Small satellites enable many important applications for both economic and scientific purposes. Many of these applications are inherently data-centric and rely on large amounts of high-resolution satellite imagery to be delivered in a timely manner. However, communicating this data to Earth is challenging due to intermittent connectivity, high packet losses, low data rates, and similar issues. Therefore, efficient onboard prioritization and data processing are essential for future satellite missions. Machine learning methods, such as deep neural networks, are very suitable for such prioritization, as they are already used extensively for satellite imagery processing and they can be deployed onboard of satellites. However, updating them to support new classification requirements when the satellite is already in orbit is difficult, as often multiple passes are required to complete model transmission due to the communication challenges. To cope with this issue, we propose a progressive transmission mechanism for model updates, which leverages vector quantization and arithmetic coding. Our mechanism allows to achieve high accuracies even with partially updated models. Evaluation results show that our mechanism significantly outperforms other less optimized transmission schemes.

Small satellites are widely used today as cost effective means to perform Earth observation and other tasks that generate large amounts of high-dimensional data, such as multispectral imagery. These satellites typically operate in low earth orbit, which poses significant challenges for data transmission due to short contact times with ground stations, low bandwidth, and high packet loss probabilities. In this paper, we introduce JSCC-SAT, which applies joint source-and-channel coding using neural networks to provide efficient and robust transmission of compressed image data for satellite applications. We evaluate our mechanism against traditional transmission schemes with separate source and channel coding and demonstrate that it outperforms the existing approaches when applied to Earth observation data of the Sentinel-2 mission.

Small satellites networks are characterized by intermittent connectivity and rare communication opportunities between satellites and ground stations. To overcome this problem, inter-satellite links can be used and predictability of satellite movements can be exploited to plan the communication in advance. Mathematical optimization techniques are typically applied to create such communication plans. However, the resulting optimization problems are often large and therefore problematic to solve. To handle them more efficiently, we propose a novel decomposition approach based on Benders decomposition. To the best of our knowledge, we are the first to apply Benders decomposition in the context of satellite networks. Our evaluation results show that our approach considerably outperforms the state-of-the-art solution.

The protocol evaluation is an integral part of network protocol design. From the perspective of experimental design, discrete event simulations constitute a middle ground between analytical protocol evaluation and testbeds. They allow precise control of otherwise external influences while supporting more detailed protocol models than analytical evaluations. Compared to testbeds, a major restriction is that existing protocols require a separate implementation in the discrete event model. Creating this implementation model may cause differences between the protocol's simulator-based model and the native implementation, invalidating simulation results. We propose a novel architecture to evaluate unmodified, binary protocol implementations in the state-of-the-art discrete event simulators by utilizing the operating system's system call barrier. Notably, our approach does not affect discrete simulation properties, such as repeatability, and it does not require the native protocol implementation's source code. The evaluation results using existing network protocols show the feasibility of our approach in combination with the ns-3 simulator core. We show that our approach more closely resembles realistic protocol performance when compared to simulator-based protocol models. Moreover, our approach performs better than existing solutions for more realistic protocol simulations.

Many privacy-enhancing architectures require an anonymous communication channel as a component to acquire tokens or other material from a centralized authority without revealing the requester's identity. While anonymous routing overlays like Tor can provide the required anonymity for TCPlike data streams, it increases dependence on third party infrastructure, and widespread use may overload the Tor network's current capacity. Therefore, we propose to use requests in the Domain Name System as an anonymization layer by forwarding packets over public DNS resolvers operating in recursive mode. In contrast to existing approaches, we focus on UDP datagram transport. As an example use case, we implemented a system for anonymous issuance of blind signatures. We evaluate our domaintailored solution against existing approaches like Tor and analyze its performance and privacy guarantees. Evaluation results show that the achievable data rate using the DNS-based anonymous channel is sufficient for many practical use cases, and that the provided anonymity is comparable to that provided by the Tor network.

Recent works have shown that the wireless link between two nodes is neither a shared secret nor unforgeable by an adversary-questioning the effectiveness of many physical layer security mechanisms. In this work, we argue that physical layer security is still valuable when multiple nodes combine their measurements of overheard adversarial transmissions. This assumption is applicable to, e.g., industrial wireless networks, where nodes are spatially close and wireless links are unique. First, we design a novel physical layer adversary detection algorithm particularly suited for the scenario of multiple sensing nodes. Second, we evaluate our algorithm with real-world measurements, which show that already four detecting nodes are sufficient to significantly increase the detection performance.

The trend towards smart factories necessitates protocols for wireless transmission of production process information. Protocols must transfer process information timely even with temporary wireless interference on the factory floor and regardless of machines' location in the network topology. TANDEM is a topology-independent wireless multi-hop network protocol that implements in-network prioritization to provide a useful, early approximation of process information. Tailored to often required time series data transmission, TANDEM applies a time-frequency transformation to prioritize more valuable information parts, decouples multi-hop communication steps to improve robustness, and uses a periodic acknowledgment overhearing mechanism to save transmissions. We analytically discuss reliability and fairness aspects and show using simulations that TANDEM's prioritization is beneficial to obtain an early preview of sensor information even in large factories. In addition, we implement TANDEM on industrial grade hardware and provide a performance evaluation.

Bonus point systems are widely used for rewarding customer loyalty in both traditional and electronic commerce scenarios. Merchants aim to additionally increase revenue by targeted advertising using customer data. At the same time, customers wish to protect their privacy of what they purchase. Common systems neither provide sufficient protection of privacy, nor protect the customers' identities. Anonymity makes it significantly harder to properly resolve claims of fraudulent transactions, because identities are not recorded for any purpose after a transaction has been committed. We propose an anonymous bonus system based on digital payment systems using blind cryptographic signatures. We implement a protocol extension that allows proof of bonus point possession in case of reported misbehavior, and we identify the implications on security, privacy, and performance of our proposals. Our results show that we can resolve these cases of fraud within the system without losing functionality in the bonus point protocol.

In recent years, many visions for hitherto considered-futuristic computing applications gained momentum. The vision of smart factories is one example for these trends, which all share a common requirement: the prolific dissemination of sensor information. As wireless communication in smart factories needs to cope with harsh environments, the amount of sensor information produced by sources will likely surpass the communication channel's available capacity. This discrepancy calls for efficient communication and filtering protocols, as well as compression mechanisms, as a foundation for dependable applications. We propose such a compression algorithm that is lossless and tailored towards the requirements of the manufacturing industry. Our algorithm employs a two-step stochastic model that uses lossy compression to extract an approximation from the signal and a separate noise model to accommodate the remaining error. Evaluation results validate that our algorithm achieves better compression rates than existing approaches for several types of real world sensor data from the industry.

While location management is a key component of cellular networks, it is also a major privacy issue: location management empowers the network operator to track users. In today's public and scientific discussion, the centralized storage of location data is mostly taken as a fact, and users are expected to trust the network operator. With ANOTEL we present a novel, clean-slate approach of location management in cellular networks that challenges this assumption. The design is able to route calls to users who move through cellular networks, without violating their location privacy.

Random linear network coding simplifies routing decisions, improves throughput, and increases tolerance against packet loss. A substantial limitation, however, is delay: decoding requires as many independent linear combinations as data blocks. Hierarchical network coding purportedly solves this delay problem. It introduces layers to decode prioritized data blocks early, which may benefit video streaming applications or applications for sensor information collection. While hierarchical network coding reduces decoding delays, it introduces significant space complexity and additional decoding time. We propose a decoding algorithm that manages all prioritization layers in a joint decoder matrix. Analytical evaluation and performance measurements show that we maintain prioritization benefits without increased space complexity and improve decoding performance. With memory requirements independent of the number of layers, our algorithm facilitates more fine-grained prioritization layers to further the benefits of hierarchical network coding.

Vehicular ad-hoc networks are a promising research area. Besides improving safety, traffic efficiency enhancements are a major expected benefit. In this paper, we present a novel security mechanism for traffic efficiency applications that leverages on velocity-based vehicle clustering and uses HyperLogLog estimators to create bandwidth-efficient integrity proofs. Evaluation results show that our mechanism achieves high protection against plausible attacker models, and that it is more bandwidth efficient than a comparably secure security mechanism that does not employ clustering.

Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication aims to increase safety, efficiency, and comfort of drivers. Vehicles periodically broadcast their current status, such as position, velocity, and other information. Received information is stored in a local knowledge base, often called world model, and used for application decisions. Because of the potential impact, V2V communication is an interesting target for malicious attackers. Message integrity protection using cryptographic signatures only protects against outsider attackers. In addition to signatures, misbehavior detection mechanisms comparable to intrusion detection systems (IDS) are needed to detect insider attackers. Given the complexity and large number of foreseen V2V and V2I applications, misbehavior detection cannot be a one-size-fits-all solution. In this paper, we present a flexible framework that can combine a range of different misbehavior detection mechanisms by modeling their outputs using subjective logic. We demonstrate the feasibility of our framework by using a combination of existing detection mechanisms to increase their misbehavior detection results.

In vehicular ad-hoc networks (VANETs), a use case for mobile ad-hoc networks (MANETs), the ultimate goal is to let vehicles communicate using wireless message exchange to provide safety, traffic efficiency, and entertainment applications. Especially traffic efficiency applications benefit from wide-area message dissemination, and aggregation of information is an important tool to reduce bandwidth requirements and enable dissemination in large areas. The core idea is to exchange high quality summaries of the current status rather than forwarding all individual messages. Securing aggregation schemes is important, because they may be used for decisions about traffic management, as well as traffic statistics used in political decisions concerning road safety and availability. The most important challenge for security is that aggregation removes redundancy and the option to directly verify signatures on atomic messages. Existing proposals are limited, because they require roads to be segmented into small fixed-size regions, beyond which aggregation cannot be performed. In this paper, we introduce SeDyA, a scheme that allows more dynamic aggregation compared to existing work, while also providing stronger security guarantees. We evaluate SeDyA against existing proposals to show the benefits in terms of information accuracy, bandwidth usage, and resilience against attacks.

Ubiquitous computing is characterized by the merger of physical and virtual worlds as physical artifacts gain digital sensing, processing, and communication capabilities. Maintaining an appropriate level of privacy in the face of such complex and often highly dynamic systems is challenging. We argue that context awareness not only enables novel UbiComp applications but can also support dynamic regulation and configuration of privacy mechanisms. We propose a higher level context model that abstracts from low level details and contains only privacy relevant context features. Context changes in our model can trigger reconfiguration of privacy mechanisms or facilitate context-specific privacy recommendations to the user. Based on our model, we analyze potential privacy implications of context changes and discuss how these results could inform actual reconfiguration of privacy mechanisms.

Network communication using unprotected air as a medium leads to unique challenges ensuring confidentiality, integrity and availability. While newer amendments of IEEE 802.11 provide acceptable confidentiality and integrity, availability is still questionable despite broad usage of Wi-Fi technologies for tasks where availability is critical. We will present new security weaknesses that we have identified in the 802.11 standard and especially the 802.11h amendment. Our results are underlined by an extensive analysis of attacks addressing the quiet information element and channel switch announcement in management frames. For some stations a complete DoS effect can be achieved with a single packet for more than one minute. This shows that the newly identified attacks are more efficient than earlier approaches like a deauthentication attack. Tests were performed with a large variety of network interface cards, mobile devices, and operating systems.