Addressing Future Internet Research Challenges for Smart Cities
The Future Internet will integrate large-scale systems constructed from the composition of thousands of distributed services, while interacting directly with the physical world via sensors and actuators, which compose the Internet of Things. This Future Internet will enable the realization of the Smart Cities vision, in which the urban infrastructure will be used to its fullest extent to offer a better quality of life for its citizens. Key to the ef?cient and effective realization of Smart Cities is the scienti?c and technological research covering the multiple layers that make up the Internet. This paper discusses the research challenges and initiatives related to Future Internet and Smart Cities in the scope of the InterSCity project. The challenges and initiatives are organized in three fronts: (1) Networking and High-Performance Distributed Computing; (2) Software Engineering for the Future Internet; and (3) Analysis and Mathematical Modeling for the Future Internet and Smart Cities. InterSCity aims at developing an integrated open-source platform containing all the major building blocks for the development of robust, integrated, sophisticated applications for the smart cities of the future. Index Terms—Smart Cities, Internet of Things, Future Internet, Big Data, Machine Learning, Mathematical Modeling.




Towards Real-time Semantic Reasoning for the Internet of Things

As distributed IoT applications become larger and more complex, the pure processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new pieces of knowledge. And since many IoT applications require almost real-time reactivity to stimulus of the environment such information inference process has to de performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time reasoning based on theconceptsofSemanticStreamandFactStream”,asa natural extensions of Complex Event Processing(CEP) and RDF (graph-based knowledge model). The main advantages of our approach are that: (a) it considers time as a key relation between pieces of information, (b) the processing of streams can be implemented using CEP and that (c) it is general enough to be applied to any Data Steam Management System (DSMS).