The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.

Abstract-Smart environments are places where different kind of embedded devices are interconnected in order to provide their occupants intelligent services improving their comfort and convenience. These smart environments are seen to be important for the future urban ecosystems in terms of user friendliness, quality of life, energy efficiency and sustainability. Lately such environments have become economically and technologically feasible due to the advancements in embedded and distributed technologies. Most of the novel infrastructures adopt smart technologies, while old infrastructures need a transition towards smart environments. Even though different technologies and devices are available, there is a need for an appropriate methodology and a system architecture for a smooth and profitable transition towards smart environments. In this paper we present a framework for creating personalized smart environments using wireless sensor networks. This framework, among other services that it provides, is able to identify people and take personalized actions such as control electrical devices based on their preferences and needs. We present, as a proof of concept, a real world deployment where two scenarios are implemented in two office premises.

Abstract-As we are moving towards to the Internet of Things (IoT) era, Wireless Sensor Networks (WSN) in smart buildings delineate the heart of such systems' architecture. WSN systems are mature enough to support the IoT vision and different architectural designs and communication protocols are developed to realize this vision. In this paper, two different WSN architectural approaches for smart building systems are presented. In the first one, IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) deployment is used, which is designed specifically for constrained embedded devices. In the second one, the system is developed without the usage of IP. To evaluate these two approaches we implemented a scenario of a smart building environment on top of them. We analyze and compare them, both from theoretical and practical point of view. Finally, as a proof of concept we evaluated them experimentaly in our testbed and we reported our conclusions.

Abstract-Geographic routing based on virtual coordinates has been studied extensively, especially in environments expensive localization techniques are infeasible. Even though, the construction of virtual coordinate system is theoretically understood, their practical deployment is questionable due to computational requirements. An alternative approach is to use raw range measures from a special set of nodes called "anchors" as virtual coordinates, which only preserve partial geographic knowledge. In this paper we follow a similar approach, but focus on answering the question "what are the minimal geometric primitives required to perform geometric routing?". We take the first step towards answering this question, based on a node centric local geometric view of localized nodes. We define local geometric primitives and show that geographic face routing can be performed with those primitives.