The massive diffusion of smartphones and the exponential rise of location based services (LBS) have made the problem of localization and navigation inside buildings one of the most important technological challenges of the last years. Indoor positioning systems have a huge market in the retail sector and contextual advertising; moreover, they can be fundamental to increase the quality of life for the citizens. Various approaches have been proposed in scientific literature. Recently, thanks to the high performances of the smartphones’ cameras, marker-less and marked-based computer vision approaches have been investigated. In a previous paper, we proposed a technique for indoor navigation using both Bluetooth Low Energy (BLE) and a 2D visual markers system deployed into the floor. In this paper, we present a qualitative performance evaluation of three 2D visual markers suitable for real-time applications.

Smart environment scenarios are characterised by the presence of users, with different needs and preferences, and everyday life objects exploited to meet the expectations of users themselves. Connecting objects to the internet and making them accessible from remote is not sufficient to make an environment 'smart' since such ecosystems should also be able to enable context-sensitive actions along with a management of the interaction between objects and users. In this work, we propose GOOSE, a platform which aimed at interpreting users goals expressed in natural language in order to generate, select, and safely enforce a set of plans to be executed to fulfill target goals. After highlighting the main challenges affecting typical machine-to-machine (M2M) communication scenarios, we show how the use of a semantic reasoner can be used to allow the composition of plans consisting of sequences of services to be called on the smart environment objects. Finally, we address the issue of secure communications between platform and objects, and the management of potentially inconsistent goals.

Magnetic fingerprint-based indoor localization systems are a promising alternative to GPS in indoor environments by using magnetic fields (MFs) to determine accurate indoor locations. However, the optimal number of MFs required for precise location prediction on the client side (CS), i.e., mobile phone, remains a critical yet unanswered question. Hence, resource-constrained devices face limitations in terms of computational power and energy supply. Therefore, this study aims to optimize prediction error and prediction time at CS with minimal MF samples during the online location prediction phase. This study tackles this challenge by employing efficient clustering techniques and machine learning classifiers to minimize prediction error and prediction time on client devices using multi-object optimization instead of single-objective optimization. Our research findings reveal an optimal sample size of 14 per second that provides an ideal balance, offering precise accuracy while reducing the computational burden and energy consumption, thus contributing to the advancement of efficient indoor positioning systems.