City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

Prospects in Agricultural Engineering in the Information Age - Technological Development for the Producer and the Consumer

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is an Invited article from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 1 (1999): N. Sigrimis, Y. Hashimoto, A. Munack and J. De Baerdemaker. Prospects in Agricultural Engineering in the Information Age - Technological Development for the Producer and the Consumer

Human-in-the-loop: Role in Cyber Physical Agricultural Systems

With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS

Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.\ud \u

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature

Using agent based simulation to empirically examine complexity in carbon footprint business process

Through the critical analysis of the extant literature, it is observed that Simulation is widely used as a research method in Natural Sciences, Engineering and Social Sciences, in addition to argumentation and formalisation as the third way of carrying out research. Simulation is not so widely used in Business and Management research as it ought to have been, though this is changing for the better with the technological advances in computers and their computational power. These technological advances enhance the capability of theoretical research models, in defining a problem and their use in empirically examining a solution to the problem in simulated reality, like never before. Management journal searches for “Simulation and Complexity Theory” returned nil or zero returns, which explain that this combination is not popular in management research, though they are used individually more often. The major objective of this paper is to analyse some of the conceptual (or theoretical) and methodological (or empirical) contributions that Agent Based Simulation and Complexity Theory can make to the business and management community in their business process related research In view of this, some basic ideas are discussed of using Agent Based Simulation as a method in Business and Management Studies research and how an Agent Based Model can be applied to a business process as complex as Carbon Footprint. It is in this context that the use of Complexity as the base theory to empirically examine a business process is discussed. Throughout this article, our research on complex adaptive systems (e.g., Accounting Information System) in continuously changing organisations managing complex business processes (e.g., Carbon Footprint business process) is considered as the basis for illustrating some of the concepts. Through this article, avenues for further management research using these tools and methodology are suggested