SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing

Mobile Cloud Computing (MCC) is the state-ofthe- art mobile computing technology aims to alleviate resource poverty of mobile devices. Recently, several approaches and techniques have been proposed to augment mobile devices by leveraging cloud computing. However, long-WAN latency and trust are still two major issues in MCC that hinder its vision. In this paper, we analyze MCC and discuss its issues. We leverage Service Oriented Architecture (SOA) to propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our architecture consists of three major layers, namely SOA, arbitrator, and infrastructure. The main strength of this architecture is in its multi-tier infrastructure layer which leverages infrastructures from three main sources of Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top of the infrastructure layer, an arbitrator layer is designed to classify Services and allocate them the suitable resources based on several metrics such as resource requirement, latency and security. Utilizing SAMI facilitate development and deployment of service-based platform-neutral mobile applications.Comment: 6 full pages, accepted for publication in IEEE MobiCC'12 conference, MobiCC 2012:IEEE Workshop on Mobile Cloud Computing, Beijing, Chin

Scaling AI Ventures: How to Navigate Tensions between Automation and Augmentation

AI ventures promise to automate and augment ever more human tasks. This provides rich opportunities for growth. Yet, digital and human resources that involve AI are oftentimes task-specific and hard to scale. Furthermore, clients remain skeptical to be fully automated by external services. Thus, it remains unclear how AI ventures achieve growth. We adopt a grounded theory approach on an interview study with founders, product managers and investors to inquire how resources afford or constrain scaling in AI ventures. For this, we blend the notion of (non-)scale free resources with the layered architecture of digital technologies. Our study suggests that AI ventures scale by organizing digital and human resources for replicability in that they keep AI-specific resources distant from clients while simultaneously externalizing human-intensive tasks to their clients. As we inquire the roles of human and digital resources, our study suggests that ventures seek to quickly find an optimal degree on the continuum between augmentation and automation when bundling resources

The Economics of Water Resource Allocation: Valuation Methods and Policy Implications

In this chapter a ‘watershed economics approach’ that could be applied in Cyprus is proposed which is composed of two important stages. In Stage I economic valuation techniques are used to establish the economic value of the competing demands for surface and groundwater, incorporating where necessary an analysis of water quality. The valuation exercise allows the objective balancing of demands based upon the equi-marginal principle to achieve economic efficiency. In Stage II a policy impact analysis is proposed which addresses issues of social equity and the value of water for environmental/ecological purposes. The analysis is undertaken within the confines of the watershed; the most natural unit for the analysis of water allocation and scarcity since it determines the hydrological links between competing users and thus the impacts of one user upon another. The methodology is encapsulated by a case study of the Kouris watershed in Cyprus

Dublin Smart City Data Integration, Analysis and Visualisation

Data is an important resource for any organisation, to understand the in-depth working and identifying the unseen trends with in the data. When this data is efficiently processed and analysed it helps the authorities to take appropriate decisions based on the derived insights and knowledge, through these decisions the service quality can be improved and enhance the customer experience. A massive growth in the data generation has been observed since two decades. The significant part of this generated data is generated from the dumb and smart sensors. If this raw data is processed in an efficient manner it could uplift the quality levels towards areas such as data mining, data analytics, business intelligence and data visualisation

Smart cities:engaging users and developers to foster innovation ecosystems

Increasingly, city planners and government officials understand that cities are engines of innovation and wealth creation. Equally, there is a growing understanding that the application of technology in support of Smart Cities helps grow the urban economy and deliver better services to citizens. However, often Smart City projects are top- down projects focused on improving city infrastructure using technology. We argue, and our experience over the last decade has shown, that often, citizen driven, or grass-roots based Smart City projects deliver better value and sustainable success. In this paper we report on our work to engage citizens and the technology community in smart city projects and highlight some lessons learnt from our experiences. We show how a modest investment in a Smart City Data Hub (using our IoT platform – WoTKit) plus development tools based on Node-RED helps bootstrap a Smart City innovation cluster