<p><strong>Abstract.</strong> According to the advances in Information &amp; Communication Technology (ICT), nowadays, the use of Internet of Things (IoT) has become a normal part of daily life. It allows interconnections among a wide variety of devices and sensors such as smartphones, smartwatches, automobiles, or any object with a built-in sensor. However, these devices and sensors are developed by numerous different manufacturers which leads to technology lock-in in terms of data formats and protocols. In order of address this heterogeneity, an interoperable sensor protocol is the need of the hour. To address this, we propose a sensor data management system for monitoring <i>pedelec</i> usage and user fitness level. Using a proof-of-concept prototype the study is carried out in downtown of Stuttgart city. The result of the integrated analyzed data is visualized in 3D digital globe CESIUM.</p>

Abstract. With rising population, cities face new challenges. A key challenge for city administrators is to address the overall well-being of its citizens. This includes both physical and emotional health. Towards this objective, cities around the world are heavily investing in green mobility with support for sustainable modes (e.g. public transport, cycling, walking) as an alternative to individual motorized transport using combustion engine. However, very little attention is paid towards identifying the effect of green mobility on the emotional states of citizens. Several studies show a link between an upbeat emotional state and physical signs of good health. Furthermore, as urban centres expand it is imperative to find a balanced combination of physical and emotional health during last mile urban commute. In this paper, we try to find a feasible method for urban emotion detection in the age of last mile green mobility. Our approach relies on Machine Learning (ML) techniques to predict emotions with real-time data.

Abstract. Over the last few years, social networks, mobile devices and personalized services have been heavily responsible for a substantial increase in remote services available over Internet. Consequently, service consumers have to discover remote services anytime, anywhere across networks boundaries making thus service discovery, and their underlying Service Discovery Protocols (SDPs) more important than ever.In this paper, we introduce ZigZag, a middleware to reuse and extend current SDP, designed for local networks, to discover available services across network boundaries as required in Future Internet. Our approach is based on protocol translation to enable service discovery irrespective of their underlying SDP. Further, we provide a thorough evaluation to validate our approach.

Abstract. Emotions are one of the manner humans use to indicate how they feel about a particular event, place or things. To date there is no consensus about the correlation of measured data to an unambiguously defined emotional state. The selection of parameters, their weight and range, which derive at an emotion, are not clearly defined. Especially, if measurements took place outdoors and during a physical activity. This work is based on previous work and focuses on the parameters and methods to classify measured data to an emotional state. We took a closer look to the values, defined ranges for parameters and performed further pre-processing steps. Furthermore, we revised the assignment of an emotion, analyzed the parameter weights and their correlation. Moreover, we compared our previous approach with further Machine Learning (ML) methods. The results are in line with previous work, however, indicate the need for more and heterogeneous data to endorse the outcome. Further results from the parameter analysis suggest an importance of the skin conductance level (SCL) depending on the method used.