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

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Insights in the cost of continuous broadband Internet on trains for multi-service deployments by multiple actors with resource sharing

The economic viability of broadband Internet services on trains has always been proved difficult, mainly due to a high investment cost and low willingness to pay by train passengers, but also due to unused opportunities such as non-passenger services (e.g. train performance monitoring, crew services) and optimization of the resources consumed to offer Internet services. Evaluating opportunities to improve the return on investment is therefore essential towards profitability of the business case. By efficiently sharing resources amongst services, costs can be pooled over several services in order to reduce the investment cost per service. Current techno-economic evaluation models are hard to apply to cost allocation in a multi-service deployment with multiple actors and resource sharing. We therefore propose a new evaluation model and apply it to a deployment of Internet services on trains. We start with a detailed analysis of the technical architecture required to provide Internet access on trains. For each component, we investigate the impact by the different services on resource consumption. The proposed techno-economic evaluation model is then applied in order to calculate the total cost and allocate the used and unused resources to the appropriate services. In a final step, we calculate the business case for each stakeholder involved in the offering of these services. This paper details the proposed model and reports on our findings for a multi-service deployment by multiple actors. Results show important benefits for the case that considers the application of resource sharing in a multi-service, multi-actor scenario and the proposed model produces insights in the contributors to the cost per service and the unused amount of a resource. In addition, ex-ante insights in the cost flows per involved actor are obtained and the model can easily be extended to include revenue flows to evaluate the profitability per actor. As a consequence, the proposed model should be considered to support and stimulate upcoming multi-actor investment decisions for Internet-based multi-service offerings on-board trains with resource sharing

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems