Detecting and controlling the diffusion of infectious diseases such as COVID-19 is crucial to managing epidemics. One common measure taken to contain or reduce diffusion is to detect infected individuals and trace their prior contacts so as to then selectively isolate any individuals likely to have been infected. These prior contacts can be traced using mobile devices such as smartphones or smartwatches, which can continuously collect the location and contacts of their owners by using their embedded localisation and communications technologies, such as GPS, Cellular networks, Wi-Fi, and Bluetooth. This paper evaluates the effectiveness of these technologies and determines the impact of contact tracing precision on the spread and control of infectious diseases. To this end, we have created an epidemic model that we used to evaluate the efficiency and cost (number of people quarantined) of the measures to be taken, depending on the smartphone contact tracing technologies used. Our results show that in order to be effective for the COVID-19 disease, the contact tracing technology must be precise, contacts must be traced quickly, and a significant percentage of the population must use the smartphone contact tracing application. These strict requirements make smartphone-based contact tracing rather ineffective at containing the spread of the infection during the first outbreak of the virus. However, considering a second wave, where a portion of the population will have gained immunity, or in combination with some other more lenient measures, smartphone-based contact tracing could be extremely useful.

Abstract-Simulation is one of the most powerful tools we have for evaluating the performance of Opportunistic Networks. In this survey, we focus on available tools and models, compare their performance and precision and experimentally show the scalability of different simulators. We also perform a gap analysis of state-of-the-art Opportunistic Network simulations and sketch out possible further development and lines of research.This survey is targeted at students starting work and research in this area while also serving as a valuable source of information for experienced researchers.

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One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective.

Abstract-Mobile ad-hoc networks (MANETs) are composed of mobile nodes connected by wireless links without using any pre-existent infrastructure. MANET nodes rely on network cooperation schemes to properly work, forwarding traffic unrelated to its own use. However, in the real world, most nodes may have a selfish behavior, being unwilling to forward packets for others in order to save resources. Therefore, detecting these nodes is essential for network performance.Watchdogs are used to detect selfish nodes in computer networks. A way to reduce the detection time and to improve the accuracy of watchdogs is the collaborative approach. This paper proposes a collaborative watchdog based on contact dissemination of the detected selfish nodes. Then, we introduce an analytical model to evaluate the detection time and the cost of this collaborative approach. Numerical results show that our collaborative watchdog can dramatically reduce the overall detection time with a reduced overhead.

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Mobile crowdsensing (MCS) is a technique where people with computing and sensing devices such as smartphones collectively share data that are of potential interest to the rest of society. MCS includes two different trends (i) mobile sensing, which shares raw data generated from the sensors that are embedded in mobile devices, and (ii) social sensing, which uses the information shared by people in online social networks (OSNs). In this study, the authors present the timeline evolution of the COVID-19 pandemic in Spain, and summarise the MCS research efforts that are being undertaken by the Spanish community to address COVID-19 outbreak. Indeed, the COVID-19 pandemic is putting today's society at risk; lockdown and social distancing measures proposed by governments are dramatically affecting economies. In this regard, MCS tools can become a powerful solution to provide smart quarantine strategies in periods of a steep decrease of infections, or new outbreaks.

One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.