Technology Trends in ICT – Towards Data-Driven, Farmer-Centered and Knowledge-Based Hybrid Cloud Architectures for Smart Farming

Over the past four decades, advances in Information and Communication Technology (ICT) have resulted in unprecedented opportunity and innovation for improving farming outcomes. Ongoing innovations such as mobile, social media, agricultural drones, Internet of Things (IoT), Big Data, and cloud computing present new challenges and opportunities for agribusinesses to redefine and rethink the role of ICT towards achieving better farming outcomes. With recent advances in infrastructure, data (collection, storage and retrieval), and a better understanding of all aspects of the food chain, new challenges and opportunities are presented. Unstructured data is now being generated real time, in large volumes, at high speed and unknown quality that results in challenges to current approaches for decision making, and require a focus on analytics. These new sources of data create the opportunity to inform and drive a change in decision making from one that is highly intuitive to one that is data driven and processed in real-time. This paper highlights recent trends in ICT and introduces a hybrid cloud architecture for smart farming. The proposed architecture emphasizes data-driven, farmer-centered, and knowledge-based decision tools through service integration, aggregation and interoperation. As a customized solution for farmers, the proposed architecture contains components of 1) data integration of on-farm sensors and data from public sources, 2) farm management modules, 3) knowledge-based software solutions from different providers, 4) service integration, aggregation and interoperation, and 5) a customized dashboard focused on usefulness and usability. This cloud-based solution allows the integration of businesses services, things, and technology from any channel and can be used anywhere. At this time, hybrid cloud environments have shown promise to integrate these different services and provide smart farming solutions to both big and smallholder farmers. 

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

A Hybrid Approach for Data Analytics for Internet of Things

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.Comment: Accepted to be published in the Proceedings of the 7th ACM International Conference on the Internet of Things (IoT 2017

Enabling Interactive Analytics of Secure Data using Cloud Kotta

Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, such enclaves do not readily support discovery science---a form of exploratory or interactive analysis by which researchers execute a range of (sometimes large) analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks; however it is far from ideal for day-to-day discovery science. As researchers must submit jobs to queues and wait for results, the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing, Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page