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.

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.

Location-based services (LBSs) flood mobile phones nowadays, but their use poses an evident privacy risk. The locations accompanying the LBS queries can be exploited by the LBS provider to build the user profile of visited locations, which might disclose sensitive data, such as work or home locations. The classic concept of entropy is widely used to evaluate privacy in these scenarios, where the information is represented as a sequence of independent samples of categorized data. However, since the LBS queries might be sent very frequently, location profiles can be improved by adding temporal dependencies, thus becoming mobility profiles, where location samples are not independent anymore and might disclose the user’s mobility patterns. Since the time dimension is factored in, the classic entropy concept falls short of evaluating the real privacy level, which depends also on the time component. Therefore, we propose to extend the entropy-based privacy metric to the use of the entropy rate to evaluate mobility profiles. Then, two perturbative mechanisms are considered to preserve locations and mobility profiles under gradual utility constraints. We further use the proposed privacy metric and compare it to classic ones to evaluate both synthetic and real mobility profiles when the perturbative methods proposed are applied. The results prove the usefulness of the proposed metric for mobility profiles and the need for tailoring the perturbative methods to the features of mobility profiles in order to improve privacy without completely loosing utility.