We introduce AI Harmonics ( AIH ), a novel metric designed to quantify the concentration of harms across stakeholder groups affected by AI systems. Unlike traditional approaches that rely on arbitrary numerical assignments to ordinal severity levels, AIH provides a principled framework grounded in inequality theory, extending concepts from the Gini index to purely ordinal data. The metric evaluates how harm is distributed among stakeholders, capturing whether severe impacts are concentrated within specific groups or more evenly spread. Experiments on annotated incident data show that the proposed metric exhibits a strong monotonic relationship with the Criticality Index (CI), preserving harm category rankings while capturing additional variation in concentration patterns. The method demonstrates high robustness, with Spearman rank correlations above 0.97 under severity perturbations and stable prioritization even under up to 80% random data removal. Political and physical harms consistently exhibit the highest concentration, indicating the need for urgent mitigation. Political harms erode public trust, while physical harms pose serious, even life-threatening risks, underscoring the real-world relevance of our approach. The AIH metric is particularly well-suited for policy-making and risk management, where only ordinal assessments are available, and it enables more informed prioritization of mitigation strategies.

The Internet Yellow Pages (IYP) aggregates information from multiple sources about Internet routing into a unified, graph-based knowledge base. However, querying it requires knowledge of the Cypher language and the exact IYP schema, thus limiting usability for non-experts. In this paper, we propose ChatIYP, a domain-specific Retrieval-Augmented Generation (RAG) system that enables users to query IYP through natural language questions. Our evaluation demonstrates solid performance on simple queries, as well as directions for improvement, and provides insights for selecting evaluation metrics that are better fit for IYP querying AI agents.

In this paper, we introduce the Internet Quality Barometer (IQB), a framework aiming to redefine Internet quality beyond "speed".IQB (i) defines Internet quality in a user-centric way by considering popular use cases, (ii) maps network requirements to use cases through a set of weights and quality thresholds, and (iii) leverages publicly available Internet performance datasets, to calculate the IQB score, a composite metric that reflects the quality of Internet experience.

In today’s connected society, many people rely on mHealth and self-tracking (ST) technology to help them adopt healthier habits with a focus on breaking their sedentary lifestyle and staying fit. However, there is scarce evidence of such technological interventions’ effectiveness, and there are no standardized methods to evaluate their impact on people’s physical activity and health. This work aims to help ST practitioners and researchers by empowering them with systematic guidelines and a framework for designing and evaluating technological interventions to facilitate health behavior change and user engagement, focusing on increasing physical activity and decreasing sedentariness. To this end, we conduct a literature review of 129 papers between 2008 and 2022, which identifies the core ST design principles and their efficacy, as well as the most comprehensive list to date of user engagement evaluation metrics for ST. Based on the review’s findings, we propose PAST SELF, a framework to guide the design and evaluation of ST technology that has potential applications in industrial and scientific settings. Finally, to facilitate researchers and practitioners, we complement this article with an open corpus and an online, adaptive exploration tool for the PAST SELF data.

Caching algorithms try to predict content popularity, and place the content closer to the users. Additionally, nowadays requests are increasingly driven by recommendation systems (RS). These important trends, point to the following: \emph{make RSs favor locally cached content}, this way operators reduce network costs, and users get better streaming rates. Nevertheless, this process should preserve the quality of the recommendations (QoR). In this work, we propose a Markov Chain model for a stochastic, recommendation-driven \emph{sequence} of requests, and formulate the problem of selecting high quality recommendations that minimize the network cost \emph{in the long run}. While the original optimization problem is non-convex, it can be convexified through a series of transformations. Moreover, we extend our framework for users who show preference in some positions of the recommendations' list. To our best knowledge, this is the first work to provide an optimal polynomial-time algorithm for these problems. Finally, testing our algorithms on real datasets suggests significant potential, e.g.,$2\times$improvement compared to baseline recommendations, and 80\% compared to a greedy network-friendly-RS (which optimizes the cost for I.I.D. requests), while preserving at least 90\% of the original QoR. Finally, we show that taking position preference into account leads to additional performance gains.

Content recommendation systems, also known as recommenders, are pervasive and impact a significant portion of users demands over the Internet. Although recommenders have been primarily devised to account for users interests with respect to the content catalog, mobile users are typically served by a network that is unreliable and subject to losses and low QoS. Can content recommenders compensate for low QoS? To answer this question, we conducted experiments over the Internet, and report our findings on (i) the characterization of QoS and (ii) the compensation for low QoS. Our measurements suggest that content that is far from the trends tends to be far from the user. We quantify the extent at which unpopular content tends to be served with lower QoS and establish a methodology to determine the relationship between contents' popularity and its physical proximity to the users. Then, we verify that making requests a bit trendier can hit much closer content. In particular, our results suggest conditions under which a recommender can compensate for low QoS, at zero costs for operators.

Joint caching and recommendation has been recently proposed as a new paradigm for increasing the efficiency of mobile edge caching. Early findings demonstrate significant gains for the network performance. However, previous works evaluated the proposed schemes exclusively on simulation environments. Hence, it still remains uncertain whether the claimed benefits would change in real settings. In this paper, we propose a methodology that enables to evaluate joint network and recommendation schemes in real content services by only using publicly available information. We apply our methodology to the YouTube service, and conduct extensive measurements to investigate the potential performance gains. Our results show that significant gains can be achieved in practice; e.g., 8 to 10 times increase in the cache hit ratio from cache-aware recommendations. Finally, we build an experimental testbed and conduct experiments with real users; we make available our code and datasets to facilitate further research. To our best knowledge, this is the first realistic evaluation (over a real service, with real measurements and user experiments) of the joint caching and recommendations paradigm. Our findings provide experimental evidence for the feasibility and benefits of this paradigm, validate assumptions of previous works, and provide insights that can drive future research.

Users in Online Social Networks (OSNs,) leave traces that reflect their personality characteristics. The study of these traces is important for several fields, such as social science, psychology, marketing, and others. Despite a marked increase in research on personality prediction based on online behavior, the focus has been heavily on individual personality traits, and by doing so, largely neglects relational facets of personality. This study aims to address this gap by providing a prediction model for holistic personality profiling in OSNs that includes socio-relational traits (attachment orientations) in combination with standard personality traits. Specifically, we first designed a feature engineering methodology that extracts a wide range of features (accounting for behavior, language, and emotions) from the OSN accounts of users. Subsequently, we designed a machine learning model that predicts trait scores of users based on the extracted features. The proposed model architecture is inspired by characteristics embedded in psychology; i.e, it utilizes interrelations among personality facets and leads to increased accuracy in comparison with other state-of-the-art approaches. To demonstrate the usefulness of this approach, we applied our model on two datasets, namely regular OSN users and opinion leaders on social media, and contrast both samples’ psychological profiles. Our findings demonstrate that the two groups can be clearly separated by focusing on both Big Five personality traits and attachment orientations. The presented research provides a promising avenue for future research on OSN user characterization and classification.

BGP prefix hijacking is a critical threat to the resilience and security of communications in the Internet. While several mechanisms have been proposed to prevent, detect or mitigate hijacking events, it has not been studied how to accurately quantify the impact of an ongoing hijack. When detecting a hijack, existing methods do not estimate how many networks in the Internet are affected (before and/or after its mitigation). In this paper, we study fundamental and practical aspects of the problem of estimating the impact of an ongoing hijack through network measurements. We derive analytical results for the involved trade-offs and limits, and investigate the performance of different measurement approaches (control/data-plane measurements) and use of public measurement infrastructure. Our findings provide useful insights for the design of accurate hijack impact estimation methodologies. Based on these insights, we design (i) a lightweight and practical estimation methodology that employs ping measurements, and (ii) an estimator that employs public infrastructure measurements and eliminates correlations between them to improve the accuracy. We validate the proposed methodologies and findings against results from hijacking experiments we conduct in the real Internet.

As mobile traffic is dominated by content services (e.g., video), which typically use recommendation systems, the paradigm of network-friendly recommendations (NFR) has been proposed recently to boost the network performance by promoting content that can be efficiently delivered (e.g., cached at the edge). NFR increase the network performance, however, at the cost of being unfair towards certain contents when compared to the standard recommendations. This unfairness is a side effect of NFR that has not been studied in literature. Nevertheless, retaining fairness among contents is a key operational requirement for content providers. This paper is the first to study the fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics that capture different notions of fairness, and study the unfairness created by existing NFR schemes. Our analysis reveals that NFR can be significantly unfair. We identify an inherent trade-off between the network gains achieved by NFR and the resulting unfairness, and derive bounds for this trade-off. We show that existing NFR schemes frequently operate far from the bounds, i.e., there is room for improvement. To this end, we formulate the design of Fair-NFR (i.e., NFR with fairness guarantees compared to the baseline recommendations) as a linear optimization problem. Our results show that the Fair-NFR can achieve high network gains (similar to non-fair-NFR) with little unfairness.

Internet eXchange Points (IXPs) are Internet hubs that mainly provide the switching infrastructure to interconnect networks and exchange traffic. While the initial goal of IXPs was to bring together networks residing in the same city or country, and thus keep local traffic local, this model is gradually shifting. Many networks connect to IXPs without having physical presence at their switching infrastructure. This practice, called Remote Peering, is changing the Internet topology and economy, and has become the subject of a contentious debate within the network operators' community. However, despite the increasing attention it attracts, the understanding of the characteristics and impact of remote peering is limited. In this work, we introduce and validate a heuristic methodology for discovering remote peers at IXPs. We (i) identify critical remote peering inference challenges, (ii) infer remote peers with high accuracy (>97%) and coverage (94%) per IXP, and (iii) characterize different aspects of the remote peering ecosystem by applying our methodology to 30 large IXPs. We observe that remote peering is a significantly common practice in all the studied IXPs; for the largest IXPs, remote peers account for 40% of their member base. We also show that today, IXP growth is mainly driven by remote peering, which contributes two times more than local peering.

Caching popular content at the network edge can benefit both the operator and the client by alleviating the backhaul traffic and reducing access latency, respectively. Recommendation systems, on the other hand, try to offer interesting content to the user and impact her requests, but independently of the caching policy. Nevertheless, it has been recently proposed that designing caching and recommendation policies separately is suboptimal. Caching could benefit by knowing the recommender's actions in advance, and recommendation algorithms could try to favor cached content (among equally interesting options) to improve network performance and user experience. In this paper we tackle the problem of optimally making caching and recommendation decisions jointly, in the context of the recently introduced “soft cache hits” setup. We show that even the simplest (one user, one cache) problem is NP-hard, but that the most generic problem (multiple users, femtocaching network) is approximable to a constant. To the best of our knowledge, this is the first polynomial algorithm with approximation guarantees for the joint problem. Finally, we compare our algorithm to existing schemes using a range of real-world data-sets.

Caching has recently attracted a lot of attention in the wireless communications community, as a means to cope with the increasing number of users consuming web content from mobile devices. Caching offers an opportunity for a win-win scenario: nearby content can improve the video streaming experience for the user, and free up valuable network resources for the operator. At the same time, recent works have shown that recommendations of popular content apps are responsible for a significant percentage of users requests. As a result, some very recent works have considered how to nudge recommendations to facilitate the network (e.g., increase cache hit rates). In this paper, we follow up on this line of work, and consider the problem of designing cache friendly recommendations for long viewing sessions; specifically, we attempt to answer two open questions in this context: (i) given that recommendation position affects user click rates, what is the impact on the performance of such network-friendly recommender solutions? (ii) can the resulting optimization problems be solved efficiently, when considering both sequences of dependent accesses (e.g., YouTube) and position preference? To this end, we propose a stochastic model that incorporates position-aware recommendations into a Markovian traversal model of the content catalog, and derive the average cost of a user session using absorbing Markov chain theory. We then formulate the optimization problem, and after a careful sequence of equivalent transformations show that it has a linear program equivalent and thus can be solved efficiently. Finally, we use a range of real datasets we collected to investigate the impact of position preference in recommendations on the proposed optimal algorithm. Our results suggest more than 30% improvement with respect to state-of-the-art methods.

Caching has been successfully applied in wired networks, in the context of Content Distribution Networks (CDNs), and is quickly gaining ground for wireless systems. Storing popular content at the edge of the network (e.g, at small cells) is seen as a “win-win” for both the user (reduced access latency) and the operator (reduced load on the transport network and core servers). Nevertheless, the much smaller size of such edge caches, and the volatility of user preferences suggest that standard caching methods do not suffice in this context. What is more, simple popularity-based models commonly used (e.g, IRM) are becoming outdated, as users often consume multiple contents in sequence (e.g. YouTube, Spotify), and this consumption is driven by recommendation systems. The latter presents a great opportunity to bias the recommender to minimize content access cost (e.g, maximizing cache hit rates). To this end, in this paper we first propose a Markovian model for recommendation-driven user requests. We then formulate the problem of biasing the recommendation algorithm to minimize access cost, while maintaining acceptable recommendation quality. We show that the problem is non-convex, and propose an iterative ADMM-based algorithm that outperforms existing schemes, and shows significant potential for performance improvement on real content datasets.

Pushing popular content to small cells with local storage (“helper” nodes) has been proposed to cope with the ever-growing data demand. Nevertheless, the collective storage of a few nearby helper nodes may not suffice to achieve a high hit rate in practice. In this paper, we introduce the concept of “soft cache hits” (SCHs). An SCH occurs if a user's requested content is not in the local cache, but the user can be (partially) satisfied by a related content that is. In case of a cache miss, an application proxy (e.g., YouTube) running close to the helper node (e.g., at a multi-access edge computing server) can recommend the most related files that are locally cached. This system could be activated during periods of predicted congestion, or for selected users (e.g., low-cost plans), to improve cache hit ratio with limited (and tunable) user quality of experience performance impact. Beyond introducing a model for soft cache hits, our next contribution is to show that the optimal caching policy should be revisited when SCHs are allowed. In fact, we show that optimal caching with SCH is NP-hard even for a single cache. To this end, we formulate the optimal femto-caching problem with SCH in a sufficiently generic setup and propose efficient algorithms with provable performance. Finally, we use a large range of real datasets to corroborate our proposal.

Border gateway protocol (BGP) prefix hijacking is a critical threat to Internet organizations and users. Despite the availability of several defense approaches (ranging from RPKI to popular third-party services), none of them solves the problem adequately in practice. In fact, they suffer from: (i) lack of detection comprehensiveness, allowing sophisticated attackers to evade detection; (ii) limited accuracy, especially in the case of third-party detection; (iii) delayed verification and mitigation of incidents, reaching up to days; and (iv) lack of privacy and of flexibility in post-hijack counteractions, on the side of network operators. In this paper, we propose ARTEMIS, a defense approach (a) based on accurate and fast detection operated by the autonomous system itself, leveraging the pervasiveness of publicly available BGP monitoring services and their recent shift towards real-time streaming and thus (b) enabling flexible and fast mitigation of hijacking events. Compared to the previous work, our approach combines characteristics desirable to network operators, such as comprehensiveness, accuracy, speed, privacy, and flexibility. Finally, we show through real-world experiments that with the ARTEMIS approach, prefix hijacking can be neutralized within a minute.

Pushing popular content to cheap ``helper'' nodes (e.g., small cells with local storage) during off-peak hours has recently been proposed to cope with the increase in mobile data traffic. If the requested content is available locally at a helper node, both user and operator performance could benefit. Nevertheless, the collective storage of a few nearby helper nodes does not usually suffice to achieve a high hit rate in practice. In this paper, we investigate the concept of ``soft cache hits'' where, if the original content is not available, some locally cached related contents can be recommended. Given that Internet content consumption is entertainment-oriented, we argue that there exist scenarios where a user might accept an alternative content (e.g., better download rate for alternative content, low rate plans), thus avoiding to access expensive/congested links. We formulate the problem of optimal edge caching with soft cache hits in a sufficiently generic setup, propose an efficient algorithm, and analyze the expected gains. We then show using synthetic and real datasets of related video contents that promising caching gains could be achieved in practice.

Epidemic algorithms have found their way into many areas of computer science, such as databases and distributed systems, and recently for communication in Opportunistic or Delay Tolerant Networks (DTNs). To ensure analytical tractability, existing analyses of epidemic spreading predominantly consider homogeneous contact rates between nodes. However, this assumption is generally not true in real scenarios. In this paper, we consider classes of contact/mobility models with heterogeneous contact rates. Through an asymptotic analysis, we prove that a first-order, mean value approximation for the basic epidemic spreading step becomes exact in the limiting case (large network size). We further derive simple closed form approximations, based on higher order statistics of the mobility heterogeneity, for the case of finite-size networks. To demonstrate the utility of our results, we use them to predict the delay of epidemic-based routing schemes and analyze scenarios with node selfishness. We validate the analytic results through extensive simulations on synthetic scenarios, as well as on real traces to demonstrate that our expressions can be useful also in scenarios with significantly more complex structure. We believe these results are an important step forward towards analyzing the effects of heterogeneity (of mobility and/or other characteristics) on the performance of epidemic-based algorithms.

Prefix hijacking is a common phenomenon in the Internet that often causes routing problems and economic losses. In this demo, we propose ARTEMIS, a tool that enables network administrators to detect and mitigate prefix hijacking incidents, against their own prefixes. ARTEMIS is based on the real-time monitoring of BGP data in the Internet, and software-defined networking (SDN) principles, and can completely mitigate a prefix hijacking within a few minutes (e.g., 5-6mins in our experiments) after it has been~launched.

Mobile users are envisioned to exploit direct communication opportunities between their portable devices, in order to enrich the set of services they can access through cellular or WiFi networks. Sharing contents of common interest or providing access to resources or services between peers can enhance a mobile node's capabilities, offload the cellular network, and disseminate information to nodes without Internet access. Interest patterns, i.e., how many nodes are interested in each content or service (popularity), as well as how many users can provide a content or service (availability) impact the performance and feasibility of envisioned applications. In this paper, we establish an analytical framework to study the effects of these factors on the delay and success probability of a content/service access request through opportunistic communication. We also apply our framework to the mobile data offloading problem and provide insights for the optimization of its performance. We validate our model and results through realistic simulations, using datasets of real opportunistic networks.

Opportunistic networking has been proposed to support a number of novel applications, like content sharing or mobile data offloading, that follow a content-centric communication model, i.e., many users are interested in the same content. Users' traffic demand patterns can crucially affect the performance of such applications, but our knowledge about the characteristics of content demand is limited. Nevertheless, opportunistic networking is known to exhibit strong locality and social characteristics. For this reason, in this paper we argue that some initial insights about opportunistic traffic patterns could be inferred from geo-social network data. In particular, we study the check-in patterns of users in datasets of two real Location-Based Social Networks, towards understanding potential traffic characteristics and implications for opportunistic networking.

In opportunistic networks, direct communication between mobile devices is used to extend the set of services accessible through cellular or WiFi networks. Mobility patterns and their impact in such networks have been extensively studied. In contrast, this has not been the case with communication traffic patterns, where homogeneous traffic between all nodes is usually assumed. This assumption is generally not true, as node mobility and social characteristics can significantly affect the end-to-end traffic demand between them. To this end, in this paper, we explore the joint effect of traffic patterns and node mobility on the performance of popular forwarding mechanisms, both analytically and through simulations. Among the different insights stemming from our analysis, we identify conditions under which heterogeneity renders the added value of using extra relays more/less useful. Furthermore, we confirm the intuition that an increasing amount of heterogeneity closes the performance gap between different forwarding policies, making end-to-end routing more challenging in some cases, or less necessary in others. To our best knowledge, this is the first effort to model, analyze, and quantify effects of traffic heterogeneity. We believe this is an important step towards better protocol design and evaluation of the feasibility of applications in opportunistic networks.

Mobile users are envisioned to exploit direct communication opportunities between their portable devices, in order to enrich the set of services they can access through cellular or WiFi networks. Sharing contents of common interest or providing access to resources or services between peers can enhance a mobile node's capabilities, offload the cellular network, and disseminate information to nodes without internet access. Interest patterns, i.e. how many nodes are interested in each content or service (popularity), as well as how many users can provide a content or service (availability) impact the performance and feasibility of envisioned applications. In this paper, we establish an analytical framework to study the effects of these factors on the delay and success probability of a content/service access request through opportunistic communication. We also apply our framework to the data offloading problem and provide insights for its optimization.

In technological or social networks, diffusion processes (e.g. information dissemination, rumour/virus spreading) strongly depend on the structure of the network. In this paper, we focus on epidemic processes over one such class of networks, Opportunistic Networks, where mobile nodes within range can communicate with each other directly. As the node degree distribution is a salient property for process dynamics on complex networks, we use the well known Configuration Model, that captures generic degree distributions, for modeling and analysis. We also assume that information spreading between two neighboring nodes can only occur during random contact times. Using this model, we proceed to derive closed-form approximative formulas for the information spreading delay that only require the first and second moments of the node degree distribution. Despite the simplicity of our model, simulations based on both synthetic and real traces suggest a considerable accuracy for a large range of heterogeneous contact networks arising in this context, validating its usefulness for performance prediction.