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  • 1.
    Afifi, Haitham
    et al.
    Hasso Platter Institute, Germany.
    Ramaswamy, Arunselvan
    Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
    Karl, Holger
    Hasso Platter Institute, Germany.
    Reinforcement learning for autonomous vehicle movements in wireless multimedia applications2023Ingår i: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589, Vol. 92, artikel-id 101799Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We develop a Deep Reinforcement Learning (DeepRL)-based, multi-agent algorithm to efficiently control autonomous vehicles that are typically used within the context of Wireless Sensor Networks (WSNs), in order to boost application performance. As an application example, we consider wireless acoustic sensor networks where a group of speakers move inside a room. In a traditional setup, microphones cannot move autonomously and are, e.g., located at fixed positions. We claim that autonomously moving microphones improve the application performance. To control these movements, we compare simple greedy heuristics against a DeepRL solution and show that the latter achieves best application performance. As the range of audio applications is broad and each has its own (subjective) per-formance metric, we replace those application metrics by two immediately observable ones: First, quality of information (QoI), which is used to measure the quality of sensed data (e.g., audio signal strength). Second, quality of service (QoS), which is used to measure the network's performance when forwarding data (e.g., delay). In this context, we propose two multi-agent solutions (where one agent controls one microphone) and show that they perform similarly to a single-agent solution (where one agent controls all microphones and has a global knowledge). Moreover, we show via simulations and theoretical analysis how other parameters such as the number of microphones and their speed impacts performance.

  • 2. Christin, Delphine
    et al.
    Roßkopf, Christian
    Hollick, Matthias
    Martucci, Leonardo
    Telecooperation Lab, Technische Universit¨at Darmstadt, Darmstadt, Germany.
    Kanhere, Salil
    IncogniSense: An Anonymity-preserving Reputation Frameworkfor Participatory Sensing Applications2013Ingår i: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589, Vol. 9, nr 3, s. 353-371Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Reputation systems are fundamental for assessing the quality of user contributions inparticipatory sensing. However, naively associating reputation scores to contributionsallows adversaries to establish links between multiple contributions and thus deanonymizeusers. We present the IncogniSense framework as a panacea to these privacythreats. IncogniSense utilizes periodic pseudonyms generated using blind signatureand relies on reputation transfer between these pseudonyms. Simulations are used toanalyze various reputation cloaking schemes that address the inherent trade-off betweenanonymity protection and loss in reputation. Our threat analysis confirms the robustnessof IncogniSense and a prototype demonstrates that associated overheads are minimal.

  • 3.
    Murmann, Patrick
    et al.
    Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
    Matthias, Beckerle
    Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
    Fischer-Hübner, Simone
    Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
    Reinhardt, Delphine
    University of Göttingen.
    Reconciling the what, when and how of privacy notifications in fitness tracking scenarios2021Ingår i: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589, Vol. 77, artikel-id 101480Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The increasing number of fitness tracking wearables deployed worldwide poses challenges to the privacy of their users, esp. in terms of transparency. Privacy notifications facilitate transparency by providing users with situational awareness about the pro-cessing of their personal data. We present the results of two online surveys including English-speaking (n(Eng) = 154) and German-speaking (n(Ger) = 150) users of fitness track-ing devices from Europe, conducted to elicit determinants of notification settings. We found evidence for the perceived usefulness of privacy notifications, and for concordant predictors in terms of when and how users prefer to be notified about personal data processing in 12 scenarios related to fitness tracking.

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