Davide Conzon

The VIRTUS Middleware: An XMPP Based Architecture for Secure IoT Communications

Before a wider adoption of the Internet of Things (IoT) vision occurs, many urgent technological and social challenging issues still need to be addressed, including device interoperability, systems autonomy, privacy and security concerns, which could have a significant impact on several aspects of everyday-life or potential end-user. Due to the very large number of technologies normally in place within the IoT paradigm, some type of middleware layer is employed to enforce seamless integration of devices and data within the same information network. Within such middleware, data must be exchanged respecting strict protection constraints. Both the networking and security issues have driven the design and the development of the VIRTUS Middleware, an IoT middleware relying on the open XMPP protocol to provide secure event- driven communications within an IoT scenario. Leveraging the standard security features provided by XMPP, the middleware offers a reliable and secure communication channel for distributed applications, protected with both authentication (through TLS protocol) and encryption (SASL protocol) mechanisms. The proposed architecture provides the possibility to isolate an instance of VIRTUS, allowing the exchange of data only within a private network. This paper presents an overview of VIRTUS, providing an overall platform description and details regarding its security features.

Industrial application development exploiting IoT vision and model driven programming

In recent years there has been a huge discussion about the IoT (Internet of Things) concept, and even more about IoT within industrial environment. The real IoT is a network of devices with local intelligence (sensors, lights, gas pumps, HVAC systems), which shares access & control mechanisms to push and pull status and command information from the networked world. However, there are still some issues, limiting the IoT diffusion: the devices involved are heterogeneous, with proprietary system of chips, protocols and interface; furthermore, there is a lack of development toolkits, enabling developers to create and evaluate IoT prototypes in simple and flexible manner. The ebbits [1] platform addressed those issues, taking advantages from the IoT vision and providing a middleware infrastructure for the integration of heterogeneous industrial devices and sensors, transforming them into web services and thus enabling their seamless integration into existing legacy systems. This paper will introduce the platform and its software architecture, describing features like semantic devices interoperability and entity virtualization. Furthermore, the paper will describe an innovative, IoT oriented, model driven development toolkit. This toolkit leverages on the semantic discovery service, allowing to dynamically selecting and locating available resources or devices, and provides a flexible instrument, including a graphical interface, that enables developers to compose mashup applications.

Heterogeneous Applications, Tools, and Methodologies in the Car Manufacturing Industry Through an IoT Approach

Due to the growth of industrial Internet services, todays production environment is on the edge of a new era of innovations and changes. This is taking place through the convergence of the global industrial system with the power of advanced computing, analytics, low-cost sensing, and new levels of connectivity enabled by the Internet of Things (IoT). These innovations will bring higher efficiency, flexibility, and interoperability among industries, although they belong to different production ecosystems. This paper describes an IoT platform and the related prototypes developed within the project: Enabling Business-Based Internet of Things and Services (ebbits), with a focus on the industrial domain. Heterogeneous applications were deployed and tested, including a wireless sensor and actuator network for industrial machines monitoring and a radio-frequency-identification-based system for operator management, locating, and authorization, which also includes an interactive user interface for portable devices to visualize real-time information from physical-world devices. Moreover, tools for model-driven development are used to simplify the process of building IoT applications. Those developments are based on IoT middleware that is developed and deployed by the project to enable the seamless integration of heterogeneous technologies and processes into mainstream enterprise systems. This paper also presents the prototypal deployment of the developed prototypes in the car manufacturing industry.

Enhancing traceability and industrial process automation through the VIRTUS middleware

Information and Communication Technologies (ICT) are considered a key instrument to improve efficiency and flexibility of industrial processes. This paper provides an experience report about the application of an ICT-based approach, derived from the Internet-of-Things (IoT) concept, to logistics in industrial manufacturing environments, aimed at enhancing awareness and control of logistic flows. The described solution performs assets management and inbound-outbound monitoring of goods by interconnecting business processes entities and devices providing physical-world data through an existing IoT-oriented middleware named VIRTUS. The VIRTUS Middleware, based on the open XMPP standard protocol and leveraging the OSGi framework, provides a scalable, agile, event-driven, network independent tool to manage an ecosystem of heterogeneous interconnected objects. The described solution has been validated within an actual industrial environment made of geographically-separated production plants.

XMPP-based infrastructure for IoT network management and rapid services and applications development

The information technology ecosystem is today facing many radical and methodological changes driven by the Internet-of-Things (IoT): those innovations impact at various levels, ranging from the device-to-device communication paradigms to the value-added services built on top of them. Though several IoT platforms addressing IoT design requirements have recently been raised in State-of-the-Art (SoTA), there is still a lack of platforms and tools which can help end-users to easily develop IoT applications and to configure and manage IoT infrastructures. In order to address these challenges, this work introduces the system development platform (SDP) developed within the IMPReSS project and specifically one of its components, namely, the IoT Platform’s Infrastructure for Configurations (IoT-PIC). It supports developers and users in arranging, configuring, and monitoring the various components of an IoT platform. Specifically, the paper highlights the solution adopted to face two services: IoT Network Management (NM) and platform commissioning. The proposed infrastructure, based on the eXtensible Messaging and Presence Protocol (XMPP), provides means for device discovery and IoT network monitoring, enabling also the mash-up of the platform entities. The platform commissioning tool leverages this feature to compose available modules and services, to implement the desired IoT application. This paper also describes the Resource Adaptation Interface (RAI), which virtualizes physical devices within the IoT platform.

Cognitive Multi-Radio Prototype for Industrial Environment

Today, a large number of cost saving and energy efficient applications are enabled by Wireless Sensor and Actuator Networks (WSANs). Usually, these solutions have serious connectivity problems in scenarios where other wireless technologies are co-located sharing the frequency spectrum (e.g. industrial shop-floors). To cope with this issue, the concept of Multi-Radio (MR) has been introduced, which promotes the simultaneous use of multiple radio communication interfaces, leveraging their different characteristics, to improve the overall system performance and reliability. The proposed approach based on cognitive algorithm, considers two wireless technologies operating at the 2.4 GHz frequency band, namely Wi-Fi and 6LoWPAN, and provides a concrete implementation of the system using a real test-bed industry scenario. The solution provides a reliable communication infrastructure for manufacturing processes, firstly combining the properties of several physical layer standards and secondly, providing the ability to recover from temporary network failures by switching from a communication channel to another one.

Cognitive-based multi-radio prototype for industrial environment

Today, a large number of cost-saving and energy-efficient applications are enabled by wireless sensor and actuator networks (WSANs). Usually, these solutions have serious connectivity problems in scenarios where other wireless technologies are co-located sharing the frequency spectrum (e.g., industrial shop-floors). To cope with this issue, the concept of multi-radio (MR) has been introduced, which promotes the simultaneous use of multiple radio communication interfaces, leveraging their different characteristics, to improve the overall system performance and reliability. The proposed approach based on cognitive algorithm considers two wireless technologies operating at the 2.4-GHz frequency band, namely Wi-Fi and 6LoWPAN, and provides a concrete implementation of the system using a real test-bed industry scenario. The solution provides a reliable communication infrastructure for manufacturing processes, firstly combining the properties of several physical layer standards and secondly providing the ability to recover from temporary network failures by switching from a communication channel to another one.

WSNs Self-Calibration Approach for Smart City Applications Leveraging Incremental Machine Learning Techniques

The diffusion of the Internet of Things paradigm, in the last few years, has led to the need of deploying and managing large-scale Wireless Sensor Networks (WSNs), composed by a multitude of geographically distributed sensors, like the ones needed for Smart City applications. The traditional way to manage WSNs is not suitable for this type of applications, because manually managing and monitoring every single sensor would be too expensive, time consuming and error prone. Moreover, unattended sensors may suffer of several issues that progressively make their measures unreliable and consequently useless. For this reason, several automatically techniques have been studied and implemented for the detection and correction of measurements from sensors which are affected by errors caused by aging and/or drift. These methods are grouped under the name of self-calibration techniques. This paper presents a distributed system, which combines an incremental machine learning technique with a non-linear Kalman Filter estimator, which allows to automatically re-calibrate sensors leveraging the correlation with measurements made by neighbor sensors. After the description of the used model and the system implementation details, the paper describes also the proof-of-concept prototype that has been built for testing the proposed solution.

Adaptive Security Framework for Resource-Constrained Internet-of-Things Platforms

The aim of this work is to investigate and define a dynamically adjustable security method, suitable especially for Wireless Sensor Networks (WSNs), usually composed by resource constrained devices. In order to support both the high level of security and the need for long lifetime of battery powered wireless devices, flexible means to adjust the level of security at runtime is needed. To this end, the paper proposes a solution, whose main component in the security between wireless sensors and the gateway is an Adaptive Security Manager (ASM), which can select the required level of security and inform the resource about it, based on the context. This makes it possible to support both high level of security and long lifetime of battery powered wireless devices when needed. Based on commands from the ASM, wireless sensors can select the suitable key from pre-shared keys (PSKs).