In pervasive computing environments, mobile devices communicate via wireless links without requiring any fixed infrastructure. These devices must be able to discover and share services dynamically. In this paper, we propose a new service discovery middleware specifically designed for this kind of environments. This middleware is composed of a service discovery protocol, Pervasive Discovery Protocol (PDP), and a service description language, Generic Service Description Language (GSDL). PDP is a fully distributed protocol that merges characteristics of both pull and push solutions; it reduces power consumption of the most limited devices. GSDL is an XML based markup language that uses a hierarchical service description designed taking into account the specific requirements of pervasive environments.
The fast growth of the Internet of Things (IoT) has made this technology one of the most promising paradigms of recent years. Wireless Sensor Networks (WSNs) are one of the most important challenges of the Internet of things. These networks are made up of devices with limited processing power, memory, and energy. The constrained nature of WSNs makes it necessary to have specific restricted protocols to work in these environments. In this paper, we present an energy consumption and network traffic study of the main IoT application layer protocols, the Constrained Application Protocol (CoAP), and the version of Message Queue Telemetry Transport (MQTT) for sensor networks (MQTT_SN). The simulations presented evaluate the performance of these protocols with different network configurations.
Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.
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