Recently, the positioning techniques based on the IEEE 802.11 signal strength are becoming the dominant solutions in the mobile device localization within indoor scenarios. Such solutions are characterized by two main pitfalls that compromise their effective usage in real application environments. First, during the calibration, a large amount of manual effort is required for acquiring a massive collection of training samples. Second, the positioning accuracy is directly related to the deployment of the wireless access points into the workspace, which is extremely time-consuming and requires human intervention. This paper presents an approach to reduce the manual calibration and to optimize the positioning accuracy, by selecting the best deployment schema of the wireless access points. The approach has been implemented in a tool, which uses an analytical signal propagation model to build the radio map of a given workspace, and exploits a multi-objective genetic algorithm to identify the best access points placement pattern that fits the required accuracy. A detailed experimental campaign is presented in order to show the benefits achievable by the proposed approach.

The next generation of critical systems requires an efficient, scalable and robust data dissemination infrastructure. Middleware solutions compliant with the novel OMG standard, called Data Distribution Service (DDS), are being traditionally used for architecting large-scale systems, because they well meet the requirements of scalability, seamless decoupling and fault tolerance. Due to such features, industrial practitioners are enforcing the adoption of such middleware solutions also within the context of critical systems. However, these systems pose serious dependability requirements, which in turn demand DDS compliant products also to realize reliable data dissemination in different and heterogeneous contexts. Hence, assessing the supported reliability degree and proposing improvement strategies becomes crucial and requires a clear understanding of DDS compliant middleware failing behavior. This paper illustrates an innovative tool to automatically evaluate the robustness of DDS-compliant middleware based on a fault injection technique. Specifically, experiments have been conducted on an actual implementation of the DDS standard, by means of injecting a set of proper invalid inputs through its API and analyzing the achieved outcomes.