Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Data-informed Building Procurement: A contractor exploration on embodied-carbon targets through Buildability and AI

The construction industry has been object of criticism due to poor productivity rates, lethargic development, and irresponsible use of natural resources. It has been pointed out that segment peculiarities, such as fragmentated, project-based, and one-of-a-kind, stand as barriers for change, compromising the urgent agenda for sustainable development. Regardless of that, recent climate agreements have input unprecedent pressure on the industry with challenging decarbonization goals. Motivated by that, this thesis explores a Swedish contractor perspective on how embodied carbon targets can be addressed in the building sector through better-informed tender briefings and buildability. For that, the study follows an abductive reasoning and exploratory mixed method approach, where a broad qualitative study informs a quantitative survey.Findings reveal a clear need for better-informed decisions on tender briefings. It exposes that data in early stages is reduced and inaccurate due to undefinitions and undeveloped assessments methods that tackles limited criteria. Additionally, it is argued that information flows are difficulted by outdated practices and the fragmented reality of conventional buildings product development, in which several stakeholders co-create, negotiate, and transfer asset ownership along the way. These actors, as investigations support, usually hold opposing interests driven by short-term economic gains. Consequently, neither environmental nor societal criteria appear to be effectively informed and spoken for during early stages of decision-making, regardless the considerably high share of emissions and relevant social issues that the sector comprise. Further, it is exposed that decarbonization roadmaps proposing investments in greener materials solutions, although longed-for, might escalate building costs considerably, possibly leading to economic and social issues linked to housing prices. Therefore, it is argued the plan may turn unfeasible, especially in face of the broad implementation which is vital to attend set targets.Accordingly, seeking to compensate such economic impacts, the thesis explores opportunities to reduce waste through the promotion of Buildability principles in the earliest stages of concept design, when it can still be addressed. As such, obstacles and inflexibilities created via client requirements and tender procedures are analyzed to propose changes.However, findings show the public procurement act challenges contractors ability to influence on more buildable solutions in public tenders. And Partnering strategies, which are often seen as a remedy for that, has been reducing due to public clients fear of volatile budgets - a viewpoint which contractors oppose since Partnering is a mean for many ends. Consequently, the thesis concludes that contractors are dependent on client’s leading role and can hardly count with better informed briefings and easier to build requirements.Nonetheless, it is suggested that contractors could develop a strategy based on data-informed ‘side-offers’, as a way around the limitations framed by the public procurement dynamics. Accordingly, a roadmap for AI-applications is advocated for contractors to reduce lead-time and resources spent for the elaboration of these side-offers. For that, it recommends the use of cutting-edge technologies to process an integrated multi-criteria design, that is informed by data collected both from the product and the market (client). Ultimately, the thesis supports that through automation, contractors can gain access to the right information at the right time, and thus promote a more valuable and sustainable alternative for public clients to procure

Measuring Developer Experience of a Digital Platform

Smart city and smart transportation are concepts that have emerged as an enabling solution which facilitates the grassroots social innovations to mitigate the problems generated by rapid urbanization and population growth. The digital service platform has fostered a new paradigm of transportation by involving all key players to create a novel environment. It is concerned developer are also the user of the platform as they are using the system development tools and methods for further development, that is why developer experience over the platform plays a vital role. Delightful developer experience not only improving the platform performance but also invokes to introduce new innovations. In this research we off to measure developer experience and answering the research questions “how to measure developer experience on top of the digital service platform” and “how to analyse the developer experience”. In the state of measuring developer experience, an application has been developed over the digital service platform and a measurement procedure has been introduced by modifying System Usability Scale (SUS) to more suit the context of the developer. The SUS has been borrowed from UX measurement tools as developers are the user of system, system development tools and methods as well as SUS is a widely accepted tool by the usability researchers for measuring usability. The result of the proposed method showed superior experience from the developer’s perspective to develop the application over the living lab bus platform. The result is almost same when it is compared with another method, but it is arguable as it showed small discrepancy. Furthermore, it can be said that, this research provides a straight forward way to measure developer experience on a digital service platform. The answer of the research questions provides a detail guideline of the measurement process and analysing criteria of developer experience. Moreover, it comes out with few recommendations that can be helpful for the developers of the platform to improve the platform in future, so that it could ensure the delightful experience for the developers

A comparison of processing techniques for producing prototype injection moulding inserts.

This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed

Reducing risk in pre-production investigations through undergraduate engineering projects.

This poster is the culmination of final year Bachelor of Engineering Technology (B.Eng.Tech) student projects in 2017 and 2018. The B.Eng.Tech is a level seven qualification that aligns with the Sydney accord for a three-year engineering degree and hence is internationally benchmarked. The enabling mechanism of these projects is the industry connectivity that creates real-world projects and highlights the benefits of the investigation of process at the technologist level. The methodologies we use are basic and transparent, with enough depth of technical knowledge to ensure the industry partners gain from the collaboration process. The process we use minimizes the disconnect between the student and the industry supervisor while maintaining the academic freedom of the student and the commercial sensitivities of the supervisor. The general motivation for this approach is the reduction of the entry cost of the industry to enable consideration of new technologies and thereby reducing risk to core business and shareholder profits. The poster presents several images and interpretive dialogue to explain the positive and negative aspects of the student process

Digital Supply Chain Twins in Urban Logistics System – Conception of an Integrative Platform

Current trends in urban areas pose several challenges to city logistics stakeholders while also offering opportunities for optimization. With its analytics, modelling and simulation capabilities, the Digital Supply Chain Twin (DSCT) technology provides a possibility to optimize urban logistics processes. However, a number of barriers have limited the implementation of holistic DSCTs so far. An integrative, collaborative platform could decrease these barriers. By applying design science research methodology and expert interviews, this paper develops an architecture for a high-level cross-institutional platform for the generation of DSCTs. This framework includes a modular design of the platform through eight functional modules. The platform can facilitate the implementation of DSCTs for urban stakeholders and thus optimize urban logistics processes

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (3/4)

Technical report about sustainable urban freight solutions, part 3 of

Integrated artificial intelligence effect on crisis management and lean production: structural equation modelling frame work

It is a goal that manufacturing companies strive towards on a regular basis, and it involves enhancing the efficiency and productivity of maintenance operations. It is especially vital to avoid unforeseen breakdowns, which may result in costly charges and production losses if they do not occur in advance. While the execution of an acceptable management plan affects maintenance productivity, it also affects the adoption of proper procedures and tools to help in the assessment processes in this field. This difficulty, among other things, affects a company's capacity to achieve high performance with the equipment it employs, as well as the judgement process and the design of the firm's maintenance plan. In order to achieve this goal, the aim of this paper is to exemplify how intelligent systems can be used to enhance judgement techniques in the implementation of the lean maintenance perspective, allowing for an advancement in the functional capabilities of the industry's technological infrastructure. The reseachers employed artificial intelligence technologies to look for connections between specific operations carried out as part of the deployment of lean maintenance and the findings achieved. The raw set notion, which was used in this situation, was used to determine whether or not the lean maintenance method was being used in this study. The crisis management process carries with it some of the most complex data technology concerns ever encountered. It necessitates, among other items, active information gathering and information transfer efforts, that are used for a range of functions, such as decreasing uncertainty, attempting to measure and manage consequences, and attempting to manage resources in a way that goes beyond what is generally possible to deal with daily problems. It also needs the employment of artificial intelligence technology, among other things, to increase crisis awareness.Campus At

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT