Abstract-This survey provides a comprehensive review of existing physical carrier sensing enhancements for IEEE 802.11 wireless networks. The original physical carrier sensing mechanism, used by wireless stations to gain access to the medium, is limited. Consequently, IEEE 802.11 networks are vulnerable to the presence of hidden and exposed nodes. Such nodes can significantly decrease system performance by increasing the collision rate and decreasing the channel spatial reuse. The value of the physical carrier sensing threshold is a key factor influencing the presence of hidden and exposed nodes in a wireless network. Several enhancements have been proposed in the literature, which attempt to mitigate the loss in performance caused by the limited carrier sensing. Firstly, the notion of an optimum carrier sensing threshold has been studied, and results indicate that it can be tuned to an optimum value. Building on the positive early results, further work was performed to develop mechanisms that dynamically adjust the threshold according to varying network conditions. This article presents an in-depth survey of the existing literature in the area, detailing the various approaches and their efficacy in addressing the problem of hidden and exposed nodes (and consequently increasing performance). It offers a comparison of the techniques, by evaluating the models, limitations, assumptions, and performance gains.

No abstract

Abstract. Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. However, the access to real microdata is highly restricted and the one that is publicly-available is usually anonymized or aggregated; hence, reducing its value for testing purposes. In this paper, we present a framework (COCOA) for the generation of realistic synthetic microdata that allows to define multi-attribute relationships in order to preserve the functional dependencies of the data. We prove how COCOA is useful to strengthen the testing of anonymization techniques by broadening the number and diversity of the test scenarios. Results also show how COCOA is practical to generate large datasets.

No abstract

Cloud computing is becoming increasingly prevalent; more and more software providers are offering their applications as Software-as-a-Service solutions rather than traditional on-premises installations. In order to ensure the efficacy of the testing phase, it is critical to create a test environment that sufficiently emulates the production environment. Thus, Cloud applications should be tested in the Cloud. Cloud providers offer command-line tools for interacting with their platforms. However, writing custom low-level scripts using the provider's tool can become very complex to maintain and manage when variability (in terms of providers and platforms) is introduced. The contributions in this paper include: the development of a high level Domain Specific Language for the abstract definition of the application deployment process, and resource requirements; and a generation process that transforms these definitions to automatically produce deployment and instantiation scripts for a variety of providers and platforms. These contributions significantly simplify and accelerate the testing process for Cloud applications.