yes / A 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.

Yes / Today’s factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company’s existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries. / Innovate UK—Knowledge Transfer Partnerships (KTP010551)

yes / The current electricity grid suffers from numerous challenges due to the lack of an effective energy management strategy that is able to match the generated power to the load demand. This problem becomes more pronounced with microgrids, where the variability of the load is obvious and the generation is mostly coming from renewables, as it depends on the usage of distributed energy sources. Building a smart microgrid would be much more economically feasible than converting the large electricity grid into a smart grid, as it would require huge investments in replacing legacy equipment with smart equipment. In this paper, application of Internet of Things (IoT) technology in different parts of the microgrid is carried out to achieve an effective IoT architecture in addition to proposing the Internet-of-Asset (IoA) concept that will be able to convert any legacy asset into a smart IoT-ready one. This will allow the effective connection of all assets to a cloud-based IoT. The role of which is to perform computations and big data analysis on the collected data from across the smart microgrid to send effective energy management and control commands to different controllers. Then the IoT cloud will send control actions to solve microgrid's technical issues such as solving energy mismatch problem by setting prediction models, increasing power quality by the effective commitment of DERs and eliminating load shedding by turning off only unnecessary loads so consumers won't suffer from power outages. The benefits of using IoT on various parts within the microgrid are also addressed.

Yes / Multi-stage attacks (MSAs) are among the most serious threats in cyberspace today. Criminals target big organisations and government critical infrastructures mainly for financial gain. These attacks are becoming more advanced and stealthier, and thus have capabilities to evade Intrusion Detection Systems (IDSs). As a result, the attack strategies used in the attack render IDSs ineffective, particularly because of new security challenges introduced by some of the key emerging technologies such as 5G wireless networks, cloud computing infrastructure and Internet of Things (IoT), Advanced persistent threats (APTs) and botnet attacks are examples of MSAs, these are serious threats on the Internet. This work analyses recent MSAs, outlines and reveals open issues, challenges and opportunities with existing detection methods.

Yes / The advances in the Internet of Things (IoT) has substantially contributed to the automation of modern societies by making physical things around us more interconnected and remotely controllable over the internet. This technological progress has inevitably created an intelligent society where various mechatronic systems are becoming increasingly efficient, innovative, and convenient. Undoubtedly, the IoT paradigm will continue to impact human life by providing efficient control of the environment with minimum human intervention. However, despite the ubiquity of IoT devices in modern society, the dependability of IoT applications remains a crucial challenge. Accordingly, this paper systematically reviews the current status and challenges of IoT dependability frameworks. Based on the review, existing IoT dependability frameworks are mainly based on informal reliability models. These informal reliability models are unable to effectively evaluate the unified treatment safety faults and cyber-security threats of IoT systems. Additionally, the existing frameworks are also unable to deal with the conflicting interaction between co-located IoT devices and the dynamic features of self-adaptive, reconfigurable, and other autonomous IoT systems. To this end, this paper suggested the design of a novel model-based dependability framework for quantifying safety faults and cyber-security threats as well as interdependencies between safety and cyber-security in IoT ecosystems. Additionally, robust approaches dealing with conflicting interactions between co-located IoT systems and the dynamic behaviours of IoT systems in reconfigurable and other autonomous systems are required.

Yes / The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

With the developments of industry 4.0, data analytics solutions and their applications have become more prevalent in the manufacturing industry. Currently, the typical software architecture supporting these solutions is modular, using separate software for data collection, storage, analytics, and visualization. The integration and maintenance of such a solution requires the expertise of an information technology team, making implementation more challenging for small manufacturing enterprises. To allow small manufacturing enterprises to more easily obtain the benefits of industry 4.0 data analytics, a full-stack data analytics framework is presented and its performance evaluated as applied in the common industrial analytics scenario of predictive maintenance. The predictive maintenance approach was achieved by using a full-stack data analytics framework, comprised of the PTC Thingworx software suite. When deployed on a lab-scale factory, there was a significant increase in factory uptime in comparison with both preventative and reactive maintenance approaches. The predictive maintenance approach simultaneously eliminated unexpected breakdowns and extended the uptime periods of the lab-scale factory. This research concluded that similar or better results may be obtained in actual factory settings, since the only source of error on predictions would not be present in real world scenarios.

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The manufacturing industry is continuously evolving as new practices and technology are adopted to improve productivity and remain competitive. There have been three well established manufacturing revolutions in recent history and some say that the fourth is occurring currently by the name of Smart Manufacturing, Indusrie 4.0, and others. This latest manufacturing revolution is highly dependent on industrial connectivity. This research aims to gage the ability of Industrial Innovation Platforms (IIPs) to reduce complexity of implementing base-line industrial connectivity for small and medium-sized enterprises (SMEs). The results of this study would be especially relevant to decision makers in industrial SMEs who are considering implementing industrial connectivity as well as providing insights into approaches for establishing base-line industrial connectivity. The research methodology consists of three main steps: 1) creation of IIP and non-IIP connectivity solutions that enable connectivity of the vast amount of industrial equipment, 2) Gathering measures from solutions in accordance with metrics identified for complexity evaluation, 3) discussion and interpretation of data To have a more complete analysis, quantitative and qualitative data was used and evaluated to address the varying elements of the broad task of establishing industrial connectivity. The research showed that IIPs can reduce complexity for select industrial equipment. Some industrial equipment have robust and streamlined connectivity solutions provided by the IIP. In these cases, the IIP almost certainly will reduce the complexity of establishing connectivity. Other industrial equipment have a solution provided by the IIP which requires piecing together and some component modifications. In these cases, the IIPs reduce complexity of establishing connectivity dependent on circumstances. Lastly, when no form of solution is available through the IIP for the industrial equipment, the IIP's has no ability to reduce complexity other than hosting the server used in connectivity. These findings open additional avenues of research which could improve the understanding of benefits IIPs may provide to SMEs.

Background. Smart door locks are either replacing or adding on to more regular mechanical locks today. They come in many different sizes and with many different functionalities. Today, most consumer advertisements focus on the functionalities of the locks and not so much on security. A few years ago when the locks started first to appear, they were very easily hacked and dedicated hackers could ride along the road and unlock people's doors with ease. As this is not the case as it was then, the problem persists. Insecure locks exist but consumer media focuses only on functionality, how are consumers making their purchases? In blind faith or with educated guesses? Objectives. This thesis tends to find out if consumers research the security of a smart lock, how do they research it, and is it enough for them? What sources do they consider before purchasing a lock and do they trust the sources? Can an application with security as its main focus aid or change consumers' methods of research? Methods. To begin answering these questions knowledge of the subject is necessary. Therefore, a literature study was conducted in order to find out what aspects of a smart door lock are more or less secure. To answer the first research question a questionnaire was created to collect quantitative data. This questionnaire contained questions about the participants' knowledge of the subject. Then, interviews were held in order to gather qualitative data for answering the second research question. During the interviews, participants got to test a prototype application helping them understand the subject of the questions. Results. The result shows that a majority of the participants are conscious of security regarding smart locks, 59.8% even think it is the most important aspect. The results from the interviews show that people do want an application suggesting smart locks with security in mind. Conclusions. The research here shows that smart door lock consumers value security. Consumers are also aware that consumer media does not value it as much. However, what is left unanswered is if the consumers care enough to do their own research before purchasing a smart door lock?