Utilizing industry 4.0 on the construction site : challenges and opportunities

In recent years a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organisations alike. This paper discusses the current state of the art in the adoption of industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of industry 4.0 technologies. This paper discusses the relevance of the following key industry 4.0 technologies to construction: data analytics and artificial intelligence; robotics and automation; buildings information management; sensors and wearables; digital twin and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This paper also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector; a three-phase use of intelligent assets from the point of manufacture up to after build and a four staged R&D process for the implementation of smart wearables in a digital enhanced construction site

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Preventing Capability Abuse through Systematic Analysis of Exposed Interface

Connectivity and interoperability are becoming more and more critical in today’s software and cyber-physical systems. Different components of the system can better collaborate, enabling new innovation opportunities. However, to support connectivity and interoperability, systems and applications have to expose certain capabilities, which inevitably expands their attack surfaces and increases the risk of being abused. Due to the complexity of software systems and the heterogeneity of cyber-physical systems, it is challenging to secure their exposed interfaces and completely prevent abuses. To address the problems in a proactive manner, in this dissertation, we demonstrate that systematic studies of exposed interfaces and their usage in the real world, leveraging techniques such as program analysis, can reveal design-level, implementation-level, as well as configuration-level security issues, which can help with the development of defense solutions that effectively prevent capability abuse. This dissertation solves four problems in this space. First, we detect inconsistent security policy enforcement, a common implementation flaw. Focusing on the Android framework, we design and build a tool that compares permissions enforced on different code paths and identifies the paths enforcing weaker permissions. Second, we propose the Application Lifecycle Graph (ALG), a novel modeling approach to describing system-wide app lifecycle, to assist the detection of diehard behaviors that abuse lifecycle interfaces. We develop a lightweight runtime framework that utilizes ALG to realize fine-grained app lifecycle control. Third, we study real-world programmable logic controller programs for identifying insecure configurations that can be abused by adversaries to cause safety violations. Lastly, we conduct the first systematic security study on the usage of Unix domain sockets on Android, which reveals both implementation flaws and configuration weaknesses.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149960/1/yurushao_1.pd