Laura Belli

Design and Deployment of an IoT Application-Oriented Testbed

The global reach and extreme heterogeneity of the Internet of Things present major application development challenges. Using the same Web-based approach underlying the Internets evolution into the IoT, the Web of Things Testbed provides a stable, open, dynamic, and secure infrastructure to simplify application design and testing.

A Graph-Based Cloud Architecture for Big Stream Real-Time Applications in the Internet of Things

The Internet of Things (IoT) will consist of billions of interconnected heterogeneous devices denoted as “smart objects.” Smart objects are generally sensor/actuator-equipped and have constrained resources in terms of: (i) processing capabilities; (ii) available ROM/RAM; and (iii) communication reliability. To meet low-latency requirements, real-time IoT applications must rely on specific architectures designed in order to handle and process gigantic (in terms of number of sources of information and rate of received data) streams of data coming from smart objects. We refer to this smart object-generated data stream as “Big Stream,” in contrast to traditional “Big Data” scenarios, where real-time constraints are not considered. In this paper, we propose a novel Cloud architecture for Big Stream applications that can efficiently handle data coming from deployed smart objects through a graph-based processing platform and deliver processed data to consumer applications with lowest latency.

An Open-Source Cloud Architecture for Big Stream IoT Applications

The Internet of Things (IoT) is shaping to a worldwide network of networks consisting of billions of interconnected heterogeneous sensor/actuator-equipped devices (denoted as “things” or “smart objects”), which are expected to exceed 50 billions by 2020. Smart objects, which will be pervasively deployed, are constrained devices with (i) limited processing power and available memory and (ii) limited communication capabilities, in terms of transmission rate and reliability. Future Smart-X applications, such as Smart Cities and Home Automation, will be fostered by the use of standard and interoperable IP-based communication protocols that smart objects are going to implement, by simplifying their development, integration, and deployment. Smart-X applications will significantly differ from traditional Internet services, in terms of: (i) the number of data sources; (ii) rate of information exchange; and, (iii) need for real-time processing. Because of these requirements, such services are denoted as “Big Stream” applications, in order to distinguish them from traditional Big Data applications. In this paper, we present an implementation of a novel Cloud architecture for Big Stream applications based on standard protocols and open-source components, which provides a scalable and efficient processing platform for IoT applications, designed to be open and extensible and to guarantee minimal latency between data generation and consumption. We also provide a performance evaluation based on experimentation in a real-world Smart Parking scenario, to assess the feasibility and scalability of the proposed architecture.

UTravel: Smart mobility with a novel user profiling and recommendation approach

Abstract The exponentially growing availability of online information calls for personalized search and recommendation. Such systems provide recommendations typically based on user profiles built taking into account user actions. Not yet fully explored, is the domain of context-aware recommendation. In this article, we introduce a novel approach, where user profiling and context-based data filtering both concur to recommendation production. Based on the aforementioned approach, UTravel is a smart mobility application that recommends points of interest (POIs) to end users. After describing the UTravel architecture and implementation, we present the results of an experimental evaluation we carried out involving both simulated and real users.

From Micro to Macro IoT: Challenges and Solutions in the Integration of IEEE 802.15.4/802.11 and Sub-GHz Technologies

Research efforts in the field of Internet of Things (IoT) are providing solutions in building new types of “network of networks,” going beyond the technological barriers due to intrinsic limitations of the constrained devices typically used in this context. Thanks to the improvement in communication/networking protocols and the hardware cost reduction, it is now possible to define new IoT architectures, combining the “micro” IoT paradigm, based on short-range radio technologies (e.g., IEEE 802.15.4 and IEEE 802.11), with the rising “macro” IoT paradigm, based on sub-GHz radio technologies. This allows the implementation of scalable network architectures, able to collect data coming from constrained devices and process them in order to provide useful services and applications to final consumers. In this paper, we focus on practical integration between micro and macro IoT approaches, providing architectural and performance details for a set of experimental tests carried out in the campus of the University of Parma. We then discuss challenges and solutions of the proposed micro–macro integrated IoT systems.

Big Stream Cloud Architecture for the Internet of Things

The Internet of Things (IoT) will consist of billions of interconnected devices denoted as “Smart Objects:” (SOs) tiny, constrained devices which are going to be pervasively deployed in several contexts. The actors involved in IoT scenarios have extremely heterogeneous characteristics (in terms of processing and communication capabilities, energy supply and consumption, availability, and mobility), spanning from constrained SOs, to smartphones and other personal devices, Internet hosts, and the Cloud. SOs are typically equipped with sensors and/or actuators and are thus capable to perceive and act on the environment where they are deployed. By 2020, 50 billions of SOs are expected to be deployed in urban, home, industrial, and rural scenarios [3], in order to collect relevant information, which may be used to build new useful applications. In a typical IoT scenario, sensed data are collected by SOs, deployed in and populating the IoT network, and sent uplink to collection entities as the Cloud. With billions of nodes capable of gathering data and generating information, the availability of efficient and scalable mechanisms for collecting, processing, and storing data is crucial. Big Data techniques, which were developed in the last few years, address the need to process extremely large amounts of heterogeneous data for multiple purposes. These techniques have been designed mainly to deal with huge volumes of information (focusing on storage, aggregation, analysis, and provisioning of data), rather than to provide real-time processing and dispatching. One of the distinctive features of IoT systems is the deployment of a huge amount of heterogeneous data sources collecting data from the environment and sending information through the internet to collectors. The work of all data sources generate, as a whole, streams with a very high frequency. Moreover, several relevant IoT scenarios need real-time or predictable latency. The number of data sources, on one side, and the subsequent frequency of incoming data, on the other side, create a new need for Cloud architectures to handle such massive information flows. Big Data approaches typically have an intrinsic

Design and experimental performance analysis of a B.A.T.M.A.N.-based double Wi-Fi interface mesh network

Abstract Mesh networks and, in particular, Wireless Mesh Networks (WMNs) are gaining a growing interest because of their scalability, robustness, and ease of deployment. These characteristics make WMNs suitable for several applications, such as distributed sensing, monitoring, and public safety. In this paper, we describe a novel WMN implementation based on the use of low-cost double Wi-Fi interface embedded IoT-oriented devices. At each node, one interface provides external connectivity, whereas the other interface is used to create a mesh backbone. On the mesh side, the Better Approach To Mobile Ad-hoc Networking (B.A.T.M.A.N.) routing algorithm is used to route the traffic flows from external clients (possibly towards an Internet gateway), which can be IoT nodes and/or mobile nodes (e.g., smartphones and tablets). After providing a description of the architecture and relevant implementation details, we carry out an extensive experimental campaign to evaluate the WMN performance, especially in terms of the trade-off between throughput and number of hops.

A Novel Smart Object-Driven UI Generation Approach for Mobile Devices in the Internet of Things

The broad adoption of the Internet of Things (IoT) is linked to the possibility to discover and interact easily with objects in the surroundings of users. Because of their characteristics and large diffusion, mobile devices are perfect to connect the IoT and common people. In order to accomplish the challenging task of enabling seamless interaction between users and smart objects, in this paper, we propose a lightweight, standard and REST compliant mechanism for the generation of user interfaces (UIs) on mobile devices driven by smart objects. This approach is expedient for a number of reasons: i) end-users are no longer required to download and use custom mobile vendor-provided apps to interact with smart objects; ii) smart objects can actually drive the interaction by letting mobile devices generate the correct UI for the intended interplay; iii) UIs can be dynamically changed over time without requiring any software update by the user. A suitable lightweight UI description format is presented, together with an implementation for Android devices. An evaluation of the proposed approach has also been conducted in order to prove its feasibility and ease of use.