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The Circular Economy – A new sustainability paradigm?

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

/pdf/context-aware-computing-for-the-internet-of-things-a-survey-3nibvvc921.pdf

Context Aware Computing for The Internet of Things: A Survey

A literature and practice review to develop sustainable business model archetypes

Thank you for reading experimental and quasi experimental designs for research. Maybe you have knowledge that, people have search numerous times for their favorite readings like this experimental and quasi experimental designs for research, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop.

Experimental And Quasi Experimental Designs For Research

Models that depict innovation as a smooth, well-behaved linear process badly misspecify the nature and direction of the causal factors at work. Innovation is complex, uncertain, somewhat disorderly, and subject to changes of many sorts. Innovation is also difficult to measure and demands close coordination of adequate technical knowledge and excellent market judgment in order to satisfy economic, technological, and other types of constraints—all simultaneously. The process of innovation must be viewed as a series of changes in a complete system not only of hardware, but also of market environment, production facilities and knowledge, and the social contexts of the innovation organization.

An Overview of Innovation

Sustainability is and will be a crucial issue for the present and future generations. The current assumption that natural resources are infinite and that the regenerative capacity of the environment is able to compensate for all human action is no longer acceptable. Hence, sustainability issues will influence all organisational aspects of the human life, from the economical, political, social and environmental points of view. The reason is simple: until now, all human activities have been based on the paradigm of unlimited resources and unlimited world's capacity for regeneration; from now on, the awareness of the termination of this assumption means that all related behavioural models must be changed. This is a very impressive objective embracing all fields of culture, economy, technology and much more. A continuing effort, together with a reasonable time span, will be required to pursue this goal. Fortunately, nature and the environment are capable of self-regulation and will give man a chance to recove...

Sustainable manufacturing: trends and research challenges

The value of Big Data in servitization

http://publications.lib.chalmers.se/records/fulltext/214181/local_214181.pdf

Energy management in production: A novel method to develop key performance indicators for improving energy efficiency

Energy Management in Manufacturing: From Literature Review to a Conceptual Framework

The paper investigates the effects of production scheduling policies aimed towards improving productive and environmental performances in a job shop system. A green genetic algorithm allows the assessment of multi-objective problems related to sustainability. Two main considerations have emerged from the application of the algorithm. First, the algorithm is able to achieve a semi-optimal makespan similar to that obtained by the best of other methods but with a significantly lower total energy consumption. Second, the study demonstrated that the worthless energy consumption can be reduced significantly by employing complex energy-efficient machine behaviour policies.

/pdf/multi-objective-genetic-algorithm-for-energy-efficient-job-2sw3abirqc.pdf

Marco Taisch

Papers

Sustainable manufacturing: trends and research challenges

The value of Big Data in servitization

Energy management in production: A novel method to develop key performance indicators for improving energy efficiency

Energy Management in Manufacturing: From Literature Review to a Conceptual Framework

Multi-objective genetic algorithm for energy-efficient job shop scheduling