting contacts between pairs of mobile devices that come within communication range. While forwarding in opportunistic networking has been explored, investigations into asynchronous service provisioning on top of opportunistic networks are unique contributions of this paper. Mobile devices are typically heterogeneous, possess disparate physical resources, and can provide a variety of services. During opportunistic contacts, the pairing peers can cooperatively provide (avail of) their (other peer's) services. This service provisioning paradigm is a key feature of the emerging opportunistic computing paradigm. We develop an analytical model to study the behaviors of service seeking nodes (seekers) and service providing nodes (providers) that spawn and execute service requests, respectively. The model considers the case in which seekers can spawn parallel executions on multiple providers for any given request, and determines: 1) the delays at different stages of service provisioning; and 2) the optimal number of parallel executions that minimizes the expected execution time. The analytical model is validated through simulations, and exploited to investigate the performance of service provisioning over a wide range of parameters.

Opportunistic computing has emerged as a new paradigm in computing, leveraging the advances in pervasive computing and opportunistic networking. Nodes in an opportunistic network avail of each others' connectivity and mobility to overcome network partitions. In opportunistic computing, this concept is generalised, as nodes avail of any resource available in the environment. Here we focus on computational resources, assuming mobile nodes opportunistically invoke services on each other. Specifically, resources are abstracted as services contributed by providers and invoked by seekers. In this paper, we present an analytical model that depicts the service invocation process between seekers and providers. Specifically, we derive the optimal number of replicas to be spawned on encountered nodes, in order to minimise the execution time and optimise the computational and bandwidth resources used. Performance results show that a policy operating in the optimal configuration largely outperforms policies that do not consider resource constraints.

It is our great pleasure to introduce this Special Section of the Journal, focused on Autonomic and Opportunistic Communications. We strongly believe autonomic and opportunistic properties will be a key feature of the Future Mobile Internet. The huge proliferation of mobile devices with wireless networking capabilities makes it possible to foresee a Future Internet environment in which users' mobile devices will spontaneously network together and build self-organizing wireless networks for enabling users interaction and content exchange. This will be a natural enabler for the take off of User Generated Content and other user-centred networking models in the area of pervasive mobile networks.

Recent studies indicate that the Internet-related energy consumption represents a significant, and increasing, part of the overall energy consumption of our society. The largest contribution to this consumption is due to Internet edge devices. Typically, users leave their PCs continuously on for satisfying the connectivity requirements of file sharing P2P applications, like BitTorrent. In this paper we propose a novel proxy-based BitTorrent architecture. BitTorrent users can delegate the download operations to the proxy and then power off, while the proxy downloads the requested files. We implemented our solution and validated it in a realistic testbed. Experimental results show that, with respect to a legacy approach, our solution is very effective in reducing the energy consumption (up to 95%) without introducing any QoS degradation.

Providing efficient transport services over multi-hop ad hoc networks is a fundamental building block for this wireless technology. The typical approach is modifying transmission control protocol (TCP) to fix one (or a few of) its inefficiency while preserving compatibility with the original protocol. However, a complete solution should include a significant number of modifications, such that the original TCP design is deeply modified. In this paper we explore a different approach. We include the desired modifications to TCP in the design of a new transport protocol [transport protocol for ad-hoc (TPA)]. In this way we are able to blend together these features in a unique design framework, and better control interactions among the different (modified) components. We then compare TCP and TPA through field tests, in terms of throughput and total number of transmitted segments. We consider several possible configurations of the protocol parameters, different routing protocols and various networking scenarios. In all the cases taken into consideration, TPA significantly outperforms TCP. To achieve a more thorough understanding of the TPA behaviour, we compare TPA and TCP also in terms of fairness and scalability (both in static and mobile configurations) over a wide range of representative topologies. To this end, we adopt a simulation approach, which is more suitable to this kind of analysis. Simulation results confirm field tests, and show that TPA is able to outperform TCP with respect to all analysed performance figures.

This paper deals with data dissemination in resource-constrained opportunistic networks, i.e., multi-hop ad hoc networks in which simultaneous paths between endpoints are not available, in general, for end-to-end communication. One of the main challenges is to make content available in those regions of the network where interested users are present, without overusing available resources (e.g., by avoiding flooding). These regions should be identified dynamically, only by exploiting local information exchanged by nodes upon encountering other peers. To this end, exploiting information about social users' behaviour turns out to be very efficient. In this paper we propose and evaluate ContentPlace, a system that exploits dynamically learnt information about users' social relationships to decide where to place data objects in order to optimise content availability. We define a number of social-oriented policies in the general ContentPlace framework, and compare them also with other reference policies proposed in the literature.

Opportunistic networks are challenging mobile ad hoc networks characterised by frequent disconnections and partitioning. In this paper we focus on data dissemination services, i.e. cases in which data should be disseminated in the network without a priori knowledge about the set of intended destinations. We propose a general autonomic data dissemination framework that exploits information about the userspsila context and social behaviour, to decide how to replicate and replace data on nodespsila buffers. Furthermore, our data dissemination scheme explicitly takes into account resource constraints, by jointly considering the expected utility of data replication and the associated costs. The results we present show that our solution is able to improve data availability, provide fairness among nodes, and reduce the network load, with respect to reference proposals available in the literature.

Opportunistic networks are mainly characterized by nodes intermittently connected among them. Available applications designed for mobile ad hoc networks are not suitable for such an environment since we cannot assume to have a stable path between pairs of nodes. Network protocols and applications themselves must be enhanced to exploit all possible communication opportunities to deliver messages on the network. In this demo we present an enhanced file sharing application based on the exchange of context information between nodes. In this case the context is defined as a combination of the user personal information, interests, and social relationships in order to implement cooperative downloading mechanisms. Besides reducing the impact of intermittent connectivity and high mobility on multi-hop communications, exploiting context also allows us to avoid flooding, thus resulting in a very efficient approach.

In this paper we deal with energy-efficient data collection in sparse sensor networks with data mules. We analyze the problem of optimal data transfer from sensors to data mules, and derive an upper bound for the performance of ARQ-based data-transfer protocols. This analysis shows that protocols currently used have low performance, which results in unnecessary energy consumption. Based on these results we define and evaluate an Adaptive Data Transfer (ADT) protocol that is able to combine efficiency and adaptability to external conditions. Simulation results show that ADT not only reduces significantly the average data-transfer time in comparison with previous protocols, but also provides quasi-optimal performance. In addition, it is able to react quickly to variations in the external conditions and adapt to new conditions in a limited time.

In opportunistic networks the existence of a simultaneous path between a sender and a receiver is not assumed. This model (which fits well to pervasive networking environments) completely breaks the main assumptions on which MANET routing protocols are built. Routing in opportunistic networks is usually based on some form of controlled flooding. But often this results in very high resource consumption and network congestion. In this paper we advocate context-based routing for opportunistic networks. We provide a general framework for managing and using context for taking forwarding decisions. We propose a context-based protocol (HiBOp), and compare it with popular solutions, i.e., Epidemic Routing and PROPHET. Results show that HiBOp is able to drastically reduce resource consumption. At the same time, it significantly reduces the message loss rate, and preserves the performance in terms of message delay.

Opportunistic networks are wireless mobile networks in which a continuous end-to-end path between a source and a destination is not necessary. Messages are stored at intermediate nodes, and opportunistically forwarded when a more suitable next hop towards the destination becomes available. A very interesting aspect is understanding how users' mobility patterns impact on the performance of routing protocols. Starting from this motivation, in this paper we take into consideration group mobility models, whose movement patterns have shown to be remarkably similar to real-world user movements. We consider routing protocols representative of a broad range of schemes, and highlight the impact of users social relationships and movement patterns on the protocols' performance.

Several previous works have shown that TCP exhibits poor performance in mobile ad hoc networks (MANETs). The ultimate reason for this is that MANETs behave in a significantly different way from traditional wired networks, like the Internet, for which TCP was originally designed. In this paper we propose a novel transport protocol - named TPA - specifically tailored to the characteristics of the MANET environment. It is based on a completely new congestion control mechanism, and designed in such a way to minimize the number of useless transmissions and, hence, power consumption. Furthermore, it is able to manage efficiently route changes and route failures. We evaluated the TPA protocol in a static scenario where TCP exhibits good performance. Simulation results show that, even in such a scenario, TPA significantly outperforms TCP.

In the near future more and more users will access Internet services by means of portable devices through wireless links. However, mobile computing is still strongly limited by the scarcity of energetic resources of portable devices. In This work we propose and evaluate an application-independent energy management policy for a Wi-Fi hotspot scenario. Unlike the IEEE 802.11 power saving mode, the proposed solution is able to adapt to the application traffic profile, thus saving a considerable amount of energy. For the same reason it is flexible, i.e., it exhibits good performance irrespectively of the specific network application, and even in the presence of concurrent applications. Experimental measurements performed on a prototype implementation with different traffic types have shown that our energy management policy is able to save up to 80% of the energy consumed in a legacy architecture, without a significant degradation on the QoS perceived by the user.

In this paper we investigate the performance of mica2 and mica2dot Berkeley motes by means of an extensive experimental analysis. This study is aimed at analyzing the main elements that characterize the performance of a sensor network, e.g., power consumption in different operating conditions, impact of weather conditions, interference between neighboring nodes, etc. Even if the analysis is related to a specific technology it provides some general useful information. Specifically, we found that the transmission range of mote sensor nodes decreases significantly in the presence of fog or rain. We also investigated the interference between neighboring nodes and, based on the experimental results, we propose a channel model for mote sensor nodes. This model is very similar to the channel model of IEEE 802.11 networks.