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The devices forming the Internet-of-Things (IoT) networks need to be re-programmed over-the-air, so that new features are added, software bugs or security vulnerabilities are resolved and their applications can be re-purposed. The limitations of IoT devices, such as installation in locations with limited physical access, resource-constraint nature, large scale and high heterogeneity, should be taken into consideration for designing an efficient and reliable pipeline for over-the-air programming (OTAP). In this work, we present a survey of OTAP techniques, which can be applied to IoT networks. We highlight the main challenges and limitations of OTAP for IoT devices and analyse the essential steps of firmware update process, along with different approaches and techniques that implement them. In addition, we discuss schemes that focus on securing the OTAP process. Finally, we present a collection of state-of-the art open-source and commercial platforms that integrate secure and reliable OTAP.

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BGP prefix hijacking is a critical threat to the resilience and security of communications in the Internet. While several mechanisms have been proposed to prevent, detect or mitigate hijacking events, it has not been studied how to accurately quantify the impact of an ongoing hijack. When detecting a hijack, existing methods do not estimate how many networks in the Internet are affected (before and/or after its mitigation).In this paper, we study fundamental and practical aspects of the problem of estimating the impact of an ongoing hijack through network measurements. We derive analytical results for the involved trade-offs and limits, and investigate the performance of different measurement approaches (control/data-plane measurements) and use of public measurement infrastructure. Our findings provide useful insights for the design of accurate hijack impact estimation methodologies. Based on these insights, we design (i) a lightweight and practical estimation methodology that employs ping measurements, and (ii) an estimator that employs public infrastructure measurements and eliminates correlations between them to improve the accuracy. We validate the proposed methodologies and findings against results from hijacking experiments we conduct in the real Internet.

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