Cloud Infrastructures, Services, and IoT Systems for Smart Cities

The capability to perform comparisons of city performances can be an important guide for stakeholders to detect strengths and weaknesses and to set up strategies for future urban development. Today, the rise of the Open Data culture in public administrations is leading to a larger availability of statistical datasets in machine-readable formats, e.g. the RDF Data Cube. Although these allow easier data access and consumption, appropriate evaluation mechanisms are still needed to perform proper comparisons, together with an explicit representation of how statistical indicators are calculated. In this work, we discuss an approach for analysis and comparison of statistical Linked Data which is based on the formal and mathematical representation of performance indicators. Relying on this knowledge model, a set of logic-based services are able to support novel typologies of comparison of different resources.

Barcelona has recently started a new strategy to control and understand Last Mile Delivery, AreaDUM. The strategy is to provide freight delivery vehicle drivers with a mobile app that has to be used every time their vehicle is parked in one of the designated AreaDUM surface parking spaces in the streets of the city. This provides a significant amount of data about the activity of the freight delivery vehicles, their patterns, the occupancy of the spaces, etc.

In this paper, we provide a preliminary set of analytics preceded by the procedures employed for the cleansing of the dataset. During the analysis we show that some data blur the results and using a simple strategy to detect when a vehicle parks repeatedly in close-by parking slots, we are able to obtain different, yet more reliable results. In our paper, we show that this behavior is common among users with \(80\%\) prevalence. We conclude that we need to analyse and understand the user behaviors further with the purpose of providing predictive algorithms to find parking lots and smart routing algorithms to minimize traffic.

In this paper, we deal with the problem of acquiring statistics on the movements of vehicles in a given environment yet preserving the identity of drivers involved. To do this, we have designed a system based on an embedded board, namely Beaglebone Black, equipped with a Logitech C920 webcam with H.256 hardware encoder. The system uses JavaANPR to acquire snapshots of cars and recognize license plates. Acquired plate numbers are anonymized by the use of hash functions to obtain plate digests, and the use of a salt prevents plate number discovery from its digest (by dictionary or brute force attacks). A recovery algorithm is also run to correct possible errors in plate number recognition. Finally, these anonymized data are used to extract several statistics, such as the time of permanence of a vehicle in the environment.

The ageing population and related diseases represent some of the most relevant challenges in the healthcare domain. All that will lead to an increasing demand of innovative solutions in order to guarantee a healthy and safe lifestyle to the elderly. In fact, many researchers are studying the use of Internet of Things (IoT) technologies in the e-health field. In this paper we report a case study where a locale middleware for portable devices has been used to facilitate the development of IoT mobile application in this respect, allowing the communication among different on board sensing technologies. The mobile middleware is built on top of the WoX (Web of Topics) platform and quickly permits the deployment of innovation services thanks to its abstraction and user centric model. A validation test bed involving 31 elderly people living in Lecce (Italy) has been carried out for the monitoring of their activities, mainly those connected to positioning and motility both in indoor and outdoor scenarios. Our approach has demonstrated a practical way to replace obtrusive monitoring technique (typical of caregivers) with unobtrusive ones, in order to obtain proactive intervention strategies for a smart city.

This paper presents emerging detection systems for improving the citizen security and comfort in vehicular and urban contexts. To this end, we firstly provide an overview of existing detection systems dealing with innovative citizen security and comfort functionalities based on vision technologies. Secondly, we expose two vision-based detection prototypes we developed for proposing services more specifically addressed to security agencies and to car drivers.

The smart cities promise to offer innovative services to citizens in order to improve the level of life quality but currently, the integration among different ecosystems of data are still lacking. It is noteworthy that data, produced and consumed by public and private institutions, and citizens, may be a precious resource if abstraction and effectiveness are guaranteed in the interface mechanism between data-producers and data-consumers. This work proposes a coupling model, based on standards, technologies, and methodologies, able to make easy and effective the distribution, access, and use of data between services providers and citizens. The system architecture has been designed and developed by using the OSGi technology exploiting a support supplied by Docker, a platform able to ensure greater freedom in the modularization of software platforms oriented to micro-services. Furthermore, for higher levels, an approach based on the OneM2M standard has been adopted in order to obtain a middle layer useful to large-scale coordination of aspects related to the gathering, discovery, security and distribution of data and services. An use case has been defined and summarized in order to clearly show potential benefits of the proposed system for all stakeholders.

The European regulation eIDAS introduces in EU States interoperable public digital identity systems whose native application is secure authentication on online services. In this paper, we try to offer an enhanced view of the potential benefits that such systems can have in our physical environments. Indeed, cities have seen a dramatic increase in the number of violent acts and crimes. The possibility of monitoring people access to physical critical places is certainly an important issue because this gives the possibility to deny the access to dangerous people, to find the offender of a crime, and, in general, to track suspicions activities. In this paper, we show how to exploit an eIDAS-compliant public digital identity system to meet the above requirements, thus offering a concrete solution with high level of interoperability.

Smart Cities work with many data collected from various sources. The data are useless unless people know how to process them effectively. The aim of the submitted paper is to find out what the attitudes of the employees working in industrial enterprises in the CEE region are towards the Knowledge Management and it also focuses on finding the means of possible improvements of the Knowledge Management implementation. In the first part, definition and importance of the knowledge is explained for better understanding of the dealing issue. The second part describes our questionnaire survey with sample of 650 respondents. Selected survey results are presented and interpreted in the following section. Main research findings and recommendations can be found in the fourth part. The last part summarizes all previous parts of the article. From the survey results can be concluded that there is a significant relationship between knowledge management performance and the ease of use of knowledge management tools.

Fresh perishables are normally stored and distributed with a proper cold chain control in the supply chain from farm to retail. Usually, the consumers break the cold chain after the point of sale. The question is whether consumers are aware of requirements during the transport to and storage at home. The handling conditions and temperature changes can significantly decrease the shelf life and cause faster spoilage of food. The study presents two examples of shelf life prediction. The first one is based on temperature measurements of fish covered with ice in a Styrofoam box with supported information of environment temperatures in the cold store, uncooled car and refrigerator. In the second, measurements from first phase of storage on temperatures (0 °C–4 °C) were used with assumption of fish stored later on higher temperatures without ice. The results show important shortening of shelf life after the point of sale.

Mobile devices are able to gather more and more functionalities useful to control people’s daily life facilities. They offer computational power and different kinds of sensors and communication interfaces, enabling users to monitor and interact with the environment by a single integrated tool. Near Field Communication (NFC) represents a suitable technology in the interaction between digital world and real world. Most NFC-enabled mobile devices exploit the smart card features as a whole: e.g., they can be used as contactless payment and authentication systems. Nevertheless at present heterogeneity in mobile and IoT technologies does not permit to fully express potentialities of mobile devices as authentication systems, since most of the proposed solutions are strictly related to specific technological platforms. Basing on smart payment card approach, Europay, MasterCard e VISA (EMV) protocols and Host Card Emulation (HCE) technology, the current work proposes a distributed architecture for using NFC-enabled mobile devices as possession factor in Multifactor Authentication (MFA) systems. The innovative idea of the proposal relies on its independence with respect to the specific software and hardware technologies. The architecture is able to distribute tokens to registered mobile devices for univocally identifying user identity, tracing its actions in the meanwhile. As proof of concept, a real case has been implemented: an Android/iOS mobile application to control a car central locking system by NFC.

Completeness and accuracy of data is probably a persistent and intrusive problem in any process related to data capture. This is especially true in the clinical field, where omitting significant information can have considerable implications for diagnosis and treatment in general. History taking from patients represents a crucial phase for physicians in order to evaluate the patient’s wellness status and to perform correct diagnoses. As a routine procedure, it is a time-consuming and not so appealing obligation. In this paper we present a smart approach to anamnesis in order to gain as much data as possible and to have high quality information by avoiding any misunderstandings or errors. The approach is mainly based on the possibility to capture data directly at the source increasing the overall effectiveness of physician’s time and of the visit itself. Its feasibility has been evaluated in the context of a complex clinical domain: maternal and fetal assessment during pregnancy.

In a highly connected world, widespread networking has imposed new needs that require new paradigms and new technologies. The mobile agent is an emerging technology that is gaining ground in the field of distributed computing for the processing and transfer of information on the network. In our previous article we study the mobile agent model which thanks to its autonomy, mobility and adaptability, can send and retrieve data in real time using a local and or distant interaction with other agents on the network. In this paper, we will discuss the case of the use of mobile agent model in a connected ambulance. This paper aims to give a brief description of the mobile agents model and illustrates some existing systems that use this model in telemedecine. Then we present the new concept of the ambulance of the future and our proposal of mobile agent model service in smart ambulance able to diagnose the patients condition and the appropriate service and data transmission to get an accurate response in real time.

This work proposes extensions to Wubby (a device-level software platform for IoT devices, a technology developed by Econais A.E.) to support wireless modules for mobile networks (4 G / LTE-A, and also supporting the forthcoming 5 G). The proposed extension leverages the use of such modules, as it allows easy programming and existing code re-use. It thus adds a compatibility layer across the different modules as it a common set of classes for the wireless modules. The system can be used to support the networking aspects of a variety of IoT applications, including applications for Smart Cities, using a variety of IoT devices. This work suggests such a case focusing on air quality monitoring.

In the “Information Age”, Smart Cities rely on a wide range of different data sources. Among them, social networks can play a big role, if information veracity is assessed. Veracity assessment has been, and is, a rather popular research field. Specifically, our work investigates the credibility of data from Twitter, an online social network and a news media, by considering not only credibility, and type, but also origin. Our analysis proceeds in four phases: Features Extraction, Features Analysis, Features Selection, and Classification. Finally, we classify whether a Tweet is credible or incredible, is rumor or spam, is generated by a human or a Bot. We use Social Media Mining and Machine Learning techniques. Our analysis reaches an overall accuracy higher than the benchmark, and it adds the origin dimension to the credibility analysis method.

An information infrastructure for modern Smart Cities must be able to integrate data from multiple heterogeneous sources such as private and public energy consumption, garbage collection and environmental conditions (pollution, citizens’ safety and security). In this context, citizens themselves become providers of data, in the form of comments, opinions and suggestions that should be integrated within the infrastructure. A vast amount of data must be collected, organized and analysed to extract useful insights that can be transformed into actions aimed at improving the quality of life in the city. In this paper, we discuss data management issues to be addressed for bringing benefits to different categories of stakeholders in a Smart City, ranging from citizens to the Public Administration and energy providers.

With the appearance of things of the Internet of Things (IoT) area, IoT Cloud systems have been formed that are supported by cloud technologies, but still needs a significant amount of research. Data users produce with IoT devices are continuously posted to online services, which require the use of cloud providers to efficiently handle, and meaningfully visualize these data. In this paper we analyze the pricing schemes of four corresponding providers, and perform usage cost calculations for a concrete IoT scenario to help users to better understand their operation. We also compare these IoT Cloud providers by estimating service costs for operating an application of a smart city use case. We also validate our cost estimation by simulating the smart city scenario in the IBM Bluemix Platform.

The emergence of the Internet of Things introduces new challenges like network congestion or data privacy. However, it also provides opportunities, such as computational resources close to data sources, which can be pooled to realize Fogs to run software applications on the edge of the network. To foster this new type of resources, we revisit the concept of mobile agents and evolve them to so-called nomadic applications, which allow addressing vital challenges for the Internet of Things.

In this paper, we propose a system design to realize nomadic applications and discuss several challenges that need to be addressed in order to apply them to real-world scenarios.

Cloud Computing infrastructures have been extensively deployed to support energy computation within built environments. This has ranged from predicting potential energy demand for a building (or a group of buildings), undertaking heat profile/energy distribution simulations, to understanding the impact of climate and weather on building operation. Cloud computing usage in these scenarios have benefited from resource elasticity, where the number and types of resources can change based on the complexity of the simulation being considered. While there are numerous advantages of using a cloud based energy management system, there are also significant limitations. For instance, many such systems assume that the data has been pre-staged at a cloud platform prior to simulation, and do not take account of data transfer times from the building to the simulation platform. The need for supporting computation at edge resources, which can be hosted within the building itself or shared within a building complex, has become important over recent year. Additionally, network connectivity between the sensing infrastructure within a built environment and a data centre where analysis is to be carried out can be intermittent or may fail. There is therefore also a need to better understand how computation/analysis can be carried out closer to the data capture site to complement analysis that would be undertaken at the data centre. We describe how the Fog computing paradigm can be used to support some of these requirements, extending the capability of a data centre to support energy simulation within built environments.

This paper presents a system developed in the Horizon 2020 BEACON project enabling the deployment of distributed applications in an OpenStack-based federated Cloud networking environment. In such a scenario, we assume that a distributed application consists of several microservices that can be instantiated in different federated Cloud providers and that users can formalize advanced geolocation deployment constrains. In particular, we focus on an Orchestration Broker that is able to create ad-hoc manifest documents including application deployment instructions for the involved federated Cloud providers and users’ requirements.

Mobile Edge Computing (MEC) supports network function virtualization and it brings network service intelligence close to the network edge. MEC services provide low level radio and network information to authorized applications. Communication between MEC services and applications is according to the principles of Service–oriented Architecture (SOA). In this paper, we propose an approach to design application programming interfaces for MEC Web Services that may be used by RAN analytics applications to adapt content delivery in real–time improving quality of experience to the end users. Web Service interfaces are mapped onto network protocols.

Nowadays, we are observing a massive digitalization of clinical tasks in Hospital Information Systems (HIS). Even more medical devices belongs to Internet of Things (IoT) applications that generate a huge amount of clinical data. Therefore, the healthcare industry is looking at modern big data storage, processing and analytics technologies. In this context, traditional HIS presents several issues, such as a mismanagement of data generated from medical devices. Starting from the experience of IRCCS Centro Neurolesi “Bonino Pulejo” placed in Messina (Italy), i.e., a clinical and research center, in this paper, we motivate the need to move traditional HIS into an innovative infrastructure based on the Osmotic computing paradigm. In particular, studying the healthcare domain, we specifically focus on production and research tasks.

Today’s spread of chronic diseases and the need to control infectious diseases outbreaks have raised the demand for integrated information systems that can support patients while moving anywhere and anytime. This has been promoted by recent evolution in telecommunication technologies, together with an exponential increase in using sensor-enabled mobile devices on a daily basis. The construction of Mobile Health Communities (MHC) supported by Mobile CrowdSensing (MCS) is essential for mobile healthcare emergency scenarios. In a previous work, we have introduced the COLLEGA middleware, which integrates modules for supporting mobile health scenarios and the formation of MHCs through MCS. In this paper, we extend the COLLEGA middleware to address the need in real time scenarios to handle data arriving continuously in streams from MHC’s members. In particular, this paper describes the novel COLLEGA support for managing the real-time formation of MHCs. Experimental results are also provided that show the effectiveness of our identification solution.

The omnipresence of resource-constrained sensors connected to the cloud has enabled numerous Internet of Things (IoT) applications. However, the trust in these IoT applications is severely compromised by security concerns. We introduce a lightweight and effective security approach for such applications by protecting the sensors. Our approach leverages Physically Unclonable Functions (PUF) on the sensor platform to ensure non-repudiation of sensed data and integrity of sensor hardware and firmware. We compare the performance of different PUF implementations on Atmel, ARM, and FPGA-based sensing platforms, analyze the security properties of the proposed protocols and determine the overhead in terms of latency, storage and logic area. Our evaluation shows that it only incurs insignificant overhead on low-end sensors.

Analysing data acquired from one or more buildings (through specialist sensors, energy generation capability such as PV panels or smart meters) via a cloud-based Local Energy Management System (LEMS) is increasingly gaining in popularity. In a LEMS, various smart devices within a building are monitored and/or controlled to either investigate energy usage trends within a building, or to investigate mechanisms to reduce total energy demand. However, whenever we are connecting externally monitored/controlled smart devices there are security and privacy concerns. We describe the architecture and components of a LEMS and provide a survey of security and privacy concerns associated with data acquisition and control within a LEMS. Our scenarios specifically focus on the integration of Electric Vehicles (EV) and Energy Storage Units (ESU) at the building premises, to identify how EVs/ESUs can be used to store energy and reduce the electricity costs of the building. We review security strategies and identify potential security attacks that could be carried out on such a system, while exploring vulnerable points in the system. Additionally, we will systematically categorize each vulnerability and look at potential attacks exploiting that vulnerability for LEMS. Finally, we will evaluate current counter measures used against these attacks and suggest possible mitigation strategies.

The Internet of Things (IoT) introduces a future vision where users, computer, computing devices and daily objects possessing sensing and actuating capabilities cooperate with unprecedented convenience and benefits. We are moving towards IoT trend, where the number of smart sensing devices deployed around the world is growing at a rapid speed. With considering the number of sources and types of data from smart sources, the sensed data tends to new trend of research i.e. big data. Security will be a fundamental enabling factor of most IoT applications and big data, mechanisms must also be designed to protect communications enabled by such technologies. This paper analyses existing protocols and mechanisms to secure the IoT and big data, as well as security threats in the domain. We have broadly divided the IoT architecture into several layers to define properties, security issues and related works to solve the security concerns.

The IoT (Internet of Things) has emerged to increase the potentiality of pervasive monitoring devices. However, the implementation and integration of IoT devices, data storage and the development of applications are still considered challenging. This paper presents an infrastructure for aggregating and storing data from different sources from IoT devices to the cloud. In order to evaluate the infrastructure regarding the quality in storage, it has been implemented and verified in an AAL (Ambient Assisted Living) case scenario, the main application being Human Biometeorology. The evaluation of metrics related to sending, receiving and storing data demonstrate that the experimental environment is completely reliable and appropriate for the case study in question.