Data Analytics Service Composition and Deployment on IoT Devices.

Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are the driving force of online service providers such as Amazon. Finance analytics has quickly adopted ML to harness large volume of data in such areas as fraud detection and risk-management. Deep Neural Network (DNN) is the technology behind voice-based personal assistance, self-driving cars [1], image processing [3], etc. Many popular data analytics are deployed on cloud computing infrastructures. However, they require aggregating users’ data at central server for processing. This architecture is prone to issues such as increased service response latency, communication cost, single point failure, and data privacy concerns.Thiswork is funded in part by the EPSRC Databox project (EP/N028260/2), NaaS (EP/K031724/2) and Contrive (EP/N028422/1)

Empowering Cyber-Physical Systems with FADEX.

The proliferation of smart devices in close proximity to end users has massively increased availability of data about our surroundings and hence stimulated a plethora of new services. However, it has also increased the chances of leaking sensitive and private information about end users (e.g., geolocation data, biometric signatures). Loss of trust towards a Cloud provider can lead to a user boycott and requests for deletion of the their remotely stored personal information. While many Cloud services can handle this relatively easily, it is far more cumbersome for many smart services. In fact, the current market of smart services is composed of black-box systems dependent on tight coupling between deployed hardware and the Cloud hosted software stack leaving virtually no freedom to change service provider without considerable redeployment costs

Empath-D: VR-based empathetic app design for accessibility

Singapore National Research Foundation under IDM Futures Funding Initiative; Ministry of Education, Singapore under its Academic Research Funding Tier

Decentralised Edge-Computing and IoT through Distributed Trust

The emerging Internet of Things needs edge-computing - this is an established fact. In turn, edge computing needs infrastructure decentralisation. What is not necessarily established yet is that infrastructure decentralisation needs a distributed model of Internet governance and decentralised trust schemes. We discuss the features of a decentralised IoT and edge-computing ecosystem and list the components that need to be designed, as well the challenges that need to be addressed

Authentication Beyond Desktops and Smartphones: Novel Approaches for Smart Devices and Environments

Much of the research on authentication in the past decades focused on developing authentication mechanisms for desktop computers and smartphones with the goal of making them both secure and usable. At the same time, the increasing number of smart devices that are becoming part of our everyday life creates new challenges for authentication, in particular since many of those devices are not designed and developed with authentication in mind. Examples include but are not limited to wearables, AR and VR glasses, devices in smart homes, and public displays. The goal of this workshop is to develop a common understanding of challenges and opportunities smart devices and environments create for secure and usable authentication. Therefore, we will bring together researchers and practitioners from HCI, usable security, and specific application areas (e.g., smart homes, wearables) to develop a research agenda for future approaches to authentication

SoC-Cluster as an Edge Server: an Application-driven Measurement Study

Huge electricity consumption is a severe issue for edge data centers. To this end, we propose a new form of edge server, namely SoC-Cluster, that orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip network. For the first time, we have developed a concrete SoC-Cluster server that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server has been commercialized successfully and deployed in large scale on edge clouds. The current dominant workload on those deployed SoC-Clusters is cloud gaming, as mobile SoCs can seamlessly run native mobile games. The primary goal of this work is to demystify whether SoC-Cluster can efficiently serve more general-purpose, edge-typical workloads. Therefore, we built a benchmark suite that leverages state-of-the-art libraries for two killer edge workloads, i.e., video transcoding and deep learning inference. The benchmark comprehensively reports the performance, power consumption, and other application-specific metrics. We then performed a thorough measurement study and directly compared SoC-Cluster with traditional edge servers (with Intel CPU and NVIDIA GPU) with respect to physical size, electricity, and billing. The results reveal the advantages of SoC-Cluster, especially its high energy efficiency and the ability to proportionally scale energy consumption with various incoming loads, as well as its limitations. The results also provide insightful implications and valuable guidance to further improve SoC-Cluster and land it in broader edge scenarios