Cryptocurrency applications of distributed ledger methods such as blockchains are now well established, but their implications for more general topics are just beginning to be appreciated. Beyond applications in finance and banking, new applications are emerging in supply chain management, manufacturing, agricultural product tracking, advertising verification, Internet of Things, healthcare, and the pharmaceutical industry, among others.

ICT is becoming increasingly pervasive to urban environments and providing the necessary basis for sustainability and resilience of the smart future cities. Often ICT tools for a smart city deal with different application domains e.g. land use, transport, energy, and rarely provide an integrated information perspective to deal with sustainability and socioeconomic growth of the city. Smart cities can benefit from such information using Big, and often real-time cross-thematic, data collection, processing, integration and sharing through inter-operable services deployed in a Cloud environment. However, such information utilisation requires appropriate software tools, services and technologies to collect, store, analyse and visualise large amounts of data from the city environment, citizens and various departments and agencies at city scale. This paper presents a theoretical perspective on the smart cities focused Big data processing and analysis by proposing a Cloud-based analysis service that can be further developed to generate information intelligence and support decision-making in smart future cities context.

With the emergence of new methodologies and technologies it has now become possible to manage large amounts of environmental sensing data and apply new integrated computing models to acquire information intelligence. This paper advocates the application of cloud technologies to support the information, communication and decision making needs of a wide variety of stakeholders in the complex business of the management of urban and regional development. The complexity is evident in the socio-economic and environmental interactions and impacts embodied in the concept of the urban-ecosystem. This highlights the need for more effective integrated environmental management systems. A key to understanding the nature of integrated environmental management systems is the identification of the need for horizontal integration of information across sectoral inter-agency boundaries at the local level, and the need for vertical coordination between levels of governance. This paper offers a user-oriented approach to the specification of requirements for the effective management of urban areas and the potential contributions that can be supported by cloud computing. The commonality of the influence of the drivers of change at the urban level offers the opportunity for the cloud computing community to develop generic solutions that can serve the needs of hundreds of cities throughout Europe and indeed globally. In this respect, different cloud based architecture scenarios are presented which utilise capabilities compliant to various standards in generating information and intelligence for urban governance.

With increasing availability and use of Internet of Things (IoT) devices large amounts of streaming data is now being produced at high velocity. Applications which require low latency response such as video surveillance demand a swift and efficient analysis of this data. Existing approaches employ cloud infrastructure to store and perform machine learning based analytics on this data. This centralized approach has limited ability to support analysis of real-time, large-scale streaming data due to network bandwidth and latency constraints between data source and cloud. We propose RealEdgeStream (RES) an edge enhanced stream analytics system for large-scale, high performance data analytics. The proposed approach investigates the problem of video stream analytics by proposing (i) filtration and (ii) identification phases. The filtration phase reduces the amount of data by filtering low value stream objects using configurable rules. The identification phase uses deep learning inference to perform analytics on the streams of interest. The stages are mapped onto available in-transit and cloud resources using a placement algorithm to satisfy the Quality of Service (QoS) constraints identified by a user. The job completion in the proposed system takes 49\% less time and saves 99\% bandwidth compared to a centralized cloud-only based approach.