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Existing Internet protocols assume persistent end-to-end connectivity, which cannot be guaranteed in disruptive and high-latency space environments. To operate over these challenging networks, a store-carry-and-forward communication architecture called Delay/Disruption Tolerant Networking (DTN) has been proposed. This work provides the first examination of the performance and robustness of Contact Graph Routing (CGR) algorithm, the state-of-the-art routing scheme for space-based DTNs. To this end, after a thorough description of CGR, two appealing satellite constellations are proposed and evaluated by means of simulations. Indeed, the DtnSim simulator is introduced as another relevant contribution of this work. Results enabled the authors to identify existing CGR weaknesses and enhancement opportunities.

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During the COVID-19 pandemic, contact tracing apps based on the Bluetooth Low Energy (BLE) technology found in smartphones have been deployed by multiple countries despite BLE’s debatable performance for determining close contacts among users. Current solutions estimate proximity based on a single feature: the mean attenuation of the BLE signal. In this context, a new generation of these apps which better exploits data from the BLE signal and other sensors available on phones can be fostered. Collected data can be used to extract multiple features that feed machine learning models which can potentially improve the accuracy of today’s solutions. In this work, we consider the use of machine learning models to evaluate different feature sets that can be extracted from the received BLE signal, and assess the performance gain as more features are introduced in these models. Since indoor conditions have a strong impact in assessing the risk of being exposed to the SARS-CoV-2, we analyze the environment (indoor or outdoor) role in these models, aiming at understanding the need for apps that could increase proximity accuracy if aware of its environment. Results show that a better accuracy can be obtained in outdoor locations with respect to indoor ones, and that indoor proximity estimation can benefit more from the introduction of more features with respect to the outdoor estimation case. Accuracy can be increased about 10% when multiple features are considered if the device is aware of its environment, reaching a performance of up to 83% in indoor spaces and up to 91% in outdoor ones. These results encourage future contact tracing apps to integrate this awareness not only to better assess the associated risk of a given environment but also to improve the proximity accuracy for detecting close contacts.

a b s t r a c tDelay-Tolerant Networking (DTN) has been proposed for satellite networks with no expectation of continuous or instantaneous end-to-end connectivity, which are known as Delay-Tolerant Satellite Networks (DTSNs). Path computation over large and highly-dynamic yet predictable topologies of such networks requires complex algorithms such as Contact Graph Routing (CGR) to calculate route tables, which can become extremely large and limit forwarding performance if all possible routes are considered. In this work, we discuss these issues in the context of CGR and propose alternatives to the existing route computation scheme: first-ending, first-depleted, one-route , and per-neighbor strategies. Simulation results over realistic DTSN constellation scenarios show that network flow metrics and overall calculation effort can be significantly improved by adopting these novel route table computation strategies. .ar (J.A. Fraire).DTN can be precisely computed in advance based on orbital elements. Also, power-conserving spacecrafts may communicate on infrequent, fixed intervals established by configuration. In any case, forthcoming episodes of communications (a.k.a. contacts ) are typically scheduled weeks or months before they occur and can be imprinted in a contact plan . The resulting contact plan can be either distributed in advance to DTN nodes, or used by a centralized node (i.e., mission control) to execute route determination procedures.A DTN paradigm can indeed be used to forward data on near-Earth satellite networks with sporadic satellite-to-satellite and satellite-to-ground communication opportunities. If so, we define them as Delay-Tolerant Satellite Networks (DTSNs). DTSNs differ from other space DTNs in the size of the topology and the speed at which it changes. In particular, interplanetary networks are rather scarce in terms of spacecrafts as a few rovers on a remote planet plus some orbiters are typically assumed in the literature [7] . While this density of deep-space nodes is unlikely to change in the near future, DTSNs topologies are expected to be promptly based on dozens or even hundreds of satellites [1] . Furthermore, while in interplanetary DTNs the topological changes are dictated by planetary dynamics, communication opportunities in DTSNs typically occur much more frequently between satellites in Low-Earth Orbit (LEO). As a result, the scalability limits of current DTN protocols and algorithms are likely to be met sooner in DTSN than in deep-space applications. Thus, DTSNs become an immediate object of study for evaluating efficient routing strategies which will also, in the long term, be valuable in the interplanetary domain.

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