Epidemics, such as Corona Virus Disease 2019 (COVID-19), have serious consequences globally, of which the most effective way to control the infection is contact tracing. Nowadays, research related to privacy-preserving epidemic infection control has been conducted, nevertheless, current researchers do not regard the authenticity of records and infection facts as well as poor traceability. Moreover, with the emergence of quantum computing, there is a bottleneck in upholding privacy, security and efficiency. Our paper proposes a privacy-preserving epidemic infection control scheme through lattice-based linkable ring signature in blockchain, called AQRS. Firstly, our scheme adopts a blockchain with three ledgers to store information in a distributed manner, which offers transparency and immunity from the Single Point of Failure (SPoF) and Denial of Service (DoS) attacks. Moreover, we design a lattice-based linkable ring signature scheme to secure privacy-preserving of epidemic infection control. Significantly, we are the first to introduce the lattice-based linkable ring signature into privacy preserving in epidemic control scenario. Security analysis indicates that our scheme ensures unconditional users anonymity, record unforgeability, signature linkability, link non-slanderability and contact traceability. Finally, the comprehensive performance evaluation demonstrates that our scheme has an efficient time-consuming, storage consumption and system communication overhead and is practical for epidemic and future pandemic privacy-preserving.

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE (Transferable Traffic Prediction in MUlti-Service Edge Networks), a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning.

The COVID-19 pandemic has reshaped Internet traffic due to the huge modifications imposed to lifestyle of people resorting more and more to collaboration and communication apps to accomplish daily tasks. Accordingly, these dramatic changes call for novel traffic management solutions to adequately countermeasure such unexpected and massive changes in traffic characteristics. In this paper, we focus on communication and collaboration apps whose traffic experienced a sudden growth during the last two years. Specifically, we consider nine apps whose traffic we collect, reliably label, and publicly release as a new dataset (MIRAGE-COVID-CCMA-2022) to the scientific community. First, we investigate the capability of state-of-art single-modal and multimodal Deep Learning-based classifiers in telling the specific app, the activity performed by the user, or both. While we highlight that state-of-art solutions reports a more-than-satisfactory performance in addressing app classification (96%-98% F-measure), evident shortcomings stem out when tackling activity classification (56%-65% F-measure) when using approaches that leverage the transport-layer payload and/or per-packet information attainable from the initial part of the biflows. In line with these limitations, we design a novel set of inputs (namely Context Inputs) providing clues about the nature of a biflow by observing the biflows coexisting simultaneously. Based on these considerations, we propose Mimetic-All a novel early traffic classification multimodal solution that leverages Context Inputs as an additional modality, achieving F-measure in activity classification. Also, capitalizing the multimodal nature of Mimetic-All, we evaluate different combinations of the inputs. Interestingly, experimental results witness that Mimetic-ConSeq-a variant that uses the Context Inputs but does not rely on payload information (thus gaining greater robustness to more opaque encryption sub-layers possibly going to be adopted in the future)-experiences only F-measure drop in performance w.r.t. Mimetic-All and results in a shorter training time.

The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial-terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach.

Small-scale data centers at the edge are becoming prominent in offering various services to the end-users following the cloud model while avoiding the high latency inherent to the classic cloud environments when accessed from remote Internet regions. However, we should address several challenges to facilitate the end-users finding and consuming the relevant services from the edge at the Internet scale. First, the scale and diversity of the edge hinder seamless access. Second, a framework where researchers openly share their services and data in a secured manner among themselves and with external consumers over the Internet does not exist. Third, the lack of a unified interface and trust across the service providers hinder their interchangeability in composing workflows by chaining the services. Thus, creating a workflow from the services deployed on the various edge nodes is presently impractical. This paper designs Viseu, a latency-aware blockchain framework to provide Virtual Internet Services at the Edge. Viseu aims to solve the puzzle of network service discovery at the edge, considering the peers' reputation and latency when choosing the service instances. Viseu enables peers to share their computational resources, services, and data among each other in an untrusted environment, rather than relying on a set of trusted service providers. By composing workflows from the peers' services, rather than confining them to the pre-established service provider and consumer roles, Viseu aims to facilitate scientific collaboration across the peers natively. Furthermore, by offering services from multiple peers close to the end-users, Viseu also minimizes end-to-end latency and data loss in the service execution at the Internet scale.

In 2020, the information about Corona Virus Disease 2019 (COVID-19) is overwhelming, which is mixed with a lot of rumors. Rumor and truth can change people's believes more than once, depending on who is more credible. Here we use credibility to measure the influence one person has on others. Considering costs, we often hope to find the people with the smallest credibility but can achieve the maximum influence. Therefore, we focus on how to use minimal credibility in a given amount of time to clarify rumors. Given the time , the minimum credibility rumor clarifying problem aims to find a seed set with users such that the total credibility can be minimized when the total number of the users influenced by positive information reaches a given number at time . In this paper, we propose a Longest-Effective-Hops algorithm called LEH to solve this problem that supposes each user can be influenced two or more times. The theoretical analysis proves that our algorithm is universal and effective. Extensive contrast experiments show that our algorithm is more efficient in both time and performance than the state-of-the art methods.

The COVID-19 pandemic dramatically changed the way of living of billions of people in a very short time frame. In this paper, we evaluate the impact on the Internet latency caused by the increased amount of human activities that are carried out on-line. The study focuses on Italy, which experienced significant restrictions imposed by local authorities, but results about Spain, France, Germany, Sweden, and the whole of Europe are also included. The analysis of a large set of measurements shows that the impact on the network can be significant, especially in terms of increased variability of latency. In Italy we observed that the standard deviation of the average additional delay - the additional time with respect to the minimum delay of the paths in the region - during lockdown is times as much as the value before the pandemic. Similarly, in Italy, packet loss is times as much as before the pandemic. The impact is not negligible also for the other countries and for the whole of Europe, but with different levels and distinct patterns.

Web server logs have been extensively used as a source of data on the characteristics of Web traffic and users' navigational patterns. In particular, Web bot detection and online purchase prediction using methods from artificial intelligence (AI) are currently key areas of research. However, in reality, it is hard to obtain logs from actual online stores and there is no common dataset that can be used across different studies. Moreover, there is a lack of studies exploring Web traffic over a longer period of time, due to the unavailability of long-term data from server logs. The need to develop reliable models of Web traffic, Web user navigation, and e-customer behaviour calls for an up-to-date, large-volume e-commerce dataset on Web traffic. Similarly, AI problems require a sufficient amount of solid, real-life data to train and validate new models and methods. Thus, to meet a demand of a publicly available long-term e-commerce dataset, we collected access log data describing the operation of an online store over a six-month period. Using a program written in the C# language, data were aggregated, transformed, and anonymized. As a result, we release this EClog dataset in CSV format, which covers 183 days of HTTP-level e-commerce traffic. The data will be beneficial for research in many areas, including computer science, data science, management, and sociology.

Nowadays the sharing of trade in counterfeit and pirated goods is constantly growing and fake products are found in a large number of industries - particularly pharmaceuticals, food, and medical equipment - that can pose serious health and safety risks. With the intention of avoiding any loss of client confidence and any disclosure of sensitive information, Internet of Things (IoT) solutions are increasingly used to fulfill this need for a reliable and secure infrastructure in medical & pharmaceutical industry. When looking at the technologies used to identify products and packaging, balancing security and hardware limitations is often a difficult task and using cost-effective techniques such as bit-oriented lightweight functions is a challenge. In this study, we first assess the security level of a recently proposed protocol and prove its vulnerabilities, due to a lack of complexity in bit-oriented functions. Then, to address these exposed flaws, a lightweight improved protocol based on Authenticated Encryption (AE) cryptosystems is presented. Security analysis results demonstrate that weaknesses of previous efforts have all been adequately addressed; additionally, the improved protocol has a robust security posture in terms of confidentiality and integrity. Moreover, FPGA and ASIC simulations are carried out using five different AE schemes from CAESAR competition to develop three use-cases, in whose best scenario the proposed tag has 731 LUT and needs 3335 gates for the security module.

Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.

The COVID-19 pandemic led to the adoption of severe measures to counteract the spread of the infection. Social distancing and lockdown measures modified people's habits, while the Internet gained a major role in supporting remote working, e-teaching, online collaboration, gaming, video streaming, etc. All these sudden changes put unprecedented stress on the network. In this paper, we analyze the impact of the lockdown enforcement on the Politecnico di Torino campus network. Right after the school shutdown on the 25 of February, PoliTO deployed its own in-house solution for virtual teaching. Ever since, the university provides about 600 virtual classes daily, serving more than 16 000 students per day. Here, we report a picture of how the pandemic changed PoliTO's network traffic. We first focus on the usage of remote working and collaboration platforms. Given the peculiarity of PoliTO online teaching solution that is hosted in-house, we drill down on the traffic, characterizing both the audience and the network footprint. Overall, we present a snapshot of the abrupt changes seen on campus traffic due to COVID-19, and testify how the Internet has proved robust to successfully cope with challenges while maintaining the university operations.