Enrico Ferrera

Hybrid WSN and RFID indoor positioning and tracking system

Wireless sensor networks (WSNs), consisting of a large number of nodes to detect ambient environment, are widely deployed in a predefined area to provide more sophisticated sensing, communication, and processing capabilities, especially concerning the maintenance when hundreds or thousands of nodes are required to be deployed over wide areas at the same time. Radio frequency identification (RFID) technology, by reading the low-cost passive tags installed on objects or people, has been widely adopted in the tracing and tracking industry and can support an accurate positioning within a limited distance. Joint utilization of WSN and RFID technologies is attracting increasing attention within the Internet of Things (IoT) community, due to the potential of providing pervasive context-aware applications with advantages from both fields. WSN-RFID convergence is considered especially promising in context-aware systems with indoor positioning capabilities, where data from deployed WSN and RFID systems can be opportunistically exploited to refine and enhance the collected data with position information. In this papera, we design and evaluate a hybrid system which combines WSN and RFID technologies to provide an indoor positioning service with the capability of feeding position information into a general-purpose IoT environment. Performance of the proposed system is evaluated by means of simulations and a small-scale experimental set-up. The performed analysis demonstrates that the joint use of heterogeneous technologies can increase the robustness and the accuracy of the indoor positioning systems.

Application development for the Internet of Things: A context-aware mixed criticality systems development platform

Abstract The Internet of Things (IoT) is gaining momentum and may positively influence the automation of energy-efficiency management of smart buildings. However, the development of IoT-enabled applications still takes tremendous efforts due to the lack of proper tools. Many software components have to be developed from scratch, thus requiring huge amounts of effort, as developers must have a deep understanding of the technologies, the new application domain, and the interplay with legacy systems. In this paper we introduce the IMPReSS Systems Development Platform (SDP) that aims at reducing the complexity of developing IoT-enabled applications for supporting sensor data collection in buildings, managing automated system changes according to the context, and real-time prioritization of devices for controlling energy usage. The effectiveness of the SDP for the development of IoT-based context-aware and mixed-criticality applications was assessed by using it in four scenarios involving energy efficiency management in public buildings. Qualitative studies were undertaken with application developers in order to evaluate their perception of five key components of the SDP with regard to usability. The study revealed significant and encouraging results. Further, a quantitative performance analysis explored the scalability limits of the IMPReSS communication components.

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.

Optimization for Sustainable Manufacturing - Application of Optimization Techniques to Foster Resource Efficiency.

Resource efficiency assessment methods, along with eco-efficiency assessment methods are needed for various industrial sectors to support sustainable development, decision-making and evaluate efficiency performance. The combination of eco-efficiency with efficiency assessment allows to identify major inefficiencies and provides means to foster sustainability, through the efficient and effective material and energy use. Despite the available information for decision making, this proves to be a difficult task in the manufacturing industry, therefore, there is a real need to develop and use optimization techniques to enhance resource efficiency. In this context, and due to the lack of simple and integrated tools to assess and optimize resource efficiency, crossing the different environmental and economic aspects, arises the need to develop optimisations models, enabling support and optimize sustainable decision making process and identification of potential improvements. The optimisation method should provide robust knowledge to support decisionmaking, allow comparability of the results and consider a cost-saving approach to help set priorities. Moreover, the optimisation techniques should centre the process through design/configuration of the production system, without considering time, in order not to limit the physical agents.

Toward Industry 4.0: Efficient and Sustainable Manufacturing Leveraging MAESTRI Total Efficiency Framework

This paper presents an overview of the work under development within MAESTRI EU-funded collaborative project. The MAESTRI Total Efficiency Framework (MTEF) aims to advance the sustainability of manufacturing and process industries by providing a management system in the form of a flexible and scalable platform and methodology. The MTEF is based on four pillars: (a) an effective management system targeted at process continuous improvement; (b) Efficiency assessment tools to support improvements, optimisation strategies and decision support; (c) Industrial Symbiosis paradigm to gain value from waste and energy exchange; (d) an Internet-of-Things infrastructure to support easy integration and data exchange among shop-floor, business systems and tools.

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).