International audience

Modern geolocation ecosystems rely on diverse technical solutions and architectures, often proprietary, making it difficult to develop a global understanding of the privacy implications of location data production. This challenge is particularly critical given the ubiquity of geolocation in modern digital infrastructures and the central role of location data in privacy concerns. Yet, existing work largely focuses on isolated case studies, resulting in a fragmented understanding of the privacy risks associated with location data production. In this work, we introduce an abstract model of geolocation ecosystems together with a systematic methodology for analyzing their privacy implications, which we apply to nine representative case studies spanning a broad range of architectures, from OS-level geolocation services to object-tracking platforms. This comparative analysis identifies structural design choices that significantly impact users' privacy and reveals common structural privacy risks across heterogeneous ecosystems, which reflect architectural design decisions rather than security vulnerabilities or poor system design. These findings, together with gaps identified in existing defense mechanisms, motivate research directions aimed at strengthening privacy in future geolocation architectures.

Location underpins critical Internet services, yet our primary mechanism for Internet localization, IP-based geolocation, fails to meet the needs of all stakeholders. User location is conflated with network location, leading to a fundamental mismatch between the goals of content providers, infrastructure operators, and regulators. As users increasingly adopt privacy-preserving technologies that obscure their network identity, this mismatch becomes more pronounced, making localization even more challenging. This paper argues that the problem cannot be solved by simply improving the accuracy of incumbent mechanisms that are inappropriately applied today to solve multiple, unrelated problems. Instead, we require a new approach for localization on the Internet.

Software-Defined Networking (SDN) is a networking paradigm that decouples the forwarding plane from the control plane. The orchestration of the control plane is a critical challenge in SDN deployment, addressed by the Controller Placement Problem (CPP). At the same time, securing the control plane is another major concern, as controllers are primary targets for attacks. To address this, mechanisms such as consensus protocols are implemented. However, these mechanisms introduce additional costs, such as increased controller response time, which can render them unsuitable for delay-sensitive applications. This work introduces the concept of integrating security constraints into the CPP to analyze the impact of such mechanisms on network performance, particularly in terms of response time. As a case study, a consensus mechanism is examined, and an efficient algorithm is proposed to optimize the problem. The proposed algorithm is compared against a solver-generated solution on the real network topology GEANT. Results demonstrate that the proposed algorithm is time-efficient and with a gap of at most 10% compared to the optimal one. It permits to analyze the trade-off and show that it is possible to implement security mechanisms, such as consensus, at the control plane level and still meet performance constraints for delay-sensitive applications, provided that a certain level of security is accepted.

Monitoring the quality of streaming video applications is important for Internet service providers (ISPs) to detect network issues and facilitate capacity planning. Machine Learning (ML) inference models have emerged as an effective solution to determine service quality using network traffic. However, while much focus has been on enhancing model performance, little attention has been given to deploying these models across entire networks. This paper introduces a new placement approach of quality inference models and their associated tasks to enhance the monitoring of video streaming applications over an entire mobile traffic network. Starting from the observation that inference tasks require the deployment of multiple components to, first, calculate input features from raw traffic, and then execute the inference models, we define the placement problem as an integer programming problem and, given its NP-hardness, we provide a heuristic solution, experimentally close to the optimum, based on the relaxation and the rounding of fractional solutions. We highlight that decoupling these components for the inference of network traffic can be beneficial in terms of total accuracy of the ML inference tasks. Finally, we experimentally show that our solution outperforms state-of-the-art placement techniques by ~30% of accuracy of the deployed inference models.

Target Wake Time (TWT) is a key energy-saving mechanism in IEEE 802.11 WLANs, allowing stations (STAs) to reduce power consumption by restricting transmissions to scheduled service periods (SPs). Despite its potential, TWT remains underutilized due to concerns over performance degradation, especially under congested network conditions. In this paper, we address this limitation by proposing the Power Mode Selection Algorithm (PMSA), a lightweight and standard-compliant mechanism implemented at the Access Point (AP). PMSA operates on top of existing TWT schedulers and dynamically determines whether each STA should operate in power save (PS) mode or temporarily switch to active mode (and disable TWT) based on current network congestion. Our simulation results demonstrate that PMSA enhances the robustness of TWT scheduling by adapting to traffic conditions, maintaining low delays and avoiding packet losses even under heavy load, while preserving energy savings when feasible.

UAV-based wireless networks can be deployed to provide a network coverage to users who have no or poor network connection. Unlike traditional model-based approaches that require predefined assumptions before UAV deployment, reinforcement learning (RL) offers a promising alternative but requires a realistic simulator for training the proposed strategies. None of the existing open-source simulators provide both realistic wireless communications while enabling the training, in a cluttered environment, of multi-UAVs movement strategies using RL. Thus, in this paper, we present a simulator where we focus on improving the modeling of the network access part, i.e., the communications between the UAVs and the users, by integrating signal propagation, physical rate adaptation and medium access sharing models. We evaluate the performance of a standard independent RL algorithm trained in our simulator across various use case scenarios, and compare the results obtained using different learning objectives. Results show that the classical formulation, commonly found in the literature and based on a a simplified wireless network model, underperforms in terms of communication quality.

Software Defined Networking (SDN) is a networking paradigm which proposed to decouple the forwarding and the control planes. Security and safety threat challenges at the control level are divided into the reinforcement of the controller, whatever the reason. This work aims to consider both threats and pave the way for a multi-controller architecture without East-West interface. Considering one nominal controller in charge of the data plane computation, we designed a second one in order to control the consistency of the decisions made by the controller, i.e. only through observing the activity of the command (i.e. the management traffic). Compared to related works, no direct exchanges between the controllers are required. The detection logic is introduced theoretically and it mainly relies on two phases: the learning of the decisions and the verification that each decision taken fits with the data plane estimate. The algorithm, implemented on ONOS, is discussed in a case study.