Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services

[EN] This paper presents the design and validation of a traceability system, based on radio frequency identification (RFID) technology and Internet of Things (IoT) services, intended to address the interconnection and cost-implementation problems typical in traceability systems. The RFID layer integrates temperature sensors into RFID tags, to track and trace food conditions during transportation. The IoT paradigm makes it possible to connect multiple systems to the same platform, addressing interconnection problems between different technology providers. The cost-implementation issues are addressed following the Data as a Service (DaaS) billing scheme, where users pay for the data they consume and not the installed equipment, avoiding the big initial investment that these high-tech solutions commonly require. The developed system is validated in two case scenarios, one carried out in controlled laboratory conditions, monitoring chopped pumpkin. 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Exploring Data Security and Privacy Issues in Internet of Things Based on Five-Layer Architecture

Data Security and privacy is one of the serious issues in internet-based computing like cloud computing, mobile computing and Internet of Things (IoT). This security and privacy become manifolded in IoT because of diversified technologies and the interaction of Cyber Physical Systems (CPS) used in IoT. IoTs are being adapted in academics and in many organizations without fully protecting their assets and also without realizing that the traditional security solutions cannot be applied to IoT environment. This paper explores a comprehensive survey of IoT architectures, communication technologies and the security and privacy issues of them for a new researcher in IoT. This paper also suggests methods to thwart the security and privacy issues in the different layers of IoT architecture

Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing

yesA large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases intergation, cooperation, and can also lead to innovative use of the data. Multicloud, privacy-aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads

Testing of transactional services in NoSQL key-value databases

Transactional services guarantee the consistency of shared data during the concurrent execution of multiple applications. They have been used in various domains ranging from classical databases through to service-oriented computing systems to NoSQL databases and cloud. Though transactional services aim to ensure data consistency, NoSQL databases prioritize efficiency/availability over data consistency. In order to address these issues various transaction models and protocols have been proposed in the literature. However, testing of transactions in NoSQL database has not been addressed. In this paper, we investigate into the testing of transactional services in NoSQL databases in order to test and analyse the data consistency by taking into account the characteristics of NoSQL databases such as efficiency, velocity, etc. Accordingly, we develop a framework for testing transactional services in NoSQL databases. The novelty and contributions are that we develop a context-aware transactional model that takes into account contextual requirements of NoSQL clients and the system level setting in relation to the data consistency. This can assist NoSQL application developers in choosing between transactional and non-transactional services based on their requirements of the level of data consistency. The framework also provides ways to analyse the impact of the big data requirements and characteristics (e.g., velocity, efficiency) on the data consistency of NoSQL databases. The evaluation and testing are carried out using a widely used NoSQL key/value database, Riak, and a real (open) and big data from the Council of London for public transportation of the London bus services

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms

International audienceSmart City represents one of the most promising, prominent and challenging Internet of Things (IoT) applications. In the last few years, indeed, the smart city concept has played an important role in academic and industry fields, with the development and deployment of various middleware platforms and IoT-based infrastructures. However, this expansion has followed distinct approaches creating, therefore, a fragmented scenario, in which different IoT ecosystems are not able to communicate between them. To fill this gap, there is a need to re-visit the smart city IoT semantic and offer a global common approach. To this purpose, this paper browses the semantic annotation of the sensors in the cloud, and innovative services can be implemented and considered by bridging Cloud and Internet of Things. Things-like semantic will be considered to perform the aggregation of heterogeneous resources by defining the Cloud of Things (CoT) paradigm. We survey the smart city vision, providing information on the main requirements and highlighting the benefits of integrating different IoT ecosystems within the cloud under this new CoT vision. This paper also discusses relevant challenges in this research area

Application of Machine Learning techniques in Cloud Services

Τα τελευταία χρόνια η Μηχανική Μάθηση και το Υπολογιστικό Νέφος γίνονται ολοένα και πιο δημοφιλή, εξαιτίας της ραγδαίας αύξησης του μεγέθους των δεδομένων και της αναγκαιότητας της γρήγορης επεξεργασίας τους για την εξαγωγή χρήσιμης πληροφορίας από αυτά. Στόχος της παρούσας διπλωματικής είναι η διερεύνηση τεχνικών μηχανικής μάθησης με σκοπό την εφαρμογή τους στις υπηρεσίες του υπολογιστικού νέφους. Σε αυτή την εργασία, επικεντρωθήκαμε στη βελτίωση της επεξεργασίας και της ανάλυσης των δεδομένων ενός πραγματικού σιδηροδρομικού συστήματος (συγκεκριμένα ενός τραμ μιας πόλης της Γαλλίας, της Ρενς) με την πρόβλεψη της κατανάλωσης ισχύος του τρένου. Αναπτύσσουμε μια τεχνική μηχανικής μάθησης, τα Νευρωνικά Δίκτυα για να επεξεργαστούμε το σύνολο δεδομένων, το οποίο αποτελείται απο διαφορετικές φυσικές ποσότητες σχετιζόμενες με το τραμ της Ρενς. Οι μετρήσεις που χρησιμοποιήσαμε λαμβάνονταν από διαφορετικούς αισθητήρες εγκαταστημένους στο τραμ, περιοδικά κάθε δευτερόλεπτο στη διάρκεια μιας μέρας. Στην παρούσα διπλωματική εργασία, αρχικά παρέχουμε το απαραίτητο θεωρητικό υπόβαθρο και στη συνέχεια παρουσιάζουμε κάποια σχετικά πειραματικά αποτελέσματα. Το θεωρητικό υπόβαθρο περιλαμβάνει την εισαγωγή στην έννοια «Μεγάλα Δεδομένα», μια παρουσίαση διάφορων μεθόδων Εξόρυξης Δεδομένων και αλγορίθμων Μηχανικής Μάθησης που μπορούν να χρησιμοποιηθούν για την επεξεργασία των μεγάλων δεδομένων και μια επισκόπηση στις έννοιες «Υπολογιστικό Νέφος», «Διαδίκτυο των Πραγμάτων» και «SiteWhere». Στο Κεφάλαιο 2 ορίζουμε το πρόβλημα που θα επιλύσουμε και εισάγουμε τους όρους «Υπολογιστικό Νέφος» και «Διαδίκτυο των Πραγμάτων». Στο Κεφάλαιο 3 εισάγουμε τις έννοιες «Μεγάλα Δεδομένα», «Εξόρυξη Δεδομένων» και «Μηχανική Μάθηση». Στο Κεφάλαιο 4 ξεκινάμε με την παρουσίαση της αρχιτεκτονικής και των δυνατοτήτων του «SiteWhere» και καταλήγουμε με την απεικόνιση των δεδομένων του τρένου, χρησιμοποιώντας ένα Γραφικό Περιβάλλον Διεπαφής Χρήστη (GUI), γνωστό ως MongoDB Compass. Το σύνολο δεδομένων, που περιέχει τα δεδομένα από το τρένο αποθηκεύτηκαν στη βάση δεδομένων «MongoDB», η οποία υποστηρίζεται από την πλατφόρμα SiteWhere. Στο Κεφάλαιο 5 παρέχουμε μια γρήγορη επισκόπιση των Νευρωνικών Δικτύων καθώς και των βασικών σχετικών αλγορίθμων. Στο Κεφάλαιο 6 εισάγουμε την έννοια της Χρονοσειράς, εφαρμόζουμε δύο διαφορετικά είδη Νευρωνικών Δικτύων, το Multilayer Perceptron (MLP) και το Long Short-Term Memory (LSTM) στα δεδομένα που συλλέχθηκαν από το τρένο και παρουσιάζουμε τα αποτελέσματά μας. Το Κεφάλαιο 7 παρέχει την περίληψη της παρούσας διπλωματικής εργασίας και των συμπερασμάτων των προηγούμενων κεφαλαίων.Machine Learning and Cloud Computing are gaining increased popularity over that past few years. The reason behind this is the rapid increase of the volume of data and the necessity of their fast processing, in order to extract useful information. The goal of this master thesis is to investigate the application of machine learning techniques in support of cloud services. In this work, we focus our effort to improve the process and analysis of the data of a real railway system (the Reims tramway) by forecasting the train power consumption. We develop a machine learning technique based on Neural Networks to process the dataset composed of various physical quantities related with the Reims tramway, each one measured periodically every second during the period of a day. The measurements were collected by different sensors, which were installed on the train. Initially, we provide the required theoretical background and then we present some relevant experimental results. The theoretical background involves the introduction of the concept of Big Data, the presentation of different Data Mining methods and Machine Learning algorithms that can be used in data processing and a review on Cloud Computing, Internet of Things (IoT) and SiteWhere. In Chapter 2, the problem definition is presented, and the terms of Cloud Computing and Internet of Things are introduced. In Chapter 3, we introduce the notions of Big Data, Data Mining and Machine Learning. In Chapter 4, we begin with the presentation of the architecture and capabilities of SiteWhere and we conclude with the visualization of the train data, using a Graphical User Interface (GUI), referred to as MongoDB Compass. The train dataset was stored in the MongoDB database, which is supported by the SiteWhere platform. Chapter 5 provides a brief overview of Neural Networks and the relevant basic algorithms. In Chapter 6 we introduce the concept of Time Series, implement two different types of Neural Networks, the Multilayer Perceptrons (MLP) and the Long Short-Term Memory (LSTM) on the train dataset and we present a set of relevant results. Chapter 7 provides the summary of the thesis and the conclusions derived and presented in the previous chapters

Design and implementation of an open framework for ubiquitous carbon footprint calculator applications

As climate change is becoming an important global issue, more and more people are beginning to pay attention to reducing greenhouse gas emissions. To measure personal or household carbon dioxide emission, there are already plenty of carbon footprint calculators available on the web. Most of these calculators use quantitative models to estimate carbon emission caused by a user\u27s activities. Although these calculators can promote public awareness regarding carbon emission due to an individual\u27s behavior, there are concerns about the consistency and transparency of these existing CO2 calculators. Apart from a small group of smart phone based carbon footprint calculator applications, most of the existing CO2 calculators require users to input data manually. This not only provides a poor user experience but also makes the calculation less accurate. The use of a standard framework for various carbon footprint application developments can increase the accuracy of overall calculations, which in turn may increase energy awareness at the individual human level. We aim for developing a carbon footprint calculation framework that can serve as a platform for various carbon footprint calculator applications. Therefore, in this paper, we propose a platform-agnostic Open Carbon Footprint Framework (OCFF) that will provide the necessary interfaces for software developers to incorporate the latest scientific knowledge regarding climate change into their applications. OCFF will maintain a clouded knowledge base that will give developers access to a dynamic source of computational information that can be brought to bear on real-time sensor data. Based on the OCFF platform, we developed a Ubiquitous Carbon Footprint Calculator application (UCFC) that allows the user to be aware of their personal carbon footprint based on their ubiquitous activity and act accordingly. The major contribution of this paper is the presentation of the quantitative model of the platform along with the entire design and implementation of UCFC application. We also present the results, analysis, and findings of an extensive survey that has been conducted to find users’ awareness of increased carbon footprint, feature requirements, and expectations and desires to alleviate CO2 emissions by using a footprint calculator. The design of UCFC application incorporates the analysis and inferences of the survey results. We are also developing a fuel efficient mobile GPS application for iPhone suggesting the greenest/most fuel efficient route to the user. In this paper, we also point out some important features of such an application

USING BLOCKCHAIN TO SUPPORT PROVENANCE IN THE INTERNET OF THINGS

The Internet of Things (IoT) has gained traction in all sectors and pervades all spheres of our lives. With statistics projecting an increase in the number of devices by 87% as well as increase in security concerns, traceability within this IoT will become a major problem. As more devices communicate with each other via the Internet, it will be crucial to determine the origins of requests and responses. Being able to store records related to the life cycle of requests and responses in an immutable form will provide documentary evidence that will help to establish transparency and accountability within the IoT. Previous works employed provenance techniques to address this problem but focuses on the request perspective. However, little or nothing has been done regarding the response perspective. Consequently, this thesis proposes and develops a blockchain-based provenance system to trace bi-directionally the sources of requests and responses in the IoT. This is achieved through the investigation of historical communication records. Furthermore, a performance evaluation of the system is provided. The results show that the developed system is scalable under real-world setting

Smart home - opportunity to make life easier

A great deal of contemporary research is showing that it is not work that goes home but home that goes to work. I would like to write my thesis about smart home possibilities (theoretically), which are available, or proposed to the market. Then I would like to make a business plan for hypothetical company which wants to arrive to the market with smart homes development. Finally, I would like to design (practically) part of smart home according to available technologies. I am more considered in the SW part of the problem, but I want to use real hardware if it would be possible rather then the simulator. The SW should be portable written in JAVA, developed on LINUX ( better hardware support ). It should be able to manage all smart home interfaces and extensible. Brain of the system should be Neural Network, which should be able to learn automatically the inhabitants behaviour and help them in their everyday routine. Make them disappear and you have succeeded. For example, if system finds out that some switch is turning on every time after doors are open, it will turn it on automatically. But it should consult his decisions in the beginning t avoid collisions with human decision (according to Asimov rules :) These shall avoid starting mixer while nothing in there, or even turning lights on when nobody is at home ( only if it is necessary according to security policies). The system shall communicate throw the network, which could be wired, or wireless. I search the technologies available and found some of them but I will need to decide which one to use. I prefer the "over power lines" transmitting because it is less expensive in real houses, but may be it's possible to use bluetooth or wi-fi as well