Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy

Value Propositions in the Internet of Things: A Taxonomy of B2B Smart Services

Connected and smart products give rise to smart services that leverage their advanced capabilities and promise profitable business models. However, many companies in the Internet of Things domain are still struggling to incorporate smart services into their portfolios, and more research is needed to facilitate service innovation and adoption. We, therefore, identify common characteristics of the value propositions of B2B smart services and summarize them in a taxonomy. The taxonomy development follows established methods and is based on a systematic literature review and the study of 100 empirical objects. To confirm the validity of our findings, we conduct two ex-post evaluations. Our research provides descriptive knowledge about B2B smart services that can serve as a foundation for further research on smart service innovation

An openbim approach to iot integration with incomplete as-built data

Digital Twins (DT) are powerful tools to support asset managers in the operation and maintenance of cognitive buildings. Building Information Models (BIM) are critical for Asset Management (AM), especially when used in conjunction with Internet of Things (IoT) and other asset data collected throughout a building’s lifecycle. However, information contained within BIM models is usually outdated, inaccurate, and incomplete as a result of unclear geometric and semantic data modelling procedures during the building life cycle. The aim of this paper is to develop an openBIM methodology to support dynamic AM applications with limited as-built information availability. The workflow is based on the use of the IfcSharedFacilitiesElements schema for processing the geometric and semantic information of both existing and newly created Industry Foundation Classes (IFC) objects, supporting real-time data integration. The methodology is validated using the West Cambridge DT Research Facility data, demonstrating good potential in supporting an asset anomaly detection application. The proposed workflow increases the automation of the digital AM processes, thanks to the adoption of BIM-IoT integration tools and methods within the context of the development of a building DT.Innovate UK, Construction Innovation Hub, Centre for Digital Built Britai

Design of the phonocardiography appliance for coronary artery disease diagnosing and monitoring : business perspectives analysis of innovative medical technologies for cardiovascular diseases in Finland

The topic of this study is the application of modern medical technology to cardiovascular conditions. The main purpose of that research is to evaluate myocardium disorders from the versatile perspectives and propose the design of a socially-demanding and financially-efficient technological solution targeted to coronary artery disease (CAD) diagnosing and monitoring. Phonocardiography and audial CAD detection are discussed as innovative methods for personalized healthcare applications and based on that, digital product design is developed in the form of functional specification, wearable device model, iOS and WatchOS applications interface architecture. In addition to the diseases study, myocardium signals acquisition discussion and to device design itself, market research is conducted. It is focused on medical technologies segment in general and cardiological systems in particular. Finland and Nordic Europe are the major covered regions, while global trends are outlined to collect the vision on the general market tendency. Core assessment topics are medical technology product distribution models, investment potential and development barriers. The final result could be used as a foundation for further product development and as an overview or guidelines for businesses interested in healthcare Internet-of-Things and cardiological systems

Software product quality metrics : a systematic mapping study

In the current competitive world, producing quality products has become a prominent factor to succeed in business. In this respect, defining and following the software product quality metrics (SPQM) to detect the current quality situation and continuous improvement of systems have gained tremendous importance. Therefore, it is necessary to review the present studies in this area to allow for the analysis of the situation at hand, as well as to enable us to make predictions regarding the future research areas. The present research aims to analyze the active research areas and trends on this topic appearing in the literature during the last decade. A Systematic Mapping (SM) study was carried out on 70 articles and conference papers published between 2009 and 2019 on SPQM as indicated in their titles and abstract. The result is presented through graphics, explanations, and the mind mapping method. The outputs include the trend map between the years 2009 and 2019, knowledge about this area and measurement tools, issues determined to be open to development in this area, and conformity between conference papers, articles and internationally valid quality models. This study may serve as a foundation for future studies that aim to contribute to the development in this crucial field. Future SM studies might focus on this subject for measuring the quality of network performance and new technologies such as Artificial Intelligence (AI), Internet of things (IoT), Cloud of Things (CoT), Machine Learning, and Robotics.publishedVersio

Adversarial samples on android malware detection systems for IoT systems

Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.This research was funded by the National Natural Science Foundation of China under Grant No. 61672170, No. 61871313 and No. 61572115, in part by the National Key R&D Plan under Grant CNS 2016QY06X1205.Scopu

Cloud computing adoption barriers faced by Saudi manufacturing SMEs

Cloud Computing is arguably the most significant technological development after the Internet. It accelerated technology adoption and gave birth to new business models. More importantly, it is acting as a foundation for new technologies like the Internet of Things (IoT) and Artificial Intelligence (AI). The Cloud Computing paradigm provides a level playing field for Small and Medium-sized Enterprises (SMEs) as they are able to adopt technologies that were not affordable before. Governments, vendors and business support organisations across the world have a plethora of initiatives to encourage SME adoption of Cloud Computing technologies. Despite these initiatives, many SME decision makers are still hesitant to adopt the cloud. This research reports an engagement with 16 information technology (IT) managers working for Saudi manufacturing SMEs. This paper presents their perceived barriers to migrating their applications to cloud services

An Efficient Collaboration and Incentive Mechanism for Internet-of-Vehicles (IoVs) with Secured Information Exchange Based on Blockchains

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordWith the rapid development of Internet-of-Things (IoT), mobile crowdsensing, i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, mobile crowdsensing lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet-ofVehicles (IoV) – a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multi-vehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we firstly propose a new model for the scenario of two vehicles collaboration, considering the situation of emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Secondly, for an emergent task, a novel time-window based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.National Key Research and Development Program of ChinaNational Natural Science Foundation of China (NSFC)Purple Mountain Laboratory: Networking, Communications and SecurityAcademician Expert Workstation of Bitvalue Technology (Hunan) Company Limite