Among all the Internet of Things (IoT) applications, the Smart City concept has received significant attention in the last few years. The main motivation behind this interest is attributable to population growth and urbanization trend. Cities need, indeed, to be ready to face new challengese.g., traffic congestion, wast management, etc.-caused by this new amount of citizens. To address those issues, many IoT solutions have already been proposed and many others are still under investigation; anyway, those initiatives are all based on different standards and protocols, while the Smart City concept requires integration among all the stakeholders. In this paper, we present the VITAL architecture, which aims to integrate Internet-Connected Objects (ICOs) among multiple IoT platforms and ecosystems. In particular, we introduce the "ICOs and Services Discovery" module, which makes completely transparent, for users, the exploration of data-sources that are appropriate for his/her business context. This mechanism is at the basis of the Cloud of Things paradigm and a key feature as the platform agnostic property is an essential goal for VITAL. Index Terms-Smart Cities, Internet of Things, Cloud of Things, Discovery.
Abstract. Received Signal Strength Indicator (RSSI) is commonly considered and is very popular for target localization applications, since it does not require extra-circuitry and is always available on current devices. Unfortunately, target localizations based on RSSI are affected with many issues, above all in indoor environments. In this paper, we focus on the pervasive localization of target objects in an unknown environment. In order to accomplish the localization task, we implement an Associative Search Network (ASN) on the robots and we deploy a real test-bed to evaluate the effectiveness of the ASN for target localization. The ASN is based on the computation of weights, to "dictate" the correct direction of movement, closer to the target. Results show that RSSI through an ASN is effective to localize a target, since there is an implicit mechanism of correction, deriving from the learning approach implemented in the ASN.
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