Mechatronics & the cloud

Conventionally, the engineering design process has assumed that the design team is able to exercise control over all elements of the design, either directly or indirectly in the case of sub-systems through their specifications. The introduction of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) means that a design team’s ability to have control over all elements of a system is no longer the case, particularly as the actual system configuration may well be being dynamically reconfigured in real-time according to user (and vendor) context and need. Additionally, the integration of the Internet of Things with elements of Big Data means that information becomes a commodity to be autonomously traded by and between systems, again according to context and need, all of which has implications for the privacy of system users. The paper therefore considers the relationship between mechatronics and cloud-basedtechnologies in relation to issues such as the distribution of functionality and user privacy

Managing Climatic Risks to Combat Land Degradation and Enhance Food security: Key Information Needs

This paper discusses the key information needs to reduce the negative impacts of weather variability and climate change on land degradation and food security, and identifies the opportunities and barriers between the information and services needed. It suggests that vulnerability assessments based on a livelihood concept that includes climate information and key socio-economic variables can overcome the narrow focus of common one-dimensional vulnerability studies. Both current and future climatic risks can be managed better if there is appropriate policy and institutional support together with technological interventions to address the complexities of multiple risks that agriculture has to face. This would require effective partnerships among agencies dealing with meteorological and hydrological services, agricultural research, land degradation and food security issues. In addition a state-of-the-art infrastructure to measure, record, store and disseminate data on weather variables, and access to weather and seasonal climate forecasts at desired spatial and temporal scales would be needed

Establishing a Central Archive for Transit Passenger Data

This report describes the rationale, background, establishing organization, and future steps of CATPAD, the Central Archive for Transit Passenger Data. The Central Archive for Transit Passenger Data is a repository that collects, indexes, archives, and makes available online the transit survey instruments, data, and reports collected across the country. This resource is unique in its focus on the disaggregated information of individual transit users – information that is critical for a range of transportation planning analyses. In addition, where available, CATPAD contains aggregated information, such as station boardings and service and fare schedules, to provide key context for the disaggregate person-level data. The Central Archive for Transit Passenger Data seeks to overcome the current impediments to accessing transit survey data by providing a single, searchable, internet archive to store and disseminate this valuable information. The Central Archive for Transit Passenger Data explicitly aims to expand the public return on the considerable investment made to gather transit passenger data. The resource is designed from the start to serve the needs of a range of use cases from transportation planners and policy makers to researchers and community advocates. The goal of CATPAD is to make useful data available to inform transit decision making at all levels and to foster ongoing refinement of the nation’s transit network

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit