The initial vision of the Internet of Things (IoT) was of a world in which all physical objects are tagged and uniquelly identified by RFID transponders. However, the concept has grown into multiple dimensions, encompassing sensor networks able to provide real-world intelligence and goal-oriented collaboration of distributed smart objects via local networks or global interconnections such as the Internet. Despite significant technological advances, difficulties associated with the evaluation of IoT solutions under realistic conditions, in real world experimental deployments still hamper their maturation and significant roll out. In this article we identify requirements for the next generation of the IoT experimental facilities. While providing a taxonomy, we also survey currently available research testbeds, identify existing gaps and suggest new directions based on experience from recent efforts in this field.

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Smart city represents one of the most promising, prominent and challenging Internet of Things (IoT) applications [1]. In the last few years, indeed, the smart city concept has played an important role in academic and industry fields, with the development and deployment of various middleware platforms and IoT‐based infrastructures. However, this expansion has followed distinct approaches creating, therefore, a fragmented scenario, in which different IoT ecosystems are not able to communicate between them. To fill this gap, there is a need to re‐visit the smart city IoT semantic and to offer a global common approach. To this purpose, this paper browses the semantic annotation of the sensors in Cloud, and innovative services can be implemented and considered by bridging Cloud of Things (CoT) and IoT. Things like semantic will be considered to perform the aggregation of heterogeneous resources by defining the CoT paradigm. We survey the smart city vision, providing information on the main requirements and highlighting the benefits of integrating different IoT ecosystems within Cloud under this new CoT vision. This paper also discusses relevant challenges in this research area. Copyright © 2015 John Wiley & Sons, Ltd.

In the current worldwide ICT scenario, a constantly growing number of ever more powerful devices (smartphones, sensors, household appliances, RFID devices, etc.) join the Internet, significantly impacting the global traffic volume (data sharing, voice, multimedia, etc.) and foreshadowing a world of (more or less) smart devices, or "things" in the Internet of Things (IoT) perspective. Heterogeneous resources can be aggregated and abstracted according to tailored thing-like semantics, thus enabling Things as a Service paradigm, or better a "Cloud of Things". In the Future Internet initiatives, sensor networks will assume even more of a crucial role, especially for making smarter cities. Smarter sensors will be the peripheral elements of a complex future ICT world. However, due to differences in the "appliances" being sensed, smart sensors are very heterogeneous in terms of communication technologies, sensing features and elaboration capabilities. This article intends to contribute to the design of a pervasive infrastructure where new generation services interact with the surrounding environment, thus creating new opportunities for contextualization and geo-awareness. The architecture proposal is based on Sensor Web Enablement standard specifications and makes use of the Contiki Operating System for accomplishing the IoT. Smart cities are assumed as the reference scenario.

In large scale multihop wireless networks, flat architectures are not scalable. In order to overcome this major drawback, clusterization is introduced to support selforganization and to enable hierarchical routing. When dealing with multihop wireless networks the robustness is a main issue due to the dynamicity of such networks. Several algorithms have been designed for the clusterization process. As far as we know, very few studies check the robustness feature of their clusterization protocols. Moreover, when it is the case, the evaluation is driven by simulations and never by a theoretical approach.In this paper, we show that a clusterization algorithm, that seems to present good properties of robustness, is self-stabilizing. We propose several enhancements to reduce the stabilization time and to improve stability. The use of a Directed Acyclic Graph ensures that the selfstabilizing properties always hold regardless of the underlying topology. These extra criterion are tested by simulations.