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Construction is known as one of the most dangerous industries in terms of worker safety. Collisions due the excessive proximity of workers to moving construction vehicles are one of the leading causes of fatal and non-fatal accidents on construction sites internationally. Proximity warning systems (PWS) have been proposed in the literature as a solution to detect the risk for collision and to alert workers and equipment operators in time to prevent collisions. Although the role of sensing technologies for situational awareness has been recognised in previous studies, several factors still need to be considered. This paper describes the design of a prototype sensor-based PWS, aimed mainly at small and medium-sized construction companies, to collect real-time data directly from construction sites and to warn workers of a potential risk of collision accidents. It considers, in an integrated manner, factors such as cost of deployment, the actual nature of a construction site as an operating environment and data protection. A low-cost, ultra-wideband (UWB)-based proximity detection system has been developed that can operate with or without fixed anchors. In addition, the PWS is compliant with the General Data Protection Regulation (GDPR) of the European Union. A privacy-by-design approach has been adopted and privacy mechanisms have been used for data protection. Future work could evaluate the PWS in real operational conditions and incorporate additional factors for its further development, such as studies on the timely interpretation of data. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) approach relies on the use of the Internet Protocol (IP) as a pervasive network protocol. IP acts as a "glue" for interconnecting end devices (on the field side) and end users, leveraging on very diverse lower-level and upper-level protocols. The need for scalability would suggest the adoption of IPv6, but the large overhead and payloads do not match with the constraints dictated by common wireless solutions. For this reason, compression strategies have been proposed to avoid redundant information in the IPv6 header and to provide fragmentation and reassembly of long messages. For example, the Static Context Header Compression (SCHC) protocol has been recently referenced by the LoRa Alliance as a standard IPv6 compression scheme for LoRaWAN-based applications. In this way, IoT end points can seamlessly share an end-to-end IP link. However, implementation details are out of the specifications' scope. For this reason, formal test procedures for comparing solutions from different providers are important. In this paper, a test method for assessing architectural delays of real-world deployments of SCHC-over-LoRaWAN implementations is presented. The original proposal includes a mapping phase, for identifying information flows, and a subsequent evaluation phase, in which flows are timestamped and time-related metrics are computed. The proposed strategy has been tested in different use cases involving LoRaWAN backends deployed all around the world. The feasibility of the proposed approach has been tested by measuring the end-to-end latency of IPv6 data in sample use cases, obtaining a delay of less than 1 s. However, the main result is the demonstration that the suggested methodology permits a comparison of the behavior of IPv6 with SCHC-over-LoRaWAN, allowing the optimization of choices and parameters during deployment and commissioning of both infrastructure components and software. [ABSTRACT FROM AUTHOR]

As the number of installed Battery Energy Storage Systems (BESSs) increases, the concerns related to possible cyber-attacks to these systems rise accordingly. The most of BESS owners knows their systems may be vulnerable, but they often consider only denial of service attacks in their risk assessment. Unfortunately, other, subtler and more dangerous attacks exist. In this paper we show that a stealth attack to BESSs can be performed by applying a Man-in-the-Middle (MitM) approach. The aim of the attack is to stealthily manage the physical system by hiding the actual behavior of the system to its supervisory controller. In this case the attacker would be able to: (i) degrade the BESS by reducing its expected lifetime, (ii) produce economic losses for the prosumer, and (iii) affect the security and stability of the grid. The feasibility of the attack is demonstrated by providing an example of a stealth MitM attack on a real BESS coupled with a photovoltaic power plant. The proposed case study demonstrates that such attack can be performed without being discovered by end-users and shows that its effects can be severe. Finally, possible strategies to avoid or detect such kind of attack are discussed. [ABSTRACT FROM AUTHOR]

In this article, industrial control systems include networked control systems (NCS), which use real-time Ethernet (RTE) protocols since many years, well before the time sensitive networking initiative debut. Today, ethernet-based control systems are used all across Industry 4.0, including in critical applications, allowing for straight integration with information technology layers. Even if it is known that current RTE protocols do not have strong authentication or ciphering options, it is still very challenging to perform undetected cyber-attacks to these protocols while the NSC is in operation, in particular because such attacks must comply with very strict and small temporal constraints. In this article, a model-based attack is proposed for service degradation of NCS. The attack is carried out in real-time and it can remain undetected for the entire plant life. The attack can be applied to any RTE protocols and, without loss of generality, a detailed analysis of stealth techniques is provided for a specific real use case based on PROFINET. The experimental results demonstrate the feasibility of the proposed attack and its high effectiveness. The article also points out some possible future investigation directions in order to mitigate the attack. [ABSTRACT FROM AUTHOR]

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities' lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema. [ABSTRACT FROM AUTHOR]

Ambient assisted-living (AAL) systems, including wearables and home automation, could improve the quality of life of fragile people. As analyzed in this paper, the typical architecture of AAL systems is based on wireless sensors networks and proprietary gateways and it can be complex, expensive, and hardly includes interoperable multivendor devices. Emerging Android-based gateways can help in reducing costs, but flexibility and interoperability issues will still remain. On the basis of this fact, the development of an AAL system by a framework-oriented approach is presented. Thanks to the use of the SAndroidE framework, the proposed approach leads to the reduction of the programming effort as well as improves interoperability among different vendors ensuring high customization levels. Results obtained in a typical use case indicate that the programming effort in integrating multivendor devices is reduced by a factor of ten. The presented approach paves the way to the implementation of new open multivendor AAL systems, also encouraging redundant architectures and improving robustness of these emerging solutions. [ABSTRACT FROM AUTHOR]

In the last few years, several wearables appeared in the market, for fitness and healthcare applications. Such smart devices have been proposed as a possible solution for lowering the costs of healthcare, leading to the mHealth revolution. In the typical scenario, each wearable, embedding sensors, processing units and communication modules, adopts a smartphone for data collection, data displaying, and remote communication. In this paper, authors modify this paradigm simplifying the wearables (e.g., relying only on simple analog front ends and communication interfaces) and exploiting the (relatively large) computational capability of the smartphone, not only for implementing gateway features but also for processing raw biosignals as well. Several experiments verify the feasibility of the proposed approach and demonstrate that “local” biosensor virtualization is possible, expanding possibilities of mHealth. In particular, tests have been carried out to evaluate the performance of hearth rate computation and respiratory rate virtual sensor, starting from a single-lead electrocardiogram signal. [ABSTRACT FROM PUBLISHER]

In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as "Database as a service"—DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations. [ABSTRACT FROM AUTHOR]

In the last few years, the technology re-evolution has deeply transformed several aspects of everyday life. For sure, one technology with a strong impact is the so-called Internet of Things (IoT). The IoT paradigm made it possible to break down the data barrier between the vertical domains on which the traditional information and communication technology (ICT) world was organized. Recently, the designers of home automation systems have begun looking to the IoT paradigm to ease the deployment of systems that are able to collect data from different plants. Such a situation has driven further evolution from the traditional automation system, where logic is defined by the programmer or by the user, to a cognitive system that is able to learn from the user' habits regarding what should be the best configuration of plants. Several countries are funding renovations of public and private buildings for improving energy efficiency. Generally, such renovations are only focusing on the structure of the building and of its energy performance (e.g., the thermal envelope, window units, air-conditioning plants, and renewable generators) and largely ignoring the use of intelligent devices. On the contrary, scientific literature and practice have demonstrated that the wider use of IoT sensors, as well as distributed and remote intelligence, is fundamental to optimize energy consumption. This research work aimed to identify issues due the application of cognitive solutions during the renovation phase of buildings. In particular, the paper presents a cognitive architecture to support the operation and management phases of buildings, thanks to the massive digitalization of the entire supply chain of the construction sector from the single building element to the entire construction process. Such an architecture is capable of combining data from the IoT sensors and actuators of smart objects installed during the renovation phase, as well as legacy building automation systems. As an indication of the capability of the proposed solution, an intelligent window device was developed and validated. Within the Energy, Life Styled, and Seismic Innovation for Regenerated Buildings (ELISIR) project, window units are equipped with sensors to monitor indoor and outdoor condition behaviours of users. In addition, windows are able to react to changes in the environment by means of actuators that enable motorized opening and shading. Thanks to the cognitive layer designed in the project, the window is able to automatically define the best rules for opening and shading by using the local controller to satisfy user's habits and energy efficiency targets. The cognitive layer defines the appropriate rules for opening and shading using the decision tree algorithm applied to the data generated by the sensors in order to infer users' preferences. For this research, two prototypes of the window units were installed in two offices of Politecnico di Milano, Italy. The accuracy of this algorithm to classify the users' behaviour and preferences was found to be around 90%, considering an observation interval of two months. [ABSTRACT FROM AUTHOR]

The adoption of the distributed generation paradigm is introducing several changes in the design and operation of modern distribution networks. Modern grid codes are becoming more and more complex, and the adoption of smart protection systems is becoming mandatory. However, the adoption of newer and smarter units is only half of the story. Proper communication networks must be provided as well, and the overall costs may become critical. In this work, the adoption of the Long-Range Wide Area Network (LoRaWAN) technology is suggested as a viable approach to implement the coordination of Interface Protection Systems. A proper communication architecture based on the LoRaWAN Class B technology was proposed and evaluated in order to assess its feasibility for the considered application. A scalability analysis was carried out, by computing the number of devices that can be handled by a single LoRaWAN Gateway (GW) and the maximum expected time of response between a triggering event and the arrival of the related coordination command. The results of the study showed that up to 312 devices can be managed by a single GW, by assuring a maximum response time of 22.95 s. A faster maximum response time of 6.2 s is also possible by reducing the number of managed devices to 12. [ABSTRACT FROM AUTHOR]

Human errors are probably the most critical cause of the large amount of medical accidents. Medical cyber-physical systems (MCPS) have been suggested as a possible approach for detecting and limiting the impact of errors and wrong procedures. However, during the initial development phase of medical instruments, regular MCPS systems are not a viable approach, because of the high costs of repeating complex validation procedures, due to modifications of the prototype instrument. In this work, a communication architecture, inspired by recent Internet of Things (IoT) advances, is proposed for connecting prototype instruments to the cloud, to allow direct and real-time interaction between developers and instrument operators. Without loss of generality, a real-world use case is addressed, dealing with the use of transcranial magnetic stimulation (TMS) for neurodegenerative disease diagnosis. The proposed infrastructure leverages on a message-oriented middleware, complemented by historical database for further data processing. Two of the most diffused protocols for cloud data exchange (MQTT and AMQP) have been investigated. The experimental setup has been focused on the real-time performance, which are the most challenging requirements. Time-related metrics confirm the feasibility of the proposed approach, resulting in an end-to-end delay on the order of few tens of milliseconds for local networks and up to few hundreds of milliseconds for geographical scale networks. [ABSTRACT FROM AUTHOR]