Accurately predicting whether consent for organ donation will be granted is essential for optimizing timing and resource use in donor management.This study develops and evaluates machine learning models to estimate the likelihood of obtaining consent based on donor and contextual factors.The goal is to support early clinical decision-making by identifying cases where consent is more or less likely.Using real-world data from a regional transplant center operating under an opt-in system, we conduct data preprocessing, feature selection, and model training with various algorithms.Model performance is assessed using standard classification metrics, and key predictors of consent outcomes are identified.Results show accuracy levels exceeding 80%, highlighting the importance of including information about the relatives responsible for the decision.We also find that prediction accuracy varies with donor nationality, being higher for non-Italian donors.These findings demonstrate the value of predictive analytics in improving organ procurement efficiency and reducing unnecessary costs.

In Beyond5G and 6G networks, a common theme is that sensing will play a more significant role than ever before. Over this trend, Integrated Sensing and Communications (ISAC) is focused on unifying the sensing functionalities and the communications ones and to pursue direct tradeoffs between them as well as mutual performance gains. We frame the resource tradeoff between the SAC functionalities within an economic setting. We model a service provision by one operator to the users, the utility of which is derived from both SAC functionalities. The tradeoff between the resources that the operator assigns to the SAC functionalities is analyzed from the point of view of the service prices, quantities and profits. We demonstrate that equilibrium quantities and prices exist. And we provide relevant recommendations for enforcing regulatory limits of both power and bandwidth.

Smart speakers equipped with intelligent voice processing functions, like Siri and Amazon Echo, have become widespread globally. However, their recording and networking capabilities spur concerns about users’ privacy. In this research, we report the results of a text-mining analysis of customers’ perception of privacy in smart speakers. The corpus comprises over 4,500 reviews of the Echo line of smart speakers collected from Amazon. Smart speakers owners appear to be mostly oblivious to privacy issues since the fraction of those mentioning privacy in their reviews is largely below 3%. The average sentiment towards privacy is positive, though the average hides a significant fraction exhibiting a negative sentiment. However, those negative perceptions do not affect the overall sentiment polarity towards the product, which stays positive.

The insurgence of COVID-19 requires fast mass vaccination, hampered by scarce availability and uncertain supply of vaccine doses and a tight schedule for boosters.In this paper, we analyze planning strategies for the vaccination campaign to vaccinate as many people as possible while meeting the booster schedule.We compare a conservative strategy and q-days-ahead strategies against the clairvoyant strategy.The conservative strategy achieves the best trade-off between utilization and compliance with the booster schedule.Q-days-ahead strategies with q < 7 provide a larger utilization but run out of stock in over 30% of days.

Cryptography key management system plays a very central role in the cloud data security. Nonetheless, a great part of the current commercial solutions rely on cloud providers that hold both the encrypted data and the related private master key of their served customers in their secure key vaults, having a de-facto total control on their customer digital assets. Conversely, entrusting customer companies for key holding can be dangerous as witnessed by many cases of key loss or theft. In this work we present CoProtect, a novel architecture to protect the cryptography keys in cloud systems that leverage on the cooperation between the cloud provider and the customer company. With such trust model, we present the proposed data management strategy, the key generation and the crypto procedures, and a proof of concept.

Cloud availability is a major performance parameter in cloud Service Level Agreements (SLA).Its correct evaluation is essential to SLA enforcement and possible litigation issues.Current methods fail to correctly identify the fault location, since they include the network contribution.We propose a procedure to identify the failures actually due to the cloud itself and provide a correct cloud availability measure.The procedure employs tools that are freely available, i.e. traceroute and whois, and arrives at the availability measure by first identifying the boundaries of the cloud.We evaluate our procedure by testing it on three major cloud providers: Google Cloud, Amazon AWS, and Rackspace.The results show that the procedure arrives at a correct identification in 95% of cases.The cloud availability obtained in the test after correct identification lies between 3 and 4 nines for the three platforms under test.

A brokering platform distributing the data collected by a set of Wireless Sensor Networks (WSNs) to end customers is considered, where WSNs receive a payment from the broker proportional to the amount of data they provide and pay the network provider a flat fee, while end customers pay a flat fee to access those data. In the case where the broker can set both prices, the optimal pricing strategy, maximizing the broker's profit, is analysed as to its outcomes for all the stakeholders. The attractiveness for end customers is evaluated through the overall amount of data on offer and the service penetration rate (which is quite low for a typical context). The service penetration rate among WSNs (which is instead quite high), as well as their profit, are considered as the variables measuring the platform's success among WSNs. Additionally, the network fee is absorbed by the broker, with a negligible impact on the broker's profit and even a positive impact on the average WSN's profit. Finally, the overall sensing data rate and its impact on the platform's profit are also assessed.

In order that the Internet of Things (IoT) realize its full potential, it has been claimed for the IoT industry to follow the smartphone recipe of market-creating innovation. Indeed, the main mobile apps platforms derive a significant part of their brand value and pricing power from their reputation to have most innovative apps coming to them. Having witnessed that the same steady stream of developer-driven innovation is already emerging in IoT, demand for IoT-based services will be driven by this growing innovative community of IoT developers. In order to make it possible, main players should focus on building platforms that support third party applications instead of developing applications for their own devices. As a way to materialize the above idea, we propose a business model for IoT-based services where a platform creates a multisided market. The business model comprises a platform that serves as an intermediary between human users, app developers and Wireless Sensor Networks, so that the users use the apps, and the apps process the data supplied by the sensor networks. The platform, acting as a monopolist, posts a fee for each of the three sides so as to maximize its profit. We conduct an analysis of the profit maximization problem faced by the platform. We show that the relative strength of the value that advertisers attach to the users determines the platform price structure. All in all, a high valuation is beneficial for all agents (users, platform, developers, and WSNs).

The advent of the Internet of Things (IoT) is expected to bring major benefits to a wide range of areas. However, the successful deployment of the IoT calls for the existence of sustainable and well-understood business models. In this paper, we propose and analyze a business model for a likely scenario in the IoT, which is made up of WSNs, service providers and users. The service providers compete against each other in the intermediation between the virtualized WSNs and the users that benefit from enhanced services built on the sensed data. The service providers pay to the WSNs for the data and charge the users for the service. The model is analyzed by applying oligopoly theory and game theory, the conditions for the existence and uniqueness of the Nash equilibrium are established, and the equilibrium and the social optimum are obtained. Our results show that the business model is sustainable, provided that the users' sensitivity to the value-to-price ratio is not negligible and, in this situation, the number of active service providers is upper bounded by a value that depends on the sensitivity and the market size. Furthermore, the operation of such a market is shown to efficiently use the information provided by the WSNs, and, when compared to the social optimum, to produce an increase in users' and service providers' surpluses, but a reduction in WSNs' surplus.

A brokering platform is considered, acting as an intermediary to deliver the data collected by a set of wireless sensor networks as a bundle to end customers. The wireless sensor networks provide data at sensing rates following a Zipf law and are rewarded by a linear pricing scheme, while end customers pay a flat fee to access the whole bundle of data. The optimal pricing policy to be adopted by the broker is derived. Even sensor networks with a very small sensing rate find it profitable to join the platform, unlike the case of uniform distribution of sensing rates. The optimal price paid to WSNs grows as the sensing rate grows, acting as an incentive to deploy better technology. Instead, the penetration rate among end customers is equal to the case of uniform distribution, being quite low unless a pronounced sensitivity for prices forces the broker to lower prices so as to make the service attractive to end customers.

Cloud storage has fast become a widespread alternative to in-house costly storage infrastructures. However, the migration to cloud storage is not necessarily everybody’s best choice and should be evaluated in a rigorous quantitative way against the alternative over a long time horizon. We propose a methodological approach for the comparison of cloud vs in-house solutions, based on the use of the Net Present Value and employing stochastic models for storage prices and memory needs. We analyse two decision criteria, which employ the median and the mean value of the Differential Net Present Value (DNPV), respectively. Through three appropriate risk measures, we show that the mean DNPV is the less risky decision criterion. Since the DNPV is a stochastic quantity, we also consider a protection measure against the risk of taking the wrong decision, which relies on underwriting an insurance policy. Through the real options approach, we propose a pricing formula for such policy, showing that it is an affordable means to hedge against risk for smaller companies and over a limited time horizon. Both the decision criteria and the insurance pricing formula are applied in a typical scenario.

The search neutrality debate questions the ranking methods of search engines. We analyze the issue when content providers offer content for free, but get revenues from advertising. We investigate the noncooperative game among competing content providers under different ranking policies. When the search engine is not involved with high-quality content providers, it should adopt neutral ranking, also maximizing user quality-of-experience. If the search engine controls high-quality content, favoring its ranking and adding advertisement yield a larger revenue. Though user perceived quality may not be impaired, the advertising revenues of the other content providers drastically decrease.

We consider three approaches to the modeling of systems with repairable components by a multivariate stochastic on-off process. First, we discuss the Palm calculus framework for stationary processes and its power in the derivation of general formulae for joint downtime statistics in the case of statistically independent components. Second, a class of Generalized Semi-Markov (GSMP) models is proposed for incorporating both arbitrary component downtime distributions and statistical dependence of component failures. The case of two components is studied in detail. Third, we define the property referred to as weakened-by-failures for a system of repairable components, and prove that it implies association under fairly general conditions. We also give sufficient conditions for our GSMP models to possess this property.

Content providers are often accused of free riding by exploiting the network to distribute their content without sharing their revenues with the network providers. In order to assess the correctness of such an accusation, we set up a game-theoretical model, where content providers releasing their contents for free and under the payment of a usage-based charge are both present. In this model, the network provider charges both content providers a usage-based charge (network charge) as well. The network provider and the paid content provider act as the players, using the respective retail prices as strategic leverages. Both the cases where network charges are set by the network provider and by a regulatory authority are examined. The Nash equilibrium is determined in a closed form. In a typical scenario, the solution represented by zero network charges maximizes both the network provider's revenues and the social welfare: free riding for content providers appears as the best choice under both the viewpoints of the selfish network provider and the regulatory authority.

Network clustering is traditionally accomplished by relying just on the topology information, while a traffic-aware clustering approach has been recently proposed. The latter approach employs traffic matrices to take into account the intensity of the relationship between nodes. The extra effort needed to gather the traffic matrices is warranted if the composition of the clusters obtained in the traffic-based approach is significantly different from that obtained under the topology-based approach. In this paper we compare the outcomes of the two approaches, using the Rand Index as a similarity metric. For a variety of established clustering algorithms, and two large datasets, we show that the two approaches provide significantly different results, since the Rand Index lies far below one.

While network clustering is traditionally accomplished just relying on the topology of the network, the new traffic-aware clustering approach employs traffic matrices to take into account the intensity of the relationship between nodes. In the context of traffic-aware clustering we propose a new Evolutionary Clustering algorithm and compare it with the Spectral Filtering algorithm. We compare them using both the Modularity and the Traffic-aware Scaled Coverage metrics, and two real-world datasets, each made of 1000 traffic matrices, respectively from Abilene and Géant networks. Our experiments show that Evolutionary Clustering performs better on all traffic matrices, excepting a minor number of traffic matrices in the Abilene network when the Modularity metric is employed.

Network clustering is traditionally approached just relying on the topology of the network, and neglecting the information on the traffic intensity between the nodes. In this paper we propose traffic-aware clustering, whereby networks are clustered on the basis of their traffic matrices. We redefine two clustering metrics for the context of traffic matrices, and perform an exploratory analysis by comparing four well known algorithms against two real-world datasets, each made of 1000 traffic matrices, respectively from Abilene and Géant networks. The Spectral Filtering algorithm appears as the best performer. However, in the Géant network dataset the two metrics provide different rankings for the algorithms under examination, and Newman's algorithm can perform marginally better under one of the two metrics.

A call center is a system used by companies to provide a number of services to customers, which may vary from providing simple information to gathering and dealing with complaints or more complex transactions. The design of this kind of system is an important task, since the trend is that companies and institutions choose call centers as the primary option for customer relationship management. This paper presents an evolutionary algorithm based on Dandelion encoding to obtain near-optimal service trees which represent the structure of the desired call center. We introduce several modifications to the original Dandelion encoding in order to adapt it to the specific problem of service tree design. Two search space size reduction procedures improve the performance of the algorithm. Systematic experiments have been tackled in order to show the performance of our approach: first, we tackle different synthetic instances, where we discuss and analyze several aspects of the proposed evolutionary algorithm, and second, we tackle a real application, the design of the call center of an Italian telecommunications company. In all the experiments carried out we compare our approach with a lower bound for the problem based on information theory, and also with the results of a Huffman algorithm we have used for reference.

In the telecommunication world, competition among providers to attract and keep customers is fierce. On the other hand customers churn between providers due to better prices, better reputation or better services. We propose in this paper to study the price war between two providers in the case where users' decisions are modeled by a Markov chain, with price-dependent transition rates. Each provider is assumed to look for a maximized revenue, which depends on the strategy of the competitor. Therefore, using the framework of non-cooperative game theory, we show how the price war can be analyzed and show the influence of various parameters.

Migration processes of customers between alternative providers are becoming more and more relevant. Providers competing for migrating customers may adopt a delaying strategy to retain customers who are willing to leave, facing regulatory sanctions for that unfair behaviour. The contribution of this paper is to propose a game-theoretic model to describe the resulting competition among providers. For that model, both stable and unstable Nash equilibria are shown to exist and the providers' equilibrium strategies can be derived, in general numerically. In the stable equilibrium case the delaying strategy predicted by the model introduces a mean delay that is a strongly nonlinear (decaying) function of the sanction value.

Call centers represent now an important channel for customer care activities. Most services provided by call centers are now accomplished in automated way to save operational costs. The customer is guided through a menu of options, which can be represented as a service tree, down to the desired service. In order to meet QoS constraints the service tree must be designed to minimize the average service time. An optimization algorithm, based on the combined use of Dandelion encoding and evolutionary operators, is proposed here to design the service trees with minimum average service time. The proposed algorithm provides trees whose average service time is lower than that obtained by the reference Huffman coding and is typically within 2% of the absolute lower bound represented by the entropic bound.