Defeating jamming with the power of silence: a game-theoretic analysis

The timing channel is a logical communication channel in which information is encoded in the timing between events. Recently, the use of the timing channel has been proposed as a countermeasure to reactive jamming attacks performed by an energy-constrained malicious node. In fact, whilst a jammer is able to disrupt the information contained in the attacked packets, timing information cannot be jammed and, therefore, timing channels can be exploited to deliver information to the receiver even on a jammed channel. Since the nodes under attack and the jammer have conflicting interests, their interactions can be modeled by means of game theory. Accordingly, in this paper a game-theoretic model of the interactions between nodes exploiting the timing channel to achieve resilience to jamming attacks and a jammer is derived and analyzed. More specifically, the Nash equilibrium is studied in the terms of existence, uniqueness, and convergence under best response dynamics. Furthermore, the case in which the communication nodes set their strategy and the jammer reacts accordingly is modeled and analyzed as a Stackelberg game, by considering both perfect and imperfect knowledge of the jammer's utility function. Extensive numerical results are presented, showing the impact of network parameters on the system performance.Comment: Anti-jamming, Timing Channel, Game-Theoretic Models, Nash Equilibriu

Social-Aware Stateless Forwarding in Pocket Switched Networks

Several social-aware routing protocols for pocket switched networks have been recently introduced in the literature. The main idea underlying these protocols is to exploit state information (e.g., history of past encounters) to deduce information on the social structure of the network, and to optimize routing based on this information. While social-aware routing protocols have been shown to have superior performance to social-oblivious, stateless routing protocols such as, e.g., BinarySW, the improvement comes at the cost of considerable storage overhead required on the nodes, which is instead not required for stateless approaches. So, whether the benefits of social-aware routing protocols would still be present when storage capacity at the nodes is constrained is not clear. In this paper we present SANE, the first forwarding mechanism that combines the advantages of both social-aware and stateless approaches. SANE is based on the observation-that we validate on real-world traces-that individuals with similar interests tend to meet more often. In our approach, individuals (network members) are characterized by their interest profile, a compact representation of their interests. By implementing a simple interest profile similarity based forwarding rule, SANE is free of network state information, thus overcoming the storage capacity problem with existing social-aware approaches. Through extensive experiments, we show the superiority of social-aware, stateless forwarding over existing stateful, social-aware and stateless, social-oblivious routing approaches. An important byproduct of our interest-based approach is that it easily enables innovative routing primitives, such as interest-casting. An interest-casting protocol is also introduced in this paper, and extensively evaluated through experiments based on both real-world and synthetic mobility traces

Monitoring Bicycle Safety through GPS data and Deep Learning Anomaly Detection

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE

Analysis of Location Privacy/Energy Eciency Tradeos in Wireless Sensor Networks

Abstract. In this paper an analytical framework is proposed for the evaluation of the tradeos between location privacy and energy eciency in wireless sensor networks. We assume that random routing is utilized to improve privacy. However, this involves an increase in the average path length and thus an increase in energy consumption. The privacy loss is measured using information theory concepts; indeed, it is calculated as the dierence between the uncertainties on the target location before and after the attack. To evaluate both privacy loss and average energy consumption the behavior of the routing protocol is modeled through a Markov chain in which states represent the nodes traversed by a packet in its way to the sink. The analytical framework can be used by designers to evaluate the most appropriate setting of the random routing parameters depending on the privacy and/or energy eciency requirements

Willage: A Two-Tiered Peer-to-Peer Resource Sharing Platform for Wireless Mesh Community Networks

The success of experiences such as Seattle and Houston Wireless has attracted the attention on the so called wireless mesh community networks. These are wireless multihop networks spontaneously deployed by users willing to share communication resources. Due to the community spirit characterizing such networks, it is likely that users will be willing to share other resources besides communication resources, such as data, images, music, movies, disk quotas for distributed backup, and so on. In other words, it is expected that peer-to-peer applications will be deployed in such type of networks. In this paper we propose Willage, a platform for resource localization in wireless mesh community networks with mobile users. The platform is based on a two-tiered architecture: resources are made available at the lower tier, which is composed of mobile terminals, whereas information on their localization is managed at the upper layer, which is composed of wireless mesh routers. We also introduce Georoy, an algorithm for the efficient retrieval of the information on resource localization based on the Viceroy algorithm. Simulation results show that Willage achieves its goal of enabling efficient and scalable peer-to-peer resource sharing in wireless mesh community networks

Air temperature-related human health outcomes: Current impact and estimations of future risks in Central Italy

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Management of Acute Kidney Injury and Extracorporeal Blood Purification Therapies During the COVID-19 Pandemic: The Italian SIN-SIAARTI Joint Survey (and Recommendations for Clinical Practice)

Background and aim: The novel coronavirus disease 2019 remains challenging. A large number of hospitalized patients are at a high risk of developing AKI. For this reason, we conducted a nationwide survey to assess the incidence and management of AKI in critically ill patients affected by the SARS-CoV-2 infection. Methods: This is a multicenter, observational, nationwide online survey, involving the Italian Society of Nephrology and the critical care units in Italy, developed in partnership between the scientific societies such as SIN and SIAARTI. Invitations to participate were distributed through emails and social networks. Data were collected for a period of 1 week during the COVID-19 pandemic. Results: A total of 141 responses were collected in the SIN-SIAARTI survey: 54.6% from intensivists and 44.6% from nephrologists. About 19,000 cases of COVID-19 infection have been recorded in hospitalized patients; among these cases, 7.3% had a confirmed acute kidney injury (AKI), of which 82.2% were managed in ICUs. Only 43% of clinicians routinely used the international KDIGO criteria. Renal replacement therapy (RRT) was performed in 628 patients with continuous techniques used most frequently, and oliguria was the most common indication (74.05%). Early initiation was preferred, and RRT was contraindicated in the case of therapeutic withdrawal or in the presence of severe comorbidities or hemodynamic instability. Regional anticoagulation with citrate was the most common choice. About 41.04% of the interviewed physicians never used extracorporeal blood purification therapies (EBPTs) for inflammatory cytokine or endotoxin removal. Moreover, 4.33% of interviewed clinicians used these techniques only in the presence of AKI, whereas 24.63% adopted them even in the absence of AKI. Nephrologists made more use of EBPT, especially in the presence of AKI. HVHF was never used in 58.54% of respondents, but HCO membranes and adsorbents were used in more than 50% of cases. Conclusion: This joint SIN-SIAARTI survey at the Italian Society of Nephrology and the critical care units in Italy showed that, during the COVID-19 pandemic, there was an underestimation of AKI based on the "non-use" of common diagnostic criteria, especially by intensivists. Similarly, the use of specific types of RRT and, in particular, blood purification therapies for immune modulation and organ support strongly differed between centers, suggesting the need for the development of standardized clinical guidelines

Costs of clinical trials with anticancer biological agents in an Oncologic Italian Cancer Center using the activity-based costing methodology

The aim of the present study was to assess the estimated "per patient" total cost for a single Oncologic Italian Cancer Center participating in a multicenter clinical trial with new anticancer biological agents using the activity-based costing (ABC) methodology

New national and regional Annex I Habitat records: from #60 to #82

New Italian data on the distribution of the Annex I Habitats are reported in this contribution. Specifically, 8 new occurrences in Natura 2000 sites are presented and 49 new cells are added in the EEA 10 km × 10 km reference grid. The new data refer to the Italian administrative regions of Campania, Calabria, Marche, Piedmont, Sardinia, Sicily, Tuscany and Umbria. Relevés and figures are provided as Supplementary material respectively 1 and 2