1,721,152 research outputs found
Hybrid approach to path planning in autonomous agents
No full textThis paper focuses on the integration of symbolic and sub-symbolic knowledge for the execution of path-planning tasks in autonomous agents. Environmental knowledge is represented through a multilayered architecture whose different abstraction levels are identified by means of meta-knowledge for classification and clustering of distinctive places. The path-planning problem we consider consists in determining the cheapest path for visiting a set of resources in the environment, each resource being expressed as either a cluster or a category of clusters at any abstraction level. Time windows and precedence constraints between resources are taken into account. The algorithm we propose finds a sub-optimal solution to this problem by decomposing it at the different abstraction levels through a divide-et-impera technique
Bloom filter variants for multiple sets: a comparative assessment
Full text linkIn this paper we compare two probabilistic data structures for association queries derived from the well-known Bloom filter: the shifting Bloom filter (ShBF), and the spatial Bloom filter (SBF). With respect to the original data structure, both variants add the ability to store multiple subsets in the same filter, using different strategies. We analyse the performance of the two data structures with respect to false positive probability, and the inter-set error probability (the probability for an element in the set of being recognised as belonging to the wrong subset). As part of our analysis, we extended the functionality of the shifting Bloom filter, optimising the filter for any non-trivial number of subsets. We propose a new generalised ShBF definition with applications outside of our specific domain, and present new probability formulas. Results of the comparison show that the ShBF provides better space efficiency, but at a significantly higher computational cost than the SBF
Privacy preservation in outsourced mobility traces through compact data structures
No full textIndoor localization is widely used as enabling technology for location-based services, such as advertising, indoor routing, and behavioral analysis. To keep these features available, service providers passively collect a large amount of data that may reveal strictly personal information about an individual. As an example, a timestamped mobility trace acquired in a mall may help the business owner to rearrange the user surroundings relying on a punctual analysis of the user behavior. In this paper we discuss some information processing techniques relying on probabilistic data structures designed to mitigate the user’s privacy leakage. The work is also accompanied by a case study. Our experiments were carried out using well-known networking equipment, Cisco Meraki, which is provided in combination with several primitives designed to passively infer and collect the user position in an indoor environment
Benchmarking Cloud Providers on Serverless IoT Back-End Infrastructures
No full textIoT is one of the trending topics in the technological revolution of the last decade. The huge amount of sensors composing IoT systems implies the need for powerful back-end infrastructures that find a perfect habitat in cloud services. Nowadays many players offer cloud services and it is thus essential for the user to consciously learn which one mostly fits his needs. Among cloud providers, three conquered a leader position in the sector: Amazon Web Sevices, Google Cloud Platform, and Microsoft Azure. In this paper, we thoroughly test these providers to highlight their strengths and weaknesses. To produce relevant results, we stress a back-end infrastructure designed to handle a national-sized network of IoT nodes. Our analysis is not limited to the cloud provider performance as a whole, while it also investigates and compares several cloud components separately. As part of the contribution, we also test different time series databases and we discuss the advantages of such kind of technologies. Finally, an in-depth pricing analysis is conducted to better understand the differences between each platform from an economic perspective
A multimodal approach for human activity recognition based on skeleton and RGB data
Full text linkHuman action recognition plays a fundamental role in the design of smart solution for home environments, particularly in relation to ambient assisted living applications, where the support of an automated system could improve the quality of life of humans trying to interpret and anticipate user needs, recognizing unusual behaviors or preventing dangerous situations (e.g. falls). In this work the potentialities of the Kinect sensor are fully exploited to design a robust approach for activity recognition combining the analysis of skeleton and RGB data streams. The skeleton representation is designed to capture the most representative body postures, while the temporal evolution of actions is better highlighted by the representation obtained from RGB images. The experimental results confirm that the combination of these two data sources allow to capture highly discriminative features resulting in an approach able to achieve state-of-the-art performance on public benchmarks
A privacy-aware zero interaction smart mobility system
No full textSmart cities often rely on technological innovations to improve citizens’ safety and quality of life. This paper presents a novel smart mobility system that facilitates people’s access to public mobility while preserving their privacy. In contrast to several well-known smart mobility systems discussed in this paper, the one we propose combines privacy guarantees with user friendliness. Specifically, the system is based on a zero-interaction approach whereby a person can use public transport services without any need to perform explicit actions. Operations related to ticket purchases and validation were fully automated. The system is also designed with the privacy-by-design paradigm to preserve user privacy as much as possible. Throughout the paper, several technical details are discussed as well to describe a prototype version of the system that was implemented. The prototype was successfully tested in the city of Imola (Emilia Romagna, Italy) to prove the validity of the system in the field
An efficient fingerprint verification system using integrated Gabor filters and Parzen Window Classifier
No full textThis paper proposes a novel method of image-based fingerprint matching based on the features extracted by “FingerCode”. The experiments show that our system outperforms the standard “FingerCode” recognition method and other image-based approaches. Combining the matching score generated by the proposed technique with that obtained from a minutiae-based matcher results in an overall improvement in performance of the fingerprint matching
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